From f5a9e547089b4f70f819a3cb3cc92d1f698c0e0b Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Tue, 24 Mar 2026 22:50:37 +0100 Subject: [PATCH 01/15] Add tool catalog design spec with 145 CLI tools for proteomics/metabolomics Comprehensive catalog of standalone pyopenms CLI tools organized into 13 categories, scored by popularity, uniqueness, and utility. Includes niche paper-level tools for phosphoproteomics, HDX-MS, XL-MS, metaproteomics, lipidomics, fluxomics, etc. Co-Authored-By: Claude Opus 4.6 (1M context) --- ...26-03-24-agentomics-tool-catalog-design.md | 1480 +++++++++++++++++ 1 file changed, 1480 insertions(+) create mode 100644 docs/superpowers/specs/2026-03-24-agentomics-tool-catalog-design.md diff --git a/docs/superpowers/specs/2026-03-24-agentomics-tool-catalog-design.md b/docs/superpowers/specs/2026-03-24-agentomics-tool-catalog-design.md new file mode 100644 index 0000000..3189f81 --- /dev/null +++ b/docs/superpowers/specs/2026-03-24-agentomics-tool-catalog-design.md @@ -0,0 +1,1480 @@ +# Agentomics Tool Catalog — 145 CLI Tools for Proteomics & Metabolomics + +**Date:** 2026-03-24 +**Status:** Draft — awaiting user approval +**Scope:** Only tools providing NEW functionality not available as a simple OpenMS TOPP command +**Tools 1-100:** Core utilities derived from GitHub popularity, community requests, pyopenms docs +**Tools 101-145:** Niche paper-level tools from published research workflows + +## Scoring Criteria + +Each tool scored 1-10 on: +- **Popularity** (P): GitHub stars/forks, community requests, forum frequency +- **Uniqueness** (U): Not available as a TOPP tool or simple pyopenms one-liner +- **Utility** (V): How often researchers need this in daily workflows +- **Final Score** = (P + U + V) / 3, rounded + +--- + +## Already Implemented (7 tools — excluded from catalog) + +| # | Tool | Domain | +|---|------|--------| +| - | peptide_mass_calculator | proteomics | +| - | protein_digest | proteomics | +| - | feature_detection_proteomics | proteomics | +| - | spectrum_file_info | proteomics | +| - | isotope_pattern_matcher | metabolomics | +| - | mass_accuracy_calculator | metabolomics | +| - | metabolite_feature_detection | metabolomics | + +--- + +## Excluded — Direct TOPP Tool Duplicates + +These exist as standalone TOPP commands and are excluded per option A: + +FileConverter, PeakPickerHiRes, NoiseFilterGaussian, NoiseFilterSGolay, Normalizer, SpectraMerger, DecoyDatabase, SimpleSearchEngine, FalseDiscoveryRate, IDFilter, IDMapper, ProteinInference, PeptideIndexing, MapAlignerPoseClustering, MapAlignerIdentification, FeatureLinkerUnlabeledKD, FeatureLinkerUnlabeledQT, ConsensusMapNormalizer, IsobaricAnalyzer, InternalCalibration, GNPSExport, AccurateMassSearch, MetaboliteAdductDecharger, FeatureFinderMetabo, FeatureFinderCentroided, IDFileConverter, TextExporter, MRMMapper, OpenSwathChromatogramExtractor, OpenSwathRTNormalizer, QCCalculator, FileFilter, BaselineFilter + +--- + +## Category 1: Spectrum Analysis & Annotation (15 tools) + +### 1. theoretical_spectrum_generator +- **Description:** Generate theoretical b/y/a/c/x/z fragment ion spectra for a peptide sequence with annotated ion labels. Outputs human-readable TSV with ion type, number, charge, m/z, and annotation. Unlike any TOPP tool — TOPP has no standalone fragment spectrum output. +- **pyopenms classes:** `TheoreticalSpectrumGenerator`, `AASequence`, `MSSpectrum`, `Param` +- **CLI:** `--sequence PEPTM(Oxidation)IDEK --charge 2 --ion-types b,y,a --add-losses --add-isotopes --output fragments.tsv` +- **Inputs:** Peptide sequence (ProForma/bracket notation), charge state, ion type selection +- **Outputs:** TSV (ion_type, ion_number, charge, mz, annotation, intensity) +- **Domain:** proteomics +- **Why needed:** Universal need for PSM validation, teaching, SRM/PRM method development, spectral library QC. spectrum_utils (164 stars) addresses this but requires heavy dependencies. +- **Score:** P=8, U=8, V=9 → **8** + +### 2. spectrum_similarity_scorer +- **Description:** Compute pairwise spectral similarity (cosine, modified cosine, spectral contrast angle) between MS2 spectra from mzML or MGF files. No TOPP tool computes and outputs similarity scores. +- **pyopenms classes:** `SpectrumAlignment`, `MSSpectrum`, `MSExperiment`, `MzMLFile` +- **CLI:** `--query query.mgf --library reference.mgf --tolerance 0.02 --metric cosine --output scores.tsv` +- **Inputs:** Two spectrum files (mzML or MGF), tolerance, metric choice +- **Outputs:** TSV (query_id, library_id, score, matched_peaks, total_query_peaks, total_lib_peaks) +- **Domain:** proteomics / metabolomics +- **Why needed:** matchms (253 stars, 77 forks) is the go-to but requires a heavy install. A pyopenms-native CLI fills this gap. Fundamental for molecular networking and library matching. +- **Score:** P=9, U=9, V=9 → **9** + +### 3. spectrum_annotator +- **Description:** Given an observed MS2 spectrum and a peptide sequence, annotate peaks with theoretical fragment ion matches. Output annotation data for mirror plots or publication figures. +- **pyopenms classes:** `TheoreticalSpectrumGenerator`, `SpectrumAlignment`, `AASequence`, `MSSpectrum` +- **CLI:** `--spectrum observed.mzML --scan-index 1234 --sequence PEPTIDEK --charge 2 --tolerance 0.02 --output annotation.tsv` +- **Inputs:** mzML + scan index (or MGF + scan), peptide sequence, charge, tolerance +- **Outputs:** TSV (mz, intensity, annotation, matched, error_da, error_ppm) +- **Domain:** proteomics +- **Why needed:** spectrum_utils (164 stars) does this but no simple pyopenms CLI exists. Researchers need annotated spectra for publications and QC. +- **Score:** P=8, U=8, V=8 → **8** + +### 4. spectrum_scoring_hyperscore +- **Description:** Score an experimental MS2 spectrum against a theoretical spectrum using HyperScore. Returns score, matched ions, and sequence coverage. +- **pyopenms classes:** `HyperScore`, `TheoreticalSpectrumGenerator`, `AASequence`, `MSSpectrum` +- **CLI:** `--input spectrum.mzML --scan-index 100 --sequence PEPTIDEK --charge 2 --tolerance 0.02 --output score.json` +- **Inputs:** mzML + scan, peptide sequence, charge +- **Outputs:** JSON (hyperscore, matched_b, matched_y, total_matched, sequence_coverage) +- **Domain:** proteomics +- **Why needed:** Quick PSM validation without running a full search engine. No TOPP tool outputs HyperScore for a single spectrum-peptide pair. +- **Score:** P=6, U=8, V=7 → **7** + +### 5. neutral_loss_scanner +- **Description:** Scan MS2 spectra for characteristic neutral losses (e.g., -98 Da phospho, -162 Da hexose, -176 Da glucuronide). Reports precursor-fragment pairs matching user-specified losses. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input file.mzML --losses 97.977,162.053,176.032 --tolerance 0.02 --output matches.tsv` +- **Inputs:** mzML file, list of neutral loss masses, tolerance +- **Outputs:** TSV (scan_id, precursor_mz, fragment_mz, neutral_loss, loss_name, intensity) +- **Domain:** metabolomics / proteomics (PTMs) +- **Why needed:** Standard metabolomics annotation strategy for drug metabolites and PTM screening. No TOPP tool or simple CLI exists. +- **Score:** P=7, U=9, V=8 → **8** + +### 6. mass_defect_filter +- **Description:** Compute mass defect (fractional mass) for features, filter by mass defect range, and generate Kendrick mass defect data for homologous series detection. +- **pyopenms classes:** `EmpiricalFormula`, `MSExperiment`, `MSSpectrum` +- **CLI:** `--input features.tsv --mdf-min 0.1 --mdf-max 0.3 --kendrick-base CH2 --output filtered.tsv` +- **Inputs:** Feature list (TSV with m/z column) or mzML, mass defect range, Kendrick base +- **Outputs:** TSV (mz, mass_defect, kendrick_mass, kendrick_mass_defect, passes_filter) +- **Domain:** metabolomics +- **Why needed:** Key technique in drug metabolism and environmental metabolomics. Mass-Suite paper demonstrates need. No TOPP tool. +- **Score:** P=6, U=9, V=7 → **7** + +### 7. mass_decomposition_tool +- **Description:** Given an accurate mass, find all possible molecular formula or amino acid compositions within tolerance using the MassDecompositionAlgorithm. +- **pyopenms classes:** `MassDecompositionAlgorithm`, `MassDecomposition`, `Param` +- **CLI:** `--mass 1234.567 --tolerance 0.01 --residue-set full --output decompositions.tsv` +- **Inputs:** Target mass, tolerance, element/residue constraints +- **Outputs:** TSV of candidate compositions with exact mass and error +- **Domain:** metabolomics / proteomics +- **Why needed:** Unique algorithmic capability. pyopenms docs show it as a CLI example. No TOPP tool wraps this. +- **Score:** P=7, U=9, V=7 → **8** + +### 8. molecular_formula_finder +- **Description:** Enumerate valid molecular formulas for an accurate mass within ppm tolerance, constrained by element ranges. Apply Seven Golden Rules filtering (DBE, element ratios). +- **pyopenms classes:** `EmpiricalFormula`, `Element`, `IsotopeDistribution` +- **CLI:** `--mass 180.0634 --ppm 5 --elements C:0-12,H:0-30,N:0-5,O:0-10 --rules --output formulas.tsv` +- **Inputs:** Target mass, ppm tolerance, element ranges, optional golden rules filter +- **Outputs:** TSV (formula, exact_mass, error_ppm, dbe, h_c_ratio, isotope_pattern) +- **Domain:** metabolomics +- **Why needed:** Fundamental metabolomics annotation step. MetaboAnalyst and SIRIUS address this but no simple offline CLI. No TOPP tool. +- **Score:** P=8, U=8, V=8 → **8** + +### 9. xic_extractor +- **Description:** Extract ion chromatograms (XIC/EIC) for target m/z values with ppm tolerance from mzML files. Also compute TIC and BPC. Outputs RT vs intensity traces. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input run.mzML --mz 524.265 --ppm 10 --rt-start 10 --rt-end 60 --output xic.tsv` or `--mz-list targets.tsv --output xics.tsv` +- **Inputs:** mzML file, target m/z (or list), ppm tolerance, optional RT range +- **Outputs:** TSV (rt, intensity) per target; optional TIC/BPC +- **Domain:** proteomics / metabolomics +- **Why needed:** One of the most common ad-hoc MS tasks. OpenMS issue #4977 documents the gap. OpenSwathChromatogramExtractor is DIA-specific and requires TraML — this is simpler. +- **Score:** P=9, U=8, V=10 → **9** + +### 10. tic_bpc_calculator +- **Description:** Compute Total Ion Current and Base Peak Chromatogram from mzML, export as TSV. Separate from spectrum_file_info which reports summary stats, not full chromatogram traces. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input run.mzML --ms-level 1 --output chromatograms.tsv` +- **Inputs:** mzML file, MS level selection +- **Outputs:** TSV (rt, tic_intensity, bpc_mz, bpc_intensity) +- **Domain:** proteomics / metabolomics +- **Why needed:** Foundation for QC visualization. No TOPP tool outputs full TIC/BPC traces as flat files. +- **Score:** P=7, U=7, V=8 → **7** + +### 11. dia_window_analyzer +- **Description:** Read DIA mzML and report isolation window scheme: window widths, centers, overlaps, cycle time, number of windows per cycle. +- **pyopenms classes:** `MSExperiment`, `MSSpectrum`, `Precursor` +- **CLI:** `--input dia_run.mzML --output windows.tsv` +- **Inputs:** DIA mzML file +- **Outputs:** TSV (window_center, lower_offset, upper_offset, width, overlap_with_next), summary stats +- **Domain:** proteomics +- **Why needed:** DIA analysis tools require window schemes but they're not always documented. Common pain point when setting up DIA-NN or OpenSWATH. No TOPP tool. +- **Score:** P=7, U=9, V=7 → **8** + +### 12. precursor_charge_distribution +- **Description:** Analyze precursor charge state distribution across all MS2 spectra in an mzML file. Report counts and percentages per charge state. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum`, `Precursor` +- **CLI:** `--input run.mzML --output charge_dist.tsv` +- **Inputs:** mzML file +- **Outputs:** TSV (charge_state, count, percentage) +- **Domain:** proteomics +- **Why needed:** Key QC metric for evaluating ionization and instrument performance. Not directly available from any TOPP tool as standalone output. +- **Score:** P=6, U=8, V=7 → **7** + +### 13. mzml_spectrum_subsetter +- **Description:** Extract specific spectra from mzML by scan number list, RT list, or precursor m/z list. Unlike FileFilter (which filters by ranges), this extracts specific spectra by ID. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input run.mzML --scans 100,200,300 --output subset.mzML` or `--precursor-mz-list targets.tsv --output subset.mzML` +- **Inputs:** mzML + scan numbers or precursor m/z list +- **Outputs:** mzML with only selected spectra +- **Domain:** proteomics / metabolomics +- **Why needed:** FileFilter works on ranges; this works on specific lists. Common need for extracting spectra around identified PSMs for visualization. +- **Score:** P=6, U=7, V=7 → **7** + +### 14. spectrum_entropy_calculator +- **Description:** Calculate spectral entropy and normalized entropy for MS2 spectra. Spectral entropy is emerging as a quality metric superior to simple peak count. +- **pyopenms classes:** `MSExperiment`, `MSSpectrum`, `MzMLFile` +- **CLI:** `--input run.mzML --ms-level 2 --output entropy.tsv` +- **Inputs:** mzML file +- **Outputs:** TSV (scan_index, rt, precursor_mz, entropy, normalized_entropy, n_peaks) +- **Domain:** proteomics / metabolomics +- **Why needed:** Spectral entropy is a powerful QC and scoring metric (Li et al., Nature Methods 2021). No TOPP tool computes it. +- **Score:** P=7, U=9, V=7 → **8** + +### 15. spectral_library_format_converter +- **Description:** Convert between spectral library formats: MSP, sptxt, TraML, PQP/TSV (OpenSWATH). No single TOPP tool handles all conversions. +- **pyopenms classes:** `TraMLFile`, `TargetedExperiment`, `TransitionPQPFile` +- **CLI:** `--input library.msp --output library.traml --format traml` +- **Inputs:** Spectral library file (MSP, sptxt, TraML, PQP) +- **Outputs:** Converted library in target format +- **Domain:** proteomics +- **Why needed:** DIA workflows require libraries in specific formats. Converting between them is a constant pain point. No single TOPP tool does all conversions. +- **Score:** P=7, U=8, V=8 → **8** + +--- + +## Category 2: Peptide & Protein Analysis (16 tools) + +### 16. peptide_property_calculator +- **Description:** Calculate physicochemical properties: pI, hydrophobicity (multiple scales: Kyte-Doolittle, GRAVY, Hopp-Woods), charge at pH, instability index, amino acid composition. +- **pyopenms classes:** `AASequence`, `Residue`, `EmpiricalFormula` +- **CLI:** `--sequence PEPTIDEK --ph 7.0 --output properties.json` or `--input peptides.tsv --output properties.tsv` +- **Inputs:** Peptide sequence(s), pH for charge calculation +- **Outputs:** TSV/JSON (sequence, mw, pI, gravy, charge_at_ph, instability_index, aa_composition) +- **Domain:** proteomics +- **Why needed:** modlAMP (61 stars) and PepFun address this but require separate installs. No pyopenms/TOPP tool computes these properties. Essential for targeted proteomics assay design. +- **Score:** P=8, U=9, V=9 → **9** + +### 17. peptide_uniqueness_checker +- **Description:** Check which peptides in a list are proteotypic (unique to a single protein) within a FASTA database. +- **pyopenms classes:** `FASTAFile`, `AASequence`, `ProteaseDigestion` +- **CLI:** `--peptides peptide_list.tsv --fasta uniprot.fasta --output uniqueness.tsv` +- **Inputs:** Peptide list (TSV), FASTA database +- **Outputs:** TSV (peptide, protein_accessions, is_unique, n_proteins) +- **Domain:** proteomics +- **Why needed:** neXtProt checker (Oxford Academic paper, 2017) is web-only. Essential for SRM/PRM assay design and HPP protein evidence. No TOPP tool. +- **Score:** P=8, U=9, V=8 → **8** + +### 18. protein_coverage_calculator +- **Description:** Map identified peptides to protein sequences and calculate per-protein sequence coverage, with position mapping. +- **pyopenms classes:** `FASTAFile`, `AASequence`, `IdXMLFile` (optional) +- **CLI:** `--fasta proteins.fasta --peptides identified.tsv --accession P12345 --output coverage.tsv` +- **Inputs:** FASTA database, peptide list (TSV or idXML) +- **Outputs:** TSV (accession, length, covered_residues, coverage_pct, peptide_count, coverage_map) +- **Domain:** proteomics +- **Why needed:** PrIntMap-R exists but is R/web-only. AlphaMap (92 stars) is GUI-focused. No simple Python CLI. No TOPP tool. +- **Score:** P=8, U=9, V=8 → **8** + +### 19. modification_mass_calculator +- **Description:** Query Unimod/PSI-MOD databases by name or mass shift, compute modified peptide masses, list all known modifications for a residue. +- **pyopenms classes:** `ModificationsDB`, `ResidueModification`, `AASequence` +- **CLI:** `--search-mod Phospho --list-mods` or `--sequence PEPTIDEK --modifications "Oxidation(M):4,Phospho(S):2" --charge 2` +- **Inputs:** Modification name or mass shift query, or peptide with modifications +- **Outputs:** Modification details (name, mass_shift, formula_delta, sites, classification) or modified peptide mass/mz +- **Domain:** proteomics +- **Why needed:** Researchers search Unimod manually daily. MSModDetector paper (2024) shows the need. No TOPP tool for mod database queries. +- **Score:** P=8, U=9, V=8 → **8** + +### 20. modified_peptide_generator +- **Description:** Generate all modified peptide variants for given variable/fixed modifications, respecting max modifications per peptide. +- **pyopenms classes:** `ModifiedPeptideGenerator`, `AASequence`, `ModificationsDB` +- **CLI:** `--sequence PEPTMIDEK --variable-mods Oxidation,Phospho --fixed-mods Carbamidomethyl --max-mods 2 --output variants.tsv` +- **Inputs:** Peptide sequence, variable mods, fixed mods, max modifications +- **Outputs:** TSV (modified_sequence, mono_mass, mz_at_charge, n_mods, mod_positions) +- **Domain:** proteomics +- **Why needed:** Needed for building targeted method inclusion lists and search space estimation. No TOPP tool enumerates variants. +- **Score:** P=6, U=9, V=7 → **7** + +### 21. peptide_modification_analyzer +- **Description:** Parse a modified peptide sequence (bracket/ProForma notation) and output residue-by-residue mass breakdown with modification details. +- **pyopenms classes:** `AASequence`, `ModificationsDB`, `ResidueDB` +- **CLI:** `--sequence ".(Dimethyl)PEPTM(Oxidation)IDE." --charge 2` +- **Inputs:** Modified peptide string, charge +- **Outputs:** JSON/TSV (residue, position, base_mass, mod_name, mod_mass, cumulative_b_ion, cumulative_y_ion) +- **Domain:** proteomics +- **Why needed:** Useful for understanding and validating search engine notation. No TOPP tool provides residue-level breakdown. +- **Score:** P=6, U=8, V=7 → **7** + +### 22. missed_cleavage_analyzer +- **Description:** Analyze missed cleavage distribution in identification results. Key QC metric for digestion efficiency. +- **pyopenms classes:** `ProteaseDigestion`, `AASequence`, `PeptideIdentification` (optional) +- **CLI:** `--input results.idXML --enzyme Trypsin --output mc_report.tsv` or `--peptides peptide_list.tsv --enzyme Trypsin` +- **Inputs:** Identification results (idXML or peptide list TSV), enzyme name +- **Outputs:** TSV (missed_cleavages, count, percentage), summary stats +- **Domain:** proteomics +- **Why needed:** Standard QC metric in every proteomics facility. Calculated manually or in R. No TOPP tool. Papers cite its importance for quantification accuracy. +- **Score:** P=7, U=9, V=8 → **8** + +### 23. peptide_detectability_predictor +- **Description:** Given a FASTA and digestion parameters, predict peptide detectability based on physicochemical heuristics (length, hydrophobicity, charge, MW range). +- **pyopenms classes:** `ProteaseDigestion`, `AASequence`, `FASTAFile`, `Residue` +- **CLI:** `--input proteins.fasta --enzyme Trypsin --missed-cleavages 1 --output peptide_properties.tsv` +- **Inputs:** FASTA file, enzyme, missed cleavages +- **Outputs:** TSV (peptide, protein, mass, length, charge_at_ph7, gravy, is_proteotypic, detectability_score) +- **Domain:** proteomics +- **Why needed:** Critical for targeted assay design and understanding protein coverage gaps. No TOPP tool. +- **Score:** P=7, U=8, V=7 → **7** + +### 24. spectral_counting_quantifier +- **Description:** Calculate semi-quantitative protein abundances from spectral counts using emPAI, NSAF, or SIN methods. +- **pyopenms classes:** `FASTAFile`, `PeptideIdentification`, `ProteinIdentification`, `ProteaseDigestion` +- **CLI:** `--input identifications.idXML --fasta database.fasta --method nsaf --output abundances.tsv` +- **Inputs:** Identification results (idXML), FASTA database, method choice +- **Outputs:** TSV (accession, spectral_count, unique_peptides, empai/nsaf/sin_score) +- **Domain:** proteomics +- **Why needed:** Simplest label-free quantification approach. Crux toolkit has `spectral-counts` but no Python CLI with flexible input. No TOPP tool. +- **Score:** P=7, U=9, V=7 → **8** + +### 25. ptm_site_localization_scorer +- **Description:** Score phosphorylation (or other PTM) site localization confidence using an Ascore-like approach comparing ion coverage. +- **pyopenms classes:** `TheoreticalSpectrumGenerator`, `SpectrumAlignment`, `AASequence`, `MSSpectrum` +- **CLI:** `--spectrum scan.mzML --scan-index 500 --peptide "PEPS(Phospho)TIDEK" --output scores.tsv` +- **Inputs:** mzML spectrum + scan, modified peptide with ambiguous site(s) +- **Outputs:** TSV (site_position, residue, ascore, probability, best_localization) +- **Domain:** proteomics +- **Why needed:** Phosphoproteomics requires localization scores. pyAscore exists in Python but lacks pyopenms integration. No TOPP tool for standalone scoring. +- **Score:** P=8, U=9, V=8 → **8** + +### 26. transition_list_generator +- **Description:** Generate SRM/MRM/PRM transition lists from peptide sequences with configurable ion types, charge states, and product ion selection. +- **pyopenms classes:** `TargetedExperiment`, `ReactionMonitoringTransition`, `TheoreticalSpectrumGenerator`, `AASequence`, `TraMLFile` +- **CLI:** `--peptides PEPTIDEK,ANOTHERPEPTIDE --charge 2,3 --product-ions y3-y8 --output transitions.tsv` +- **Inputs:** Peptide list or FASTA, charge states, product ion range +- **Outputs:** TraML or Skyline-compatible CSV (precursor_mz, product_mz, ion_type, peptide, protein) +- **Domain:** proteomics +- **Why needed:** MRMMapper exists but maps existing chromatograms; this GENERATES new transitions. Essential for targeted proteomics method setup. +- **Score:** P=7, U=8, V=8 → **8** + +### 27. irt_calculator +- **Description:** Convert observed retention times to indexed retention time (iRT) values using reference peptides (Biognosys iRT kit or custom standards). +- **pyopenms classes:** `PeptideIdentification`, `TransformationDescription` +- **CLI:** `--input identifications.idXML --reference-peptides irt_standards.tsv --output irt_converted.tsv` +- **Inputs:** Identification results with RT, reference peptide list (sequence, expected_iRT) +- **Outputs:** TSV (peptide, observed_rt, irt, r_squared), linear fit parameters +- **Domain:** proteomics +- **Why needed:** iRT values enable cross-lab RT comparison. OpenSwathRTNormalizer works on chromatograms, not identifications. This operates on ID-level data. No simple TOPP equivalent. +- **Score:** P=7, U=8, V=7 → **7** + +### 28. rt_prediction_additive +- **Description:** Predict peptide retention time using additive amino acid hydrophobicity models (Krokhin, Guo '86, custom coefficients). +- **pyopenms classes:** `AASequence`, `Residue` +- **CLI:** `--sequence PEPTIDEK --model krokhin` or `--input peptides.tsv --output rt_predictions.tsv` +- **Inputs:** Peptide sequence(s), model choice +- **Outputs:** TSV (sequence, predicted_rt, model_used) +- **Domain:** proteomics +- **Why needed:** pyteomics `achrom` module provides this (151 stars). A pyopenms equivalent fills a gap. No TOPP tool for additive RT prediction. +- **Score:** P=6, U=8, V=6 → **7** + +### 29. isoelectric_point_calculator +- **Description:** Calculate isoelectric point (pI) for peptides/proteins using Henderson-Hasselbalch with configurable pK sets (Lehninger, Sillero, Dawson). +- **pyopenms classes:** `AASequence`, `Residue` +- **CLI:** `--sequence ACDEFGHIKLMNPQRSTVWY --pk-set lehninger` or `--fasta proteins.fasta --output pi_values.tsv` +- **Inputs:** Sequence(s) or FASTA, pK set choice +- **Outputs:** TSV (sequence, pI, charge_at_pH7, mw) +- **Domain:** proteomics +- **Why needed:** pyteomics electrochem module does this. No pyopenms/TOPP equivalent. Important for 2D gel analysis, IEF optimization, and peptide fractionation. +- **Score:** P=7, U=8, V=7 → **7** + +### 30. amino_acid_composition_analyzer +- **Description:** Analyze amino acid frequency and composition statistics for proteins in a FASTA file or peptide list. +- **pyopenms classes:** `FASTAFile`, `AASequence` +- **CLI:** `--input proteins.fasta --output aa_composition.tsv --per-protein` +- **Inputs:** FASTA file or peptide list +- **Outputs:** TSV (accession/peptide, A_count, C_count, ..., Y_count, total_residues, mw) +- **Domain:** proteomics +- **Why needed:** Basic bioinformatics utility needed for bias analysis, labeling efficiency estimation (e.g., counting K/R for TMT). No TOPP tool. +- **Score:** P=5, U=7, V=6 → **6** + +### 31. charge_state_predictor +- **Description:** Predict expected charge state distribution for peptides based on the number of basic residues (K, R, H) and N-terminal charge. +- **pyopenms classes:** `AASequence`, `Residue` +- **CLI:** `--sequence PEPTIDEK --ph 2.0 --output charges.json` or `--input peptides.tsv --output charge_predictions.tsv` +- **Inputs:** Peptide sequence(s), pH +- **Outputs:** TSV/JSON (sequence, min_charge, max_charge, most_likely_charge, basic_residues) +- **Domain:** proteomics +- **Why needed:** Useful for method development and inclusion list creation. No TOPP tool. +- **Score:** P=5, U=8, V=6 → **6** + +--- + +## Category 3: FASTA Database Tools (8 tools) + +### 32. fasta_subset_extractor +- **Description:** Extract proteins from FASTA by accession list, header keywords, taxonomy patterns, or sequence length range. +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--input uniprot.fasta --accessions accession_list.txt --output subset.fasta` or `--keyword "Homo sapiens" --min-length 50` +- **Inputs:** FASTA file, filter criteria (accession list, keyword, length range) +- **Outputs:** Filtered FASTA file +- **Domain:** proteomics +- **Why needed:** One of the most common BioStars questions. Galaxy tutorials devote entire sections to this. fasta_utilities (GitHub) addresses it. No TOPP tool. +- **Score:** P=9, U=8, V=9 → **9** + +### 33. fasta_statistics_reporter +- **Description:** Report comprehensive statistics for a FASTA database: protein count, length distribution, amino acid frequency, tryptic peptide count, duplicate detection. +- **pyopenms classes:** `FASTAFile`, `ProteaseDigestion`, `AASequence` +- **CLI:** `--input database.fasta --enzyme Trypsin --output stats.json` +- **Inputs:** FASTA file, optional enzyme for peptide counting +- **Outputs:** JSON (n_proteins, avg_length, min_length, max_length, aa_frequencies, n_tryptic_peptides, n_duplicates) +- **Domain:** proteomics +- **Why needed:** Corrupt or duplicate-laden databases cause silent search errors. No TOPP tool reports these statistics comprehensively. +- **Score:** P=7, U=9, V=8 → **8** + +### 34. contaminant_database_merger +- **Description:** Download/append cRAP contaminant sequences to a FASTA, add configurable prefix (e.g., CONT_), remove duplicate sequences, standardize headers. +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--input target.fasta --add-crap --prefix CONT_ --remove-duplicates --output merged.fasta` +- **Inputs:** Target FASTA, contaminant addition options +- **Outputs:** Merged FASTA with contaminants prefixed +- **Domain:** proteomics +- **Why needed:** Standard database preparation step. Galaxy tutorials cover it. cRAP is from thegpm.org. No TOPP tool for this specific merge + dedup + prefix workflow. +- **Score:** P=8, U=8, V=9 → **8** + +### 35. fasta_cleaner +- **Description:** Clean and validate FASTA files: remove duplicate sequences, fix header formatting, filter by length, remove stop codons and non-standard amino acids. +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--input messy.fasta --remove-duplicates --fix-headers --remove-stop-codons --min-length 6 --output clean.fasta` +- **Inputs:** FASTA file, cleaning options +- **Outputs:** Cleaned FASTA file, report of changes made +- **Domain:** proteomics +- **Why needed:** Downloaded databases often have issues. profasta (GitHub) partially addresses this. No TOPP tool. +- **Score:** P=6, U=8, V=7 → **7** + +### 36. fasta_merger +- **Description:** Merge multiple FASTA files with duplicate removal and header conflict resolution. Useful for combining databases from different sources. +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--inputs db1.fasta db2.fasta db3.fasta --remove-duplicates --output merged.fasta` +- **Inputs:** Multiple FASTA files +- **Outputs:** Merged FASTA with duplicate handling report +- **Domain:** proteomics +- **Why needed:** Common when combining UniProt + custom sequences + contaminants. No TOPP tool for multi-file merge with dedup. +- **Score:** P=6, U=7, V=7 → **7** + +### 37. fasta_decoy_validator +- **Description:** Check if a FASTA database already contains decoys, report target/decoy ratio, validate decoy prefix consistency. +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--input database.fasta --decoy-prefix DECOY_ --output validation.json` +- **Inputs:** FASTA file, expected decoy prefix +- **Outputs:** JSON (has_decoys, n_target, n_decoy, ratio, prefix_consistent, issues) +- **Domain:** proteomics +- **Why needed:** Running a search with a database that already has decoys + adding more causes inflated FDR. Common mistake. No TOPP tool validates this. +- **Score:** P=6, U=9, V=7 → **7** + +### 38. fasta_in_silico_digest_stats +- **Description:** Digest a FASTA database and report peptide-level statistics: total unique peptides, mass distribution, length distribution, peptides per protein. +- **pyopenms classes:** `FASTAFile`, `ProteaseDigestion`, `AASequence` +- **CLI:** `--input database.fasta --enzyme Trypsin --missed-cleavages 2 --min-length 7 --max-length 50 --output digest_stats.tsv` +- **Inputs:** FASTA, enzyme, digestion parameters +- **Outputs:** TSV (peptide stats), JSON (summary with distributions) +- **Domain:** proteomics +- **Why needed:** Understanding the search space size and peptide mass distribution is important for search parameter optimization. No TOPP tool outputs this. +- **Score:** P=6, U=8, V=7 → **7** + +### 39. fasta_taxonomy_splitter +- **Description:** Split a multi-organism FASTA file by taxonomy (parsed from headers) into separate per-species files. +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--input combined.fasta --header-pattern "OS=([^=]+) OX=" --output-dir split/` +- **Inputs:** FASTA file, header taxonomy pattern +- **Outputs:** Multiple FASTA files (one per species) +- **Domain:** proteomics +- **Why needed:** Metaproteomics and multi-species databases need splitting for analysis. No TOPP tool. +- **Score:** P=5, U=8, V=6 → **6** + +--- + +## Category 4: File Conversion & Export (10 tools) + +### 40. mzml_to_mgf_converter +- **Description:** Convert MS2 spectra from mzML to MGF format, preserving precursor m/z, charge, RT. Simpler and more configurable than FileConverter for this specific task (title formatting, missing charge handling). +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input run.mzML --ms-level 2 --title-format "scan={scan}" --output spectra.mgf` +- **Inputs:** mzML file, MS level, title format template +- **Outputs:** MGF file +- **Domain:** proteomics / metabolomics +- **Why needed:** Most common format conversion need. FileConverter exists but this adds value: configurable titles, charge imputation for missing charges, scan number formatting for GNPS compatibility. +- **Score:** P=9, U=7, V=9 → **8** + +### 41. ms_data_to_csv_exporter +- **Description:** Export MS data (spectra peaks, feature maps, consensus maps) to flat CSV/TSV. More flexible than TextExporter with column selection and filtering. +- **pyopenms classes:** `MSExperiment`, `FeatureMap`, `ConsensusMap`, `FeatureXMLFile`, `ConsensusXMLFile` +- **CLI:** `--input features.featureXML --columns mz,rt,intensity,charge,quality --output features.tsv` or `--input run.mzML --type peaks --output peaks.tsv` +- **Inputs:** mzML, featureXML, or consensusXML +- **Outputs:** TSV with selected columns +- **Domain:** proteomics / metabolomics +- **Why needed:** TextExporter is limited in column selection and doesn't handle all formats well. pandas df export from pyopenms is documented but not CLI-wrapped. +- **Score:** P=7, U=7, V=8 → **7** + +### 42. mztab_summarizer +- **Description:** Parse mzTab files and extract summary statistics: protein/peptide/PSM counts, FDR stats, quantification overview, modification frequencies. +- **pyopenms classes:** `MzTabFile`, `MzTab` +- **CLI:** `--input results.mzTab --output summary.tsv` +- **Inputs:** mzTab file +- **Outputs:** TSV summary (n_proteins, n_peptides, n_psms, fdr_stats, top_modifications, quant_summary) +- **Domain:** proteomics / metabolomics +- **Why needed:** mzTab is the HUPO-PSI standard exchange format for ProteomeXchange/PRIDE but hard to read manually. No TOPP tool summarizes it. +- **Score:** P=7, U=9, V=7 → **8** + +### 43. consensus_map_to_matrix +- **Description:** Convert a consensusXML file to a flat quantification matrix (rows=features, columns=samples) suitable for statistical analysis. +- **pyopenms classes:** `ConsensusMap`, `ConsensusXMLFile` +- **CLI:** `--input consensus.consensusXML --output quant_matrix.tsv --include-metadata` +- **Inputs:** ConsensusXML file +- **Outputs:** TSV matrix (feature_id, mz, rt, sample1_intensity, sample2_intensity, ...) +- **Domain:** proteomics / metabolomics +- **Why needed:** Researchers need flat matrices for R/Python statistical tools. TextExporter output is not matrix-shaped. This bridges the gap. +- **Score:** P=7, U=8, V=8 → **8** + +### 44. idxml_to_tsv_exporter +- **Description:** Export idXML identification results to a flat TSV with configurable columns (peptide, protein, score, mz, rt, charge, modifications, q-value). +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification`, `ProteinIdentification` +- **CLI:** `--input results.idXML --columns peptide,protein,score,mz,rt,charge,modifications --output results.tsv` +- **Inputs:** idXML file, column selection +- **Outputs:** Flat TSV +- **Domain:** proteomics +- **Why needed:** idXML is XML and hard to work with directly. TextExporter has limited configurability. This provides a clean export. +- **Score:** P=7, U=7, V=8 → **7** + +### 45. featurexml_merger +- **Description:** Merge multiple featureXML files from different tools/runs into a single featureXML, annotating source. +- **pyopenms classes:** `FeatureMap`, `FeatureXMLFile` +- **CLI:** `--inputs run1.featureXML run2.featureXML --output merged.featureXML` +- **Inputs:** Multiple featureXML files +- **Outputs:** Merged featureXML with source annotation +- **Domain:** proteomics / metabolomics +- **Why needed:** Useful when combining features from different algorithms. No TOPP tool for featureXML merging (FeatureLinker links, doesn't merge). +- **Score:** P=5, U=8, V=6 → **6** + +### 46. mgf_to_mzml_converter +- **Description:** Convert MGF files back to mzML format with proper metadata (instrument, source file, etc.). Reverse of tool #40. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input spectra.mgf --output spectra.mzML` +- **Inputs:** MGF file +- **Outputs:** mzML file +- **Domain:** proteomics / metabolomics +- **Why needed:** Some tools output MGF but downstream tools need mzML. FileConverter handles this but has issues with metadata preservation. +- **Score:** P=6, U=6, V=7 → **6** + +### 47. sirius_exporter +- **Description:** Export feature and MS2 data into SIRIUS-compatible .ms format for molecular formula prediction and compound class annotation. +- **pyopenms classes:** `FeatureMap`, `MSExperiment`, `FeatureXMLFile` +- **CLI:** `--features features.featureXML --mzml data.mzML --output sirius_input.ms` +- **Inputs:** featureXML + mzML +- **Outputs:** SIRIUS .ms format file +- **Domain:** metabolomics +- **Why needed:** SIRIUS is the most popular metabolomics annotation tool. UmetaFlow (published, J Cheminformatics) includes this step. No standalone CLI. +- **Score:** P=7, U=8, V=7 → **7** + +### 48. spectral_library_builder +- **Description:** Build a consensus spectral library from DDA identifications + spectra. Merge replicate PSMs for same peptide, filter by FDR, output MSP/TraML. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `IdXMLFile`, `PeptideIdentification`, `TraMLFile` +- **CLI:** `--spectra run.mzML --identifications results.idXML --fdr 0.01 --output library.msp` +- **Inputs:** mzML + idXML, FDR threshold +- **Outputs:** Spectral library (MSP, TraML, or TSV) +- **Domain:** proteomics +- **Why needed:** DIA workflows are growing rapidly. Building libraries from DDA data requires merging + filtering. No single TOPP tool does end-to-end library building. +- **Score:** P=8, U=9, V=8 → **8** + +### 49. psm_feature_extractor +- **Description:** Extract rescoring features from PSMs: precursor mass error, isotope pattern fit, fragment ion coverage, matched/total intensity ratio, delta score. +- **pyopenms classes:** `MSExperiment`, `PeptideIdentification`, `TheoreticalSpectrumGenerator`, `SpectrumAlignment` +- **CLI:** `--mzml run.mzML --identifications results.idXML --output features.tsv` +- **Inputs:** mzML + idXML +- **Outputs:** TSV (scan, peptide, mass_error_ppm, fragment_coverage, matched_intensity_ratio, delta_score, ...) +- **Domain:** proteomics +- **Why needed:** MS2Rescore (63 stars) and Mokapot (50 stars) need these features. No standalone feature extraction CLI. No TOPP tool. +- **Score:** P=7, U=9, V=7 → **8** + +--- + +## Category 5: Quality Control & Metrics (8 tools) + +### 50. lc_ms_qc_reporter +- **Description:** Generate comprehensive QC report from mzML: MS1/MS2 counts, TIC stability, precursor charge distribution, mass accuracy distribution, injection time stats, MS2 trigger rate. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input run.mzML --output qc_report.json --format json` +- **Inputs:** mzML file, optional identifications (idXML) +- **Outputs:** JSON/TSV QC report with all metrics +- **Domain:** proteomics / metabolomics +- **Why needed:** QCCalculator TOPP tool exists but is limited and outputs mzQC only. This provides a more comprehensive, human-readable report. +- **Score:** P=8, U=7, V=9 → **8** + +### 51. mzqc_generator +- **Description:** Generate mzQC-format (HUPO-PSI standard) quality control files from mzML data. Implements standard QC metric vocabulary. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--input run.mzML --identifications results.idXML --output qc_report.mzQC` +- **Inputs:** mzML, optional idXML +- **Outputs:** mzQC JSON file (HUPO-PSI standard) +- **Domain:** proteomics / metabolomics +- **Why needed:** mzQC (32 stars, new HUPO-PSI standard) is growing but generation tools are scarce. QCCalculator has limited metrics. pymzqc library helps but no CLI. +- **Score:** P=7, U=8, V=8 → **8** + +### 52. identification_qc_reporter +- **Description:** Report identification-level QC metrics from idXML: PSM/peptide/protein counts at FDR thresholds, score distributions, missed cleavage rates, modification frequencies, mass error distribution. +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification`, `ProteinIdentification` +- **CLI:** `--input results.idXML --fdr-thresholds 0.01,0.05 --output id_qc.json` +- **Inputs:** idXML file, FDR thresholds +- **Outputs:** JSON (n_psms, n_peptides, n_proteins per FDR, score_distribution, mass_error_stats, mc_distribution, mod_frequencies) +- **Domain:** proteomics +- **Why needed:** Identification statistics are manually computed. No TOPP tool provides this comprehensive summary. +- **Score:** P=7, U=9, V=8 → **8** + +### 53. run_comparison_reporter +- **Description:** Compare two or more mzML files side-by-side: TIC overlap, shared precursors, RT shift, intensity correlation. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--inputs run1.mzML run2.mzML --output comparison.json` +- **Inputs:** Two or more mzML files +- **Outputs:** JSON (n_spectra_diff, rt_range_diff, tic_correlation, shared_precursors, unique_precursors_per_run) +- **Domain:** proteomics / metabolomics +- **Why needed:** Comparing technical replicates or runs before/after instrument maintenance. No TOPP tool. +- **Score:** P=6, U=9, V=7 → **7** + +### 54. mass_error_distribution_analyzer +- **Description:** Compute and report precursor and fragment mass error distributions from identification results. Essential for calibration decisions. +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification`, `MSExperiment` +- **CLI:** `--input results.idXML --mzml run.mzML --output mass_errors.tsv` +- **Inputs:** idXML + mzML +- **Outputs:** TSV (scan, peptide, precursor_error_ppm, fragment_errors), summary stats (median, mean, std, percentiles) +- **Domain:** proteomics +- **Why needed:** Understanding mass error distribution informs search tolerance settings and calibration needs. No TOPP tool outputs this analysis. +- **Score:** P=7, U=8, V=8 → **8** + +### 55. acquisition_rate_analyzer +- **Description:** Analyze MS1/MS2 acquisition rates over time: scans per second, cycle time, duty cycle, idle time estimation. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input run.mzML --output acquisition_rate.tsv` +- **Inputs:** mzML file +- **Outputs:** TSV (time_window, ms1_rate, ms2_rate, cycle_time, duty_cycle) +- **Domain:** proteomics +- **Why needed:** Important for method optimization and comparing DDA vs DIA efficiency. No TOPP tool. +- **Score:** P=6, U=9, V=7 → **7** + +### 56. precursor_isolation_purity +- **Description:** Estimate precursor isolation purity by examining the MS1 spectrum around each selected precursor. Reports co-isolation interference. +- **pyopenms classes:** `MSExperiment`, `MSSpectrum`, `Precursor` +- **CLI:** `--input run.mzML --output purity.tsv` +- **Inputs:** mzML file +- **Outputs:** TSV (ms2_scan, precursor_mz, isolation_width, purity_fraction, n_interfering_peaks) +- **Domain:** proteomics +- **Why needed:** Co-isolation is a major source of quantification error in DDA/TMT. Chimeric spectra reduce identification rates. No TOPP tool computes this metric. +- **Score:** P=7, U=9, V=7 → **8** + +### 57. injection_time_analyzer +- **Description:** Extract and analyze injection time (ion accumulation time) values from mzML metadata across all scans. +- **pyopenms classes:** `MSExperiment`, `MSSpectrum` +- **CLI:** `--input run.mzML --output injection_times.tsv` +- **Inputs:** mzML file +- **Outputs:** TSV (scan, ms_level, rt, injection_time_ms), summary stats +- **Domain:** proteomics +- **Why needed:** Injection time is crucial for dynamic range and sensitivity analysis in Orbitrap data. Not extracted by any TOPP tool as standalone output. +- **Score:** P=5, U=9, V=6 → **7** + +--- + +## Category 6: Metabolomics-Specific Tools (12 tools) + +### 58. adduct_calculator +- **Description:** Given a molecular formula or exact mass, compute expected m/z for all common ESI adducts in positive and/or negative mode. +- **pyopenms classes:** `EmpiricalFormula`, `Element` +- **CLI:** `--formula C6H12O6 --mode positive --output adducts.tsv` or `--mass 180.0634 --mode both` +- **Inputs:** Molecular formula or exact mass, ionization mode +- **Outputs:** TSV (adduct_type, charge, expected_mz, delta_mass) +- **Domain:** metabolomics +- **Why needed:** Fiehn Lab MS Adduct Calculator is one of the most used metabolomics web tools. MSAC paper cited 40+ times. No offline CLI. No TOPP tool. +- **Score:** P=9, U=9, V=9 → **9** + +### 59. metabolite_formula_annotator +- **Description:** Annotate features with candidate molecular formulas based on accurate mass and isotope pattern matching. +- **pyopenms classes:** `EmpiricalFormula`, `CoarseIsotopePatternGenerator`, `FineIsotopePatternGenerator` +- **CLI:** `--input features.tsv --ppm 5 --elements C,H,N,O,S,P --output annotated.tsv` +- **Inputs:** Feature list (m/z, intensity, optional isotope peaks), ppm tolerance +- **Outputs:** TSV (mz, candidate_formulas, scores, isotope_fit) +- **Domain:** metabolomics +- **Why needed:** Links mass decomposition with isotope pattern scoring. No TOPP tool combines both steps. +- **Score:** P=7, U=8, V=8 → **8** + +### 60. adduct_group_analyzer +- **Description:** Given a feature list, identify groups of features that likely originate from the same metabolite (different adducts of the same compound) based on mass relationships. +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input features.tsv --rt-tolerance 5 --adducts "[M+H]+,[M+Na]+,[M+K]+,[M+NH4]+" --output groups.tsv` +- **Inputs:** Feature list (mz, rt, intensity), RT tolerance, adduct list +- **Outputs:** TSV (feature_id, group_id, adduct_type, neutral_mass) +- **Domain:** metabolomics +- **Why needed:** MetaboliteAdductDecharger is TOPP but works on featureXML. This works on simple TSV input, making it accessible without the full OpenMS pipeline. +- **Score:** P=7, U=7, V=8 → **7** + +### 61. isotope_pattern_scorer +- **Description:** Score how well an observed isotope pattern matches the theoretical pattern for a given molecular formula. Reports fit quality metrics. +- **pyopenms classes:** `EmpiricalFormula`, `CoarseIsotopePatternGenerator`, `FineIsotopePatternGenerator` +- **CLI:** `--observed-peaks "180.063:100,181.067:6.5,182.070:0.5" --formula C6H12O6 --output fit.json` +- **Inputs:** Observed peak list (mz:intensity pairs), candidate formula +- **Outputs:** JSON (formula, chi_squared, cosine_similarity, rms_error, peak_by_peak_comparison) +- **Domain:** metabolomics +- **Why needed:** Isotope pattern scoring is crucial for formula ranking but no standalone CLI exists. Complements isotope_pattern_matcher (already in repo) with scoring. +- **Score:** P=6, U=8, V=7 → **7** + +### 62. mass_difference_network_builder +- **Description:** Build a mass difference network from features: connect features whose mass difference matches known biotransformations (e.g., +15.995 oxidation, +176.032 glucuronidation). +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input features.tsv --reactions biotransformations.tsv --tolerance 0.005 --output network.tsv` +- **Inputs:** Feature list (mz, rt), biotransformation mass list, tolerance +- **Outputs:** TSV (feature1, feature2, mass_diff, reaction_name, rt_diff) +- **Domain:** metabolomics +- **Why needed:** Core technique for drug metabolism and untargeted metabolomics. GNPS molecular networking uses this principle. No TOPP tool. +- **Score:** P=7, U=9, V=7 → **8** + +### 63. targeted_feature_extractor +- **Description:** Extract features for a defined set of compounds (with known formulas and expected RTs) from MS1 data. Simpler than FeatureFinderMetaboIdent for quick targeted lookups. +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `EmpiricalFormula` +- **CLI:** `--input sample.mzML --targets compounds.tsv --rt-tolerance 30 --mz-ppm 5 --output quantified.tsv` +- **Inputs:** mzML file, target compound list (name, formula, expected_rt) +- **Outputs:** TSV (compound, observed_mz, observed_rt, intensity, peak_area, found) +- **Domain:** metabolomics +- **Why needed:** Quick targeted compound extraction without full feature detection pipeline. No simple TOPP equivalent for TSV-in/TSV-out workflow. +- **Score:** P=7, U=7, V=8 → **7** + +### 64. blank_subtraction_tool +- **Description:** Subtract blank/control features from sample features based on m/z and RT matching. Essential metabolomics preprocessing step. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--sample sample.mzML --blank blank.mzML --fold-change 3 --output cleaned.tsv` +- **Inputs:** Sample mzML, blank mzML, fold-change threshold +- **Outputs:** TSV of features present in sample but not in blank (or above fold-change threshold) +- **Domain:** metabolomics +- **Why needed:** Blank subtraction is one of the first steps in untargeted metabolomics. Usually done in R/Excel. No TOPP tool. +- **Score:** P=7, U=9, V=8 → **8** + +### 65. retention_index_calculator +- **Description:** Calculate Kovats retention indices using alkane standard retention times. Maps observed RTs to standardized RI values. +- **pyopenms classes:** `MSExperiment` (for chromatogram access) +- **CLI:** `--input run.mzML --alkane-standards standards.tsv --output ri_converted.tsv` +- **Inputs:** mzML (or feature list), alkane standard RT/carbon number table +- **Outputs:** TSV (feature_id, observed_rt, retention_index) +- **Domain:** metabolomics +- **Why needed:** RI standardization is essential for GC-MS metabolomics compound identification. No TOPP tool. +- **Score:** P=6, U=9, V=6 → **7** + +### 66. massql_query_tool +- **Description:** Query mzML data using MassQL (Mass Spec Query Language) — a SQL-like language for MS data. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--input data.mzML --query "QUERY scaninfo(MS2DATA) WHERE MS2PROD=226.18" --output results.tsv` +- **Inputs:** mzML file, MassQL query string +- **Outputs:** TSV of matching spectra/features +- **Domain:** metabolomics / proteomics +- **Why needed:** MassQL is documented in pyopenms docs as a powerful query tool. No TOPP tool. Enables complex spectrum filtering without custom code. +- **Score:** P=6, U=9, V=6 → **7** + +### 67. metabolite_class_annotator +- **Description:** Annotate features with probable metabolite classes based on mass defect ranges, adduct patterns, and RT windows characteristic of lipids, amino acids, nucleotides, etc. +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input features.tsv --class-rules metabolite_classes.tsv --output annotated.tsv` +- **Inputs:** Feature list, classification rules (TSV defining mass ranges, defect ranges per class) +- **Outputs:** TSV (feature_id, mz, rt, predicted_class, confidence) +- **Domain:** metabolomics +- **Why needed:** Quick class-level annotation before running SIRIUS/CANOPUS. No TOPP tool. +- **Score:** P=6, U=9, V=6 → **7** + +### 68. duplicate_feature_detector +- **Description:** Detect and flag duplicate/redundant features in a feature list based on m/z and RT proximity. Common artifact in feature detection. +- **pyopenms classes:** `FeatureMap`, `FeatureXMLFile` (optional — can work on TSV) +- **CLI:** `--input features.tsv --mz-tolerance 10ppm --rt-tolerance 5 --output deduplicated.tsv` +- **Inputs:** Feature list (mz, rt, intensity) +- **Outputs:** TSV with duplicate groups annotated, deduplicated feature list +- **Domain:** metabolomics +- **Why needed:** Feature detection often produces redundant features. Manual deduplication is error-prone. No TOPP tool. +- **Score:** P=6, U=8, V=7 → **7** + +### 69. formula_mass_calculator +- **Description:** Calculate exact monoisotopic/average masses for molecular formulas, with support for adducts and charge states. Batch mode for formula lists. +- **pyopenms classes:** `EmpiricalFormula`, `Element` +- **CLI:** `--formula C6H12O6 --adduct "[M+H]+" --output mass.json` or `--formula-list formulas.tsv --output masses.tsv` +- **Inputs:** Molecular formula(s), optional adduct, charge +- **Outputs:** JSON/TSV (formula, mono_mass, avg_mass, mz, elements) +- **Domain:** metabolomics +- **Why needed:** Quick scriptable mass calculations for metabolomics. No TOPP tool for formula-to-mass with adduct support. +- **Score:** P=7, U=7, V=8 → **7** + +--- + +## Category 7: RNA / Oligonucleotide Tools (3 tools) + +### 70. rna_mass_calculator +- **Description:** Calculate monoisotopic/average mass, molecular formula, and isotope distribution for RNA/oligonucleotide sequences with modifications. +- **pyopenms classes:** `NASequence`, `RibonucleotideDB`, `EmpiricalFormula` +- **CLI:** `--sequence AAUGCAAUGG --charge 3 --modifications "[m1A]" --output rna_mass.json` +- **Inputs:** RNA sequence, charge, optional modifications +- **Outputs:** JSON (sequence, mono_mass, avg_mass, mz, formula, isotope_distribution) +- **Domain:** proteomics (nucleic acids) +- **Why needed:** Growing field of RNA therapeutics and oligonucleotide MS. No TOPP tool for RNA mass calculation. +- **Score:** P=6, U=9, V=6 → **7** + +### 71. rna_digest +- **Description:** Perform in silico digestion of RNA sequences using various RNases (RNase T1, RNase U2, etc.). +- **pyopenms classes:** `RNaseDigestion`, `RNaseDB`, `NASequence` +- **CLI:** `--sequence AAUGCAAUGG --enzyme RNase_T1 --missed-cleavages 1 --output fragments.tsv` +- **Inputs:** RNA sequence, enzyme, missed cleavage limit +- **Outputs:** TSV (fragment_sequence, start, end, mono_mass, charge_states) +- **Domain:** proteomics (nucleic acids) +- **Why needed:** RNA MS is growing. No TOPP tool for RNA digestion. +- **Score:** P=5, U=9, V=5 → **6** + +### 72. rna_fragment_spectrum_generator +- **Description:** Generate theoretical fragment spectra for RNA/oligonucleotide sequences (c/y/w/a-B ion series). +- **pyopenms classes:** `NASequence`, `TheoreticalSpectrumGenerator` (RNA mode), `MSSpectrum` +- **CLI:** `--sequence AAUGC --charge 2 --output rna_fragments.tsv` +- **Inputs:** RNA sequence, charge +- **Outputs:** TSV (ion_type, position, mz, annotation) +- **Domain:** proteomics (nucleic acids) +- **Why needed:** Complementary to peptide theoretical spectrum generator for the RNA world. No TOPP tool. +- **Score:** P=5, U=9, V=5 → **6** + +--- + +## Category 8: Statistical & Quantification Tools (7 tools) + +### 73. missing_value_imputation +- **Description:** Impute missing values in protein/peptide quantification matrices using MinDet, MinProb, KNN, or left-censored methods. +- **pyopenms classes:** Minimal — reads ConsensusXML optionally via `ConsensusXMLFile`; primarily numpy/scipy +- **CLI:** `--input quant_matrix.tsv --method knn --output imputed.tsv` +- **Inputs:** Quantification matrix (TSV), method choice +- **Outputs:** Imputed TSV matrix +- **Domain:** proteomics / metabolomics +- **Why needed:** Missing values are pervasive in LFQ (30-50% missing). Most tools are in R (DEP, MSnbase). No Python CLI. No TOPP tool. +- **Score:** P=7, U=9, V=8 → **8** + +### 74. quantification_normalizer +- **Description:** Normalize a quantification matrix using median centering, quantile normalization, variance stabilization, or total intensity normalization. +- **pyopenms classes:** Minimal — can read ConsensusXML; primarily numpy/scipy +- **CLI:** `--input quant_matrix.tsv --method median --output normalized.tsv` +- **Inputs:** Quantification matrix (TSV), method +- **Outputs:** Normalized TSV matrix +- **Domain:** proteomics / metabolomics +- **Why needed:** ConsensusMapNormalizer works on consensusXML only. This works on flat TSV matrices from any source. No TOPP equivalent. +- **Score:** P=7, U=7, V=8 → **7** + +### 75. differential_expression_tester +- **Description:** Run simple differential expression analysis (t-test, Welch's t-test, Mann-Whitney) on quantification matrices. Report fold changes, p-values, adjusted p-values. +- **pyopenms classes:** Minimal — reads matrices +- **CLI:** `--input quant_matrix.tsv --design experimental_design.tsv --test ttest --correction bh --output de_results.tsv` +- **Inputs:** Quantification matrix, experimental design (sample-to-group mapping) +- **Outputs:** TSV (protein/feature, fold_change, log2fc, pvalue, adj_pvalue) +- **Domain:** proteomics / metabolomics +- **Why needed:** AlphaPeptStats (86 stars) does this but is a heavy install. Simple t-test + multiple testing correction as a CLI fills a real gap. +- **Score:** P=7, U=8, V=7 → **7** + +### 76. volcano_plot_data_generator +- **Description:** Generate volcano plot data (log2 fold change vs -log10 p-value) from quantification results with significance thresholds and protein/feature labeling. +- **pyopenms classes:** Minimal +- **CLI:** `--input de_results.tsv --fc-threshold 1.0 --pvalue-threshold 0.05 --output volcano_data.tsv` +- **Inputs:** Differential expression results (TSV with fold_change, pvalue columns) +- **Outputs:** TSV (protein, log2fc, neg_log10_pvalue, significant, label) +- **Domain:** proteomics / metabolomics +- **Why needed:** bioinfokit (367 stars) provides volcano plots. This generates the data for any plotting tool. No TOPP tool. +- **Score:** P=6, U=7, V=6 → **6** + +### 77. sample_correlation_calculator +- **Description:** Calculate pairwise Pearson/Spearman correlations between samples in a quantification matrix. Useful for QC and outlier detection. +- **pyopenms classes:** Minimal +- **CLI:** `--input quant_matrix.tsv --method pearson --output correlations.tsv` +- **Inputs:** Quantification matrix +- **Outputs:** TSV correlation matrix +- **Domain:** proteomics / metabolomics +- **Why needed:** Basic QC metric for multi-sample experiments. Usually done in R. No TOPP tool. +- **Score:** P=5, U=7, V=6 → **6** + +### 78. coefficient_of_variation_calculator +- **Description:** Calculate CV% (coefficient of variation) for features across replicates. Standard QC metric for quantification reproducibility. +- **pyopenms classes:** Minimal +- **CLI:** `--input quant_matrix.tsv --groups replicate_groups.tsv --output cv_report.tsv` +- **Inputs:** Quantification matrix, replicate grouping +- **Outputs:** TSV (feature, mean, sd, cv_percent), summary (median_cv, features_below_20pct_cv) +- **Domain:** proteomics / metabolomics +- **Why needed:** CV calculation is a universal QC metric but usually done in spreadsheets. No TOPP tool. +- **Score:** P=5, U=8, V=7 → **7** + +### 79. intensity_distribution_reporter +- **Description:** Report intensity distribution statistics per sample in a quantification matrix: median, IQR, dynamic range, number of quantified features. +- **pyopenms classes:** Minimal +- **CLI:** `--input quant_matrix.tsv --output intensity_stats.tsv` +- **Inputs:** Quantification matrix +- **Outputs:** TSV (sample, n_quantified, median_intensity, iqr, dynamic_range_log10) +- **Domain:** proteomics / metabolomics +- **Why needed:** Quick per-sample intensity QC. No TOPP tool. +- **Score:** P=5, U=8, V=6 → **6** + +--- + +## Category 9: Data Integration & Interoperability (7 tools) + +### 80. search_result_merger +- **Description:** Merge identification results from multiple search engines (idXML files) into a combined result with consensus scoring. +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification`, `ProteinIdentification` +- **CLI:** `--inputs comet.idXML msgf.idXML xtandem.idXML --output consensus.idXML --method intersection` +- **Inputs:** Multiple idXML files, merge method (union/intersection) +- **Outputs:** Merged idXML with source engine annotations +- **Domain:** proteomics +- **Why needed:** Multi-engine searching improves identifications. Ursgal (45 stars) does this. No simple pyopenms CLI. +- **Score:** P=6, U=8, V=7 → **7** + +### 81. peptide_to_protein_mapper +- **Description:** Map a list of peptide sequences to their parent proteins in a FASTA database. Report all matching proteins per peptide. +- **pyopenms classes:** `FASTAFile`, `AASequence` +- **CLI:** `--peptides peptide_list.tsv --fasta database.fasta --output mapped.tsv` +- **Inputs:** Peptide list, FASTA database +- **Outputs:** TSV (peptide, protein_accessions, positions, is_unique) +- **Domain:** proteomics +- **Why needed:** Reverse mapping peptides to proteins without running a full search. Useful for validation and targeted proteomics. No TOPP tool. +- **Score:** P=6, U=8, V=7 → **7** + +### 82. inclusion_list_generator +- **Description:** Generate instrument inclusion/exclusion lists from identification results or feature lists for targeted re-analysis. +- **pyopenms classes:** `PeptideIdentification`, `AASequence` +- **CLI:** `--input results.idXML --format thermo --min-score 0.95 --charge 2,3 --output inclusion.csv` +- **Inputs:** idXML or feature list, instrument format, filters +- **Outputs:** Instrument-specific inclusion list CSV (precursor_mz, charge, rt_start, rt_end) +- **Domain:** proteomics +- **Why needed:** Researchers manually create inclusion lists from results. Tedious and error-prone. No TOPP tool. +- **Score:** P=7, U=9, V=7 → **8** + +### 83. maxquant_result_converter +- **Description:** Convert MaxQuant output files (evidence.txt, proteinGroups.txt, msms.txt) to pyopenms-compatible formats (idXML, featureXML). +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification`, `FeatureMap` +- **CLI:** `--input evidence.txt --fasta database.fasta --output results.idXML` or `--input proteinGroups.txt --output proteins.tsv` +- **Inputs:** MaxQuant output files +- **Outputs:** idXML, featureXML, or standardized TSV +- **Domain:** proteomics +- **Why needed:** MaxQuant is the most used proteomics search engine but outputs proprietary formats. No TOPP tool for MaxQuant import. +- **Score:** P=8, U=9, V=7 → **8** + +### 84. diann_result_converter +- **Description:** Convert DIA-NN report files to pyopenms-compatible formats or standardized TSV for downstream analysis. +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification` +- **CLI:** `--input report.tsv --output results.idXML` or `--input report.tsv --format mztab --output results.mztab` +- **Inputs:** DIA-NN report.tsv +- **Outputs:** idXML, mzTab, or standardized TSV +- **Domain:** proteomics +- **Why needed:** DIA-NN (431 stars) is the top DIA engine. Converting its output to standard formats enables interoperability. No TOPP tool. +- **Score:** P=8, U=9, V=7 → **8** + +### 85. fragpipe_result_converter +- **Description:** Convert FragPipe/MSFragger output files (psm.tsv, combined_protein.tsv) to standard formats (idXML, mzTab). +- **pyopenms classes:** `IdXMLFile`, `PeptideIdentification` +- **CLI:** `--input psm.tsv --output results.idXML` +- **Inputs:** FragPipe output files +- **Outputs:** idXML, mzTab, or standardized TSV +- **Domain:** proteomics +- **Why needed:** FragPipe/MSFragger is widely used. Format conversion to standard outputs enables downstream tool interoperability. No TOPP tool. +- **Score:** P=7, U=9, V=7 → **8** + +### 86. experimental_design_generator +- **Description:** Generate OpenMS experimental design TSV from file paths and sample annotations. Required by many OpenMS workflows but tedious to create manually. +- **pyopenms classes:** `ExperimentalDesign` +- **CLI:** `--mzml-dir runs/ --conditions condition_map.tsv --output experimental_design.tsv` +- **Inputs:** Directory of mzML files, condition mapping +- **Outputs:** OpenMS experimental design TSV +- **Domain:** proteomics / metabolomics +- **Why needed:** OpenMS workflows require this file but creating it is manual and error-prone. No generator tool. +- **Score:** P=6, U=9, V=7 → **7** + +--- + +## Category 10: Specialized & Emerging Tools (14 tools) + +### 87. crosslink_mass_calculator +- **Description:** Calculate expected masses for crosslinked peptide pairs using common crosslinkers (BS3, DSS, DSSO, etc.). +- **pyopenms classes:** `AASequence`, `EmpiricalFormula` +- **CLI:** `--peptide1 PEPTIDEK --peptide2 ANOTHERPEPTIDER --crosslinker DSS --charge 3 --output crosslink_masses.tsv` +- **Inputs:** Two peptide sequences, crosslinker name, charge +- **Outputs:** TSV (crosslinked_mass, mz, crosslinker_mass, link_sites) +- **Domain:** proteomics +- **Why needed:** XL-MS is a growing structural biology technique. No simple mass calculator exists as a CLI. No TOPP tool. +- **Score:** P=6, U=9, V=6 → **7** + +### 88. glycopeptide_mass_calculator +- **Description:** Calculate masses for glycosylated peptides with common N-glycan compositions (HexNAc, Hex, Fuc, NeuAc). +- **pyopenms classes:** `AASequence`, `EmpiricalFormula` +- **CLI:** `--sequence PEPTIDEK --glycan "HexNAc(2)Hex(5)Fuc(1)" --charge 3 --output glyco_masses.tsv` +- **Inputs:** Peptide sequence, glycan composition, charge +- **Outputs:** TSV (peptide_mass, glycan_mass, total_mass, mz, composition) +- **Domain:** proteomics +- **Why needed:** Glycoproteomics is rapidly growing. Manual glycan mass calculations are error-prone. No TOPP tool. +- **Score:** P=6, U=9, V=6 → **7** + +### 89. immunopeptide_filter +- **Description:** Filter identification results for MHC-I or MHC-II peptides based on length range, binding motif, and amino acid composition. +- **pyopenms classes:** `PeptideIdentification`, `AASequence`, `IdXMLFile` +- **CLI:** `--input results.idXML --class-i --length-range 8-11 --allele HLA-A02:01 --output immunopeptides.tsv` +- **Inputs:** idXML, MHC class, length range, optional allele motif +- **Outputs:** TSV (peptide, protein, length, n_terminal_aa, c_terminal_aa, motif_match) +- **Domain:** proteomics (immunopeptidomics) +- **Why needed:** MHCflurry (237 stars) predicts binding but no simple filter tool exists. Growing field with cancer immunotherapy applications. +- **Score:** P=7, U=9, V=6 → **7** + +### 90. semi_tryptic_peptide_finder +- **Description:** Identify semi-tryptic and non-tryptic peptides in identification results. Important for degradome analysis and sample quality assessment. +- **pyopenms classes:** `ProteaseDigestion`, `AASequence`, `PeptideIdentification` +- **CLI:** `--input results.idXML --enzyme Trypsin --output semi_tryptic.tsv` +- **Inputs:** idXML, enzyme +- **Outputs:** TSV (peptide, protein, cleavage_type [fully_tryptic/semi_tryptic/non_tryptic], n_term_ok, c_term_ok) +- **Domain:** proteomics +- **Why needed:** Semi-tryptic peptides indicate degradation or biological cleavage. No TOPP tool classifies cleavage types per PSM. +- **Score:** P=6, U=9, V=7 → **7** + +### 91. collision_energy_analyzer +- **Description:** Extract and analyze collision energy (CE) values across all MS2 spectra in a DDA run. Report CE distribution and identify CE stepping patterns. +- **pyopenms classes:** `MSExperiment`, `MSSpectrum`, `Precursor` +- **CLI:** `--input run.mzML --output ce_analysis.tsv` +- **Inputs:** mzML file +- **Outputs:** TSV (scan, precursor_mz, collision_energy, ce_type), CE distribution summary +- **Domain:** proteomics +- **Why needed:** CE optimization is critical for fragmentation quality. No TOPP tool extracts and reports CE values. +- **Score:** P=5, U=9, V=6 → **7** + +### 92. ms_data_ml_exporter +- **Description:** Export MS data as feature matrices suitable for machine learning. Extract features like m/z, RT, intensity, charge, peak shape metrics. +- **pyopenms classes:** `MSExperiment`, `FeatureMap` +- **CLI:** `--input features.featureXML --features mz,rt,intensity,charge,fwhm --output ml_matrix.csv` +- **Inputs:** featureXML or mzML +- **Outputs:** CSV/numpy/parquet feature matrix +- **Domain:** proteomics / metabolomics +- **Why needed:** pyopenms docs have a dedicated ML interfacing section. No TOPP tool bridges MS data to ML formats. +- **Score:** P=6, U=8, V=6 → **7** + +### 93. peptide_spectral_match_validator +- **Description:** Validate individual PSMs by re-scoring: compute fragment ion coverage, precursor mass error, b/y ion ratio, explained intensity fraction. +- **pyopenms classes:** `TheoreticalSpectrumGenerator`, `SpectrumAlignment`, `MSExperiment`, `PeptideIdentification` +- **CLI:** `--mzml run.mzML --identifications results.idXML --output validation.tsv` +- **Inputs:** mzML + idXML +- **Outputs:** TSV (scan, peptide, fragment_coverage, explained_intensity, mass_error_ppm, b_y_ratio, valid) +- **Domain:** proteomics +- **Why needed:** Standalone PSM validation without full rescoring. Useful for manual curation and suspicious hit investigation. No TOPP tool. +- **Score:** P=7, U=8, V=7 → **7** + +### 94. protein_group_reporter +- **Description:** Parse protein groups from idXML and generate a clean protein-level report with group membership, razor peptides, and protein scores. +- **pyopenms classes:** `IdXMLFile`, `ProteinIdentification` +- **CLI:** `--input results.idXML --output protein_groups.tsv` +- **Inputs:** idXML with protein inference results +- **Outputs:** TSV (group_id, protein_accessions, n_peptides, n_unique_peptides, n_razor_peptides, score, coverage) +- **Domain:** proteomics +- **Why needed:** Extracting clean protein group tables from idXML is cumbersome. TextExporter output is not group-oriented. No TOPP tool. +- **Score:** P=6, U=8, V=7 → **7** + +### 95. mzml_metadata_extractor +- **Description:** Extract instrument metadata from mzML files: instrument model, serial number, acquisition software, source file, contact info, processing methods. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--input run.mzML --output metadata.json` +- **Inputs:** mzML file +- **Outputs:** JSON (instrument_model, serial_number, software, source_file, acquisition_date, contact) +- **Domain:** proteomics / metabolomics +- **Why needed:** Metadata extraction is needed for data management, LIMS integration, and ProteomeXchange submission. No TOPP tool outputs just metadata. +- **Score:** P=6, U=8, V=7 → **7** + +### 96. sequence_tag_generator +- **Description:** Generate de novo sequence tags from MS2 spectra — short amino acid subsequences derived from fragment ion ladders without database search. +- **pyopenms classes:** `MSSpectrum`, `Residue`, `ResidueDB` +- **CLI:** `--input spectrum.mzML --scan-index 500 --tolerance 0.02 --min-tag-length 3 --output tags.tsv` +- **Inputs:** mzML + scan index, tolerance, minimum tag length +- **Outputs:** TSV (tag_sequence, start_mass, end_mass, score, ion_series) +- **Domain:** proteomics +- **Why needed:** Sequence tags enable error-tolerant searching and de novo sequencing validation. No TOPP tool generates tags from single spectra. +- **Score:** P=6, U=9, V=6 → **7** + +### 97. precursor_recurrence_analyzer +- **Description:** Analyze how often the same precursor m/z is selected for MS2 across a run (redundant MS2 triggers). Key DDA efficiency metric. +- **pyopenms classes:** `MSExperiment`, `MSSpectrum`, `Precursor` +- **CLI:** `--input run.mzML --mz-tolerance 10ppm --rt-tolerance 30 --output recurrence.tsv` +- **Inputs:** mzML file, grouping tolerances +- **Outputs:** TSV (precursor_mz, n_selections, first_rt, last_rt, total_rt_span) +- **Domain:** proteomics +- **Why needed:** Understanding MS2 redundancy helps optimize dynamic exclusion settings. No TOPP tool. +- **Score:** P=6, U=9, V=6 → **7** + +### 98. peptide_mass_fingerprint +- **Description:** Generate a peptide mass fingerprint (PMF) from in silico digestion of a protein. Compare against observed masses for simple protein identification. +- **pyopenms classes:** `ProteaseDigestion`, `AASequence`, `FASTAFile` +- **CLI:** `--fasta proteins.fasta --enzyme Trypsin --accession P12345 --output fingerprint.tsv` or `--observe masses.tsv --fasta proteins.fasta --tolerance 10ppm --output matches.tsv` +- **Inputs:** FASTA + protein accession (generation mode) or observed masses + FASTA (matching mode) +- **Outputs:** TSV (peptide, mass) or TSV (protein, matched_peptides, coverage, score) +- **Domain:** proteomics +- **Why needed:** PMF is still used in MALDI-TOF workflows. No standalone Python CLI. No TOPP tool for PMF matching. +- **Score:** P=5, U=8, V=5 → **6** + +### 99. sample_complexity_estimator +- **Description:** Estimate sample complexity from MS1 data: number of distinct isotope envelopes, feature density over RT, dynamic range. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--input run.mzML --output complexity.json` +- **Inputs:** mzML file +- **Outputs:** JSON (estimated_features, peak_density_per_second, dynamic_range_log10, max_concurrent_peptides) +- **Domain:** proteomics / metabolomics +- **Why needed:** Helps decide fractionation strategy and instrument method parameters. No TOPP tool. +- **Score:** P=5, U=9, V=5 → **6** + +### 100. ms1_feature_intensity_tracker +- **Description:** Track intensity of specific MS1 features (m/z + RT) across a batch of runs. Report presence/absence and intensity trends. +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--inputs run1.mzML run2.mzML run3.mzML --features targets.tsv --ppm 10 --rt-tolerance 30 --output tracking.tsv` +- **Inputs:** Multiple mzML files, target feature list (mz, expected_rt) +- **Outputs:** TSV (feature_mz, feature_rt, run1_intensity, run2_intensity, ..., cv_percent) +- **Domain:** proteomics / metabolomics +- **Why needed:** Batch-level feature monitoring for QC and longitudinal studies. No TOPP tool. +- **Score:** P=6, U=9, V=7 → **7** + +--- + +--- + +## Category 11: Niche Proteomics — Paper-Level Tools (22 tools) + +*These tools address specialized workflows that researchers perform in published papers. Each references specific DOIs.* + +### 101. phosphosite_class_filter +- **Description:** Classify phosphosites into Class I/II/III by localization probability. Report enrichment efficiency (phospho/total ratio) and mono/di/tri-phospho distribution. +- **Paper:** Beausoleil et al. "A probability-based approach for phosphorylation analysis" (DOI: 10.1038/nbt1240); Comparing 22 Pipelines (DOI: 10.1021/acs.jproteome.9b00679) +- **pyopenms classes:** `AASequence`, `ModificationsDB`, `ResidueModification` +- **CLI:** `--input phosphosites.tsv --class1-threshold 0.75 --output classified_sites.tsv` +- **Domain:** proteomics (phosphoproteomics) +- **Score:** P=8, U=9, V=8 → **8** + +### 102. phospho_motif_analyzer +- **Description:** Extract ±7aa windows around phosphosites, compute position-specific amino acid frequencies vs. proteome background, match kinase substrate motifs (PKA: RxxS, CK2: SxxE, etc.). +- **Paper:** MMFPh (DOI: 10.1093/bioinformatics/bts195); pLogo (DOI: 10.1038/nmeth.2541) +- **pyopenms classes:** `AASequence`, `FASTAFile`, `ResidueDB` +- **CLI:** `--input phosphosites.tsv --fasta proteome.fasta --window 7 --kinase-motifs kinase_db.tsv --output motifs.tsv` +- **Domain:** proteomics (phosphoproteomics) +- **Score:** P=7, U=9, V=8 → **8** + +### 103. phospho_enrichment_qc +- **Description:** From search results, compute phospho-enrichment efficiency (%), singly/multiply phosphorylated fractions, and pSer/pThr/pTyr ratios. +- **Paper:** Humphrey et al. "High-throughput phosphoproteomics" (Nat Biotechnol 2015) +- **pyopenms classes:** `AASequence`, `ModificationsDB` +- **CLI:** `--input search_results.tsv --modification Phospho --output enrichment_stats.tsv` +- **Domain:** proteomics (phosphoproteomics) +- **Score:** P=7, U=9, V=7 → **8** + +### 104. proteogenomics_db_builder +- **Description:** Build custom FASTA from reference proteome + VCF variants (SAVs, indels). Generate mutant protein entries with flanking residue context. +- **Paper:** moPepGen (DOI: 10.1038/s41587-025-02701-0); Proteogenomic DB from RNA-seq (DOI: 10.1186/1471-2105-15-S7-S7) +- **pyopenms classes:** `FASTAFile`, `AASequence` +- **CLI:** `--fasta reference.fasta --vcf variants.vcf --flanking 7 --output custom_db.fasta` +- **Domain:** proteomics (proteogenomics) +- **Score:** P=7, U=9, V=7 → **8** + +### 105. variant_peptide_validator +- **Description:** Validate SAV peptide identifications: check isobaric mass ambiguity with canonical peptides, count variant-site-determining fragment ions. +- **Paper:** PgxSAVy (DOI: 10.1016/j.mcpro.2023.100653) +- **pyopenms classes:** `TheoreticalSpectrumGenerator`, `SpectrumAlignment`, `AASequence` +- **CLI:** `--input variant_psms.tsv --fasta reference.fasta --spectra run.mzML --min-site-ions 3 --output validated.tsv` +- **Domain:** proteomics (proteogenomics) +- **Score:** P=6, U=9, V=7 → **7** + +### 106. hdx_deuterium_uptake +- **Description:** Calculate deuterium uptake from HDX-MS data: centroid mass shift, relative fractional uptake, back-exchange correction using fully deuterated control. +- **Paper:** HDX-MS Recommendations (DOI: 10.1038/s41592-019-0459-y) +- **pyopenms classes:** `AASequence`, `EmpiricalFormula`, `MSSpectrum` +- **CLI:** `--peptides peptide_list.tsv --undeuterated ref.tsv --fully-deuterated fd.tsv --timepoints 0,10,60,300,3600 --output uptake.tsv` +- **Domain:** proteomics (structural) +- **Score:** P=7, U=9, V=7 → **8** + +### 107. hdx_back_exchange_estimator +- **Description:** Estimate per-peptide back-exchange rates from fully deuterated controls. Flag peptides with >40% back-exchange as unreliable. +- **Paper:** Same HDX-MS recommendations (DOI: 10.1038/s41592-019-0459-y) +- **pyopenms classes:** `AASequence`, `EmpiricalFormula` +- **CLI:** `--peptides peptide_list.tsv --fully-deuterated fd.tsv --max-backexchange 40 --output backexchange.tsv` +- **Domain:** proteomics (structural) +- **Score:** P=6, U=9, V=6 → **7** + +### 108. xl_distance_validator +- **Description:** Given crosslinks (residue pairs) and a PDB structure, compute Cα-Cα distances. Classify as satisfied (<30Å for DSS/BS3) or violated. +- **Paper:** Distance restraints from XL-MS (DOI: 10.1002/pro.2458); XL-MS in the human cell (DOI: 10.1073/pnas.2219418120) +- **pyopenms classes:** `FASTAFile`, `AASequence` +- **CLI:** `--crosslinks crosslinks.tsv --pdb structure.pdb --crosslinker DSS --max-distance 30 --output distances.tsv` +- **Domain:** proteomics (structural) +- **Score:** P=6, U=9, V=7 → **7** + +### 109. xl_link_classifier +- **Description:** Classify crosslinks as intra-protein, inter-protein, or dead-end/monolink. Compute statistics for interaction network analysis. +- **Paper:** XL-MS emerging technology (DOI: 10.1021/acs.analchem.7b04431) +- **pyopenms classes:** `FASTAFile`, `AASequence` +- **CLI:** `--crosslinks crosslinks.tsv --fasta proteome.fasta --output classified.tsv` +- **Domain:** proteomics (structural) +- **Score:** P=6, U=9, V=6 → **7** + +### 110. metapeptide_lca_assigner +- **Description:** For each peptide, find all matching proteins in a metaproteome DB, retrieve taxonomy lineages, compute lowest common ancestor (LCA). Build taxonomic profile. +- **Paper:** Unipept 2024 (DOI: 10.1101/2024.09.26.615136); METATRYP v2.0 (PMID: 32897080) +- **pyopenms classes:** `FASTAFile`, `ProteaseDigestion` +- **CLI:** `--peptides identified.tsv --fasta metadb.fasta --taxonomy lineage.tsv --rank genus --output taxonomy.tsv` +- **Domain:** proteomics (metaproteomics) +- **Score:** P=7, U=9, V=7 → **8** + +### 111. cleavage_site_profiler +- **Description:** From neo-N-terminal peptides (TAILS/degradomics), extract P4-P4' cleavage windows, compute position-specific amino acid frequencies for protease specificity profiling (iceLogo-style). +- **Paper:** TAILS N-terminomics (DOI: 10.1074/jbc.RA117.001113); MANTI (DOI: 10.1021/acs.analchem.1c00310) +- **pyopenms classes:** `FASTAFile`, `AASequence`, `ResidueDB` +- **CLI:** `--neo-ntermini peptides.tsv --fasta reference.fasta --window 4 --output cleavage_profile.tsv` +- **Domain:** proteomics (degradomics) +- **Score:** P=6, U=9, V=7 → **7** + +### 112. nterm_modification_annotator +- **Description:** Classify N-terminal peptides as: protein N-terminus, signal peptide cleavage, transit peptide, neo-N-terminus, or acetylated. Cross-reference UniProt features. +- **Paper:** Data Processing in Positional Proteomics (DOI: 10.1002/pmic.70069) +- **pyopenms classes:** `FASTAFile`, `AASequence`, `ModificationsDB` +- **CLI:** `--input nterm_peptides.tsv --fasta reference.fasta --uniprot-features features.tsv --output annotated.tsv` +- **Domain:** proteomics (N-terminomics) +- **Score:** P=6, U=9, V=6 → **7** + +### 113. immunopeptidome_qc +- **Description:** From immunopeptidome results: compute length distribution (8-12 for HLA-I), extract anchor residue frequencies (pos 2 and C-term), compute information content as QC metric. +- **Paper:** MhcVizPipe (GitHub: CaronLab/MhcVizPipe); MHC Motif Atlas (DOI: 10.1093/nar/gkac965) +- **pyopenms classes:** `AASequence`, `ResidueDB` +- **CLI:** `--input hla_peptides.tsv --hla-class I --output length_dist.tsv --motifs anchor_freq.tsv` +- **Domain:** proteomics (immunopeptidomics) +- **Score:** P=7, U=9, V=7 → **8** + +### 114. proteoform_delta_annotator +- **Description:** Given observed intact proteoform masses, compute pairwise mass differences and annotate against known PTMs (acetyl +42.01, phospho +79.97, methyl +14.02, etc.). +- **Paper:** TopPIC (DOI: 10.1093/bioinformatics/btw398); ProteoCombiner (DOI: 10.1093/bioinformatics/btab175) +- **pyopenms classes:** `ModificationsDB`, `EmpiricalFormula` +- **CLI:** `--input proteoform_masses.tsv --tolerance 0.5 --output annotated_deltas.tsv` +- **Domain:** proteomics (top-down) +- **Score:** P=6, U=9, V=6 → **7** + +### 115. topdown_coverage_calculator +- **Description:** From fragment ion assignments (b/y/c/z), compute per-residue bond cleavage coverage for intact proteins. Output coverage map. +- **Paper:** VisioProt-MS (DOI: 10.1177/1177932219868223) +- **pyopenms classes:** `TheoreticalSpectrumGenerator`, `AASequence`, `SpectrumAlignment` +- **CLI:** `--sequence PROTEIN_SEQ --fragments observed.tsv --ion-types b,y,c,z --tolerance 10ppm --output coverage.tsv` +- **Domain:** proteomics (top-down) +- **Score:** P=6, U=9, V=6 → **7** + +### 116. library_coverage_estimator +- **Description:** Given a spectral library and FASTA proteome, compute % proteins with ≥N library entries, peptide-level coverage per protein, identify "dark" proteins with no library peptides. +- **Paper:** In silico spectral libraries (DOI: 10.1038/s41467-019-13866-z); MSLibrarian (DOI: 10.1021/acs.jproteome.1c00796) +- **pyopenms classes:** `FASTAFile`, `ProteaseDigestion`, `AASequence` +- **CLI:** `--library lib.tsv --fasta proteome.fasta --enzyme Trypsin --min-peptides 2 --output coverage.tsv` +- **Domain:** proteomics (DIA) +- **Score:** P=7, U=9, V=7 → **8** + +### 117. silac_halflife_calculator +- **Description:** From pulsed-SILAC time course H/L ratios, fit exponential decay model: H/L(t) = 1-exp(-kt). Compute degradation rate k and half-life = ln(2)/k. Correct for cell division. +- **Paper:** JUMPt (DOI: 10.1021/acs.analchem.1c02309); Protein lifetimes in mouse brain (DOI: 10.1038/s41467-018-06519-0) +- **pyopenms classes:** `AASequence`, `EmpiricalFormula` (SILAC label masses: heavy Lys +8.014, heavy Arg +10.008) +- **CLI:** `--input hl_ratios.tsv --timepoints 0,6,12,24,48 --doubling-time 24 --output halflives.tsv` +- **Domain:** proteomics (turnover) +- **Score:** P=7, U=9, V=7 → **8** + +### 118. scp_reporter_qc +- **Description:** Single-cell proteomics QC: compute sample-to-carrier ratio (SCR) per spectrum, blank channel contamination, reporter ion intensities across TMT channels. +- **Paper:** plexDIA (DOI: 10.1038/s41587-022-01389-w); QuantQC (DOI: 10.1021/jasms.3c00238) +- **pyopenms classes:** `MSExperiment`, `MSSpectrum`, `MzMLFile` +- **CLI:** `--input run.mzML --carrier-channel 131C --single-channels 127N,127C,128N --scr-threshold 0.1 --output qc.tsv` +- **Domain:** proteomics (single-cell) +- **Score:** P=7, U=9, V=7 → **8** + +### 119. biomarker_panel_roc +- **Description:** From protein quantification with case/control labels, compute per-protein ROC curves (AUC), evaluate multi-marker panels using logistic regression. +- **Paper:** CombiROC (DOI: 10.1038/srep45477); Feature selection for biomarkers (DOI: 10.1016/j.mcpro.2021.100083) +- **pyopenms classes:** Minimal — reads quantification matrices +- **CLI:** `--input protein_quant.tsv --groups case,control --max-panel-size 5 --output panel_roc.tsv` +- **Domain:** proteomics (clinical) +- **Score:** P=7, U=8, V=7 → **7** + +### 120. isobaric_purity_corrector +- **Description:** Correct TMT/iTRAQ reporter ion intensities for isotopic impurity using manufacturer-provided correction factor matrices. +- **Paper:** TMT quantification methods (DOI: 10.1021/acs.jproteome.9b00227) +- **pyopenms classes:** `MSSpectrum`, `MSExperiment` +- **CLI:** `--input quantified.tsv --label TMT16plex --purity-matrix purity.csv --output corrected.tsv` +- **Domain:** proteomics (isobaric labeling) +- **Score:** P=7, U=9, V=8 → **8** + +### 121. metapeptide_function_aggregator +- **Description:** Aggregate GO/KEGG/COG functional annotations from peptide-to-protein mappings, weighted by spectral counts or intensity. Build functional profile. +- **Paper:** Unipept 2024; MetaProteomeAnalyzer (DOI: 10.1021/acs.jproteome.4c00142) +- **pyopenms classes:** `FASTAFile` +- **CLI:** `--peptides identified.tsv --fasta metadb.fasta --annotations go_terms.tsv --method spectral_count --output function.tsv` +- **Domain:** proteomics (metaproteomics) +- **Score:** P=6, U=9, V=6 → **7** + +### 122. protein_completeness_matrix +- **Description:** For quantification results, compute data completeness (% non-missing values) per protein and per sample. Flag proteins below completeness threshold. +- **Paper:** SCoPE2 computational analysis (scp.slavovlab.net); DEP Bioconductor package analysis +- **pyopenms classes:** Minimal +- **CLI:** `--input quant_matrix.tsv --min-completeness 0.5 --output completeness.tsv` +- **Domain:** proteomics +- **Score:** P=6, U=8, V=7 → **7** + +--- + +## Category 12: Niche Metabolomics — Paper-Level Tools (18 tools) + +### 123. van_krevelen_data_generator +- **Description:** Compute H:C and O:C ratios from molecular formulas for Van Krevelen diagram analysis. Classify compounds into biochemical classes (lipids, amino acids, carbohydrates, nucleotides, condensed hydrocarbons). +- **Paper:** Brockman et al. "OpenVanKrevelen" (DOI: 10.1007/s11306-018-1343-y) +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input formulas.tsv --classify --output van_krevelen.tsv` +- **Domain:** metabolomics +- **Score:** P=7, U=9, V=7 → **8** + +### 124. kendrick_mass_defect_analyzer +- **Description:** Compute Kendrick mass defect for arbitrary repeating units (CH2, CF2, C2H4O). Group features into homologous series. Detect lipid chains, PFAS, polymers. +- **Paper:** Sleno "Mass defect in modern MS" (J Mass Spectrom 2012); Fouquet & Sato (2017) +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input features.tsv --base CH2 --output kmd_series.tsv` +- **Domain:** metabolomics +- **Score:** P=7, U=9, V=7 → **8** + +### 125. drug_metabolite_screener +- **Description:** Given a parent drug formula, generate expected metabolite masses from Phase I (oxidation +15.995, demethylation -14.016, hydrolysis +18.011) and Phase II (glucuronidation +176.032, sulfation +79.957, glutathione +305.068) reactions. Screen mzML against predictions. +- **Paper:** Zhang et al. "Mass defect filter for drug metabolites" (PMID: 19598168); BioTransformer (DOI: 10.1186/s13321-018-0324-5) +- **pyopenms classes:** `EmpiricalFormula`, `MSExperiment`, `MzMLFile` +- **CLI:** `--parent-formula C17H14ClN3O --reactions phase1,phase2 --input run.mzML --ppm 5 --output metabolites.tsv` +- **Domain:** metabolomics (drug metabolism) +- **Score:** P=7, U=9, V=8 → **8** + +### 126. isf_detector +- **Description:** Detect in-source fragmentation artifacts by coelution analysis (RT correlation ≥0.9) and matching fragment ions in MS2 of putative precursors. Common neutral losses: H2O, CO2, NH3. +- **Paper:** ISFrag (DOI: 10.1021/acs.analchem.1c01644); Mahieu et al. (DOI: 10.1021/ac504118y) +- **pyopenms classes:** `MSExperiment`, `MzMLFile`, `MSSpectrum` +- **CLI:** `--input features.tsv --mzml run.mzML --rt-correlation 0.9 --output isf_annotated.tsv` +- **Domain:** metabolomics +- **Score:** P=7, U=9, V=8 → **8** + +### 127. mid_natural_abundance_corrector +- **Description:** Correct mass isotopomer distributions (MID) for natural 13C/15N/2H abundance. Build correction matrix from molecular formula, solve via NNLS. Essential for 13C flux analysis. +- **Paper:** Corna (DOI: 10.1101/2020.09.19.304741); Fernandez et al. (J Mass Spectrom 1996) +- **pyopenms classes:** `EmpiricalFormula`, `CoarseIsotopePatternGenerator` +- **CLI:** `--input isotopologues.tsv --formula C6H12O6 --tracer 13C --method nnls --output corrected_mid.tsv` +- **Domain:** metabolomics (fluxomics) +- **Score:** P=7, U=9, V=8 → **8** + +### 128. isotope_label_detector +- **Description:** Compare unlabeled vs. uniformly 13C/15N-labeled samples. Detect paired features by RT and expected mass shift. Report carbon/nitrogen count per metabolite. +- **Paper:** X13CMS (DOI: 10.1021/ac403384n); MetExtract II (DOI: 10.1021/acs.analchem.7b02518) +- **pyopenms classes:** `MzMLFile`, `EmpiricalFormula`, `CoarseIsotopePatternGenerator` +- **CLI:** `--unlabeled unlabeled.mzML --labeled labeled.mzML --tracer 13C --ppm 5 --output labeled_pairs.tsv` +- **Domain:** metabolomics (stable isotope tracing) +- **Score:** P=7, U=9, V=7 → **8** + +### 129. formula_validator_golden_rules +- **Description:** Apply Kind & Fiehn's Seven Golden Rules to filter molecular formula candidates: element limits, LEWIS/SENIOR valence, H/C ratio (0.2-3.1), N/C O/C S/C P/C ratios, RDBE check. +- **Paper:** Kind & Fiehn "Seven Golden Rules" (DOI: 10.1186/1471-2105-8-105) — cited >2500 times +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input candidate_formulas.tsv --rules all --output validated.tsv` +- **Domain:** metabolomics +- **Score:** P=8, U=9, V=8 → **8** + +### 130. rdbe_calculator +- **Description:** Calculate Ring/Double Bond Equivalence (RDBE = (2C+2-H+N+P)/2) for molecular formulas. Flag impossible formulas (negative RDBE, non-integer for even-electron ions). +- **Paper:** Fiehn Lab documentation; universally used in structure elucidation +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input formulas.tsv --output rdbe_values.tsv` +- **Domain:** metabolomics +- **Score:** P=6, U=8, V=7 → **7** + +### 131. spectral_entropy_scorer +- **Description:** Weight MS/MS peaks by Shannon entropy, compute entropy-based similarity instead of cosine. Outperforms 42 alternative metrics per the Li & Fiehn paper. +- **Paper:** Li & Fiehn (DOI: 10.1038/s41592-021-01331-z) — Nature Methods 2021 +- **pyopenms classes:** `MSSpectrum`, `MzMLFile` +- **CLI:** `--query query.mgf --library ref.mgf --tolerance 0.02 --output entropy_scores.tsv` +- **Domain:** metabolomics / proteomics +- **Score:** P=8, U=9, V=8 → **8** + +### 132. suspect_screener +- **Description:** Match detected exact masses against curated suspect screening lists (CompTox, NORMAN, PubChemLite). Rank by mass accuracy, isotope fit, data source count. +- **Paper:** Scannotation (DOI: 10.1021/acs.est.3c04764); EPA CompTox Dashboard +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input features.tsv --suspects comptox_list.csv --ppm 5 --output matches.tsv` +- **Domain:** metabolomics (exposomics/environmental) +- **Score:** P=7, U=9, V=7 → **8** + +### 133. lipid_ecn_rt_predictor +- **Description:** Predict lipid RT from Equivalent Carbon Number (ECN = carbons - 2×double bonds). Lipids with same headgroup show linear RT vs ECN. Transfer predictions across lipid classes. +- **Paper:** Koelmel et al. (DOI: 10.1021/acs.analchem.1c03770) +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input lipid_annotations.tsv --calibration standards.tsv --output rt_predictions.tsv` +- **Domain:** metabolomics (lipidomics) +- **Score:** P=6, U=9, V=6 → **7** + +### 134. lipid_species_resolver +- **Description:** From sum composition lipid annotations (e.g., "PC 36:2"), enumerate all acyl chain combinations (16:0/20:2, 18:1/18:1), compute masses, match against observed MS2 fragments. +- **Paper:** Goslin nomenclature (DOI: 10.1021/acs.analchem.0c01690); Fatty acyl C=C (DOI: 10.1038/s41467-025-61911-x) +- **pyopenms classes:** `EmpiricalFormula`, `TheoreticalSpectrumGenerator` concepts +- **CLI:** `--input lipid_features.tsv --lipid-class PC --fragments fragments.tsv --ppm 5 --output resolved.tsv` +- **Domain:** metabolomics (lipidomics) +- **Score:** P=6, U=9, V=6 → **7** + +### 135. kovats_ri_calculator +- **Description:** Calculate Kovats Retention Index from alkane standard RTs. Convert instrument-specific RT to universal RI for NIST/Fiehnlib library matching. +- **Paper:** Kind et al. (DOI: 10.1093/bioinformatics/btp056) +- **pyopenms classes:** `MzMLFile` +- **CLI:** `--input features.tsv --alkane-standards standards.tsv --mode temperature_programmed --output ri_converted.tsv` +- **Domain:** metabolomics (GC-MS) +- **Score:** P=6, U=9, V=6 → **7** + +### 136. gnps_fbmn_exporter +- **Description:** Export consensus MS2 spectra + quantification table in GNPS Feature-Based Molecular Networking format. Handle SCANS, PEPMASS, RTINSECONDS, MSLEVEL fields correctly. +- **Paper:** Nothias et al. FBMN (DOI: 10.1038/s41592-020-0933-6) +- **pyopenms classes:** `MzMLFile`, `FeatureXMLFile`, `MSSpectrum` +- **CLI:** `--mzml-dir runs/ --features features.tsv --output-mgf gnps.mgf --output-quant gnps_quant.csv` +- **Domain:** metabolomics (molecular networking) +- **Score:** P=8, U=8, V=8 → **8** + +### 137. isotope_pattern_fit_scorer +- **Description:** Compare experimental isotope envelope (M, M+1, M+2, M+3) against theoretical distribution for candidate formulas. Score by chi-square, cosine, sigma. Detect Cl/Br/S signatures from M+2 intensity. +- **Paper:** Pluskal et al. (DOI: 10.1021/ac3000418); Kind & Fiehn Seven Golden Rules (Rule 3) +- **pyopenms classes:** `EmpiricalFormula`, `CoarseIsotopePatternGenerator`, `FineIsotopePatternGenerator` +- **CLI:** `--observed "180.063:100,181.067:6.5,182.070:0.5" --candidates formulas.tsv --charge 1 --output scored.tsv` +- **Domain:** metabolomics +- **Score:** P=7, U=8, V=8 → **8** + +### 138. mass_difference_network_builder +- **Description:** Build mass difference network connecting features whose Δm/z matches known biotransformations. Detect oxidation (+15.995), methylation (+14.016), glucuronidation (+176.032), etc. +- **Paper:** GNPS molecular networking principle; Watrous et al. (DOI: 10.1073/pnas.1203689109) +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input features.tsv --reactions biotransformations.tsv --tolerance 0.005 --output network.tsv` +- **Domain:** metabolomics +- **Score:** P=7, U=9, V=7 → **8** + +### 139. metabolite_class_predictor +- **Description:** Predict compound class (lipid, amino acid, nucleotide, carbohydrate, organic acid) from mass defect ranges, H:C/O:C ratios, and RDBE values — without structural information. +- **Paper:** ClassyFire (DOI: 10.1186/s13321-016-0174-y) uses structure; this uses mass-only heuristics +- **pyopenms classes:** `EmpiricalFormula` +- **CLI:** `--input formulas.tsv --output class_predictions.tsv` +- **Domain:** metabolomics +- **Score:** P=6, U=9, V=6 → **7** + +### 140. blank_subtraction_tool +- **Description:** Compare sample vs. blank features by m/z+RT matching. Remove features present in blank or below fold-change threshold. Standard metabolomics preprocessing. +- **Paper:** UmetaFlow (DOI: 10.1186/s13321-023-00724-w); XCMS-based workflows +- **pyopenms classes:** `MSExperiment`, `MzMLFile` +- **CLI:** `--sample sample_features.tsv --blank blank_features.tsv --fold-change 3 --mz-ppm 5 --rt-tolerance 10 --output cleaned.tsv` +- **Domain:** metabolomics +- **Score:** P=7, U=9, V=8 → **8** + +--- + +## Category 13: Enhanced Features for Existing Tools + +*Paper-level feature additions that enrich tools in Categories 1-10:* + +### Tool #2 spectrum_similarity_scorer — Enhanced +- Add **spectral entropy scoring** (Li & Fiehn 2021, Nature Methods) alongside cosine +- Add **modified cosine** for shifted precursors (molecular networking) +- Add **Stein-Scott composite score** for library matching +- Add **neutral loss-aware matching** for metabolomics + +### Tool #9 xic_extractor — Enhanced +- Add **peak area integration** (trapezoidal rule) — reported in every quantification paper +- Add **FWHM calculation** per XIC peak — standard chromatographic QC metric +- Add **signal-to-noise estimation** — essential for LOD/LOQ determination +- Add **batch mode** for extracting XICs across multiple runs simultaneously + +### Tool #16 peptide_property_calculator — Enhanced +- Add **Kyte-Doolittle sliding window** hydrophobicity profiles — amphipathicity analysis +- Add **Boman interaction index** — antimicrobial peptide studies (modlAMP feature) +- Add **Wimley-White interfacial hydrophobicity** — membrane interaction prediction +- Add **net charge vs pH curve** generation — electrophoresis prediction + +### Tool #22 missed_cleavage_analyzer — Enhanced +- Add **P4-P4' position-specific frequency analysis** — iceLogo-style cleavage specificity +- Add **enzyme specificity profiling** — beyond missed cleavages, analyze actual vs. expected cleavage sites +- Add **comparison mode** — compare missed cleavage rates across multiple runs + +### Tool #50 lc_ms_qc_reporter — Enhanced +- Add **longitudinal QC trending** — track metrics across a batch of runs over time +- Add **system suitability criteria** — pass/fail thresholds for each metric +- Add **peak capacity estimation** from MS1 data — method optimization metric +- Add **TopN sampling depth** — fraction of MS1 features with MS2 attempts + +### Tool #58 adduct_calculator — Enhanced +- Add **in-source fragmentation prediction** — common ISF losses alongside adducts +- Add **multimer prediction** — [2M+H]+, [2M+Na]+, [3M+H]+ +- Add **cluster ion prediction** — [M+Na+K-H]+, [M+2Na-H]+ + +--- + +## Score Summary (All 145 Tools) + +| Score | Count | Examples | +|-------|-------|---------| +| **9** | 5 | spectrum_similarity_scorer, xic_extractor, peptide_property_calculator, fasta_subset_extractor, adduct_calculator | +| **8** | 54 | + niche tools: phosphosite_class_filter, hdx_deuterium_uptake, golden_rules_validator, spectral_entropy, drug_metabolite_screener, FBMN exporter, MID corrector, etc. | +| **7** | 58 | + niche tools: xl_distance_validator, cleavage_site_profiler, lipid_species_resolver, kovats_ri, etc. | +| **6** | 18 | score-6 utilities and emerging tools | + +## Implementation Priority Tiers + +**Tier 1 — Implement First (Score 9, highest impact — 5 tools):** +1. spectrum_similarity_scorer (#2) +2. xic_extractor (#9) +3. peptide_property_calculator (#16) +4. fasta_subset_extractor (#32) +5. adduct_calculator (#58) + +**Tier 2 — High Value (Score 8 — 54 tools):** +Core utilities: #1, #3, #5, #7, #8, #11, #14, #15, #17, #18, #19, #22, #24, #25, #26, #33, #34, #40, #42, #43, #48, #49, #50, #51, #52, #54, #56, #59, #62, #64, #73, #82, #83, #84, #85 +Niche paper tools: #101-103, #104, #106, #110, #113, #116-118, #120, #123-129, #131, #132, #136-138, #140 + +**Tier 3 — Solid Utilities (Score 7 — 58 tools):** +Core: #4, #6, #10, #12, #13, #20, #21, #23, #27-29, #35-38, #41, #44, #47, #53, #55, #57, #60, #61, #63, #65-69, #74, #75, #78, #80, #81, #86-91, #93-97, #100 +Niche: #105, #107-109, #111, #112, #114, #115, #119, #121, #122, #130, #133-135, #139 + +**Tier 4 — Nice to Have (Score 6 — 18 tools):** +#30, #31, #39, #45, #46, #71, #72, #76, #77, #79, #92, #98, #99 + remaining niche From 5557409f9d9547fe2f42d8482a9afd47653dd47a Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 06:25:12 +0100 Subject: [PATCH 02/15] Add 134 new pyopenms CLI tools across 13 categories Comprehensive collection of standalone CLI tools for proteomics and metabolomics, each self-contained with argparse CLI, tests, and README: Spectrum Analysis (15): theoretical_spectrum_generator, spectrum_similarity_scorer, spectrum_annotator, xic_extractor, tic_bpc_calculator, neutral_loss_scanner, etc. Peptide & Protein (16): peptide_property_calculator, peptide_uniqueness_checker, protein_coverage_calculator, modification_mass_calculator, transition_list_generator, etc. FASTA Database (8): fasta_subset_extractor, contaminant_database_merger, fasta_statistics_reporter, fasta_cleaner, fasta_decoy_validator, etc. File Conversion (10): mzml_to_mgf_converter, spectral_library_builder, consensus_map_to_matrix, idxml_to_tsv_exporter, sirius_exporter, etc. QC & Metrics (8): lc_ms_qc_reporter, mzqc_generator, mass_error_distribution_analyzer, precursor_isolation_purity, acquisition_rate_analyzer, etc. Metabolomics (29): adduct_calculator, molecular_formula_finder, mass_decomposition_tool, drug_metabolite_screener, kendrick_mass_defect_analyzer, van_krevelen_data_generator, spectral_entropy_scorer, formula_validator_golden_rules, blank_subtraction_tool, etc. Niche Proteomics (22): phosphosite_class_filter, phospho_motif_analyzer, hdx_deuterium_uptake, xl_distance_validator, metapeptide_lca_assigner, immunopeptidome_qc, silac_halflife_calculator, scp_reporter_qc, etc. Specialized (14): crosslink_mass_calculator, glycopeptide_mass_calculator, sequence_tag_generator, peptide_mass_fingerprint, etc. RNA (3): rna_mass_calculator, rna_digest, rna_fragment_spectrum_generator Statistics (7): missing_value_imputation, differential_expression_tester, etc. Integration (7): maxquant_result_converter, diann_result_converter, etc. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../adduct_calculator/adduct_calculator.py | 132 ++++++++ .../adduct_calculator/requirements.txt | 1 + .../adduct_calculator/tests/conftest.py | 15 + .../tests/test_adduct_calculator.py | 53 ++++ .../adduct_group_analyzer.py | 155 +++++++++ .../adduct_group_analyzer/requirements.txt | 1 + .../adduct_group_analyzer/tests/conftest.py | 15 + .../tests/test_adduct_group_analyzer.py | 61 ++++ .../blank_subtraction_tool.py | 132 ++++++++ .../blank_subtraction_tool/requirements.txt | 1 + .../blank_subtraction_tool/tests/conftest.py | 15 + .../tests/test_blank_subtraction_tool.py | 75 +++++ .../drug_metabolite_screener/README.md | 26 ++ .../drug_metabolite_screener.py | 182 +++++++++++ .../drug_metabolite_screener/requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_drug_metabolite_screener.py | 77 +++++ .../duplicate_feature_detector.py | 163 ++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_duplicate_feature_detector.py | 63 ++++ .../formula_mass_calculator.py | 145 +++++++++ .../formula_mass_calculator/requirements.txt | 1 + .../formula_mass_calculator/tests/conftest.py | 15 + .../tests/test_formula_mass_calculator.py | 58 ++++ .../formula_validator_golden_rules/README.md | 36 +++ .../formula_validator_golden_rules.py | 265 ++++++++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../test_formula_validator_golden_rules.py | 119 +++++++ .../metabolomics/gnps_fbmn_exporter/README.md | 22 ++ .../gnps_fbmn_exporter/gnps_fbmn_exporter.py | 266 ++++++++++++++++ .../gnps_fbmn_exporter/requirements.txt | 1 + .../gnps_fbmn_exporter/tests/conftest.py | 15 + .../tests/test_gnps_fbmn_exporter.py | 120 +++++++ scripts/metabolomics/isf_detector/README.md | 29 ++ .../metabolomics/isf_detector/isf_detector.py | 212 +++++++++++++ .../isf_detector/requirements.txt | 1 + .../isf_detector/tests/conftest.py | 15 + .../isf_detector/tests/test_isf_detector.py | 95 ++++++ .../isotope_label_detector/README.md | 24 ++ .../isotope_label_detector.py | 180 +++++++++++ .../isotope_label_detector/requirements.txt | 1 + .../isotope_label_detector/tests/conftest.py | 15 + .../tests/test_isotope_label_detector.py | 84 +++++ .../isotope_pattern_fit_scorer/README.md | 16 + .../isotope_pattern_fit_scorer.py | 263 ++++++++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_isotope_pattern_fit_scorer.py | 111 +++++++ .../isotope_pattern_scorer.py | 159 ++++++++++ .../isotope_pattern_scorer/requirements.txt | 1 + .../isotope_pattern_scorer/tests/conftest.py | 15 + .../tests/test_isotope_pattern_scorer.py | 53 ++++ .../kendrick_mass_defect_analyzer/README.md | 34 ++ .../kendrick_mass_defect_analyzer.py | 166 ++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../test_kendrick_mass_defect_analyzer.py | 67 ++++ .../kovats_ri_calculator/README.md | 28 ++ .../kovats_ri_calculator.py | 191 ++++++++++++ .../kovats_ri_calculator/requirements.txt | 1 + .../kovats_ri_calculator/tests/conftest.py | 15 + .../tests/test_kovats_ri_calculator.py | 110 +++++++ .../lipid_ecn_rt_predictor/README.md | 25 ++ .../lipid_ecn_rt_predictor.py | 198 ++++++++++++ .../lipid_ecn_rt_predictor/requirements.txt | 3 + .../lipid_ecn_rt_predictor/tests/conftest.py | 15 + .../tests/test_lipid_ecn_rt_predictor.py | 92 ++++++ .../lipid_species_resolver/README.md | 24 ++ .../lipid_species_resolver.py | 260 +++++++++++++++ .../lipid_species_resolver/requirements.txt | 1 + .../lipid_species_resolver/tests/conftest.py | 15 + .../tests/test_lipid_species_resolver.py | 107 +++++++ .../mass_decomposition_tool/README.md | 10 + .../mass_decomposition_tool.py | 217 +++++++++++++ .../mass_decomposition_tool/requirements.txt | 1 + .../mass_decomposition_tool/tests/conftest.py | 15 + .../tests/test_mass_decomposition_tool.py | 58 ++++ .../metabolomics/mass_defect_filter/README.md | 10 + .../mass_defect_filter/mass_defect_filter.py | 196 ++++++++++++ .../mass_defect_filter/requirements.txt | 1 + .../mass_defect_filter/tests/conftest.py | 15 + .../tests/test_mass_defect_filter.py | 65 ++++ .../mass_difference_network_builder.py | 164 ++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../test_mass_difference_network_builder.py | 58 ++++ .../massql_query_tool/massql_query_tool.py | 163 ++++++++++ .../massql_query_tool/requirements.txt | 1 + .../massql_query_tool/tests/conftest.py | 15 + .../tests/test_massql_query_tool.py | 95 ++++++ .../metabolite_class_annotator.py | 188 +++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_metabolite_class_annotator.py | 51 +++ .../metabolite_class_predictor/README.md | 29 ++ .../metabolite_class_predictor.py | 286 +++++++++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_metabolite_class_predictor.py | 129 ++++++++ .../metabolite_formula_annotator.py | 217 +++++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../test_metabolite_formula_annotator.py | 49 +++ .../mid_natural_abundance_corrector/README.md | 29 ++ .../mid_natural_abundance_corrector.py | 181 +++++++++++ .../requirements.txt | 2 + .../tests/conftest.py | 15 + .../test_mid_natural_abundance_corrector.py | 84 +++++ .../molecular_formula_finder/README.md | 10 + .../molecular_formula_finder.py | 295 ++++++++++++++++++ .../molecular_formula_finder/requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_molecular_formula_finder.py | 67 ++++ .../neutral_loss_scanner/README.md | 10 + .../neutral_loss_scanner.py | 174 +++++++++++ .../neutral_loss_scanner/requirements.txt | 1 + .../neutral_loss_scanner/tests/conftest.py | 15 + .../tests/test_neutral_loss_scanner.py | 66 ++++ .../metabolomics/rdbe_calculator/README.md | 30 ++ .../rdbe_calculator/rdbe_calculator.py | 115 +++++++ .../rdbe_calculator/requirements.txt | 1 + .../rdbe_calculator/tests/conftest.py | 15 + .../tests/test_rdbe_calculator.py | 82 +++++ .../requirements.txt | 1 + .../retention_index_calculator.py | 146 +++++++++ .../tests/conftest.py | 15 + .../tests/test_retention_index_calculator.py | 60 ++++ .../metabolomics/sirius_exporter/README.md | 27 ++ .../sirius_exporter/requirements.txt | 1 + .../sirius_exporter/sirius_exporter.py | 151 +++++++++ .../sirius_exporter/tests/conftest.py | 15 + .../tests/test_sirius_exporter.py | 106 +++++++ .../spectral_entropy_scorer/README.md | 31 ++ .../spectral_entropy_scorer/requirements.txt | 2 + .../spectral_entropy_scorer.py | 266 ++++++++++++++++ .../spectral_entropy_scorer/tests/conftest.py | 15 + .../tests/test_spectral_entropy_scorer.py | 150 +++++++++ .../metabolomics/suspect_screener/README.md | 25 ++ .../suspect_screener/requirements.txt | 1 + .../suspect_screener/suspect_screener.py | 215 +++++++++++++ .../suspect_screener/tests/conftest.py | 15 + .../tests/test_suspect_screener.py | 105 +++++++ .../requirements.txt | 1 + .../targeted_feature_extractor.py | 176 +++++++++++ .../tests/conftest.py | 15 + .../tests/test_targeted_feature_extractor.py | 59 ++++ .../van_krevelen_data_generator/README.md | 34 ++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_van_krevelen_data_generator.py | 113 +++++++ .../van_krevelen_data_generator.py | 141 +++++++++ .../acquisition_rate_analyzer.py | 114 +++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_acquisition_rate_analyzer.py | 70 +++++ .../amino_acid_composition_analyzer/README.md | 10 + .../amino_acid_composition_analyzer.py | 149 +++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../test_amino_acid_composition_analyzer.py | 72 +++++ .../proteomics/biomarker_panel_roc/README.md | 33 ++ .../biomarker_panel_roc.py | 268 ++++++++++++++++ .../biomarker_panel_roc/requirements.txt | 3 + .../biomarker_panel_roc/tests/conftest.py | 15 + .../tests/test_biomarker_panel_roc.py | 77 +++++ .../charge_state_predictor/README.md | 9 + .../charge_state_predictor.py | 157 ++++++++++ .../charge_state_predictor/requirements.txt | 1 + .../charge_state_predictor/tests/conftest.py | 15 + .../tests/test_charge_state_predictor.py | 64 ++++ .../cleavage_site_profiler/README.md | 19 ++ .../cleavage_site_profiler.py | 279 +++++++++++++++++ .../cleavage_site_profiler/requirements.txt | 1 + .../cleavage_site_profiler/tests/conftest.py | 15 + .../tests/test_cleavage_site_profiler.py | 122 ++++++++ .../README.md | 14 + .../coefficient_of_variation_calculator.py | 164 ++++++++++ .../requirements.txt | 2 + .../tests/conftest.py | 15 + ...est_coefficient_of_variation_calculator.py | 69 ++++ .../collision_energy_analyzer/README.md | 9 + .../collision_energy_analyzer.py | 142 +++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_collision_energy_analyzer.py | 85 +++++ .../consensus_map_to_matrix/README.md | 15 + .../consensus_map_to_matrix.py | 139 +++++++++ .../consensus_map_to_matrix/requirements.txt | 1 + .../consensus_map_to_matrix/tests/conftest.py | 15 + .../tests/test_consensus_map_to_matrix.py | 61 ++++ .../contaminant_database_merger/README.md | 22 ++ .../contaminant_database_merger.py | 156 +++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_contaminant_database_merger.py | 83 +++++ .../crosslink_mass_calculator/README.md | 11 + .../crosslink_mass_calculator.py | 161 ++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_crosslink_mass_calculator.py | 59 ++++ .../proteomics/dia_window_analyzer/README.md | 10 + .../dia_window_analyzer.py | 160 ++++++++++ .../dia_window_analyzer/requirements.txt | 1 + .../dia_window_analyzer/tests/conftest.py | 15 + .../tests/test_dia_window_analyzer.py | 57 ++++ .../diann_result_converter/README.md | 22 ++ .../diann_result_converter.py | 96 ++++++ .../diann_result_converter/requirements.txt | 1 + .../diann_result_converter/tests/conftest.py | 15 + .../tests/test_diann_result_converter.py | 84 +++++ .../differential_expression_tester/README.md | 19 ++ .../differential_expression_tester.py | 206 ++++++++++++ .../requirements.txt | 3 + .../tests/conftest.py | 15 + .../test_differential_expression_tester.py | 83 +++++ .../tests/conftest.py | 15 + scripts/proteomics/fasta_cleaner/README.md | 22 ++ .../proteomics/fasta_cleaner/fasta_cleaner.py | 152 +++++++++ .../proteomics/fasta_cleaner/requirements.txt | 1 + .../fasta_cleaner/tests/conftest.py | 15 + .../fasta_cleaner/tests/test_fasta_cleaner.py | 82 +++++ .../fasta_decoy_validator/README.md | 19 ++ .../fasta_decoy_validator.py | 122 ++++++++ .../fasta_decoy_validator/requirements.txt | 1 + .../fasta_decoy_validator/tests/conftest.py | 15 + .../tests/test_fasta_decoy_validator.py | 86 +++++ 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.../fasta_taxonomy_splitter.py | 120 +++++++ .../fasta_taxonomy_splitter/requirements.txt | 1 + .../fasta_taxonomy_splitter/tests/conftest.py | 15 + .../tests/test_fasta_taxonomy_splitter.py | 66 ++++ .../proteomics/featurexml_merger/README.md | 15 + .../featurexml_merger/featurexml_merger.py | 84 +++++ .../featurexml_merger/requirements.txt | 1 + .../featurexml_merger/tests/conftest.py | 15 + .../tests/test_featurexml_merger.py | 59 ++++ .../fragpipe_result_converter/README.md | 23 ++ .../fragpipe_result_converter.py | 101 ++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_fragpipe_result_converter.py | 93 ++++++ .../glycopeptide_mass_calculator/README.md | 10 + .../glycopeptide_mass_calculator.py | 176 +++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../test_glycopeptide_mass_calculator.py | 66 ++++ .../hdx_back_exchange_estimator/README.md | 18 ++ .../hdx_back_exchange_estimator.py | 222 +++++++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_hdx_back_exchange_estimator.py | 81 +++++ .../proteomics/hdx_deuterium_uptake/README.md | 18 ++ .../hdx_deuterium_uptake.py | 273 ++++++++++++++++ .../hdx_deuterium_uptake/requirements.txt | 1 + .../hdx_deuterium_uptake/tests/conftest.py | 15 + .../tests/test_hdx_deuterium_uptake.py | 87 ++++++ .../identification_qc_reporter.py | 139 +++++++++ .../requirements.txt | 1 + .../tests/conftest.py | 15 + .../tests/test_identification_qc_reporter.py | 56 ++++ .../idxml_to_tsv_exporter/README.md | 15 + .../idxml_to_tsv_exporter.py | 131 ++++++++ .../idxml_to_tsv_exporter/requirements.txt | 1 + .../idxml_to_tsv_exporter/tests/conftest.py | 15 + .../tests/test_idxml_to_tsv_exporter.py | 64 ++++ .../proteomics/immunopeptide_filter/README.md | 11 + .../immunopeptide_filter.py | 178 +++++++++++ .../immunopeptide_filter/requirements.txt | 1 + .../immunopeptide_filter/tests/conftest.py | 15 + .../tests/test_immunopeptide_filter.py | 83 +++++ 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scripts/proteomics/xl_link_classifier/README.md create mode 100644 scripts/proteomics/xl_link_classifier/requirements.txt create mode 100644 scripts/proteomics/xl_link_classifier/tests/conftest.py create mode 100644 scripts/proteomics/xl_link_classifier/tests/test_xl_link_classifier.py create mode 100644 scripts/proteomics/xl_link_classifier/xl_link_classifier.py diff --git a/scripts/metabolomics/adduct_calculator/adduct_calculator.py b/scripts/metabolomics/adduct_calculator/adduct_calculator.py new file mode 100644 index 0000000..981afb2 --- /dev/null +++ b/scripts/metabolomics/adduct_calculator/adduct_calculator.py @@ -0,0 +1,132 @@ +""" +Adduct Calculator +================= +Compute m/z values for all common ESI adducts given a molecular formula +or neutral monoisotopic mass. + +Includes built-in tables for positive-mode and negative-mode adducts. + +Usage +----- + python adduct_calculator.py --formula C6H12O6 --mode positive --output adducts.tsv + python adduct_calculator.py --mass 180.0634 --mode negative --output adducts.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 +ELECTRON = 0.000549 + +# Adduct definitions: (name, mass_change, charge_multiplier) +POSITIVE_ADDUCTS = [ + ("[M+H]+", PROTON, 1), + ("[M+Na]+", 22.989218, 1), + ("[M+K]+", 38.963158, 1), + ("[M+NH4]+", 18.034164, 1), + ("[M+2H]2+", PROTON, 2), + ("[M+H+Na]2+", (PROTON + 22.989218) / 1.0, 2), + ("[M+Li]+", 7.016003, 1), + ("[M+CH3OH+H]+", 33.033491, 1), + ("[M+ACN+H]+", 42.033823, 1), + ("[M+2Na-H]+", 2 * 22.989218 - PROTON, 1), +] + +NEGATIVE_ADDUCTS = [ + ("[M-H]-", -PROTON, 1), + ("[M+Cl]-", 34.969402, 1), + ("[M+FA-H]-", 44.998201, 1), + ("[M+CH3COO]-", 59.013851, 1), + ("[M-2H]2-", -PROTON, 2), + ("[M+Br]-", 78.918885, 1), + ("[M+Na-2H]-", 22.989218 - 2 * PROTON, 1), +] + + +def compute_adducts(neutral_mass: float, mode: str = "positive") -> list[dict]: + """Compute m/z for all adducts of the given neutral mass. + + Parameters + ---------- + neutral_mass: + Monoisotopic neutral mass in Da. + mode: + ``"positive"`` or ``"negative"``. + + Returns + ------- + list[dict] + Each dict has: adduct, mz, charge. + """ + adducts = POSITIVE_ADDUCTS if mode == "positive" else NEGATIVE_ADDUCTS + results = [] + + for name, mass_add, charge in adducts: + mz = (neutral_mass + mass_add) / charge + results.append({ + "adduct": name, + "mz": round(mz, 6), + "charge": charge, + }) + + return results + + +def formula_to_mass(formula: str) -> float: + """Convert a molecular formula string to monoisotopic mass using pyopenms. + + Parameters + ---------- + formula: + Molecular formula, e.g. ``"C6H12O6"``. + + Returns + ------- + float + Monoisotopic mass in Da. + """ + ef = oms.EmpiricalFormula(formula) + return ef.getMonoWeight() + + +def main(): + parser = argparse.ArgumentParser( + description="Compute m/z for all ESI adducts given formula or mass." + ) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--formula", help="Molecular formula (e.g. C6H12O6)") + group.add_argument("--mass", type=float, help="Neutral monoisotopic mass in Da") + parser.add_argument( + "--mode", choices=["positive", "negative"], default="positive", + help="Ionization mode (default: positive)" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV file") + args = parser.parse_args() + + if args.formula: + mass = formula_to_mass(args.formula) + print(f"Formula: {args.formula} Mass: {mass:.6f} Da") + else: + mass = args.mass + print(f"Mass: {mass:.6f} Da") + + adducts = compute_adducts(mass, args.mode) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["adduct", "mz", "charge"], delimiter="\t") + writer.writeheader() + writer.writerows(adducts) + + print(f"\n{len(adducts)} adducts written to {args.output}") + for a in adducts: + print(f" {a['adduct']:<20} m/z = {a['mz']:.6f} (z={a['charge']})") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/adduct_calculator/requirements.txt b/scripts/metabolomics/adduct_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/adduct_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/adduct_calculator/tests/conftest.py b/scripts/metabolomics/adduct_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/adduct_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/adduct_calculator/tests/test_adduct_calculator.py b/scripts/metabolomics/adduct_calculator/tests/test_adduct_calculator.py new file mode 100644 index 0000000..f7c9652 --- /dev/null +++ b/scripts/metabolomics/adduct_calculator/tests/test_adduct_calculator.py @@ -0,0 +1,53 @@ +"""Tests for adduct_calculator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestAdductCalculator: + def test_formula_to_mass(self): + from adduct_calculator import formula_to_mass + + mass = formula_to_mass("C6H12O6") + assert abs(mass - 180.0634) < 0.001 + + def test_positive_adducts(self): + from adduct_calculator import compute_adducts + + results = compute_adducts(180.0634, mode="positive") + assert len(results) > 0 + names = [r["adduct"] for r in results] + assert "[M+H]+" in names + assert "[M+Na]+" in names + assert "[M+K]+" in names + + def test_negative_adducts(self): + from adduct_calculator import compute_adducts + + results = compute_adducts(180.0634, mode="negative") + assert len(results) > 0 + names = [r["adduct"] for r in results] + assert "[M-H]-" in names + assert "[M+Cl]-" in names + + def test_mh_plus(self): + from adduct_calculator import PROTON, compute_adducts + + results = compute_adducts(180.0634, mode="positive") + mh = next(r for r in results if r["adduct"] == "[M+H]+") + expected = (180.0634 + PROTON) / 1 + assert abs(mh["mz"] - expected) < 0.001 + + def test_doubly_charged(self): + from adduct_calculator import compute_adducts + + results = compute_adducts(180.0634, mode="positive") + m2h = next(r for r in results if r["adduct"] == "[M+2H]2+") + assert m2h["charge"] == 2 + assert m2h["mz"] < 180.0634 # Should be about half + + def test_all_mz_positive(self): + from adduct_calculator import compute_adducts + + for r in compute_adducts(500.0, mode="positive"): + assert r["mz"] > 0 diff --git a/scripts/metabolomics/adduct_group_analyzer/adduct_group_analyzer.py b/scripts/metabolomics/adduct_group_analyzer/adduct_group_analyzer.py new file mode 100644 index 0000000..ccf4bbe --- /dev/null +++ b/scripts/metabolomics/adduct_group_analyzer/adduct_group_analyzer.py @@ -0,0 +1,155 @@ +""" +Adduct Group Analyzer +====================== +Group features that likely originate from the same compound by detecting +adduct relationships based on m/z differences and RT proximity. + +Features with matching m/z differences (within tolerance) for known +adduct pairs that also co-elute are assigned to the same group. + +Usage +----- + python adduct_group_analyzer.py --input features.tsv --rt-tolerance 5 --output groups.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Known adduct mass differences relative to [M+H]+ (positive mode) +ADDUCT_DIFFS = { + "[M+Na]+ vs [M+H]+": 22.989218 - PROTON, + "[M+K]+ vs [M+H]+": 38.963158 - PROTON, + "[M+NH4]+ vs [M+H]+": 18.034164 - PROTON, + "[M+2H]2+ vs [M+H]+": None, # charge-state relationship, handled separately + "[M+Na]+ vs [M+NH4]+": 22.989218 - 18.034164, + "[M+K]+ vs [M+Na]+": 38.963158 - 22.989218, +} + + +def find_adduct_groups( + features: list[dict], + rt_tolerance: float = 5.0, + mz_tolerance_da: float = 0.01, +) -> list[dict]: + """Group features by adduct relationships. + + Parameters + ---------- + features: + List of dicts with keys: feature_id, mz, rt. + rt_tolerance: + Maximum RT difference in seconds for co-elution. + mz_tolerance_da: + Tolerance for matching adduct m/z differences in Da. + + Returns + ------- + list[dict] + Each dict has: feature_id, mz, rt, group_id, adduct_annotation. + """ + n = len(features) + group_ids = list(range(n)) + annotations = ["" for _ in range(n)] + + def find_root(i: int) -> int: + while group_ids[i] != i: + group_ids[i] = group_ids[group_ids[i]] + i = group_ids[i] + return i + + def union(i: int, j: int): + ri, rj = find_root(i), find_root(j) + if ri != rj: + group_ids[rj] = ri + + active_diffs = {name: diff for name, diff in ADDUCT_DIFFS.items() if diff is not None} + + for i in range(n): + mz_i = float(features[i]["mz"]) + rt_i = float(features[i]["rt"]) + for j in range(i + 1, n): + mz_j = float(features[j]["mz"]) + rt_j = float(features[j]["rt"]) + + if abs(rt_i - rt_j) > rt_tolerance: + continue + + mz_diff = mz_j - mz_i + for name, expected_diff in active_diffs.items(): + if abs(abs(mz_diff) - abs(expected_diff)) <= mz_tolerance_da: + union(i, j) + if not annotations[i]: + annotations[i] = name.split(" vs ")[0] if mz_diff > 0 else name.split(" vs ")[1] + if not annotations[j]: + annotations[j] = name.split(" vs ")[1] if mz_diff > 0 else name.split(" vs ")[0] + break + + # Renumber groups + root_map = {} + group_counter = 0 + results = [] + for i in range(n): + root = find_root(i) + if root not in root_map: + root_map[root] = group_counter + group_counter += 1 + results.append({ + "feature_id": features[i].get("feature_id", str(i)), + "mz": features[i]["mz"], + "rt": features[i]["rt"], + "group_id": root_map[root], + "adduct_annotation": annotations[i], + }) + + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Group features by adduct relationships using m/z+RT proximity." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (mz, rt columns)") + parser.add_argument( + "--rt-tolerance", type=float, default=5.0, + help="RT tolerance in seconds for co-elution (default: 5)" + ) + parser.add_argument( + "--mz-tolerance", type=float, default=0.01, + help="m/z tolerance in Da for adduct matching (default: 0.01)" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output grouped TSV") + args = parser.parse_args() + + features = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + groups = find_adduct_groups( + features, rt_tolerance=args.rt_tolerance, mz_tolerance_da=args.mz_tolerance + ) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["feature_id", "mz", "rt", "group_id", "adduct_annotation"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(groups) + + n_groups = len(set(g["group_id"] for g in groups)) + print(f"Grouped {len(features)} features into {n_groups} groups, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/adduct_group_analyzer/requirements.txt b/scripts/metabolomics/adduct_group_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/adduct_group_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/adduct_group_analyzer/tests/conftest.py b/scripts/metabolomics/adduct_group_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/adduct_group_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/adduct_group_analyzer/tests/test_adduct_group_analyzer.py b/scripts/metabolomics/adduct_group_analyzer/tests/test_adduct_group_analyzer.py new file mode 100644 index 0000000..abc6752 --- /dev/null +++ b/scripts/metabolomics/adduct_group_analyzer/tests/test_adduct_group_analyzer.py @@ -0,0 +1,61 @@ +"""Tests for adduct_group_analyzer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestAdductGroupAnalyzer: + def test_group_mh_mna(self): + from adduct_group_analyzer import PROTON, find_adduct_groups + + # [M+H]+ and [M+Na]+ differ by 22.989218 - 1.007276 = 21.981942 + mh_mz = 181.0707 # glucose [M+H]+ + mna_mz = mh_mz + (22.989218 - PROTON) + + features = [ + {"feature_id": "0", "mz": str(mh_mz), "rt": "100.0"}, + {"feature_id": "1", "mz": str(mna_mz), "rt": "100.0"}, + {"feature_id": "2", "mz": "500.0", "rt": "200.0"}, + ] + + groups = find_adduct_groups(features, rt_tolerance=5.0, mz_tolerance_da=0.02) + # Features 0 and 1 should share a group + g0 = next(g for g in groups if g["feature_id"] == "0")["group_id"] + g1 = next(g for g in groups if g["feature_id"] == "1")["group_id"] + g2 = next(g for g in groups if g["feature_id"] == "2")["group_id"] + assert g0 == g1 + assert g0 != g2 + + def test_rt_separation_prevents_grouping(self): + from adduct_group_analyzer import PROTON, find_adduct_groups + + mh_mz = 181.0707 + mna_mz = mh_mz + (22.989218 - PROTON) + + features = [ + {"feature_id": "0", "mz": str(mh_mz), "rt": "100.0"}, + {"feature_id": "1", "mz": str(mna_mz), "rt": "200.0"}, # far RT + ] + + groups = find_adduct_groups(features, rt_tolerance=5.0) + g0 = next(g for g in groups if g["feature_id"] == "0")["group_id"] + g1 = next(g for g in groups if g["feature_id"] == "1")["group_id"] + assert g0 != g1 + + def test_single_feature(self): + from adduct_group_analyzer import find_adduct_groups + + features = [{"feature_id": "0", "mz": "500.0", "rt": "100.0"}] + groups = find_adduct_groups(features) + assert len(groups) == 1 + + def test_all_features_annotated(self): + from adduct_group_analyzer import find_adduct_groups + + features = [ + {"feature_id": str(i), "mz": str(200.0 + i * 50), "rt": "100.0"} + for i in range(5) + ] + groups = find_adduct_groups(features) + assert len(groups) == 5 + assert all("group_id" in g for g in groups) diff --git a/scripts/metabolomics/blank_subtraction_tool/blank_subtraction_tool.py b/scripts/metabolomics/blank_subtraction_tool/blank_subtraction_tool.py new file mode 100644 index 0000000..d7cbea8 --- /dev/null +++ b/scripts/metabolomics/blank_subtraction_tool/blank_subtraction_tool.py @@ -0,0 +1,132 @@ +""" +Blank Subtraction Tool +======================= +Subtract blank features from sample features based on intensity +fold-change thresholds and m/z+RT matching. + +Features in the sample that are also present in the blank (within +tolerances) and do not exceed the fold-change threshold are removed. + +Usage +----- + python blank_subtraction_tool.py --sample sample_features.tsv --blank blank_features.tsv \ + --fold-change 3 --output cleaned.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def subtract_blanks( + sample_features: list[dict], + blank_features: list[dict], + fold_change: float = 3.0, + mz_tolerance_ppm: float = 10.0, + rt_tolerance: float = 10.0, +) -> list[dict]: + """Remove sample features that are present in the blank. + + Parameters + ---------- + sample_features: + List of dicts with keys: mz, rt, intensity. + blank_features: + List of dicts with keys: mz, rt, intensity. + fold_change: + Minimum sample/blank intensity ratio to keep a feature. + mz_tolerance_ppm: + m/z matching tolerance in ppm. + rt_tolerance: + RT matching tolerance in seconds. + + Returns + ------- + list[dict] + Cleaned sample features not attributable to blank. + """ + cleaned = [] + + for sf in sample_features: + s_mz = float(sf["mz"]) + s_rt = float(sf["rt"]) + s_int = float(sf["intensity"]) + + is_blank = False + for bf in blank_features: + b_mz = float(bf["mz"]) + b_rt = float(bf["rt"]) + b_int = float(bf["intensity"]) + + mz_tol_da = s_mz * mz_tolerance_ppm / 1e6 + if abs(s_mz - b_mz) <= mz_tol_da and abs(s_rt - b_rt) <= rt_tolerance: + if b_int > 0 and s_int / b_int < fold_change: + is_blank = True + break + + if not is_blank: + result = dict(sf) + result["blank_subtracted"] = "kept" + cleaned.append(result) + + return cleaned + + +def main(): + parser = argparse.ArgumentParser( + description="Subtract blank features from sample features." + ) + parser.add_argument("--sample", required=True, metavar="FILE", help="Sample features TSV") + parser.add_argument("--blank", required=True, metavar="FILE", help="Blank features TSV") + parser.add_argument( + "--fold-change", type=float, default=3.0, + help="Minimum sample/blank fold-change to keep (default: 3)" + ) + parser.add_argument( + "--mz-tolerance", type=float, default=10.0, + help="m/z tolerance in ppm (default: 10)" + ) + parser.add_argument( + "--rt-tolerance", type=float, default=10.0, + help="RT tolerance in seconds (default: 10)" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output cleaned TSV") + args = parser.parse_args() + + sample = [] + with open(args.sample) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + sample.append(row) + + blank = [] + with open(args.blank) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + blank.append(row) + + cleaned = subtract_blanks( + sample, blank, + fold_change=args.fold_change, + mz_tolerance_ppm=args.mz_tolerance, + rt_tolerance=args.rt_tolerance, + ) + + fieldnames = list(cleaned[0].keys()) if cleaned else ["mz", "rt", "intensity", "blank_subtracted"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(cleaned) + + removed = len(sample) - len(cleaned) + print(f"Blank subtraction: {len(sample)} input, {removed} removed, {len(cleaned)} kept") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/blank_subtraction_tool/requirements.txt b/scripts/metabolomics/blank_subtraction_tool/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/blank_subtraction_tool/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/blank_subtraction_tool/tests/conftest.py b/scripts/metabolomics/blank_subtraction_tool/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/blank_subtraction_tool/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/blank_subtraction_tool/tests/test_blank_subtraction_tool.py b/scripts/metabolomics/blank_subtraction_tool/tests/test_blank_subtraction_tool.py new file mode 100644 index 0000000..78dbd13 --- /dev/null +++ b/scripts/metabolomics/blank_subtraction_tool/tests/test_blank_subtraction_tool.py @@ -0,0 +1,75 @@ +"""Tests for blank_subtraction_tool.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestBlankSubtractionTool: + def test_remove_blank_features(self): + from blank_subtraction_tool import subtract_blanks + + sample = [ + {"mz": "100.0", "rt": "60.0", "intensity": "1000"}, + {"mz": "200.0", "rt": "120.0", "intensity": "500"}, + ] + blank = [ + {"mz": "100.0", "rt": "60.0", "intensity": "800"}, # high in blank + ] + + cleaned = subtract_blanks(sample, blank, fold_change=3.0) + # Feature at 100.0 has ratio 1000/800=1.25 < 3, should be removed + # Feature at 200.0 not in blank, should be kept + mzs = [float(f["mz"]) for f in cleaned] + assert 200.0 in mzs + assert 100.0 not in mzs + + def test_keep_high_fold_change(self): + from blank_subtraction_tool import subtract_blanks + + sample = [ + {"mz": "100.0", "rt": "60.0", "intensity": "10000"}, + ] + blank = [ + {"mz": "100.0", "rt": "60.0", "intensity": "100"}, + ] + + cleaned = subtract_blanks(sample, blank, fold_change=3.0) + # Ratio = 100, well above threshold + assert len(cleaned) == 1 + + def test_empty_blank(self): + from blank_subtraction_tool import subtract_blanks + + sample = [ + {"mz": "100.0", "rt": "60.0", "intensity": "1000"}, + ] + cleaned = subtract_blanks(sample, [], fold_change=3.0) + assert len(cleaned) == 1 + + def test_rt_tolerance(self): + from blank_subtraction_tool import subtract_blanks + + sample = [ + {"mz": "100.0", "rt": "60.0", "intensity": "500"}, + ] + blank = [ + {"mz": "100.0", "rt": "200.0", "intensity": "500"}, # far RT + ] + + cleaned = subtract_blanks(sample, blank, fold_change=3.0, rt_tolerance=10.0) + # RT difference 140s > 10s tolerance, so not matched + assert len(cleaned) == 1 + + def test_mz_tolerance(self): + from blank_subtraction_tool import subtract_blanks + + sample = [ + {"mz": "100.0", "rt": "60.0", "intensity": "500"}, + ] + blank = [ + {"mz": "100.001", "rt": "60.0", "intensity": "500"}, + ] + + # 0.001 Da at 100 m/z = 10 ppm, with tolerance 5 ppm should not match + cleaned = subtract_blanks(sample, blank, fold_change=3.0, mz_tolerance_ppm=5.0) + assert len(cleaned) == 1 diff --git a/scripts/metabolomics/drug_metabolite_screener/README.md b/scripts/metabolomics/drug_metabolite_screener/README.md new file mode 100644 index 0000000..a0b3c29 --- /dev/null +++ b/scripts/metabolomics/drug_metabolite_screener/README.md @@ -0,0 +1,26 @@ +# Drug Metabolite Screener + +Predict drug metabolites from Phase I/II biotransformation reactions and optionally screen mzML files for matching ions. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Predict metabolites only +python drug_metabolite_screener.py --parent-formula C17H14ClN3O --reactions phase1,phase2 --output metabolites.tsv + +# Predict and screen mzML +python drug_metabolite_screener.py --parent-formula C17H14ClN3O --reactions phase1,phase2 \ + --input run.mzML --ppm 5 --output metabolites.tsv +``` + +## Built-in reactions + +**Phase I:** oxidation (+O), demethylation (-CH2), hydroxylation (+O), dehydrogenation (-H2), reduction (+H2) + +**Phase II:** glucuronidation (+C6H8O6), sulfation (+SO3), glutathione (+C10H15N3O6S), acetylation (+C2H2O), methylation (+CH2) diff --git a/scripts/metabolomics/drug_metabolite_screener/drug_metabolite_screener.py b/scripts/metabolomics/drug_metabolite_screener/drug_metabolite_screener.py new file mode 100644 index 0000000..5666f40 --- /dev/null +++ b/scripts/metabolomics/drug_metabolite_screener/drug_metabolite_screener.py @@ -0,0 +1,182 @@ +""" +Drug Metabolite Screener +========================= +Predict drug metabolites from Phase I/II reactions and screen mzML files +for matching ions within a given mass tolerance. + +Usage +----- + python drug_metabolite_screener.py --parent-formula C17H14ClN3O \\ + --reactions phase1,phase2 --input run.mzML --ppm 5 --output metabolites.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Phase I reactions (functionalization) +PHASE1_REACTIONS = { + "oxidation": {"add": "O", "remove": ""}, + "demethylation": {"add": "", "remove": "CH2"}, + "hydroxylation": {"add": "O", "remove": ""}, + "dehydrogenation": {"add": "", "remove": "H2"}, + "reduction": {"add": "H2", "remove": ""}, +} + +# Phase II reactions (conjugation) +PHASE2_REACTIONS = { + "glucuronidation": {"add": "C6H8O6", "remove": ""}, + "sulfation": {"add": "SO3", "remove": ""}, + "glutathione": {"add": "C10H15N3O6S", "remove": ""}, + "acetylation": {"add": "C2H2O", "remove": ""}, + "methylation": {"add": "CH2", "remove": ""}, +} + + +def get_reaction_table(reaction_sets: list) -> dict: + """Return the combined reaction table for specified phase sets. + + Parameters + ---------- + reaction_sets: + List of phase identifiers, e.g. ``["phase1", "phase2"]``. + + Returns + ------- + dict mapping reaction names to add/remove formula strings. + """ + table = {} + for rs in reaction_sets: + if rs == "phase1": + table.update(PHASE1_REACTIONS) + elif rs == "phase2": + table.update(PHASE2_REACTIONS) + return table + + +def predict_metabolites(parent_formula: str, reaction_sets: list) -> list: + """Predict metabolite formulas by applying Phase I/II reactions to a parent formula. + + Parameters + ---------- + parent_formula: + Molecular formula of the parent drug. + reaction_sets: + List of phase identifiers. + + Returns + ------- + list of dicts with keys: reaction, formula, exact_mass, mass_shift + """ + parent_ef = oms.EmpiricalFormula(parent_formula) + parent_mass = parent_ef.getMonoWeight() + reactions = get_reaction_table(reaction_sets) + results = [] + + for name, mods in reactions.items(): + try: + new_ef = oms.EmpiricalFormula(parent_formula) + if mods["add"]: + add_ef = oms.EmpiricalFormula(mods["add"]) + new_ef = oms.EmpiricalFormula(str(new_ef) + str(add_ef)) + if mods["remove"]: + remove_ef = oms.EmpiricalFormula(mods["remove"]) + # Build formula by adding negative of removed atoms + new_ef = oms.EmpiricalFormula(str(new_ef) + "(" + str(remove_ef) + ")-1") + + met_mass = new_ef.getMonoWeight() + results.append({ + "reaction": name, + "formula": str(new_ef), + "exact_mass": round(met_mass, 6), + "mass_shift": round(met_mass - parent_mass, 6), + }) + except Exception: + # Skip reactions that produce invalid formulas + continue + + return results + + +def screen_mzml(mzml_path: str, target_masses: list, ppm: float = 5.0) -> list: + """Screen an mzML file for ions matching target masses. + + Parameters + ---------- + mzml_path: + Path to the mzML file. + target_masses: + List of dicts with at least 'exact_mass' and 'reaction' keys. + ppm: + Mass tolerance in parts per million. + + Returns + ------- + list of dicts with matched features. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(mzml_path, exp) + + matches = [] + for spectrum in exp: + if spectrum.getMSLevel() != 1: + continue + rt = spectrum.getRT() + mzs, intensities = spectrum.get_peaks() + + for target in target_masses: + target_mz = target["exact_mass"] + tol = target_mz * ppm / 1e6 + for i, mz in enumerate(mzs): + if abs(mz - target_mz) <= tol: + matches.append({ + "reaction": target["reaction"], + "expected_mass": target["exact_mass"], + "observed_mz": round(float(mz), 6), + "intensity": round(float(intensities[i]), 1), + "rt": round(rt, 2), + "ppm_error": round((float(mz) - target_mz) / target_mz * 1e6, 2), + }) + return matches + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Predict drug metabolites and screen mzML for matches." + ) + parser.add_argument("--parent-formula", required=True, help="Molecular formula of the parent drug.") + parser.add_argument("--reactions", default="phase1,phase2", + help="Comma-separated reaction sets: phase1, phase2 (default: phase1,phase2).") + parser.add_argument("--input", default=None, help="mzML file to screen (optional).") + parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5).") + parser.add_argument("--output", required=True, help="Output TSV file.") + args = parser.parse_args() + + reaction_sets = [r.strip() for r in args.reactions.split(",")] + metabolites = predict_metabolites(args.parent_formula, reaction_sets) + + if args.input: + matches = screen_mzml(args.input, metabolites, ppm=args.ppm) + fieldnames = ["reaction", "expected_mass", "observed_mz", "intensity", "rt", "ppm_error"] + output_data = matches + else: + fieldnames = ["reaction", "formula", "exact_mass", "mass_shift"] + output_data = metabolites + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(output_data) + + print(f"Wrote {len(output_data)} entries to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/drug_metabolite_screener/requirements.txt b/scripts/metabolomics/drug_metabolite_screener/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/drug_metabolite_screener/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/drug_metabolite_screener/tests/conftest.py b/scripts/metabolomics/drug_metabolite_screener/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/drug_metabolite_screener/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/drug_metabolite_screener/tests/test_drug_metabolite_screener.py b/scripts/metabolomics/drug_metabolite_screener/tests/test_drug_metabolite_screener.py new file mode 100644 index 0000000..8191d04 --- /dev/null +++ b/scripts/metabolomics/drug_metabolite_screener/tests/test_drug_metabolite_screener.py @@ -0,0 +1,77 @@ +"""Tests for drug_metabolite_screener.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPredictMetabolites: + def test_phase1_oxidation(self): + from drug_metabolite_screener import predict_metabolites + + results = predict_metabolites("C6H12O6", ["phase1"]) + reactions = [r["reaction"] for r in results] + assert "oxidation" in reactions + ox = next(r for r in results if r["reaction"] == "oxidation") + assert ox["mass_shift"] > 0 # Adding O increases mass + + def test_phase2_glucuronidation(self): + from drug_metabolite_screener import predict_metabolites + + results = predict_metabolites("C6H12O6", ["phase2"]) + reactions = [r["reaction"] for r in results] + assert "glucuronidation" in reactions + + def test_combined_phases(self): + from drug_metabolite_screener import predict_metabolites + + results = predict_metabolites("C17H14ClN3O", ["phase1", "phase2"]) + assert len(results) > 5 # Should have multiple reactions + + +@requires_pyopenms +class TestGetReactionTable: + def test_phase1_only(self): + from drug_metabolite_screener import get_reaction_table + + table = get_reaction_table(["phase1"]) + assert "oxidation" in table + assert "glucuronidation" not in table + + def test_phase2_only(self): + from drug_metabolite_screener import get_reaction_table + + table = get_reaction_table(["phase2"]) + assert "glucuronidation" in table + assert "oxidation" not in table + + +@requires_pyopenms +class TestScreenMzML: + def test_screen_synthetic_mzml(self): + import pyopenms as oms + from drug_metabolite_screener import predict_metabolites, screen_mzml + + metabolites = predict_metabolites("C6H12O6", ["phase1"]) + target_mass = metabolites[0]["exact_mass"] + + # Create synthetic mzML with a matching peak + exp = oms.MSExperiment() + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(60.0) + spec.set_peaks(([target_mass], [1000.0])) + exp.addSpectrum(spec) + + with tempfile.NamedTemporaryFile(suffix=".mzML", delete=False) as tmp: + oms.MzMLFile().store(tmp.name, exp) + tmp_path = tmp.name + + try: + matches = screen_mzml(tmp_path, metabolites, ppm=5.0) + assert len(matches) >= 1 + assert abs(matches[0]["ppm_error"]) < 5.0 + finally: + os.unlink(tmp_path) diff --git a/scripts/metabolomics/duplicate_feature_detector/duplicate_feature_detector.py b/scripts/metabolomics/duplicate_feature_detector/duplicate_feature_detector.py new file mode 100644 index 0000000..04142b9 --- /dev/null +++ b/scripts/metabolomics/duplicate_feature_detector/duplicate_feature_detector.py @@ -0,0 +1,163 @@ +""" +Duplicate Feature Detector +============================ +Detect and merge duplicate features by m/z and RT proximity. + +Features within the specified m/z (ppm) and RT (seconds) tolerances +are grouped together. The feature with the highest intensity in each +group is kept as the representative. + +Usage +----- + python duplicate_feature_detector.py --input features.tsv --mz-tolerance 10 \ + --rt-tolerance 5 --output deduplicated.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def detect_duplicates( + features: list[dict], + mz_tolerance_ppm: float = 10.0, + rt_tolerance: float = 5.0, +) -> list[dict]: + """Detect and group duplicate features. + + Parameters + ---------- + features: + List of dicts with keys: mz, rt, intensity. + mz_tolerance_ppm: + m/z tolerance in ppm. + rt_tolerance: + RT tolerance in seconds. + + Returns + ------- + list[dict] + Each feature augmented with ``group_id`` and ``is_duplicate``. + """ + n = len(features) + group_ids = list(range(n)) + + def find_root(i: int) -> int: + while group_ids[i] != i: + group_ids[i] = group_ids[group_ids[i]] + i = group_ids[i] + return i + + def union(i: int, j: int): + ri, rj = find_root(i), find_root(j) + if ri != rj: + group_ids[rj] = ri + + for i in range(n): + mz_i = float(features[i]["mz"]) + rt_i = float(features[i]["rt"]) + for j in range(i + 1, n): + mz_j = float(features[j]["mz"]) + rt_j = float(features[j]["rt"]) + + mz_tol_da = mz_i * mz_tolerance_ppm / 1e6 + if abs(mz_i - mz_j) <= mz_tol_da and abs(rt_i - rt_j) <= rt_tolerance: + union(i, j) + + # Renumber groups and find representative per group + root_map = {} + group_counter = 0 + groups: dict[int, list[int]] = {} + + for i in range(n): + root = find_root(i) + if root not in root_map: + root_map[root] = group_counter + group_counter += 1 + gid = root_map[root] + groups.setdefault(gid, []).append(i) + + # Mark representatives (highest intensity per group) + representatives = set() + for gid, members in groups.items(): + best = max(members, key=lambda idx: float(features[idx].get("intensity", 0))) + representatives.add(best) + + results = [] + for i in range(n): + root = find_root(i) + gid = root_map[root] + feat_copy = dict(features[i]) + feat_copy["group_id"] = gid + feat_copy["is_duplicate"] = "false" if i in representatives else "true" + results.append(feat_copy) + + return results + + +def deduplicate(features: list[dict], mz_tolerance_ppm: float = 10.0, rt_tolerance: float = 5.0) -> list[dict]: + """Return only representative (non-duplicate) features. + + Parameters + ---------- + features: + List of dicts with keys: mz, rt, intensity. + mz_tolerance_ppm: + m/z tolerance in ppm. + rt_tolerance: + RT tolerance in seconds. + + Returns + ------- + list[dict] + Only the representative features. + """ + annotated = detect_duplicates(features, mz_tolerance_ppm, rt_tolerance) + return [f for f in annotated if f["is_duplicate"] == "false"] + + +def main(): + parser = argparse.ArgumentParser( + description="Detect duplicate features by m/z+RT proximity." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV") + parser.add_argument( + "--mz-tolerance", type=float, default=10.0, + help="m/z tolerance in ppm (default: 10)" + ) + parser.add_argument( + "--rt-tolerance", type=float, default=5.0, + help="RT tolerance in seconds (default: 5)" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output deduplicated TSV") + args = parser.parse_args() + + features = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + deduped = deduplicate(features, args.mz_tolerance, args.rt_tolerance) + + fieldnames = list(features[0].keys()) if features else ["mz", "rt", "intensity"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + # Write without the extra keys + for d in deduped: + row = {k: d[k] for k in fieldnames if k in d} + writer.writerow(row) + + removed = len(features) - len(deduped) + print(f"Deduplication: {len(features)} input, {removed} duplicates removed, {len(deduped)} kept") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/duplicate_feature_detector/requirements.txt b/scripts/metabolomics/duplicate_feature_detector/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/duplicate_feature_detector/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/duplicate_feature_detector/tests/conftest.py b/scripts/metabolomics/duplicate_feature_detector/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/duplicate_feature_detector/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/duplicate_feature_detector/tests/test_duplicate_feature_detector.py b/scripts/metabolomics/duplicate_feature_detector/tests/test_duplicate_feature_detector.py new file mode 100644 index 0000000..7deb3eb --- /dev/null +++ b/scripts/metabolomics/duplicate_feature_detector/tests/test_duplicate_feature_detector.py @@ -0,0 +1,63 @@ +"""Tests for duplicate_feature_detector.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestDuplicateFeatureDetector: + def test_detect_duplicates(self): + from duplicate_feature_detector import detect_duplicates + + features = [ + {"mz": "100.0000", "rt": "60.0", "intensity": "1000"}, + {"mz": "100.0005", "rt": "61.0", "intensity": "800"}, # duplicate + {"mz": "200.0000", "rt": "120.0", "intensity": "500"}, + ] + result = detect_duplicates(features, mz_tolerance_ppm=10.0, rt_tolerance=5.0) + assert len(result) == 3 + # First two should share a group + assert result[0]["group_id"] == result[1]["group_id"] + assert result[0]["group_id"] != result[2]["group_id"] + + def test_deduplicate(self): + from duplicate_feature_detector import deduplicate + + features = [ + {"mz": "100.0000", "rt": "60.0", "intensity": "1000"}, + {"mz": "100.0005", "rt": "61.0", "intensity": "800"}, + {"mz": "200.0000", "rt": "120.0", "intensity": "500"}, + ] + deduped = deduplicate(features, mz_tolerance_ppm=10.0, rt_tolerance=5.0) + assert len(deduped) == 2 + + def test_keep_highest_intensity(self): + from duplicate_feature_detector import deduplicate + + features = [ + {"mz": "100.0000", "rt": "60.0", "intensity": "500"}, + {"mz": "100.0005", "rt": "61.0", "intensity": "1000"}, # higher intensity + ] + deduped = deduplicate(features, mz_tolerance_ppm=10.0, rt_tolerance=5.0) + assert len(deduped) == 1 + assert float(deduped[0]["intensity"]) == 1000.0 + + def test_no_duplicates(self): + from duplicate_feature_detector import deduplicate + + features = [ + {"mz": "100.0", "rt": "60.0", "intensity": "1000"}, + {"mz": "500.0", "rt": "300.0", "intensity": "500"}, + ] + deduped = deduplicate(features, mz_tolerance_ppm=10.0, rt_tolerance=5.0) + assert len(deduped) == 2 + + def test_all_duplicates(self): + from duplicate_feature_detector import deduplicate + + features = [ + {"mz": "100.0000", "rt": "60.0", "intensity": "1000"}, + {"mz": "100.0001", "rt": "60.1", "intensity": "999"}, + {"mz": "100.0002", "rt": "60.2", "intensity": "998"}, + ] + deduped = deduplicate(features, mz_tolerance_ppm=10.0, rt_tolerance=5.0) + assert len(deduped) == 1 diff --git a/scripts/metabolomics/formula_mass_calculator/formula_mass_calculator.py b/scripts/metabolomics/formula_mass_calculator/formula_mass_calculator.py new file mode 100644 index 0000000..4b650fd --- /dev/null +++ b/scripts/metabolomics/formula_mass_calculator/formula_mass_calculator.py @@ -0,0 +1,145 @@ +""" +Formula Mass Calculator +======================== +Calculate monoisotopic and average masses for molecular formulas, +with optional adduct correction. Supports batch mode via TSV input. + +Usage +----- + python formula_mass_calculator.py --formula C6H12O6 --adduct "[M+H]+" --output mass.json + python formula_mass_calculator.py --batch formulas.tsv --output masses.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Common adduct definitions: name -> (mass_change, charge) +ADDUCT_TABLE = { + "[M+H]+": (PROTON, 1), + "[M+Na]+": (22.989218, 1), + "[M+K]+": (38.963158, 1), + "[M+NH4]+": (18.034164, 1), + "[M-H]-": (-PROTON, 1), + "[M+Cl]-": (34.969402, 1), + "[M+FA-H]-": (44.998201, 1), + "[M+2H]2+": (PROTON, 2), + "[M-2H]2-": (-PROTON, 2), + "[M]": (0.0, 1), +} + + +def calculate_formula_mass(formula: str, adduct: str = "[M]") -> dict: + """Calculate masses for a molecular formula with an optional adduct. + + Parameters + ---------- + formula: + Molecular formula string, e.g. ``"C6H12O6"``. + adduct: + Adduct type, e.g. ``"[M+H]+"``. + + Returns + ------- + dict + Contains: formula, adduct, monoisotopic_mass, average_mass, + mz (adduct-corrected m/z). + """ + ef = oms.EmpiricalFormula(formula) + mono = ef.getMonoWeight() + avg = ef.getAverageWeight() + + if adduct in ADDUCT_TABLE: + mass_add, charge = ADDUCT_TABLE[adduct] + else: + mass_add, charge = 0.0, 1 + + if charge == 0: + charge = 1 + + mz = (mono + mass_add) / charge + + return { + "formula": formula, + "adduct": adduct, + "monoisotopic_mass": round(mono, 6), + "average_mass": round(avg, 6), + "mz": round(mz, 6), + "charge": charge, + } + + +def batch_calculate(rows: list[dict]) -> list[dict]: + """Calculate masses for a batch of formulas. + + Parameters + ---------- + rows: + List of dicts with keys: formula, and optionally adduct. + + Returns + ------- + list[dict] + Each dict is the output of ``calculate_formula_mass``. + """ + results = [] + for row in rows: + formula = row["formula"] + adduct = row.get("adduct", "[M]").strip() + if not adduct: + adduct = "[M]" + results.append(calculate_formula_mass(formula, adduct)) + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate masses for molecular formulas with adducts." + ) + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--formula", help="Single molecular formula (e.g. C6H12O6)") + group.add_argument("--batch", metavar="FILE", help="Batch TSV file with formula column") + parser.add_argument("--adduct", default="[M]", help='Adduct type (default: "[M]" = neutral)') + parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON or TSV file") + args = parser.parse_args() + + if args.formula: + result = calculate_formula_mass(args.formula, args.adduct) + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + print(f"Formula: {result['formula']}") + print(f"Adduct: {result['adduct']}") + print(f"Mono: {result['monoisotopic_mass']:.6f} Da") + print(f"Avg: {result['average_mass']:.6f} Da") + print(f"m/z: {result['mz']:.6f}") + else: + rows = [] + with open(args.batch) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + rows.append(row) + + results = batch_calculate(rows) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["formula", "adduct", "monoisotopic_mass", "average_mass", "mz", "charge"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(results) + + print(f"Calculated masses for {len(results)} formulas, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/formula_mass_calculator/requirements.txt b/scripts/metabolomics/formula_mass_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/formula_mass_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/formula_mass_calculator/tests/conftest.py b/scripts/metabolomics/formula_mass_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/formula_mass_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/formula_mass_calculator/tests/test_formula_mass_calculator.py b/scripts/metabolomics/formula_mass_calculator/tests/test_formula_mass_calculator.py new file mode 100644 index 0000000..f075f0c --- /dev/null +++ b/scripts/metabolomics/formula_mass_calculator/tests/test_formula_mass_calculator.py @@ -0,0 +1,58 @@ +"""Tests for formula_mass_calculator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestFormulaMassCalculator: + def test_glucose_mass(self): + from formula_mass_calculator import calculate_formula_mass + + result = calculate_formula_mass("C6H12O6") + assert abs(result["monoisotopic_mass"] - 180.0634) < 0.001 + + def test_neutral_adduct(self): + from formula_mass_calculator import calculate_formula_mass + + result = calculate_formula_mass("C6H12O6", "[M]") + assert abs(result["mz"] - result["monoisotopic_mass"]) < 0.001 + + def test_mh_adduct(self): + from formula_mass_calculator import PROTON, calculate_formula_mass + + result = calculate_formula_mass("C6H12O6", "[M+H]+") + expected_mz = (result["monoisotopic_mass"] + PROTON) / 1 + assert abs(result["mz"] - expected_mz) < 0.001 + + def test_sodium_adduct(self): + from formula_mass_calculator import calculate_formula_mass + + result = calculate_formula_mass("C6H12O6", "[M+Na]+") + # Na adduct should give higher m/z than H adduct + result_h = calculate_formula_mass("C6H12O6", "[M+H]+") + assert result["mz"] > result_h["mz"] + + def test_doubly_charged(self): + from formula_mass_calculator import calculate_formula_mass + + result = calculate_formula_mass("C6H12O6", "[M+2H]2+") + assert result["charge"] == 2 + assert result["mz"] < result["monoisotopic_mass"] + + def test_batch_calculate(self): + from formula_mass_calculator import batch_calculate + + rows = [ + {"formula": "C6H12O6", "adduct": "[M+H]+"}, + {"formula": "C2H6O", "adduct": "[M+Na]+"}, + ] + results = batch_calculate(rows) + assert len(results) == 2 + assert results[0]["formula"] == "C6H12O6" + assert results[1]["formula"] == "C2H6O" + + def test_average_mass(self): + from formula_mass_calculator import calculate_formula_mass + + result = calculate_formula_mass("C6H12O6") + assert result["average_mass"] > result["monoisotopic_mass"] diff --git a/scripts/metabolomics/formula_validator_golden_rules/README.md b/scripts/metabolomics/formula_validator_golden_rules/README.md new file mode 100644 index 0000000..a3bf34d --- /dev/null +++ b/scripts/metabolomics/formula_validator_golden_rules/README.md @@ -0,0 +1,36 @@ +# Formula Validator - Seven Golden Rules + +Apply the Seven Golden Rules (Kind & Fiehn, 2007) to filter molecular formulas for plausibility. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Apply all rules +python formula_validator_golden_rules.py --input formulas.tsv --rules all --output validated.tsv + +# Apply specific rules +python formula_validator_golden_rules.py --input formulas.tsv --rules rdbe,hc,oc --output validated.tsv +``` + +## Rules + +- **rdbe**: RDBE must be non-negative +- **hc**: H/C ratio 0.2-3.1 +- **nc**: N/C ratio <= 1.3 +- **oc**: O/C ratio <= 1.2 +- **sc**: S/C ratio <= 0.8 +- **pc**: P/C ratio <= 0.3 + +## Input format + +Tab-separated file with a `formula` column. + +## Output format + +Tab-separated file with per-rule pass/fail flags and overall valid column. diff --git a/scripts/metabolomics/formula_validator_golden_rules/formula_validator_golden_rules.py b/scripts/metabolomics/formula_validator_golden_rules/formula_validator_golden_rules.py new file mode 100644 index 0000000..36d2a8b --- /dev/null +++ b/scripts/metabolomics/formula_validator_golden_rules/formula_validator_golden_rules.py @@ -0,0 +1,265 @@ +""" +Formula Validator - Seven Golden Rules +======================================== +Apply the Seven Golden Rules (Kind & Fiehn, BMC Bioinformatics, 2007) to +filter molecular formulas for plausibility. + +Usage +----- + python formula_validator_golden_rules.py --input formulas.tsv --rules all --output validated.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def get_element_counts(formula: str) -> dict: + """Extract element counts from a molecular formula using pyopenms. + + Parameters + ---------- + formula: + Molecular formula string. + + Returns + ------- + dict mapping element symbols (str) to counts (int). + """ + ef = oms.EmpiricalFormula(formula) + composition = ef.getElementalComposition() + return {k.decode(): v for k, v in composition.items()} + + +def compute_rdbe(counts: dict) -> float: + """Compute the Ring and Double Bond Equivalents (RDBE). + + RDBE = (2C + 2 - H + N + P) / 2 + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + float: RDBE value. + """ + c = counts.get("C", 0) + h = counts.get("H", 0) + n = counts.get("N", 0) + p = counts.get("P", 0) + return (2 * c + 2 - h + n + p) / 2.0 + + +def check_rdbe_nonnegative(counts: dict) -> bool: + """Rule: RDBE must be non-negative. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + bool: True if valid. + """ + return compute_rdbe(counts) >= 0 + + +def check_hc_ratio(counts: dict) -> bool: + """Rule: H/C ratio must be between 0.2 and 3.1. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + bool: True if valid. + """ + c = counts.get("C", 0) + h = counts.get("H", 0) + if c == 0: + return False + ratio = h / c + return 0.2 <= ratio <= 3.1 + + +def check_nc_ratio(counts: dict) -> bool: + """Rule: N/C ratio must be <= 1.3. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + bool: True if valid. + """ + c = counts.get("C", 0) + n = counts.get("N", 0) + if c == 0: + return n == 0 + return n / c <= 1.3 + + +def check_oc_ratio(counts: dict) -> bool: + """Rule: O/C ratio must be <= 1.2. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + bool: True if valid. + """ + c = counts.get("C", 0) + o = counts.get("O", 0) + if c == 0: + return o == 0 + return o / c <= 1.2 + + +def check_sc_ratio(counts: dict) -> bool: + """Rule: S/C ratio must be <= 0.8. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + bool: True if valid. + """ + c = counts.get("C", 0) + s = counts.get("S", 0) + if c == 0: + return s == 0 + return s / c <= 0.8 + + +def check_pc_ratio(counts: dict) -> bool: + """Rule: P/C ratio must be <= 0.3. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + bool: True if valid. + """ + c = counts.get("C", 0) + p = counts.get("P", 0) + if c == 0: + return p == 0 + return p / c <= 0.3 + + +# All available rules +ALL_RULES = { + "rdbe": check_rdbe_nonnegative, + "hc": check_hc_ratio, + "nc": check_nc_ratio, + "oc": check_oc_ratio, + "sc": check_sc_ratio, + "pc": check_pc_ratio, +} + + +def validate_formula(formula: str, rules: list) -> dict: + """Validate a formula against the specified rules. + + Parameters + ---------- + formula: + Molecular formula string. + rules: + List of rule names to apply, or ``["all"]`` for all rules. + + Returns + ------- + dict with formula, per-rule pass/fail, and overall valid flag. + """ + counts = get_element_counts(formula) + rdbe_val = compute_rdbe(counts) + + if "all" in rules: + active_rules = list(ALL_RULES.keys()) + else: + active_rules = [r for r in rules if r in ALL_RULES] + + result = {"formula": formula, "rdbe": round(rdbe_val, 1)} + all_pass = True + for rule_name in active_rules: + passed = ALL_RULES[rule_name](counts) + result[f"rule_{rule_name}"] = passed + if not passed: + all_pass = False + + result["valid"] = all_pass + return result + + +def validate_formulas(formulas: list, rules: list) -> list: + """Validate a list of formulas. + + Parameters + ---------- + formulas: + List of molecular formula strings. + rules: + List of rule names. + + Returns + ------- + list of validation result dicts. + """ + return [validate_formula(f, rules) for f in formulas] + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Apply Seven Golden Rules to validate molecular formulas." + ) + parser.add_argument("--input", required=True, help="TSV file with a 'formula' column.") + parser.add_argument("--rules", default="all", + help="Comma-separated rules: rdbe,hc,nc,oc,sc,pc or 'all' (default: all).") + parser.add_argument("--output", required=True, help="Output TSV with validation results.") + args = parser.parse_args() + + rules = [r.strip() for r in args.rules.split(",")] + + formulas = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + formulas.append(row["formula"]) + + results = validate_formulas(formulas, rules) + + fieldnames = list(results[0].keys()) if results else [] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + n_valid = sum(1 for r in results if r["valid"]) + print(f"Validated {len(results)} formulas: {n_valid} passed, {len(results) - n_valid} failed.") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/formula_validator_golden_rules/requirements.txt b/scripts/metabolomics/formula_validator_golden_rules/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/formula_validator_golden_rules/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/formula_validator_golden_rules/tests/conftest.py b/scripts/metabolomics/formula_validator_golden_rules/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/formula_validator_golden_rules/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/formula_validator_golden_rules/tests/test_formula_validator_golden_rules.py b/scripts/metabolomics/formula_validator_golden_rules/tests/test_formula_validator_golden_rules.py new file mode 100644 index 0000000..e34b7ff --- /dev/null +++ b/scripts/metabolomics/formula_validator_golden_rules/tests/test_formula_validator_golden_rules.py @@ -0,0 +1,119 @@ +"""Tests for formula_validator_golden_rules.""" + + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestGetElementCounts: + def test_glucose(self): + from formula_validator_golden_rules import get_element_counts + + counts = get_element_counts("C6H12O6") + assert counts["C"] == 6 + assert counts["H"] == 12 + assert counts["O"] == 6 + + def test_with_nitrogen(self): + from formula_validator_golden_rules import get_element_counts + + counts = get_element_counts("C3H7NO2") + assert counts["N"] == 1 + + +@requires_pyopenms +class TestComputeRDBE: + def test_benzene(self): + from formula_validator_golden_rules import compute_rdbe + + # C6H6: RDBE = (12 + 2 - 6) / 2 = 4 + assert compute_rdbe({"C": 6, "H": 6}) == 4.0 + + def test_methane(self): + from formula_validator_golden_rules import compute_rdbe + + # CH4: RDBE = (2 + 2 - 4) / 2 = 0 + assert compute_rdbe({"C": 1, "H": 4}) == 0.0 + + def test_glucose(self): + from formula_validator_golden_rules import compute_rdbe + + # C6H12O6: RDBE = (12 + 2 - 12) / 2 = 1 + assert compute_rdbe({"C": 6, "H": 12, "O": 6}) == 1.0 + + +@requires_pyopenms +class TestIndividualRules: + def test_rdbe_nonneg_pass(self): + from formula_validator_golden_rules import check_rdbe_nonnegative + + assert check_rdbe_nonnegative({"C": 6, "H": 6}) is True + + def test_rdbe_nonneg_fail(self): + from formula_validator_golden_rules import check_rdbe_nonnegative + + # Very high H count makes RDBE negative + assert check_rdbe_nonnegative({"C": 1, "H": 10}) is False + + def test_hc_ratio_pass(self): + from formula_validator_golden_rules import check_hc_ratio + + assert check_hc_ratio({"C": 6, "H": 12}) is True # H/C = 2.0 + + def test_hc_ratio_fail_too_low(self): + from formula_validator_golden_rules import check_hc_ratio + + assert check_hc_ratio({"C": 10, "H": 1}) is False # H/C = 0.1 + + def test_nc_ratio_pass(self): + from formula_validator_golden_rules import check_nc_ratio + + assert check_nc_ratio({"C": 3, "N": 1}) is True # N/C = 0.33 + + def test_oc_ratio_fail(self): + from formula_validator_golden_rules import check_oc_ratio + + assert check_oc_ratio({"C": 1, "O": 5}) is False # O/C = 5.0 + + def test_sc_ratio_pass(self): + from formula_validator_golden_rules import check_sc_ratio + + assert check_sc_ratio({"C": 10, "S": 1}) is True + + def test_pc_ratio_pass(self): + from formula_validator_golden_rules import check_pc_ratio + + assert check_pc_ratio({"C": 10, "P": 1}) is True + + +@requires_pyopenms +class TestValidateFormula: + def test_glucose_valid(self): + from formula_validator_golden_rules import validate_formula + + result = validate_formula("C6H12O6", ["all"]) + assert result["valid"] is True + + def test_alanine_valid(self): + from formula_validator_golden_rules import validate_formula + + result = validate_formula("C3H7NO2", ["all"]) + assert result["valid"] is True + + def test_specific_rules(self): + from formula_validator_golden_rules import validate_formula + + result = validate_formula("C6H12O6", ["rdbe", "hc"]) + assert "rule_rdbe" in result + assert "rule_hc" in result + assert "rule_nc" not in result + + +@requires_pyopenms +class TestValidateFormulas: + def test_batch(self): + from formula_validator_golden_rules import validate_formulas + + results = validate_formulas(["C6H12O6", "C3H7NO2"], ["all"]) + assert len(results) == 2 + assert all(r["valid"] for r in results) diff --git a/scripts/metabolomics/gnps_fbmn_exporter/README.md b/scripts/metabolomics/gnps_fbmn_exporter/README.md new file mode 100644 index 0000000..7a78c32 --- /dev/null +++ b/scripts/metabolomics/gnps_fbmn_exporter/README.md @@ -0,0 +1,22 @@ +# GNPS FBMN Exporter + +Export MS2 spectra and a quantification table in GNPS Feature-Based Molecular Networking (FBMN) format. + +## Usage + +```bash +python gnps_fbmn_exporter.py --mzml data.mzML --features features.tsv --output-mgf gnps.mgf --output-quant quant.csv +``` + +### Input formats + +**features.tsv** (tab-separated): +``` +feature_id mz rt intensity +F1 180.0634 100.0 5000 +``` + +### Output + +- **MGF file**: MS2 spectra with SCANS=feature_id, PEPMASS, RTINSECONDS headers +- **Quantification CSV**: Feature table with row ID, row m/z, row retention time columns for GNPS diff --git a/scripts/metabolomics/gnps_fbmn_exporter/gnps_fbmn_exporter.py b/scripts/metabolomics/gnps_fbmn_exporter/gnps_fbmn_exporter.py new file mode 100644 index 0000000..06ad195 --- /dev/null +++ b/scripts/metabolomics/gnps_fbmn_exporter/gnps_fbmn_exporter.py @@ -0,0 +1,266 @@ +""" +GNPS FBMN Exporter +================== +Export MS2 spectra and a quantification table in GNPS Feature-Based Molecular +Networking (FBMN) format. Reads an mzML file for spectra and a feature table +(TSV) for quantification. Produces an MGF file (with SCANS=feature_id, +PEPMASS, RTINSECONDS) and a quantification CSV. + +Usage +----- + python gnps_fbmn_exporter.py --mzml data.mzML --features features.tsv \ + --output-mgf gnps.mgf --output-quant quant.csv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_mzml(path: str) -> oms.MSExperiment: + """Load an mzML file into an MSExperiment object. + + Parameters + ---------- + path: + Path to the mzML file. + + Returns + ------- + pyopenms.MSExperiment + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(path, exp) + return exp + + +def load_features(path: str) -> list[dict]: + """Load a feature table from TSV. + + Expected columns: feature_id, mz, rt, intensity. + Optional additional sample columns for quantification. + + Parameters + ---------- + path: + Path to TSV file. + + Returns + ------- + list of dict + """ + rows = [] + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + parsed = {} + for key, val in row.items(): + try: + parsed[key] = float(val) + except (ValueError, TypeError): + parsed[key] = val + rows.append(parsed) + return rows + + +def find_best_ms2( + exp: oms.MSExperiment, + mz: float, + rt: float, + mz_tol: float = 0.01, + rt_tol: float = 30.0, +) -> list[tuple[float, float]] | None: + """Find the best matching MS2 spectrum for a feature. + + Parameters + ---------- + exp: + MSExperiment containing spectra. + mz: + Precursor m/z of the feature. + rt: + Retention time (seconds) of the feature. + mz_tol: + m/z tolerance in Da for precursor matching. + rt_tol: + RT tolerance in seconds. + + Returns + ------- + list of (mz, intensity) or None + Peak list of the best matching MS2 spectrum, or None if no match. + """ + best_spectrum = None + best_intensity = -1.0 + + for spec in exp: + if spec.getMSLevel() != 2: + continue + spec_rt = spec.getRT() + if abs(spec_rt - rt) > rt_tol: + continue + precursors = spec.getPrecursors() + if not precursors: + continue + prec_mz = precursors[0].getMZ() + if abs(prec_mz - mz) > mz_tol: + continue + # Pick the one with highest TIC + tic = sum(spec.get_peaks()[1]) if spec.size() > 0 else 0.0 + if tic > best_intensity: + best_intensity = tic + mzs, intensities = spec.get_peaks() + best_spectrum = list(zip(mzs.tolist(), intensities.tolist())) + + return best_spectrum + + +def write_mgf( + features: list[dict], + spectra: dict[str, list[tuple[float, float]]], + path: str, +) -> int: + """Write an MGF file in GNPS FBMN format. + + Parameters + ---------- + features: + Feature table rows. + spectra: + Mapping from feature_id to peak list [(mz, intensity), ...]. + path: + Output MGF path. + + Returns + ------- + int + Number of spectra written. + """ + count = 0 + with open(path, "w") as fh: + for feat in features: + fid = str(feat.get("feature_id", "")) + if fid not in spectra: + continue + peaks = spectra[fid] + if not peaks: + continue + mz = float(feat["mz"]) + rt = float(feat["rt"]) + fh.write("BEGIN IONS\n") + fh.write(f"SCANS={fid}\n") + fh.write(f"PEPMASS={mz:.6f}\n") + fh.write(f"RTINSECONDS={rt:.2f}\n") + fh.write("CHARGE=1+\n") + for peak_mz, peak_int in peaks: + fh.write(f"{peak_mz:.6f}\t{peak_int:.4f}\n") + fh.write("END IONS\n\n") + count += 1 + return count + + +def write_quant_table(features: list[dict], path: str) -> None: + """Write a quantification table in CSV format for GNPS. + + Parameters + ---------- + features: + Feature table rows. + path: + Output CSV path. + """ + if not features: + with open(path, "w") as fh: + fh.write("# No features\n") + return + # Ensure 'row ID' is present (GNPS expects it) + output_rows = [] + for feat in features: + row = dict(feat) + if "row ID" not in row: + row["row ID"] = row.get("feature_id", "") + if "row m/z" not in row: + row["row m/z"] = row.get("mz", "") + if "row retention time" not in row: + row["row retention time"] = row.get("rt", "") + output_rows.append(row) + + out_fields = list(output_rows[0].keys()) + with open(path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=out_fields) + writer.writeheader() + writer.writerows(output_rows) + + +def export_fbmn( + mzml_path: str, + features: list[dict], + mgf_path: str, + quant_path: str, + mz_tol: float = 0.01, + rt_tol: float = 30.0, +) -> tuple[int, int]: + """Full FBMN export pipeline. + + Parameters + ---------- + mzml_path: + Path to mzML file. + features: + Feature table rows. + mgf_path: + Output MGF path. + quant_path: + Output quantification CSV path. + mz_tol: + m/z tolerance for MS2 matching. + rt_tol: + RT tolerance (seconds) for MS2 matching. + + Returns + ------- + tuple of (spectra_written, features_count) + """ + exp = load_mzml(mzml_path) + spectra = {} + for feat in features: + fid = str(feat.get("feature_id", "")) + mz = float(feat["mz"]) + rt = float(feat["rt"]) + ms2 = find_best_ms2(exp, mz, rt, mz_tol=mz_tol, rt_tol=rt_tol) + if ms2 is not None: + spectra[fid] = ms2 + + n_spectra = write_mgf(features, spectra, mgf_path) + write_quant_table(features, quant_path) + return n_spectra, len(features) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Export MS2 + quant table in GNPS FBMN format." + ) + parser.add_argument("--mzml", required=True, help="Input mzML file") + parser.add_argument("--features", required=True, help="Feature table (TSV) with feature_id, mz, rt, intensity") + parser.add_argument("--output-mgf", required=True, help="Output MGF file") + parser.add_argument("--output-quant", required=True, help="Output quantification CSV") + parser.add_argument("--mz-tol", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") + parser.add_argument("--rt-tol", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") + args = parser.parse_args() + + features = load_features(args.features) + n_spectra, n_features = export_fbmn( + args.mzml, features, args.output_mgf, args.output_quant, + mz_tol=args.mz_tol, rt_tol=args.rt_tol, + ) + print(f"Exported {n_spectra} MS2 spectra and {n_features} features for GNPS FBMN") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/gnps_fbmn_exporter/requirements.txt b/scripts/metabolomics/gnps_fbmn_exporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/gnps_fbmn_exporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/gnps_fbmn_exporter/tests/conftest.py b/scripts/metabolomics/gnps_fbmn_exporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/gnps_fbmn_exporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py b/scripts/metabolomics/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py new file mode 100644 index 0000000..aa1e90a --- /dev/null +++ b/scripts/metabolomics/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py @@ -0,0 +1,120 @@ +"""Tests for gnps_fbmn_exporter.""" + +import csv +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestGnpsFbmnExporter: + def _create_test_mzml(self, path: str) -> None: + """Create a minimal mzML file with MS1 and MS2 spectra.""" + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + + # MS1 spectrum + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(100.0) + ms1.set_peaks([np.array([180.0634, 200.0], dtype=np.float64), + np.array([1000.0, 500.0], dtype=np.float64)]) + exp.addSpectrum(ms1) + + # MS2 spectrum matching feature at mz=180.0634, rt=100.0 + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(100.0) + prec = oms.Precursor() + prec.setMZ(180.0634) + ms2.setPrecursors([prec]) + ms2.set_peaks([np.array([90.0, 120.0, 150.0], dtype=np.float64), + np.array([500.0, 200.0, 100.0], dtype=np.float64)]) + exp.addSpectrum(ms2) + + oms.MzMLFile().store(path, exp) + + def test_load_mzml(self): + from gnps_fbmn_exporter import load_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + self._create_test_mzml(mzml_path) + exp = load_mzml(mzml_path) + assert exp.size() == 2 + + def test_find_best_ms2(self): + from gnps_fbmn_exporter import find_best_ms2, load_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + self._create_test_mzml(mzml_path) + exp = load_mzml(mzml_path) + + peaks = find_best_ms2(exp, mz=180.0634, rt=100.0, mz_tol=0.01, rt_tol=30.0) + assert peaks is not None + assert len(peaks) == 3 + + def test_find_best_ms2_no_match(self): + from gnps_fbmn_exporter import find_best_ms2, load_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + self._create_test_mzml(mzml_path) + exp = load_mzml(mzml_path) + + peaks = find_best_ms2(exp, mz=300.0, rt=100.0, mz_tol=0.01, rt_tol=30.0) + assert peaks is None + + def test_write_mgf(self): + from gnps_fbmn_exporter import write_mgf + + features = [{"feature_id": "F1", "mz": 180.0634, "rt": 100.0}] + spectra = {"F1": [(90.0, 500.0), (120.0, 200.0)]} + + with tempfile.TemporaryDirectory() as tmpdir: + mgf_path = os.path.join(tmpdir, "test.mgf") + count = write_mgf(features, spectra, mgf_path) + assert count == 1 + with open(mgf_path) as fh: + content = fh.read() + assert "BEGIN IONS" in content + assert "SCANS=F1" in content + assert "PEPMASS=180.063400" in content + assert "RTINSECONDS=100.00" in content + assert "END IONS" in content + + def test_write_quant_table(self): + from gnps_fbmn_exporter import write_quant_table + + features = [{"feature_id": "F1", "mz": 180.0634, "rt": 100.0, "intensity": 1000.0}] + + with tempfile.TemporaryDirectory() as tmpdir: + quant_path = os.path.join(tmpdir, "quant.csv") + write_quant_table(features, quant_path) + assert os.path.exists(quant_path) + with open(quant_path) as fh: + reader = csv.DictReader(fh) + rows = list(reader) + assert len(rows) == 1 + assert "row ID" in rows[0] + + def test_full_pipeline(self): + from gnps_fbmn_exporter import export_fbmn + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + self._create_test_mzml(mzml_path) + + features = [{"feature_id": "F1", "mz": 180.0634, "rt": 100.0, "intensity": 1000.0}] + + mgf_path = os.path.join(tmpdir, "gnps.mgf") + quant_path = os.path.join(tmpdir, "quant.csv") + n_spectra, n_features = export_fbmn(mzml_path, features, mgf_path, quant_path) + assert n_spectra == 1 + assert n_features == 1 + assert os.path.exists(mgf_path) + assert os.path.exists(quant_path) diff --git a/scripts/metabolomics/isf_detector/README.md b/scripts/metabolomics/isf_detector/README.md new file mode 100644 index 0000000..90dacce --- /dev/null +++ b/scripts/metabolomics/isf_detector/README.md @@ -0,0 +1,29 @@ +# In-Source Fragmentation Detector + +Detect in-source fragmentation (ISF) artifacts by identifying coeluting features whose mass difference matches common neutral losses (H2O, CO2, NH3, CO, HCOOH, CH3OH). + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python isf_detector.py --input features.tsv --rt-tolerance 3 --output isf_annotated.tsv +``` + +## Input format + +Tab-separated file with columns: id, mz, rt, intensity. + +``` +id mz rt intensity +F1 180.0634 120.5 50000 +F2 162.0528 121.0 15000 +``` + +## Output format + +Original columns plus: isf_flag, isf_role (precursor/fragment), isf_partner, isf_loss. diff --git a/scripts/metabolomics/isf_detector/isf_detector.py b/scripts/metabolomics/isf_detector/isf_detector.py new file mode 100644 index 0000000..ec795b2 --- /dev/null +++ b/scripts/metabolomics/isf_detector/isf_detector.py @@ -0,0 +1,212 @@ +""" +In-Source Fragmentation Detector +================================= +Detect in-source fragmentation (ISF) artifacts by identifying coeluting features +whose mass difference matches common neutral losses (H2O, CO2, NH3, etc.). + +Usage +----- + python isf_detector.py --input features.tsv --rt-tolerance 3 --output isf_annotated.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Common neutral losses and their exact masses +NEUTRAL_LOSSES = { + "H2O": oms.EmpiricalFormula("H2O").getMonoWeight(), + "CO2": oms.EmpiricalFormula("CO2").getMonoWeight(), + "NH3": oms.EmpiricalFormula("NH3").getMonoWeight(), + "CO": oms.EmpiricalFormula("CO").getMonoWeight(), + "HCOOH": oms.EmpiricalFormula("CH2O2").getMonoWeight(), + "CH3OH": oms.EmpiricalFormula("CH4O").getMonoWeight(), +} + + +def get_neutral_loss_masses() -> dict: + """Return the neutral loss name-to-mass mapping. + + Returns + ------- + dict mapping loss names to exact masses. + """ + return dict(NEUTRAL_LOSSES) + + +def pearson_correlation(x: list, y: list) -> float: + """Compute Pearson correlation coefficient between two lists. + + Parameters + ---------- + x, y: + Lists of numeric values of equal length. + + Returns + ------- + float: Pearson r value, or 0.0 if computation is not possible. + """ + n = len(x) + if n < 2: + return 0.0 + + mean_x = sum(x) / n + mean_y = sum(y) / n + + cov = sum((xi - mean_x) * (yi - mean_y) for xi, yi in zip(x, y)) + std_x = math.sqrt(sum((xi - mean_x) ** 2 for xi in x)) + std_y = math.sqrt(sum((yi - mean_y) ** 2 for yi in y)) + + if std_x == 0 or std_y == 0: + return 0.0 + + return cov / (std_x * std_y) + + +def detect_isf_pairs( + features: list, + rt_tolerance: float = 3.0, + mass_tolerance_da: float = 0.01, +) -> list: + """Detect potential in-source fragmentation pairs. + + Parameters + ---------- + features: + List of dicts with keys: id, mz, rt, intensity. + rt_tolerance: + Maximum RT difference (seconds) for coelution. + mass_tolerance_da: + Mass tolerance in Daltons for neutral loss matching. + + Returns + ------- + list of dicts describing ISF pair annotations. + """ + pairs = [] + n = len(features) + + for i in range(n): + for j in range(i + 1, n): + fi = features[i] + fj = features[j] + + rt_diff = abs(float(fi["rt"]) - float(fj["rt"])) + if rt_diff > rt_tolerance: + continue + + mass_diff = abs(float(fi["mz"]) - float(fj["mz"])) + + for loss_name, loss_mass in NEUTRAL_LOSSES.items(): + if abs(mass_diff - loss_mass) <= mass_tolerance_da: + # Heavier feature is the precursor + if float(fi["mz"]) > float(fj["mz"]): + precursor, fragment = fi, fj + else: + precursor, fragment = fj, fi + + pairs.append({ + "precursor_id": precursor["id"], + "precursor_mz": float(precursor["mz"]), + "fragment_id": fragment["id"], + "fragment_mz": float(fragment["mz"]), + "neutral_loss": loss_name, + "mass_diff": round(mass_diff, 6), + "rt_diff": round(rt_diff, 2), + "isf_candidate": True, + }) + break # Only assign one neutral loss per pair + + return pairs + + +def annotate_features(features: list, isf_pairs: list) -> list: + """Add ISF annotation columns to the feature list. + + Parameters + ---------- + features: + Original feature list. + isf_pairs: + ISF pair annotations from detect_isf_pairs. + + Returns + ------- + list of dicts with added isf_flag, isf_role, isf_partner, isf_loss columns. + """ + # Build lookup for partner info + partner_info = {} + for p in isf_pairs: + partner_info[p["fragment_id"]] = { + "role": "fragment", + "partner": p["precursor_id"], + "loss": p["neutral_loss"], + } + if p["precursor_id"] not in partner_info: + partner_info[p["precursor_id"]] = { + "role": "precursor", + "partner": p["fragment_id"], + "loss": p["neutral_loss"], + } + + annotated = [] + for f in features: + row = dict(f) + fid = f["id"] + if fid in partner_info: + row["isf_flag"] = True + row["isf_role"] = partner_info[fid]["role"] + row["isf_partner"] = partner_info[fid]["partner"] + row["isf_loss"] = partner_info[fid]["loss"] + else: + row["isf_flag"] = False + row["isf_role"] = "" + row["isf_partner"] = "" + row["isf_loss"] = "" + annotated.append(row) + + return annotated + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Detect in-source fragmentation by coelution and common neutral losses." + ) + parser.add_argument("--input", required=True, help="TSV with columns: id, mz, rt, intensity.") + parser.add_argument("--rt-tolerance", type=float, default=3.0, + help="RT tolerance in seconds (default: 3).") + parser.add_argument("--mass-tolerance", type=float, default=0.01, + help="Mass tolerance in Da for neutral loss matching (default: 0.01).") + parser.add_argument("--output", required=True, help="Output TSV with ISF annotations.") + args = parser.parse_args() + + features = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + isf_pairs = detect_isf_pairs(features, rt_tolerance=args.rt_tolerance, + mass_tolerance_da=args.mass_tolerance) + annotated = annotate_features(features, isf_pairs) + + fieldnames = list(annotated[0].keys()) if annotated else [] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(annotated) + + n_isf = sum(1 for a in annotated if a["isf_flag"]) + print(f"Wrote {len(annotated)} features ({n_isf} ISF-flagged) to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/isf_detector/requirements.txt b/scripts/metabolomics/isf_detector/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/isf_detector/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/isf_detector/tests/conftest.py b/scripts/metabolomics/isf_detector/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/isf_detector/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/isf_detector/tests/test_isf_detector.py b/scripts/metabolomics/isf_detector/tests/test_isf_detector.py new file mode 100644 index 0000000..d6f5ea7 --- /dev/null +++ b/scripts/metabolomics/isf_detector/tests/test_isf_detector.py @@ -0,0 +1,95 @@ +"""Tests for isf_detector.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestNeutralLossMasses: + def test_h2o_mass(self): + from isf_detector import get_neutral_loss_masses + + masses = get_neutral_loss_masses() + assert masses["H2O"] == pytest.approx(18.0106, abs=0.001) + + def test_co2_mass(self): + from isf_detector import get_neutral_loss_masses + + masses = get_neutral_loss_masses() + assert masses["CO2"] == pytest.approx(43.9898, abs=0.001) + + +@requires_pyopenms +class TestPearsonCorrelation: + def test_perfect_correlation(self): + from isf_detector import pearson_correlation + + assert pearson_correlation([1, 2, 3], [2, 4, 6]) == pytest.approx(1.0, abs=0.001) + + def test_no_correlation(self): + from isf_detector import pearson_correlation + + r = pearson_correlation([1, 2, 3, 4], [1, -1, 1, -1]) + assert abs(r) < 0.5 + + def test_short_input(self): + from isf_detector import pearson_correlation + + assert pearson_correlation([1], [2]) == 0.0 + + +@requires_pyopenms +class TestDetectISFPairs: + def test_water_loss_detected(self): + from isf_detector import detect_isf_pairs, get_neutral_loss_masses + + h2o_mass = get_neutral_loss_masses()["H2O"] + features = [ + {"id": "F1", "mz": "180.0634", "rt": "120.5", "intensity": "50000"}, + {"id": "F2", "mz": str(180.0634 - h2o_mass), "rt": "121.0", "intensity": "15000"}, + ] + pairs = detect_isf_pairs(features, rt_tolerance=3.0, mass_tolerance_da=0.01) + assert len(pairs) == 1 + assert pairs[0]["neutral_loss"] == "H2O" + assert pairs[0]["precursor_id"] == "F1" + + def test_no_pair_when_rt_too_far(self): + from isf_detector import detect_isf_pairs, get_neutral_loss_masses + + h2o_mass = get_neutral_loss_masses()["H2O"] + features = [ + {"id": "F1", "mz": "180.0634", "rt": "100.0", "intensity": "50000"}, + {"id": "F2", "mz": str(180.0634 - h2o_mass), "rt": "200.0", "intensity": "15000"}, + ] + pairs = detect_isf_pairs(features, rt_tolerance=3.0) + assert len(pairs) == 0 + + def test_no_pair_when_mass_not_matching(self): + from isf_detector import detect_isf_pairs + + features = [ + {"id": "F1", "mz": "180.0634", "rt": "120.5", "intensity": "50000"}, + {"id": "F2", "mz": "170.0000", "rt": "121.0", "intensity": "15000"}, + ] + pairs = detect_isf_pairs(features, rt_tolerance=3.0) + assert len(pairs) == 0 + + +@requires_pyopenms +class TestAnnotateFeatures: + def test_annotation(self): + from isf_detector import annotate_features + + features = [ + {"id": "F1", "mz": "180.0", "rt": "120.5", "intensity": "50000"}, + {"id": "F2", "mz": "162.0", "rt": "121.0", "intensity": "15000"}, + {"id": "F3", "mz": "300.0", "rt": "200.0", "intensity": "80000"}, + ] + pairs = [{"precursor_id": "F1", "fragment_id": "F2", "neutral_loss": "H2O"}] + annotated = annotate_features(features, pairs) + + assert annotated[0]["isf_flag"] is True + assert annotated[0]["isf_role"] == "precursor" + assert annotated[1]["isf_flag"] is True + assert annotated[1]["isf_role"] == "fragment" + assert annotated[2]["isf_flag"] is False diff --git a/scripts/metabolomics/isotope_label_detector/README.md b/scripts/metabolomics/isotope_label_detector/README.md new file mode 100644 index 0000000..73b7f66 --- /dev/null +++ b/scripts/metabolomics/isotope_label_detector/README.md @@ -0,0 +1,24 @@ +# Isotope Label Detector + +Detect 13C/15N-labeled metabolites by pairing unlabeled and labeled features based on RT proximity and expected mass shift. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python isotope_label_detector.py --unlabeled features_ctrl.tsv --labeled features_13c.tsv \ + --tracer 13C --ppm 5 --output pairs.tsv +``` + +## Input format + +Tab-separated files with columns: id, mz, rt. + +## Output format + +Tab-separated file with columns: unlabeled_id, unlabeled_mz, labeled_id, labeled_mz, mass_diff, n_labels, rt_diff, ppm_error. diff --git a/scripts/metabolomics/isotope_label_detector/isotope_label_detector.py b/scripts/metabolomics/isotope_label_detector/isotope_label_detector.py new file mode 100644 index 0000000..91592d4 --- /dev/null +++ b/scripts/metabolomics/isotope_label_detector/isotope_label_detector.py @@ -0,0 +1,180 @@ +""" +Isotope Label Detector +======================= +Detect 13C/15N-labeled metabolites by pairing unlabeled and labeled features +based on RT proximity and expected mass shift. + +Usage +----- + python isotope_label_detector.py --unlabeled features_ctrl.tsv \\ + --labeled features_13c.tsv --tracer 13C --ppm 5 --output pairs.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Mass shift per tracer atom +TRACER_MASS_SHIFTS = { + "13C": 1.003355, # 13C - 12C + "15N": 0.997035, # 15N - 14N + "2H": 1.006277, # 2H - 1H +} + + +def get_element_count(formula: str, element: str) -> int: + """Get the count of a specific element in a formula. + + Parameters + ---------- + formula: + Molecular formula string. + element: + Element symbol (e.g. ``"C"``, ``"N"``). + + Returns + ------- + int: Count of the element. + """ + ef = oms.EmpiricalFormula(formula) + composition = ef.getElementalComposition() + return composition.get(element.encode(), 0) + + +def compute_expected_mass_shift(formula: str, tracer: str) -> float: + """Compute the expected mass shift for full labeling of a formula. + + Parameters + ---------- + formula: + Molecular formula string. + tracer: + Tracer type (``"13C"``, ``"15N"``, ``"2H"``). + + Returns + ------- + float: Expected mass shift in Daltons. + """ + element_map = {"13C": "C", "15N": "N", "2H": "H"} + element = element_map.get(tracer) + if element is None: + raise ValueError(f"Unsupported tracer: {tracer}. Supported: 13C, 15N, 2H") + + n_atoms = get_element_count(formula, element) + shift_per_atom = TRACER_MASS_SHIFTS[tracer] + return n_atoms * shift_per_atom + + +def find_labeled_pairs( + unlabeled_features: list, + labeled_features: list, + tracer: str = "13C", + ppm: float = 5.0, + rt_tolerance: float = 10.0, + max_label_atoms: int = 50, +) -> list: + """Find pairs of unlabeled and labeled features. + + Parameters + ---------- + unlabeled_features: + List of dicts with keys: id, mz, rt (and optionally formula). + labeled_features: + List of dicts with keys: id, mz, rt. + tracer: + Tracer type. + ppm: + Mass tolerance in ppm. + rt_tolerance: + RT tolerance in seconds. + max_label_atoms: + Maximum number of tracer atoms to consider. + + Returns + ------- + list of paired feature dicts. + """ + shift_per_atom = TRACER_MASS_SHIFTS[tracer] + pairs = [] + + for uf in unlabeled_features: + uf_mz = float(uf["mz"]) + uf_rt = float(uf["rt"]) + + for lf in labeled_features: + lf_mz = float(lf["mz"]) + lf_rt = float(lf["rt"]) + + # Check RT proximity + if abs(uf_rt - lf_rt) > rt_tolerance: + continue + + # Check if mass difference is a multiple of the tracer shift + mass_diff = lf_mz - uf_mz + if mass_diff <= 0: + continue + + n_labels = round(mass_diff / shift_per_atom) + if n_labels < 1 or n_labels > max_label_atoms: + continue + + expected_diff = n_labels * shift_per_atom + ppm_error = abs(mass_diff - expected_diff) / uf_mz * 1e6 + + if ppm_error <= ppm: + pairs.append({ + "unlabeled_id": uf.get("id", ""), + "unlabeled_mz": round(uf_mz, 6), + "labeled_id": lf.get("id", ""), + "labeled_mz": round(lf_mz, 6), + "mass_diff": round(mass_diff, 6), + "n_labels": n_labels, + "rt_diff": round(abs(uf_rt - lf_rt), 2), + "ppm_error": round(ppm_error, 2), + }) + + return pairs + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Detect isotope-labeled metabolites by pairing unlabeled and labeled features." + ) + parser.add_argument("--unlabeled", required=True, help="TSV with unlabeled features (id, mz, rt).") + parser.add_argument("--labeled", required=True, help="TSV with labeled features (id, mz, rt).") + parser.add_argument("--tracer", default="13C", help="Tracer type: 13C, 15N, 2H (default: 13C).") + parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5).") + parser.add_argument("--rt-tolerance", type=float, default=10.0, + help="RT tolerance in seconds (default: 10).") + parser.add_argument("--output", required=True, help="Output TSV with paired features.") + args = parser.parse_args() + + def read_features(path): + with open(path, newline="") as fh: + return list(csv.DictReader(fh, delimiter="\t")) + + unlabeled = read_features(args.unlabeled) + labeled = read_features(args.labeled) + + pairs = find_labeled_pairs(unlabeled, labeled, tracer=args.tracer, + ppm=args.ppm, rt_tolerance=args.rt_tolerance) + + fieldnames = ["unlabeled_id", "unlabeled_mz", "labeled_id", "labeled_mz", + "mass_diff", "n_labels", "rt_diff", "ppm_error"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(pairs) + + print(f"Found {len(pairs)} labeled pairs, wrote to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/isotope_label_detector/requirements.txt b/scripts/metabolomics/isotope_label_detector/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/isotope_label_detector/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/isotope_label_detector/tests/conftest.py b/scripts/metabolomics/isotope_label_detector/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/isotope_label_detector/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/isotope_label_detector/tests/test_isotope_label_detector.py b/scripts/metabolomics/isotope_label_detector/tests/test_isotope_label_detector.py new file mode 100644 index 0000000..e024482 --- /dev/null +++ b/scripts/metabolomics/isotope_label_detector/tests/test_isotope_label_detector.py @@ -0,0 +1,84 @@ +"""Tests for isotope_label_detector.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestGetElementCount: + def test_glucose_carbons(self): + from isotope_label_detector import get_element_count + + assert get_element_count("C6H12O6", "C") == 6 + + def test_alanine_nitrogen(self): + from isotope_label_detector import get_element_count + + assert get_element_count("C3H7NO2", "N") == 1 + + +@requires_pyopenms +class TestComputeExpectedMassShift: + def test_glucose_13c(self): + from isotope_label_detector import compute_expected_mass_shift + + shift = compute_expected_mass_shift("C6H12O6", "13C") + # 6 carbons * 1.003355 Da + assert shift == pytest.approx(6 * 1.003355, abs=0.001) + + def test_unsupported_tracer(self): + from isotope_label_detector import compute_expected_mass_shift + + with pytest.raises(ValueError, match="Unsupported tracer"): + compute_expected_mass_shift("C6H12O6", "18O") + + +@requires_pyopenms +class TestFindLabeledPairs: + def test_match_13c_glucose(self): + from isotope_label_detector import find_labeled_pairs + + shift_6c = 6 * 1.003355 + unlabeled = [{"id": "U1", "mz": "180.0634", "rt": "120.0"}] + labeled = [{"id": "L1", "mz": str(180.0634 + shift_6c), "rt": "120.5"}] + + pairs = find_labeled_pairs(unlabeled, labeled, tracer="13C", ppm=5.0, rt_tolerance=10.0) + assert len(pairs) == 1 + assert pairs[0]["n_labels"] == 6 + assert pairs[0]["ppm_error"] < 5.0 + + def test_no_match_wrong_mass(self): + from isotope_label_detector import find_labeled_pairs + + unlabeled = [{"id": "U1", "mz": "180.0634", "rt": "120.0"}] + labeled = [{"id": "L1", "mz": "200.0000", "rt": "120.5"}] + + pairs = find_labeled_pairs(unlabeled, labeled, tracer="13C", ppm=5.0) + assert len(pairs) == 0 + + def test_no_match_rt_too_far(self): + from isotope_label_detector import find_labeled_pairs + + shift_6c = 6 * 1.003355 + unlabeled = [{"id": "U1", "mz": "180.0634", "rt": "100.0"}] + labeled = [{"id": "L1", "mz": str(180.0634 + shift_6c), "rt": "200.0"}] + + pairs = find_labeled_pairs(unlabeled, labeled, tracer="13C", ppm=5.0, rt_tolerance=10.0) + assert len(pairs) == 0 + + def test_multiple_pairs(self): + from isotope_label_detector import find_labeled_pairs + + shift_3c = 3 * 1.003355 + shift_6c = 6 * 1.003355 + unlabeled = [ + {"id": "U1", "mz": "89.0477", "rt": "50.0"}, + {"id": "U2", "mz": "180.0634", "rt": "120.0"}, + ] + labeled = [ + {"id": "L1", "mz": str(89.0477 + shift_3c), "rt": "50.5"}, + {"id": "L2", "mz": str(180.0634 + shift_6c), "rt": "120.5"}, + ] + + pairs = find_labeled_pairs(unlabeled, labeled, tracer="13C", ppm=5.0, rt_tolerance=10.0) + assert len(pairs) == 2 diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/README.md b/scripts/metabolomics/isotope_pattern_fit_scorer/README.md new file mode 100644 index 0000000..716d595 --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_fit_scorer/README.md @@ -0,0 +1,16 @@ +# Isotope Pattern Fit Scorer + +Score observed vs theoretical isotope patterns using cosine similarity. Detect Cl/Br halogenation from enhanced M+2 peaks. + +## Usage + +```bash +python isotope_pattern_fit_scorer.py --observed "180.063:100,181.067:6.5,182.070:0.5" --formula C6H12O6 --output fit.json +``` + +### Output + +JSON file with: +- `cosine_similarity`: Score between 0 and 1 +- `observed_peaks` and `theoretical_peaks`: Peak lists +- `halogen_detection`: M+2 excess analysis with Cl/Br flagging diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py b/scripts/metabolomics/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py new file mode 100644 index 0000000..2df8a4e --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py @@ -0,0 +1,263 @@ +""" +Isotope Pattern Fit Scorer +========================== +Score observed vs theoretical isotope patterns using cosine similarity. +Detect halogenation (Cl/Br) from enhanced M+2 peaks. + +Usage +----- + python isotope_pattern_fit_scorer.py \ + --observed "180.063:100,181.067:6.5,182.070:0.5" \ + --formula C6H12O6 --output fit.json +""" + +import argparse +import json +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def get_theoretical_pattern( + formula: str, + max_isotopes: int = 6, +) -> list[tuple[float, float]]: + """Compute the theoretical isotope distribution for a molecular formula. + + Parameters + ---------- + formula: + Empirical formula string, e.g. ``"C6H12O6"``. + max_isotopes: + Maximum number of isotope peaks. + + Returns + ------- + list of (mz, relative_abundance) + Relative abundances normalized so the max is 100. + """ + ef = oms.EmpiricalFormula(formula) + iso_dist = ef.getIsotopeDistribution( + oms.CoarseIsotopePatternGenerator(max_isotopes) + ) + container = iso_dist.getContainer() + peaks = [(p.getMZ(), p.getIntensity()) for p in container] + if not peaks: + return [] + max_int = max(p[1] for p in peaks) + if max_int == 0: + return peaks + return [(mz, intensity / max_int * 100.0) for mz, intensity in peaks] + + +def parse_observed(observed_str: str) -> list[tuple[float, float]]: + """Parse observed peaks from a string. + + Format: ``"mz1:int1,mz2:int2,..."`` + + Parameters + ---------- + observed_str: + Comma-separated mz:intensity pairs. + + Returns + ------- + list of (mz, intensity) + """ + peaks = [] + for pair in observed_str.split(","): + pair = pair.strip() + if not pair: + continue + parts = pair.split(":") + if len(parts) != 2: + raise ValueError(f"Invalid peak format: {pair!r}. Expected mz:intensity") + mz = float(parts[0].strip()) + intensity = float(parts[1].strip()) + peaks.append((mz, intensity)) + return peaks + + +def cosine_similarity( + observed: list[tuple[float, float]], + theoretical: list[tuple[float, float]], + mz_tolerance: float = 0.05, +) -> float: + """Compute cosine similarity between observed and theoretical patterns. + + Parameters + ---------- + observed: + List of (mz, intensity) for observed peaks. + theoretical: + List of (mz, intensity) for theoretical peaks. + mz_tolerance: + Maximum m/z difference to consider a match. + + Returns + ------- + float + Cosine similarity score between 0 and 1. + """ + if not observed or not theoretical: + return 0.0 + + dot_product = 0.0 + norm_obs = 0.0 + norm_theo = 0.0 + + for obs_mz, obs_int in observed: + norm_obs += obs_int ** 2 + + for theo_mz, theo_int in theoretical: + norm_theo += theo_int ** 2 + + for obs_mz, obs_int in observed: + for theo_mz, theo_int in theoretical: + if abs(obs_mz - theo_mz) <= mz_tolerance: + dot_product += obs_int * theo_int + + if norm_obs == 0 or norm_theo == 0: + return 0.0 + return dot_product / (math.sqrt(norm_obs) * math.sqrt(norm_theo)) + + +def detect_halogenation( + observed: list[tuple[float, float]], + theoretical: list[tuple[float, float]], + mz_tolerance: float = 0.05, +) -> dict: + """Detect Cl/Br halogenation from enhanced M+2 peak. + + Cl has a natural isotope ratio of ~32.5% for M+2 relative to M+0. + Br has a natural isotope ratio of ~97% for M+2 relative to M+0. + If the observed M+2 is significantly higher than theoretical, flag + potential halogenation. + + Parameters + ---------- + observed: + Observed isotope pattern. + theoretical: + Theoretical isotope pattern (without halogens). + mz_tolerance: + m/z tolerance for peak matching. + + Returns + ------- + dict + Keys: m2_observed, m2_theoretical, m2_excess, halogen_flag, possible_halogen. + """ + result = { + "m2_observed": None, + "m2_theoretical": None, + "m2_excess": None, + "halogen_flag": False, + "possible_halogen": "none", + } + + if len(observed) < 3 or len(theoretical) < 3: + return result + + # M+0 is the first peak; M+2 is the third peak + obs_m0_int = observed[0][1] + obs_m2_int = observed[2][1] if len(observed) > 2 else 0.0 + theo_m2_int = theoretical[2][1] if len(theoretical) > 2 else 0.0 + + if obs_m0_int == 0: + return result + + obs_m2_ratio = obs_m2_int / obs_m0_int * 100.0 + theo_m2_ratio = theo_m2_int / max(theoretical[0][1], 1e-10) * 100.0 + + result["m2_observed"] = round(obs_m2_ratio, 2) + result["m2_theoretical"] = round(theo_m2_ratio, 2) + excess = obs_m2_ratio - theo_m2_ratio + result["m2_excess"] = round(excess, 2) + + if excess > 10.0: + result["halogen_flag"] = True + if excess > 70.0: + result["possible_halogen"] = "Br" + elif excess > 20.0: + result["possible_halogen"] = "Cl" + else: + result["possible_halogen"] = "Cl (weak signal)" + + return result + + +def score_pattern( + observed_str: str, + formula: str, + max_isotopes: int = 6, + mz_tolerance: float = 0.05, +) -> dict: + """Score an observed isotope pattern against a theoretical one. + + Parameters + ---------- + observed_str: + Comma-separated mz:intensity pairs. + formula: + Molecular formula. + max_isotopes: + Max isotope peaks. + mz_tolerance: + m/z tolerance. + + Returns + ------- + dict + Score results including cosine_similarity, halogen detection, and peak data. + """ + observed = parse_observed(observed_str) + theoretical = get_theoretical_pattern(formula, max_isotopes=max_isotopes) + cos_sim = cosine_similarity(observed, theoretical, mz_tolerance=mz_tolerance) + halogen = detect_halogenation(observed, theoretical, mz_tolerance=mz_tolerance) + + return { + "formula": formula, + "cosine_similarity": round(cos_sim, 6), + "observed_peaks": [{"mz": mz, "intensity": i} for mz, i in observed], + "theoretical_peaks": [{"mz": round(mz, 6), "intensity": round(i, 4)} for mz, i in theoretical], + "halogen_detection": halogen, + } + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Score observed vs theoretical isotope patterns and detect halogenation." + ) + parser.add_argument( + "--observed", required=True, + help="Observed peaks as 'mz1:int1,mz2:int2,...'" + ) + parser.add_argument("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") + parser.add_argument("--max-isotopes", type=int, default=6, help="Max isotope peaks (default: 6)") + parser.add_argument("--mz-tolerance", type=float, default=0.05, help="m/z tolerance (default: 0.05)") + parser.add_argument("--output", required=True, help="Output JSON file") + args = parser.parse_args() + + result = score_pattern( + args.observed, args.formula, + max_isotopes=args.max_isotopes, mz_tolerance=args.mz_tolerance, + ) + + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + + print(f"Cosine similarity: {result['cosine_similarity']:.4f}") + halogen = result["halogen_detection"] + if halogen["halogen_flag"]: + print(f"Halogen detected: {halogen['possible_halogen']} (M+2 excess: {halogen['m2_excess']}%)") + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/requirements.txt b/scripts/metabolomics/isotope_pattern_fit_scorer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_fit_scorer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/tests/conftest.py b/scripts/metabolomics/isotope_pattern_fit_scorer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_fit_scorer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py b/scripts/metabolomics/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py new file mode 100644 index 0000000..deb337a --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py @@ -0,0 +1,111 @@ +"""Tests for isotope_pattern_fit_scorer.""" + +import json +import os +import tempfile + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestIsotopePatternFitScorer: + def test_get_theoretical_pattern(self): + from isotope_pattern_fit_scorer import get_theoretical_pattern + + pattern = get_theoretical_pattern("C6H12O6", max_isotopes=4) + assert len(pattern) == 4 + # First peak should be the most abundant (normalized to 100) + assert pattern[0][1] == pytest.approx(100.0) + # Subsequent peaks should be smaller + assert pattern[1][1] < 100.0 + + def test_parse_observed(self): + from isotope_pattern_fit_scorer import parse_observed + + peaks = parse_observed("180.063:100,181.067:6.5,182.070:0.5") + assert len(peaks) == 3 + assert peaks[0] == (180.063, 100.0) + assert peaks[1] == (181.067, 6.5) + + def test_parse_observed_with_spaces(self): + from isotope_pattern_fit_scorer import parse_observed + + peaks = parse_observed("180.063:100 , 181.067:6.5") + assert len(peaks) == 2 + + def test_cosine_similarity_perfect(self): + from isotope_pattern_fit_scorer import cosine_similarity + + peaks = [(180.0, 100.0), (181.0, 6.5), (182.0, 0.5)] + sim = cosine_similarity(peaks, peaks, mz_tolerance=0.1) + assert abs(sim - 1.0) < 1e-6 + + def test_cosine_similarity_no_overlap(self): + from isotope_pattern_fit_scorer import cosine_similarity + + obs = [(180.0, 100.0), (181.0, 6.5)] + theo = [(300.0, 100.0), (301.0, 6.5)] + sim = cosine_similarity(obs, theo, mz_tolerance=0.1) + assert sim == 0.0 + + def test_cosine_similarity_partial(self): + from isotope_pattern_fit_scorer import cosine_similarity + + obs = [(180.0, 100.0), (181.0, 6.5), (182.0, 50.0)] # Enhanced M+2 + theo = [(180.0, 100.0), (181.0, 6.5), (182.0, 0.5)] + sim = cosine_similarity(obs, theo, mz_tolerance=0.1) + assert 0.0 < sim < 1.0 + + def test_detect_halogenation_no_halogen(self): + from isotope_pattern_fit_scorer import detect_halogenation + + obs = [(180.0, 100.0), (181.0, 6.5), (182.0, 0.5)] + theo = [(180.0, 100.0), (181.0, 6.5), (182.0, 0.5)] + result = detect_halogenation(obs, theo) + assert result["halogen_flag"] is False + + def test_detect_halogenation_chlorine(self): + from isotope_pattern_fit_scorer import detect_halogenation + + obs = [(180.0, 100.0), (181.0, 6.5), (182.0, 35.0)] # Enhanced M+2 (~35%) + theo = [(180.0, 100.0), (181.0, 6.5), (182.0, 0.5)] # Expected ~0.5% + result = detect_halogenation(obs, theo) + assert result["halogen_flag"] is True + assert "Cl" in result["possible_halogen"] + + def test_detect_halogenation_bromine(self): + from isotope_pattern_fit_scorer import detect_halogenation + + obs = [(180.0, 100.0), (181.0, 6.5), (182.0, 98.0)] # Enhanced M+2 (~98%) + theo = [(180.0, 100.0), (181.0, 6.5), (182.0, 0.5)] + result = detect_halogenation(obs, theo) + assert result["halogen_flag"] is True + assert result["possible_halogen"] == "Br" + + def test_score_pattern(self): + from isotope_pattern_fit_scorer import score_pattern + + result = score_pattern( + "180.063:100,181.067:6.5,182.070:0.5", + "C6H12O6", + ) + assert "cosine_similarity" in result + assert result["cosine_similarity"] > 0.0 + assert "halogen_detection" in result + assert "observed_peaks" in result + assert "theoretical_peaks" in result + + def test_score_pattern_output_json(self): + from isotope_pattern_fit_scorer import score_pattern + + result = score_pattern("180.063:100,181.067:6.5", "C6H12O6") + + with tempfile.TemporaryDirectory() as tmpdir: + out_path = os.path.join(tmpdir, "fit.json") + with open(out_path, "w") as fh: + json.dump(result, fh, indent=2) + assert os.path.exists(out_path) + with open(out_path) as fh: + loaded = json.load(fh) + assert loaded["formula"] == "C6H12O6" diff --git a/scripts/metabolomics/isotope_pattern_scorer/isotope_pattern_scorer.py b/scripts/metabolomics/isotope_pattern_scorer/isotope_pattern_scorer.py new file mode 100644 index 0000000..b3ad299 --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_scorer/isotope_pattern_scorer.py @@ -0,0 +1,159 @@ +""" +Isotope Pattern Scorer +======================= +Score observed isotope patterns against theoretical patterns computed +from a molecular formula using pyopenms. + +The score is a cosine similarity between observed and theoretical +isotope intensity ratios. + +Usage +----- + python isotope_pattern_scorer.py --observed "180.063:100,181.067:6.5" --formula C6H12O6 --output fit.json +""" + +import argparse +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_observed(observed_str: str) -> list[tuple[float, float]]: + """Parse observed isotope pattern from a string. + + Parameters + ---------- + observed_str: + Comma-separated ``mz:intensity`` pairs, e.g. + ``"180.063:100,181.067:6.5"``. + + Returns + ------- + list[tuple[float, float]] + List of (mz, intensity) tuples. + """ + peaks = [] + for pair in observed_str.split(","): + pair = pair.strip() + if ":" in pair: + mz_str, int_str = pair.split(":", 1) + peaks.append((float(mz_str), float(int_str))) + return peaks + + +def get_theoretical_pattern(formula: str, n_peaks: int = 5) -> list[tuple[float, float]]: + """Compute theoretical isotope pattern for a formula. + + Parameters + ---------- + formula: + Molecular formula string. + n_peaks: + Number of isotope peaks to generate. + + Returns + ------- + list[tuple[float, float]] + List of (mz, relative_intensity) tuples, normalized to max=100. + """ + ef = oms.EmpiricalFormula(formula) + gen = oms.CoarseIsotopePatternGenerator(n_peaks) + iso = ef.getIsotopeDistribution(gen) + container = iso.getContainer() + + peaks = [(peak.getMZ(), peak.getIntensity()) for peak in container] + if not peaks: + return [] + + max_int = max(p[1] for p in peaks) + if max_int > 0: + peaks = [(mz, intensity / max_int * 100.0) for mz, intensity in peaks] + + return peaks + + +def score_pattern( + observed: list[tuple[float, float]], + theoretical: list[tuple[float, float]], +) -> dict: + """Score observed vs theoretical isotope patterns. + + Parameters + ---------- + observed: + Observed (mz, intensity) pairs. + theoretical: + Theoretical (mz, intensity) pairs. + + Returns + ------- + dict + Contains cosine_score, per-peak comparisons. + """ + n = min(len(observed), len(theoretical)) + if n == 0: + return {"cosine_score": 0.0, "n_peaks_compared": 0, "peaks": []} + + obs_int = [observed[i][1] for i in range(n)] + theo_int = [theoretical[i][1] for i in range(n)] + + # Normalize observed to max=100 + obs_max = max(obs_int) if obs_int else 1.0 + if obs_max > 0: + obs_int = [v / obs_max * 100.0 for v in obs_int] + + # Cosine similarity + dot = sum(a * b for a, b in zip(obs_int, theo_int)) + mag_a = sum(a ** 2 for a in obs_int) ** 0.5 + mag_b = sum(b ** 2 for b in theo_int) ** 0.5 + + cosine = dot / (mag_a * mag_b) if (mag_a > 0 and mag_b > 0) else 0.0 + + peaks = [] + for i in range(n): + peaks.append({ + "peak_index": i, + "obs_mz": round(observed[i][0], 6), + "theo_mz": round(theoretical[i][0], 6), + "obs_intensity": round(obs_int[i], 4), + "theo_intensity": round(theo_int[i], 4), + }) + + return { + "cosine_score": round(cosine, 6), + "n_peaks_compared": n, + "peaks": peaks, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Score observed vs theoretical isotope pattern for a formula." + ) + parser.add_argument( + "--observed", required=True, + help='Observed pattern as "mz:int,mz:int,..." (e.g. "180.063:100,181.067:6.5")' + ) + parser.add_argument("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") + parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON file") + args = parser.parse_args() + + observed = parse_observed(args.observed) + theoretical = get_theoretical_pattern(args.formula, n_peaks=len(observed)) + result = score_pattern(observed, theoretical) + result["formula"] = args.formula + + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + + print(f"Isotope fit written to {args.output}") + print(f" Cosine score: {result['cosine_score']:.6f}") + print(f" Peaks compared: {result['n_peaks_compared']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/isotope_pattern_scorer/requirements.txt b/scripts/metabolomics/isotope_pattern_scorer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_scorer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/isotope_pattern_scorer/tests/conftest.py b/scripts/metabolomics/isotope_pattern_scorer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_scorer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py b/scripts/metabolomics/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py new file mode 100644 index 0000000..07a8d00 --- /dev/null +++ b/scripts/metabolomics/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py @@ -0,0 +1,53 @@ +"""Tests for isotope_pattern_scorer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestIsotopePatternScorer: + def test_parse_observed(self): + from isotope_pattern_scorer import parse_observed + + peaks = parse_observed("180.063:100,181.067:6.5,182.070:0.5") + assert len(peaks) == 3 + assert peaks[0] == (180.063, 100.0) + + def test_get_theoretical_pattern(self): + from isotope_pattern_scorer import get_theoretical_pattern + + pattern = get_theoretical_pattern("C6H12O6", n_peaks=3) + assert len(pattern) == 3 + # First peak should be the most intense (normalized to 100) + assert pattern[0][1] == 100.0 + + def test_perfect_match_score(self): + from isotope_pattern_scorer import get_theoretical_pattern, score_pattern + + theo = get_theoretical_pattern("C6H12O6", n_peaks=3) + # Use theoretical as observed + result = score_pattern(theo, theo) + assert result["cosine_score"] > 0.999 + + def test_score_range(self): + from isotope_pattern_scorer import get_theoretical_pattern, score_pattern + + theo = get_theoretical_pattern("C6H12O6", n_peaks=3) + observed = [(180.063, 100.0), (181.067, 50.0), (182.070, 30.0)] + result = score_pattern(observed, theo) + assert 0.0 <= result["cosine_score"] <= 1.0 + + def test_empty_observed(self): + from isotope_pattern_scorer import score_pattern + + result = score_pattern([], []) + assert result["cosine_score"] == 0.0 + assert result["n_peaks_compared"] == 0 + + def test_peaks_detail(self): + from isotope_pattern_scorer import get_theoretical_pattern, score_pattern + + theo = get_theoretical_pattern("C6H12O6", n_peaks=2) + observed = [(180.063, 100.0), (181.067, 7.0)] + result = score_pattern(observed, theo) + assert result["n_peaks_compared"] == 2 + assert len(result["peaks"]) == 2 diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/README.md b/scripts/metabolomics/kendrick_mass_defect_analyzer/README.md new file mode 100644 index 0000000..15ecd8d --- /dev/null +++ b/scripts/metabolomics/kendrick_mass_defect_analyzer/README.md @@ -0,0 +1,34 @@ +# Kendrick Mass Defect Analyzer + +Compute Kendrick Mass Defect (KMD) for configurable base units (CH2, CF2, C2H4O) and group features into homologous series. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# From formula column +python kendrick_mass_defect_analyzer.py --input features.tsv --base CH2 --output kmd.tsv + +# From m/z column with custom tolerance +python kendrick_mass_defect_analyzer.py --input features.tsv --base CF2 --kmd-tolerance 0.01 --output kmd.tsv +``` + +## Input format + +Tab-separated file with either a `formula` or `mz` column: + +``` +formula +C16H32O2 +C18H36O2 +C20H40O2 +``` + +## Output format + +Tab-separated file with columns: formula/exact_mass, kendrick_mass, nominal_kendrick_mass, kmd, series. diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py b/scripts/metabolomics/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py new file mode 100644 index 0000000..ce8a5ba --- /dev/null +++ b/scripts/metabolomics/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py @@ -0,0 +1,166 @@ +""" +Kendrick Mass Defect Analyzer +============================== +Compute Kendrick Mass Defect (KMD) for configurable base units (CH2, CF2, C2H4O) +and group features into homologous series. + +Usage +----- + python kendrick_mass_defect_analyzer.py --input features.tsv --base CH2 --output kmd.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Nominal masses for common base units +BASE_NOMINAL_MASSES = { + "CH2": 14, + "CF2": 50, + "C2H4O": 44, +} + + +def get_base_exact_mass(base_formula: str) -> float: + """Get the exact monoisotopic mass of a base unit formula. + + Parameters + ---------- + base_formula: + Molecular formula of the base unit, e.g. ``"CH2"``. + + Returns + ------- + float: Exact monoisotopic mass. + """ + ef = oms.EmpiricalFormula(base_formula) + return ef.getMonoWeight() + + +def compute_kmd(exact_mass: float, base_formula: str) -> dict: + """Compute Kendrick mass and Kendrick mass defect for a given exact mass. + + Parameters + ---------- + exact_mass: + Exact monoisotopic mass of the compound. + base_formula: + Base unit formula (e.g. ``"CH2"``). + + Returns + ------- + dict with keys: exact_mass, kendrick_mass, nominal_kendrick_mass, kmd + """ + base_exact = get_base_exact_mass(base_formula) + nominal = BASE_NOMINAL_MASSES.get(base_formula) + if nominal is None: + ef = oms.EmpiricalFormula(base_formula) + nominal = round(ef.getMonoWeight()) + + kendrick_factor = nominal / base_exact + kendrick_mass = exact_mass * kendrick_factor + nominal_kendrick_mass = round(kendrick_mass) + kmd = nominal_kendrick_mass - kendrick_mass + + return { + "exact_mass": round(exact_mass, 6), + "kendrick_mass": round(kendrick_mass, 6), + "nominal_kendrick_mass": nominal_kendrick_mass, + "kmd": round(kmd, 6), + } + + +def compute_kmd_from_formula(formula: str, base_formula: str) -> dict: + """Compute KMD from a molecular formula string. + + Parameters + ---------- + formula: + Molecular formula of the compound. + base_formula: + Base unit formula. + + Returns + ------- + dict with keys: formula, exact_mass, kendrick_mass, nominal_kendrick_mass, kmd + """ + ef = oms.EmpiricalFormula(formula) + exact_mass = ef.getMonoWeight() + result = compute_kmd(exact_mass, base_formula) + result["formula"] = formula + return result + + +def group_homologous_series(kmd_results: list, kmd_tolerance: float = 0.005) -> list: + """Group features into homologous series based on similar KMD values. + + Parameters + ---------- + kmd_results: + List of dicts from compute_kmd / compute_kmd_from_formula. + kmd_tolerance: + Maximum KMD difference to assign features to the same series. + + Returns + ------- + list of dicts with an added 'series' integer key. + """ + if not kmd_results: + return [] + + sorted_results = sorted(kmd_results, key=lambda x: x["kmd"]) + series_id = 0 + sorted_results[0]["series"] = series_id + + for i in range(1, len(sorted_results)): + if abs(sorted_results[i]["kmd"] - sorted_results[i - 1]["kmd"]) > kmd_tolerance: + series_id += 1 + sorted_results[i]["series"] = series_id + + return sorted_results + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Compute Kendrick Mass Defect for configurable base units." + ) + parser.add_argument("--input", required=True, help="TSV file with 'mz' or 'formula' column.") + parser.add_argument("--base", default="CH2", help="Base unit formula (default: CH2).") + parser.add_argument("--kmd-tolerance", type=float, default=0.005, + help="KMD tolerance for grouping homologous series (default: 0.005).") + parser.add_argument("--output", required=True, help="Output TSV file with KMD values.") + args = parser.parse_args() + + results = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + headers = reader.fieldnames or [] + for row in reader: + if "formula" in headers: + result = compute_kmd_from_formula(row["formula"], args.base) + elif "mz" in headers: + result = compute_kmd(float(row["mz"]), args.base) + else: + sys.exit("Input TSV must have a 'formula' or 'mz' column.") + results.append(result) + + results = group_homologous_series(results, kmd_tolerance=args.kmd_tolerance) + + fieldnames = list(results[0].keys()) if results else [] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + print(f"Wrote {len(results)} entries to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/requirements.txt b/scripts/metabolomics/kendrick_mass_defect_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/kendrick_mass_defect_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/conftest.py b/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py b/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py new file mode 100644 index 0000000..8223025 --- /dev/null +++ b/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py @@ -0,0 +1,67 @@ +"""Tests for kendrick_mass_defect_analyzer.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestComputeKMD: + def test_ch2_base(self): + from kendrick_mass_defect_analyzer import compute_kmd_from_formula + + result = compute_kmd_from_formula("C16H32O2", "CH2") + assert "kendrick_mass" in result + assert "kmd" in result + assert isinstance(result["kmd"], float) + + def test_kmd_homologous_series_similar(self): + """Fatty acids differing by CH2 should have similar KMD.""" + from kendrick_mass_defect_analyzer import compute_kmd_from_formula + + r1 = compute_kmd_from_formula("C16H32O2", "CH2") + r2 = compute_kmd_from_formula("C18H36O2", "CH2") + assert abs(r1["kmd"] - r2["kmd"]) < 0.01 + + def test_compute_kmd_by_mass(self): + from kendrick_mass_defect_analyzer import compute_kmd + + result = compute_kmd(256.2402, "CH2") + assert "kendrick_mass" in result + assert "kmd" in result + + +@requires_pyopenms +class TestGetBaseExactMass: + def test_ch2_mass(self): + from kendrick_mass_defect_analyzer import get_base_exact_mass + + mass = get_base_exact_mass("CH2") + assert mass == pytest.approx(14.01565, abs=0.001) + + def test_cf2_mass(self): + from kendrick_mass_defect_analyzer import get_base_exact_mass + + mass = get_base_exact_mass("CF2") + assert mass > 49.0 + + +@requires_pyopenms +class TestGroupHomologousSeries: + def test_grouping(self): + from kendrick_mass_defect_analyzer import compute_kmd_from_formula, group_homologous_series + + formulas = ["C14H28O2", "C16H32O2", "C18H36O2", "C6H12O6"] + results = [compute_kmd_from_formula(f, "CH2") for f in formulas] + grouped = group_homologous_series(results, kmd_tolerance=0.01) + assert all("series" in r for r in grouped) + # Fatty acids should be in same series + fatty_acid_series = set() + for r in grouped: + if r["formula"] in ("C14H28O2", "C16H32O2", "C18H36O2"): + fatty_acid_series.add(r["series"]) + assert len(fatty_acid_series) == 1 + + def test_empty_input(self): + from kendrick_mass_defect_analyzer import group_homologous_series + + assert group_homologous_series([]) == [] diff --git a/scripts/metabolomics/kovats_ri_calculator/README.md b/scripts/metabolomics/kovats_ri_calculator/README.md new file mode 100644 index 0000000..cfba14c --- /dev/null +++ b/scripts/metabolomics/kovats_ri_calculator/README.md @@ -0,0 +1,28 @@ +# Kovats Retention Index Calculator + +Calculate Kovats retention indices from n-alkane standard retention times for GC-MS data. + +Formula: RI = 100n + 100 * (log(RT_x) - log(RT_n)) / (log(RT_{n+1}) - log(RT_n)) + +## Usage + +```bash +python kovats_ri_calculator.py --input features.tsv --standards alkane_rts.tsv --output ri_values.tsv +``` + +### Input formats + +**alkane_rts.tsv** (tab-separated): +``` +carbon_number rt +8 2.0 +10 5.0 +12 10.0 +``` + +**features.tsv** (tab-separated): +``` +feature_id rt +F1 3.5 +F2 7.0 +``` diff --git a/scripts/metabolomics/kovats_ri_calculator/kovats_ri_calculator.py b/scripts/metabolomics/kovats_ri_calculator/kovats_ri_calculator.py new file mode 100644 index 0000000..725e403 --- /dev/null +++ b/scripts/metabolomics/kovats_ri_calculator/kovats_ri_calculator.py @@ -0,0 +1,191 @@ +""" +Kovats Retention Index Calculator +================================= +Calculate Kovats retention indices from n-alkane standard retention times +for GC-MS data. Uses the logarithmic interpolation formula: + + RI = 100*n + 100 * (log(RT_x) - log(RT_n)) / (log(RT_{n+1}) - log(RT_n)) + +where n is the carbon number of the preceding alkane and RT_x is the +retention time of the analyte. + +Usage +----- + python kovats_ri_calculator.py --input features.tsv --standards alkane_rts.tsv --output ri_values.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_tsv(path: str) -> list[dict]: + """Load a TSV file into a list of dicts with numeric parsing. + + Parameters + ---------- + path: + Path to TSV file. + + Returns + ------- + list of dict + """ + rows = [] + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + parsed = {} + for key, val in row.items(): + try: + parsed[key] = float(val) + except (ValueError, TypeError): + parsed[key] = val + rows.append(parsed) + return rows + + +def build_alkane_table(standards: list[dict]) -> list[tuple[int, float]]: + """Build a sorted list of (carbon_number, rt) from alkane standards. + + Parameters + ---------- + standards: + List of dicts with keys: carbon_number, rt. + + Returns + ------- + list of (int, float) + Sorted by retention time. + """ + table = [] + for row in standards: + cn = int(row["carbon_number"]) + rt = float(row["rt"]) + if rt <= 0: + continue + table.append((cn, rt)) + table.sort(key=lambda x: x[1]) + return table + + +def calculate_kovats_ri( + rt: float, + alkane_table: list[tuple[int, float]], +) -> float | None: + """Calculate the Kovats retention index for a given RT. + + Parameters + ---------- + rt: + Retention time of the analyte. + alkane_table: + Sorted list of (carbon_number, rt) for n-alkane standards. + + Returns + ------- + float or None + Kovats RI, or None if the RT falls outside the alkane range. + """ + if rt <= 0: + return None + if len(alkane_table) < 2: + return None + + # Find the bracketing alkanes + for i in range(len(alkane_table) - 1): + cn_n, rt_n = alkane_table[i] + cn_n1, rt_n1 = alkane_table[i + 1] + if rt_n <= rt <= rt_n1: + if rt_n <= 0 or rt_n1 <= 0: + return None + log_rt = math.log10(rt) + log_rt_n = math.log10(rt_n) + log_rt_n1 = math.log10(rt_n1) + denom = log_rt_n1 - log_rt_n + if denom == 0: + return None + ri = 100 * cn_n + 100 * (log_rt - log_rt_n) / denom + return round(ri, 2) + return None + + +def calculate_ri_batch( + features: list[dict], + alkane_table: list[tuple[int, float]], + rt_column: str = "rt", +) -> list[dict]: + """Calculate Kovats RI for a batch of features. + + Parameters + ---------- + features: + List of feature dicts. + alkane_table: + Sorted list of (carbon_number, rt) from alkane standards. + rt_column: + Name of the RT column in features. + + Returns + ------- + list of dict + Input rows augmented with kovats_ri. + """ + results = [] + for feat in features: + result = dict(feat) + rt = float(feat[rt_column]) + ri = calculate_kovats_ri(rt, alkane_table) + result["kovats_ri"] = ri if ri is not None else "N/A" + results.append(result) + return results + + +def write_results(results: list[dict], path: str) -> None: + """Write results to a TSV file. + + Parameters + ---------- + results: + List of result dicts. + path: + Output TSV path. + """ + if not results: + with open(path, "w") as fh: + fh.write("# No results\n") + return + fieldnames = list(results[0].keys()) + with open(path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Calculate Kovats retention indices from alkane standards for GC-MS." + ) + parser.add_argument("--input", required=True, help="Feature table (TSV) with rt column") + parser.add_argument("--standards", required=True, help="Alkane standards (TSV) with carbon_number, rt") + parser.add_argument("--output", required=True, help="Output RI values (TSV)") + parser.add_argument("--rt-column", default="rt", help="Name of RT column (default: rt)") + args = parser.parse_args() + + features = load_tsv(args.input) + standards = load_tsv(args.standards) + alkane_table = build_alkane_table(standards) + results = calculate_ri_batch(features, alkane_table, rt_column=args.rt_column) + write_results(results, args.output) + print(f"Calculated RI for {len(results)} features, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/kovats_ri_calculator/requirements.txt b/scripts/metabolomics/kovats_ri_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/kovats_ri_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/kovats_ri_calculator/tests/conftest.py b/scripts/metabolomics/kovats_ri_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/kovats_ri_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/kovats_ri_calculator/tests/test_kovats_ri_calculator.py b/scripts/metabolomics/kovats_ri_calculator/tests/test_kovats_ri_calculator.py new file mode 100644 index 0000000..a8d2fc3 --- /dev/null +++ b/scripts/metabolomics/kovats_ri_calculator/tests/test_kovats_ri_calculator.py @@ -0,0 +1,110 @@ +"""Tests for kovats_ri_calculator.""" + +import csv +import math +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestKovatsRiCalculator: + def test_build_alkane_table(self): + from kovats_ri_calculator import build_alkane_table + + standards = [ + {"carbon_number": 10, "rt": 5.0}, + {"carbon_number": 12, "rt": 10.0}, + {"carbon_number": 8, "rt": 2.0}, + ] + table = build_alkane_table(standards) + assert len(table) == 3 + # Should be sorted by RT + assert table[0][1] < table[1][1] < table[2][1] + + def test_calculate_kovats_ri_at_alkane(self): + from kovats_ri_calculator import calculate_kovats_ri + + # At exactly C10 RT, RI should be ~1000 + alkane_table = [(8, 2.0), (10, 5.0), (12, 10.0)] + ri = calculate_kovats_ri(5.0, alkane_table) + assert ri is not None + assert abs(ri - 1000.0) < 0.1 + + def test_calculate_kovats_ri_between_alkanes(self): + from kovats_ri_calculator import calculate_kovats_ri + + alkane_table = [(10, 5.0), (12, 10.0)] + ri = calculate_kovats_ri(7.0, alkane_table) + assert ri is not None + # RI should be between 1000 and 1200 + assert 1000.0 < ri < 1200.0 + + def test_calculate_kovats_ri_formula(self): + from kovats_ri_calculator import calculate_kovats_ri + + # Verify the formula: RI = 100*n + 100*(log(RT_x) - log(RT_n))/(log(RT_n+1) - log(RT_n)) + alkane_table = [(10, 5.0), (12, 10.0)] + rt_x = 7.0 + expected = 100 * 10 + 100 * (math.log10(7.0) - math.log10(5.0)) / (math.log10(10.0) - math.log10(5.0)) + ri = calculate_kovats_ri(rt_x, alkane_table) + assert ri is not None + assert abs(ri - round(expected, 2)) < 0.01 + + def test_calculate_kovats_ri_out_of_range(self): + from kovats_ri_calculator import calculate_kovats_ri + + alkane_table = [(10, 5.0), (12, 10.0)] + # RT before first alkane + assert calculate_kovats_ri(1.0, alkane_table) is None + # RT after last alkane + assert calculate_kovats_ri(15.0, alkane_table) is None + + def test_calculate_kovats_ri_negative_rt(self): + from kovats_ri_calculator import calculate_kovats_ri + + alkane_table = [(10, 5.0), (12, 10.0)] + assert calculate_kovats_ri(-1.0, alkane_table) is None + + def test_calculate_ri_batch(self): + from kovats_ri_calculator import calculate_ri_batch + + alkane_table = [(8, 2.0), (10, 5.0), (12, 10.0)] + features = [ + {"feature_id": "F1", "rt": 3.0}, + {"feature_id": "F2", "rt": 7.0}, + {"feature_id": "F3", "rt": 20.0}, + ] + results = calculate_ri_batch(features, alkane_table) + assert len(results) == 3 + assert results[0]["kovats_ri"] != "N/A" + assert results[1]["kovats_ri"] != "N/A" + assert results[2]["kovats_ri"] == "N/A" + + def test_full_pipeline(self): + from kovats_ri_calculator import build_alkane_table, calculate_ri_batch, load_tsv, write_results + + with tempfile.TemporaryDirectory() as tmpdir: + std_path = os.path.join(tmpdir, "alkanes.tsv") + with open(std_path, "w", newline="") as fh: + w = csv.DictWriter(fh, ["carbon_number", "rt"], delimiter="\t") + w.writeheader() + w.writerow({"carbon_number": "8", "rt": "2.0"}) + w.writerow({"carbon_number": "10", "rt": "5.0"}) + w.writerow({"carbon_number": "12", "rt": "10.0"}) + + feat_path = os.path.join(tmpdir, "features.tsv") + with open(feat_path, "w", newline="") as fh: + w = csv.DictWriter(fh, ["feature_id", "rt"], delimiter="\t") + w.writeheader() + w.writerow({"feature_id": "F1", "rt": "3.5"}) + + standards = load_tsv(std_path) + features = load_tsv(feat_path) + alkane_table = build_alkane_table(standards) + results = calculate_ri_batch(features, alkane_table) + + out_path = os.path.join(tmpdir, "ri.tsv") + write_results(results, out_path) + assert os.path.exists(out_path) diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/README.md b/scripts/metabolomics/lipid_ecn_rt_predictor/README.md new file mode 100644 index 0000000..f407fdb --- /dev/null +++ b/scripts/metabolomics/lipid_ecn_rt_predictor/README.md @@ -0,0 +1,25 @@ +# Lipid ECN-RT Predictor + +Predict lipid retention times from Equivalent Carbon Number (ECN = total_carbons - 2 * double_bonds) using linear regression per lipid class. + +## Usage + +```bash +python lipid_ecn_rt_predictor.py --input lipids.tsv --calibration standards.tsv --output predictions.tsv +``` + +### Input formats + +**standards.tsv** (calibration, tab-separated): +``` +lipid_class total_carbons double_bonds rt +PC 32 0 10.0 +PC 34 0 12.0 +PC 36 0 14.0 +``` + +**lipids.tsv** (tab-separated): +``` +lipid_class total_carbons double_bonds +PC 34 1 +``` diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py b/scripts/metabolomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py new file mode 100644 index 0000000..07c0c89 --- /dev/null +++ b/scripts/metabolomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py @@ -0,0 +1,198 @@ +""" +Lipid ECN-RT Predictor +====================== +Predict lipid retention times from Equivalent Carbon Number (ECN). +ECN = total_carbons - 2 * double_bonds. A linear regression of RT vs ECN +is built per lipid class from calibration standards, then applied to predict +RTs for unknown lipids. + +Usage +----- + python lipid_ecn_rt_predictor.py --input lipids.tsv --calibration standards.tsv --output predictions.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np +from scipy import stats + + +def compute_ecn(total_carbons: int, double_bonds: int) -> int: + """Calculate the Equivalent Carbon Number. + + Parameters + ---------- + total_carbons: + Total number of acyl-chain carbons. + double_bonds: + Total number of double bonds. + + Returns + ------- + int + ECN = total_carbons - 2 * double_bonds. + """ + return total_carbons - 2 * double_bonds + + +def load_tsv(path: str) -> list[dict]: + """Load a TSV file into a list of dicts. + + Expected columns vary by usage but typically include: + lipid_class, total_carbons, double_bonds, rt. + + Parameters + ---------- + path: + Path to TSV file. + + Returns + ------- + list of dict + """ + rows = [] + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + parsed = {} + for key, val in row.items(): + try: + parsed[key] = float(val) + except (ValueError, TypeError): + parsed[key] = val + rows.append(parsed) + return rows + + +def build_calibration_models( + standards: list[dict], +) -> dict[str, dict]: + """Build linear regression models (RT vs ECN) per lipid class. + + Parameters + ---------- + standards: + List of dicts with keys: lipid_class, total_carbons, double_bonds, rt. + + Returns + ------- + dict + Mapping from lipid_class to dict with keys: slope, intercept, r_value, std_err. + """ + by_class: dict[str, list[tuple[int, float]]] = {} + for row in standards: + cls = str(row["lipid_class"]) + carbons = int(row["total_carbons"]) + db = int(row["double_bonds"]) + rt = float(row["rt"]) + ecn = compute_ecn(carbons, db) + by_class.setdefault(cls, []).append((ecn, rt)) + + models = {} + for cls, points in by_class.items(): + if len(points) < 2: + continue + ecns = np.array([p[0] for p in points], dtype=float) + rts = np.array([p[1] for p in points], dtype=float) + result = stats.linregress(ecns, rts) + models[cls] = { + "slope": result.slope, + "intercept": result.intercept, + "r_value": result.rvalue, + "std_err": result.stderr, + } + return models + + +def predict_rt( + lipids: list[dict], + models: dict[str, dict], +) -> list[dict]: + """Predict RT for lipids using calibration models. + + Parameters + ---------- + lipids: + List of dicts with keys: lipid_class, total_carbons, double_bonds. + models: + Calibration models from :func:`build_calibration_models`. + + Returns + ------- + list of dict + Input rows augmented with ecn, predicted_rt, and model_r_value. + """ + results = [] + for row in lipids: + cls = str(row["lipid_class"]) + carbons = int(row["total_carbons"]) + db = int(row["double_bonds"]) + ecn = compute_ecn(carbons, db) + result = dict(row) + result["ecn"] = ecn + if cls in models: + model = models[cls] + predicted = model["slope"] * ecn + model["intercept"] + result["predicted_rt"] = round(predicted, 4) + result["model_r_value"] = round(model["r_value"], 4) + else: + result["predicted_rt"] = "N/A" + result["model_r_value"] = "N/A" + results.append(result) + return results + + +def write_predictions(predictions: list[dict], path: str) -> None: + """Write prediction results to a TSV file. + + Parameters + ---------- + predictions: + List of prediction dicts. + path: + Output TSV path. + """ + if not predictions: + with open(path, "w") as fh: + fh.write("# No predictions\n") + return + fieldnames = list(predictions[0].keys()) + with open(path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(predictions) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Predict lipid RT from ECN using linear regression per lipid class." + ) + parser.add_argument( + "--input", required=True, + help="Lipid table (TSV) with lipid_class, total_carbons, double_bonds", + ) + parser.add_argument( + "--calibration", required=True, + help="Standards table (TSV) with lipid_class, total_carbons, double_bonds, rt", + ) + parser.add_argument("--output", required=True, help="Output predictions (TSV)") + args = parser.parse_args() + + lipids = load_tsv(args.input) + standards = load_tsv(args.calibration) + models = build_calibration_models(standards) + predictions = predict_rt(lipids, models) + write_predictions(predictions, args.output) + print(f"Predicted RT for {len(predictions)} lipids, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/requirements.txt b/scripts/metabolomics/lipid_ecn_rt_predictor/requirements.txt new file mode 100644 index 0000000..ba577e4 --- /dev/null +++ b/scripts/metabolomics/lipid_ecn_rt_predictor/requirements.txt @@ -0,0 +1,3 @@ +pyopenms +numpy +scipy diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/tests/conftest.py b/scripts/metabolomics/lipid_ecn_rt_predictor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/lipid_ecn_rt_predictor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py b/scripts/metabolomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py new file mode 100644 index 0000000..8ab0a9a --- /dev/null +++ b/scripts/metabolomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py @@ -0,0 +1,92 @@ +"""Tests for lipid_ecn_rt_predictor.""" + +import csv +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestLipidEcnRtPredictor: + def test_compute_ecn(self): + from lipid_ecn_rt_predictor import compute_ecn + + assert compute_ecn(36, 2) == 32 + assert compute_ecn(34, 1) == 32 + assert compute_ecn(18, 0) == 18 + assert compute_ecn(20, 4) == 12 + + def test_build_calibration_models(self): + from lipid_ecn_rt_predictor import build_calibration_models + + standards = [ + {"lipid_class": "PC", "total_carbons": 32, "double_bonds": 0, "rt": 10.0}, + {"lipid_class": "PC", "total_carbons": 34, "double_bonds": 0, "rt": 12.0}, + {"lipid_class": "PC", "total_carbons": 36, "double_bonds": 0, "rt": 14.0}, + ] + models = build_calibration_models(standards) + assert "PC" in models + assert abs(models["PC"]["r_value"] - 1.0) < 1e-6 # Perfect linear fit + + def test_build_calibration_single_point_skipped(self): + from lipid_ecn_rt_predictor import build_calibration_models + + standards = [ + {"lipid_class": "PE", "total_carbons": 32, "double_bonds": 0, "rt": 10.0}, + ] + models = build_calibration_models(standards) + assert "PE" not in models + + def test_predict_rt(self): + from lipid_ecn_rt_predictor import build_calibration_models, predict_rt + + standards = [ + {"lipid_class": "PC", "total_carbons": 32, "double_bonds": 0, "rt": 10.0}, + {"lipid_class": "PC", "total_carbons": 34, "double_bonds": 0, "rt": 12.0}, + {"lipid_class": "PC", "total_carbons": 36, "double_bonds": 0, "rt": 14.0}, + ] + models = build_calibration_models(standards) + + lipids = [ + {"lipid_class": "PC", "total_carbons": 34, "double_bonds": 1}, + ] + results = predict_rt(lipids, models) + assert len(results) == 1 + assert results[0]["predicted_rt"] != "N/A" + # ECN=32 -> RT=10.0 from the model (slope=1.0 per ECN) + assert isinstance(results[0]["predicted_rt"], float) + + def test_predict_rt_missing_class(self): + from lipid_ecn_rt_predictor import predict_rt + + lipids = [{"lipid_class": "SM", "total_carbons": 34, "double_bonds": 1}] + results = predict_rt(lipids, {}) + assert results[0]["predicted_rt"] == "N/A" + + def test_full_pipeline(self): + from lipid_ecn_rt_predictor import build_calibration_models, load_tsv, predict_rt, write_predictions + + with tempfile.TemporaryDirectory() as tmpdir: + cal_path = os.path.join(tmpdir, "standards.tsv") + with open(cal_path, "w", newline="") as fh: + w = csv.DictWriter(fh, ["lipid_class", "total_carbons", "double_bonds", "rt"], delimiter="\t") + w.writeheader() + w.writerow({"lipid_class": "PC", "total_carbons": "32", "double_bonds": "0", "rt": "10.0"}) + w.writerow({"lipid_class": "PC", "total_carbons": "34", "double_bonds": "0", "rt": "12.0"}) + w.writerow({"lipid_class": "PC", "total_carbons": "36", "double_bonds": "0", "rt": "14.0"}) + + lip_path = os.path.join(tmpdir, "lipids.tsv") + with open(lip_path, "w", newline="") as fh: + w = csv.DictWriter(fh, ["lipid_class", "total_carbons", "double_bonds"], delimiter="\t") + w.writeheader() + w.writerow({"lipid_class": "PC", "total_carbons": "34", "double_bonds": "1"}) + + standards = load_tsv(cal_path) + lipids = load_tsv(lip_path) + models = build_calibration_models(standards) + predictions = predict_rt(lipids, models) + + out_path = os.path.join(tmpdir, "predictions.tsv") + write_predictions(predictions, out_path) + assert os.path.exists(out_path) diff --git a/scripts/metabolomics/lipid_species_resolver/README.md b/scripts/metabolomics/lipid_species_resolver/README.md new file mode 100644 index 0000000..87d618b --- /dev/null +++ b/scripts/metabolomics/lipid_species_resolver/README.md @@ -0,0 +1,24 @@ +# Lipid Species Resolver + +From sum composition (e.g. PC 36:2), enumerate all possible acyl chain combinations and compute exact masses using pyopenms. + +## Usage + +```bash +python lipid_species_resolver.py --input lipids.tsv --output resolved.tsv +python lipid_species_resolver.py --input lipids.tsv --lipid-class PC --output resolved.tsv +``` + +### Input format + +**lipids.tsv** (tab-separated): +``` +lipid +PC 36:2 +PE 34:1 +TG 54:3 +``` + +### Example output + +For PC 36:2, the tool enumerates combinations like (16:0/20:2), (18:1/18:1), (18:0/18:2), etc. diff --git a/scripts/metabolomics/lipid_species_resolver/lipid_species_resolver.py b/scripts/metabolomics/lipid_species_resolver/lipid_species_resolver.py new file mode 100644 index 0000000..99bc402 --- /dev/null +++ b/scripts/metabolomics/lipid_species_resolver/lipid_species_resolver.py @@ -0,0 +1,260 @@ +""" +Lipid Species Resolver +====================== +From a sum-composition lipid notation (e.g. PC 36:2), enumerate all possible +acyl chain combinations. For each combination, compute the exact mass using +pyopenms EmpiricalFormula. + +Usage +----- + python lipid_species_resolver.py --input lipids.tsv --lipid-class PC --output resolved.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Head-group formulas for common lipid classes (without acyl chains). +# Acyl chains contribute CnH(2n-2*db-1)O for each chain (ester linkage). +# The head-group formula represents the glycerol backbone + head-group + linkage atoms. +HEADGROUP_FORMULAS = { + "PC": "C10H18NO8P", # glycerophosphocholine head group + "PE": "C5H10NO8P", # glycerophosphoethanolamine head group (adjusted) + "PS": "C6H10NO10P", # glycerophosphoserine head group (adjusted) + "PG": "C6H11O10P", # glycerophosphoglycerol head group (adjusted) + "PI": "C9H15O13P", # glycerophosphoinositol head group (adjusted) + "PA": "C3H5O8P", # phosphatidic acid head group (adjusted) + "TG": "C3H5O3", # triacylglycerol backbone (adjusted for 3 chains) + "DG": "C3H5O3", # diacylglycerol backbone (adjusted for 2 chains) +} + +# Number of acyl chains per lipid class +CHAIN_COUNT = { + "PC": 2, "PE": 2, "PS": 2, "PG": 2, "PI": 2, "PA": 2, + "TG": 3, "DG": 2, +} + +# Minimum and maximum carbon atoms per single acyl chain +MIN_CHAIN_C = 2 +MAX_CHAIN_C = 26 +# Maximum double bonds per chain (capped at (carbons - 1) / 2 physiologically) +MAX_CHAIN_DB = 6 + + +def enumerate_chain_combinations( + total_carbons: int, + total_double_bonds: int, + num_chains: int = 2, + min_c: int = MIN_CHAIN_C, + max_c: int = MAX_CHAIN_C, + max_db: int = MAX_CHAIN_DB, +) -> list[list[tuple[int, int]]]: + """Enumerate acyl chain combinations that sum to given totals. + + Parameters + ---------- + total_carbons: + Sum of carbons across all chains. + total_double_bonds: + Sum of double bonds across all chains. + num_chains: + Number of acyl chains (default 2 for diacyl lipids). + min_c: + Minimum carbon atoms per chain. + max_c: + Maximum carbon atoms per chain. + max_db: + Maximum double bonds per single chain. + + Returns + ------- + list of list of (carbons, double_bonds) + Each inner list is one combination of chains, sorted by carbons then db. + Duplicate combinations (differing only in order) are removed. + """ + results: list[list[tuple[int, int]]] = [] + + def _recurse(remaining_c: int, remaining_db: int, chains_left: int, current: list[tuple[int, int]]) -> None: + if chains_left == 0: + if remaining_c == 0 and remaining_db == 0: + results.append(sorted(current)) + return + # The minimum/maximum for this chain + lo_c = max(min_c, remaining_c - max_c * (chains_left - 1)) + hi_c = min(max_c, remaining_c - min_c * (chains_left - 1)) + for c in range(lo_c, hi_c + 1): + lo_db = max(0, remaining_db - max_db * (chains_left - 1)) + hi_db = min(max_db, remaining_db, (c - 1) // 2 if c > 1 else 0) + for db in range(lo_db, hi_db + 1): + _recurse(remaining_c - c, remaining_db - db, chains_left - 1, current + [(c, db)]) + + _recurse(total_carbons, total_double_bonds, num_chains, []) + + # Deduplicate (sorted tuples remove order-dependent duplicates) + seen: set[tuple[tuple[int, int], ...]] = set() + unique = [] + for combo in results: + key = tuple(combo) + if key not in seen: + seen.add(key) + unique.append(combo) + return unique + + +def acyl_chain_formula(carbons: int, double_bonds: int) -> str: + """Return the molecular formula contribution of one acyl chain (ester-linked). + + An ester-linked acyl chain contributes C(n)H(2n - 1 - 2*db)O to the lipid. + + Parameters + ---------- + carbons: + Number of carbon atoms. + double_bonds: + Number of double bonds. + + Returns + ------- + str + Formula string, e.g. "C16H31O" for 16:0. + """ + h_count = 2 * carbons - 1 - 2 * double_bonds + return f"C{carbons}H{h_count}O" + + +def lipid_exact_mass(lipid_class: str, chains: list[tuple[int, int]]) -> float: + """Compute the exact monoisotopic mass of a resolved lipid species. + + Parameters + ---------- + lipid_class: + Lipid class abbreviation (e.g. "PC"). + chains: + List of (carbons, double_bonds) per chain. + + Returns + ------- + float + Monoisotopic mass in Da. + """ + headgroup = HEADGROUP_FORMULAS.get(lipid_class, "") + if not headgroup: + return 0.0 + full_formula = headgroup + for c, db in chains: + full_formula += " " + acyl_chain_formula(c, db) + # pyopenms EmpiricalFormula can parse additive formula strings + ef = oms.EmpiricalFormula(full_formula) + return ef.getMonoWeight() + + +def parse_sum_composition(notation: str) -> tuple[str, int, int]: + """Parse a sum-composition notation like 'PC 36:2'. + + Parameters + ---------- + notation: + String like "PC 36:2" or "TG 54:3". + + Returns + ------- + tuple of (lipid_class, total_carbons, total_double_bonds) + """ + parts = notation.strip().split() + if len(parts) != 2: + raise ValueError(f"Cannot parse lipid notation: {notation!r}") + lipid_class = parts[0].upper() + carbon_db = parts[1].split(":") + if len(carbon_db) != 2: + raise ValueError(f"Cannot parse carbon:db from: {parts[1]!r}") + return lipid_class, int(carbon_db[0]), int(carbon_db[1]) + + +def resolve_lipids( + lipids: list[dict], + lipid_class_override: str | None = None, +) -> list[dict]: + """Resolve sum-composition lipids into acyl chain combinations. + + Parameters + ---------- + lipids: + List of dicts with a 'lipid' column containing notation like "PC 36:2". + lipid_class_override: + If set, override the lipid class parsed from notation. + + Returns + ------- + list of dict + One row per resolved species with chain notation and exact mass. + """ + results = [] + for row in lipids: + notation = row.get("lipid", "").strip() + if not notation: + continue + try: + cls, total_c, total_db = parse_sum_composition(notation) + except ValueError: + continue + if lipid_class_override: + cls = lipid_class_override.upper() + num_chains = CHAIN_COUNT.get(cls, 2) + combos = enumerate_chain_combinations(total_c, total_db, num_chains) + for combo in combos: + chain_str = "/".join(f"{c}:{db}" for c, db in combo) + mass = lipid_exact_mass(cls, combo) + results.append({ + "input_lipid": notation, + "lipid_class": cls, + "resolved_species": f"{cls} {chain_str}", + "chains": chain_str, + "exact_mass": round(mass, 6), + }) + return results + + +def load_lipids(path: str) -> list[dict]: + """Load lipid table from TSV. Expects a 'lipid' column.""" + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + return list(reader) + + +def write_resolved(resolved: list[dict], path: str) -> None: + """Write resolved species to TSV.""" + if not resolved: + with open(path, "w") as fh: + fh.write("# No resolved species\n") + return + fieldnames = list(resolved[0].keys()) + with open(path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(resolved) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Resolve sum-composition lipids into acyl chain combinations." + ) + parser.add_argument("--input", required=True, help="Lipid table (TSV) with 'lipid' column (e.g. 'PC 36:2')") + parser.add_argument("--lipid-class", default=None, help="Override lipid class (e.g. PC)") + parser.add_argument("--output", required=True, help="Output resolved species (TSV)") + args = parser.parse_args() + + lipids = load_lipids(args.input) + resolved = resolve_lipids(lipids, lipid_class_override=args.lipid_class) + write_resolved(resolved, args.output) + print(f"Resolved {len(resolved)} species from {len(lipids)} lipid(s), written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/lipid_species_resolver/requirements.txt b/scripts/metabolomics/lipid_species_resolver/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/lipid_species_resolver/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/lipid_species_resolver/tests/conftest.py b/scripts/metabolomics/lipid_species_resolver/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/lipid_species_resolver/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/lipid_species_resolver/tests/test_lipid_species_resolver.py b/scripts/metabolomics/lipid_species_resolver/tests/test_lipid_species_resolver.py new file mode 100644 index 0000000..0cdfe0f --- /dev/null +++ b/scripts/metabolomics/lipid_species_resolver/tests/test_lipid_species_resolver.py @@ -0,0 +1,107 @@ +"""Tests for lipid_species_resolver.""" + +import csv +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestLipidSpeciesResolver: + def test_parse_sum_composition(self): + from lipid_species_resolver import parse_sum_composition + + cls, c, db = parse_sum_composition("PC 36:2") + assert cls == "PC" + assert c == 36 + assert db == 2 + + def test_parse_sum_composition_tg(self): + from lipid_species_resolver import parse_sum_composition + + cls, c, db = parse_sum_composition("TG 54:3") + assert cls == "TG" + assert c == 54 + assert db == 3 + + def test_enumerate_chain_combinations_basic(self): + from lipid_species_resolver import enumerate_chain_combinations + + combos = enumerate_chain_combinations(32, 0, num_chains=2) + # 32:0 with 2 chains -> (2:0/30:0), (3:0/29:0), ..., (16:0/16:0) + assert len(combos) > 0 + for combo in combos: + assert sum(c for c, _ in combo) == 32 + assert sum(db for _, db in combo) == 0 + + def test_enumerate_chain_combinations_with_db(self): + from lipid_species_resolver import enumerate_chain_combinations + + combos = enumerate_chain_combinations(36, 2, num_chains=2) + assert len(combos) > 0 + for combo in combos: + assert sum(c for c, _ in combo) == 36 + assert sum(db for _, db in combo) == 2 + # Should include common combinations like (16:0/20:2), (18:1/18:1), (18:0/18:2) + assert (16, 0) in [c for combo in combos for c in combo] or len(combos) > 0 + + def test_enumerate_no_duplicates(self): + from lipid_species_resolver import enumerate_chain_combinations + + combos = enumerate_chain_combinations(36, 2, num_chains=2) + combo_tuples = [tuple(c) for c in combos] + assert len(combo_tuples) == len(set(combo_tuples)) + + def test_acyl_chain_formula(self): + from lipid_species_resolver import acyl_chain_formula + + # 16:0 -> C16H31O + f = acyl_chain_formula(16, 0) + assert f == "C16H31O" + # 18:1 -> C18H33O (2*18 - 1 - 2*1 = 33) + f = acyl_chain_formula(18, 1) + assert f == "C18H33O" + + def test_lipid_exact_mass(self): + from lipid_species_resolver import lipid_exact_mass + + mass = lipid_exact_mass("PC", [(16, 0), (18, 1)]) + assert mass > 0 + # PC 16:0/18:1 ~= 759.58 Da (approximate) + assert 700 < mass < 850 + + def test_resolve_lipids(self): + from lipid_species_resolver import resolve_lipids + + lipids = [{"lipid": "PC 36:2"}] + resolved = resolve_lipids(lipids) + assert len(resolved) > 0 + for r in resolved: + assert r["lipid_class"] == "PC" + assert r["exact_mass"] > 0 + + def test_resolve_lipids_with_override(self): + from lipid_species_resolver import resolve_lipids + + lipids = [{"lipid": "PC 36:2"}] + resolved = resolve_lipids(lipids, lipid_class_override="PE") + for r in resolved: + assert r["lipid_class"] == "PE" + + def test_full_pipeline(self): + from lipid_species_resolver import load_lipids, resolve_lipids, write_resolved + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "lipids.tsv") + with open(in_path, "w", newline="") as fh: + w = csv.DictWriter(fh, ["lipid"], delimiter="\t") + w.writeheader() + w.writerow({"lipid": "PC 34:1"}) + + lipids = load_lipids(in_path) + resolved = resolve_lipids(lipids) + out_path = os.path.join(tmpdir, "resolved.tsv") + write_resolved(resolved, out_path) + assert os.path.exists(out_path) + assert len(resolved) > 0 diff --git a/scripts/metabolomics/mass_decomposition_tool/README.md b/scripts/metabolomics/mass_decomposition_tool/README.md new file mode 100644 index 0000000..d6711c9 --- /dev/null +++ b/scripts/metabolomics/mass_decomposition_tool/README.md @@ -0,0 +1,10 @@ +# Mass Decomposition Tool + +Find molecular formula compositions for a given mass within tolerance. + +## Usage + +```bash +python mass_decomposition_tool.py --mass 180.0634 --tolerance 0.01 +python mass_decomposition_tool.py --mass 180.0634 --tolerance 0.01 --output decompositions.tsv +``` diff --git a/scripts/metabolomics/mass_decomposition_tool/mass_decomposition_tool.py b/scripts/metabolomics/mass_decomposition_tool/mass_decomposition_tool.py new file mode 100644 index 0000000..8508404 --- /dev/null +++ b/scripts/metabolomics/mass_decomposition_tool/mass_decomposition_tool.py @@ -0,0 +1,217 @@ +""" +Mass Decomposition Tool +======================= +Find molecular formula compositions for a given mass within tolerance. +Enumerates possible formulas with element constraints using pyopenms EmpiricalFormula. + +Features: +- Enumerate possible molecular formulas for a given mass +- Configurable element constraints +- Mass tolerance filtering +- TSV output + +Usage +----- + python mass_decomposition_tool.py --mass 180.0634 --tolerance 0.01 + python mass_decomposition_tool.py --mass 180.0634 --tolerance 0.01 --output decompositions.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Element exact masses (monoisotopic) +ELEMENT_MASSES = { + "C": 12.000000, + "H": 1.0078250, + "N": 14.003074, + "O": 15.994915, + "S": 31.972071, + "P": 30.973762, +} + +# Default element constraints: element -> (min, max) +DEFAULT_CONSTRAINTS = { + "C": (0, 20), + "H": (0, 50), + "N": (0, 5), + "O": (0, 15), + "S": (0, 3), +} + + +def decompose_mass( + target_mass: float, + tolerance: float = 0.01, + constraints: dict[str, tuple[int, int]] | None = None, +) -> list[dict]: + """Find molecular formula compositions for a given mass within tolerance. + + Parameters + ---------- + target_mass : float + Target monoisotopic mass in Da. + tolerance : float + Mass tolerance in Da. + constraints : dict or None + Element constraints as {element: (min_count, max_count)}. + Defaults to C:0-20, H:0-50, N:0-5, O:0-15, S:0-3. + + Returns + ------- + list[dict] + List of dicts with keys: formula, mass, error_da, C, H, N, O, S. + """ + if constraints is None: + constraints = dict(DEFAULT_CONSTRAINTS) + + elements = list(constraints.keys()) + results = [] + + # Use iterative approach with pruning + _enumerate_formulas(target_mass, tolerance, elements, constraints, 0, {}, results) + + # Sort by absolute error + results.sort(key=lambda x: abs(x["error_da"])) + return results + + +def _enumerate_formulas( + target_mass: float, + tolerance: float, + elements: list[str], + constraints: dict[str, tuple[int, int]], + elem_idx: int, + current: dict[str, int], + results: list[dict], +) -> None: + """Recursively enumerate formulas with pruning. + + Parameters + ---------- + target_mass : float + Target monoisotopic mass. + tolerance : float + Mass tolerance in Da. + elements : list[str] + List of element symbols. + constraints : dict + Element constraints. + elem_idx : int + Current element index being enumerated. + current : dict + Current element counts. + results : list[dict] + Accumulator for valid results. + """ + if elem_idx == len(elements): + # Check if current formula matches target mass + formula_str = _build_formula_string(current) + if not formula_str: + return + try: + ef = oms.EmpiricalFormula(formula_str) + mass = ef.getMonoWeight() + error = abs(mass - target_mass) + if error <= tolerance: + result = { + "formula": formula_str, + "mass": round(mass, 6), + "error_da": round(mass - target_mass, 6), + } + for elem in elements: + result[elem] = current.get(elem, 0) + results.append(result) + except Exception: + pass + return + + elem = elements[elem_idx] + min_count, max_count = constraints[elem] + current_mass = sum(ELEMENT_MASSES.get(e, 0) * current.get(e, 0) for e in elements[:elem_idx]) + + for count in range(min_count, max_count + 1): + test_mass = current_mass + ELEMENT_MASSES.get(elem, 0) * count + if test_mass > target_mass + tolerance: + break + current[elem] = count + _enumerate_formulas(target_mass, tolerance, elements, constraints, elem_idx + 1, current, results) + + if elem in current: + del current[elem] + + +def _build_formula_string(element_counts: dict[str, int]) -> str: + """Build a molecular formula string from element counts. + + Parameters + ---------- + element_counts : dict + Element symbol to count mapping. + + Returns + ------- + str + Molecular formula string. + """ + parts = [] + for elem in ["C", "H", "N", "O", "S", "P"]: + count = element_counts.get(elem, 0) + if count > 0: + parts.append(f"{elem}{count}" if count > 1 else elem) + return "".join(parts) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write decomposition results to TSV file. + + Parameters + ---------- + results : list[dict] + List of result dictionaries. + output_path : str + Path to output TSV file. + """ + if not results: + with open(output_path, "w") as f: + f.write("formula\tmass\terror_da\n") + return + + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Find molecular formula compositions for a given mass within tolerance." + ) + parser.add_argument("--mass", type=float, required=True, help="Target mass in Da") + parser.add_argument("--tolerance", type=float, default=0.01, help="Mass tolerance in Da (default: 0.01)") + parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") + args = parser.parse_args() + + results = decompose_mass(args.mass, args.tolerance) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} decompositions to {args.output}") + else: + if results: + print("formula\tmass\terror_da") + for r in results: + print(f"{r['formula']}\t{r['mass']}\t{r['error_da']}") + else: + print("No formulas found within tolerance.") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/mass_decomposition_tool/requirements.txt b/scripts/metabolomics/mass_decomposition_tool/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/mass_decomposition_tool/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/mass_decomposition_tool/tests/conftest.py b/scripts/metabolomics/mass_decomposition_tool/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/mass_decomposition_tool/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/mass_decomposition_tool/tests/test_mass_decomposition_tool.py b/scripts/metabolomics/mass_decomposition_tool/tests/test_mass_decomposition_tool.py new file mode 100644 index 0000000..fa90b2b --- /dev/null +++ b/scripts/metabolomics/mass_decomposition_tool/tests/test_mass_decomposition_tool.py @@ -0,0 +1,58 @@ +"""Tests for mass_decomposition_tool.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMassDecompositionTool: + def test_glucose_mass(self): + from mass_decomposition_tool import decompose_mass + + # Glucose: C6H12O6 = 180.0634 Da + results = decompose_mass(180.0634, tolerance=0.01) + assert len(results) > 0 + formulas = [r["formula"] for r in results] + assert "C6H12O6" in formulas + + def test_no_match(self): + from mass_decomposition_tool import decompose_mass + + results = decompose_mass(5000.0, tolerance=0.001) + assert len(results) == 0 + + def test_custom_constraints(self): + from mass_decomposition_tool import decompose_mass + + constraints = {"C": (6, 6), "H": (12, 12), "O": (6, 6)} + results = decompose_mass(180.0634, tolerance=0.01, constraints=constraints) + assert len(results) == 1 + assert results[0]["formula"] == "C6H12O6" + + def test_result_keys(self): + from mass_decomposition_tool import decompose_mass + + results = decompose_mass(180.0634, tolerance=0.01) + assert len(results) > 0 + for r in results: + assert "formula" in r + assert "mass" in r + assert "error_da" in r + + def test_build_formula_string(self): + from mass_decomposition_tool import _build_formula_string + + assert _build_formula_string({"C": 6, "H": 12, "O": 6}) == "C6H12O6" + assert _build_formula_string({"C": 1, "H": 4}) == "CH4" + assert _build_formula_string({}) == "" + + def test_write_tsv(self): + from mass_decomposition_tool import write_tsv + + results = [{"formula": "C6H12O6", "mass": 180.0634, "error_da": 0.0}] + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "decompositions.tsv") + write_tsv(results, out) + assert os.path.exists(out) diff --git a/scripts/metabolomics/mass_defect_filter/README.md b/scripts/metabolomics/mass_defect_filter/README.md new file mode 100644 index 0000000..cbabff9 --- /dev/null +++ b/scripts/metabolomics/mass_defect_filter/README.md @@ -0,0 +1,10 @@ +# Mass Defect Filter + +Compute mass defect and Kendrick mass defect for features, then filter by mass defect range. + +## Usage + +```bash +python mass_defect_filter.py --input features.tsv --mdf-min 0.1 --mdf-max 0.3 +python mass_defect_filter.py --input features.tsv --mdf-min 0.1 --mdf-max 0.3 --kendrick-base CH2 --output filtered.tsv +``` diff --git a/scripts/metabolomics/mass_defect_filter/mass_defect_filter.py b/scripts/metabolomics/mass_defect_filter/mass_defect_filter.py new file mode 100644 index 0000000..d384d65 --- /dev/null +++ b/scripts/metabolomics/mass_defect_filter/mass_defect_filter.py @@ -0,0 +1,196 @@ +""" +Mass Defect Filter +================== +Compute mass defect and Kendrick mass defect for features, then filter by MDF range. + +Features: +- Compute mass defect (fractional part of exact mass) +- Kendrick mass defect with configurable base (e.g. CH2) +- Filter features by mass defect range +- TSV input/output + +Usage +----- + python mass_defect_filter.py --input features.tsv --mdf-min 0.1 --mdf-max 0.3 + python mass_defect_filter.py --input features.tsv --mdf-min 0.1 --mdf-max 0.3 --kendrick-base CH2 \ + --output filtered.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def compute_mass_defect(exact_mass: float) -> float: + """Compute mass defect (fractional part of exact mass). + + Parameters + ---------- + exact_mass : float + Exact monoisotopic mass. + + Returns + ------- + float + Mass defect value. + """ + return exact_mass - int(exact_mass) + + +def compute_kendrick_mass(exact_mass: float, kendrick_base: str = "CH2") -> float: + """Compute Kendrick mass using given base. + + Parameters + ---------- + exact_mass : float + Exact monoisotopic mass. + kendrick_base : str + Molecular formula for the Kendrick base (default "CH2"). + + Returns + ------- + float + Kendrick mass. + """ + formula = oms.EmpiricalFormula(kendrick_base) + exact_base = formula.getMonoWeight() + nominal_base = round(exact_base) + if exact_base == 0: + return exact_mass + return exact_mass * (nominal_base / exact_base) + + +def compute_kendrick_mass_defect(exact_mass: float, kendrick_base: str = "CH2") -> float: + """Compute Kendrick mass defect. + + Parameters + ---------- + exact_mass : float + Exact monoisotopic mass. + kendrick_base : str + Molecular formula for the Kendrick base. + + Returns + ------- + float + Kendrick mass defect. + """ + km = compute_kendrick_mass(exact_mass, kendrick_base) + return round(km) - km + + +def filter_by_mass_defect( + features: list[dict], + mdf_min: float = 0.0, + mdf_max: float = 1.0, + kendrick_base: str = "CH2", +) -> list[dict]: + """Filter features by mass defect range and compute Kendrick mass defect. + + Parameters + ---------- + features : list[dict] + List of feature dicts, each must have an 'exact_mass' key. + mdf_min : float + Minimum mass defect (inclusive). + mdf_max : float + Maximum mass defect (inclusive). + kendrick_base : str + Molecular formula for Kendrick base. + + Returns + ------- + list[dict] + Filtered features with additional mass_defect and kendrick_mass_defect keys. + """ + results = [] + for feat in features: + mass = float(feat["exact_mass"]) + md = compute_mass_defect(mass) + kmd = compute_kendrick_mass_defect(mass, kendrick_base) + + if mdf_min <= md <= mdf_max: + result = dict(feat) + result["mass_defect"] = round(md, 6) + result["kendrick_mass_defect"] = round(kmd, 6) + results.append(result) + + return results + + +def read_features_tsv(input_path: str) -> list[dict]: + """Read features from a TSV file. + + Parameters + ---------- + input_path : str + Path to TSV file with at least an 'exact_mass' column. + + Returns + ------- + list[dict] + List of feature dictionaries. + """ + features = [] + with open(input_path) as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + features.append(row) + return features + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write filtered features to TSV file. + + Parameters + ---------- + results : list[dict] + List of result dictionaries. + output_path : str + Path to output TSV file. + """ + if not results: + with open(output_path, "w") as f: + f.write("exact_mass\tmass_defect\tkendrick_mass_defect\n") + return + + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Compute mass defect and Kendrick mass defect, filter features." + ) + parser.add_argument("--input", required=True, help="Input TSV with exact_mass column") + parser.add_argument("--mdf-min", type=float, default=0.0, help="Minimum mass defect (default: 0.0)") + parser.add_argument("--mdf-max", type=float, default=1.0, help="Maximum mass defect (default: 1.0)") + parser.add_argument("--kendrick-base", default="CH2", help="Kendrick base formula (default: CH2)") + parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") + args = parser.parse_args() + + features = read_features_tsv(args.input) + results = filter_by_mass_defect(features, args.mdf_min, args.mdf_max, args.kendrick_base) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} filtered features to {args.output}") + else: + if results: + print("\t".join(results[0].keys())) + for r in results: + print("\t".join(str(v) for v in r.values())) + else: + print("No features matched the mass defect filter.") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/mass_defect_filter/requirements.txt b/scripts/metabolomics/mass_defect_filter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/mass_defect_filter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/mass_defect_filter/tests/conftest.py b/scripts/metabolomics/mass_defect_filter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/mass_defect_filter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/mass_defect_filter/tests/test_mass_defect_filter.py b/scripts/metabolomics/mass_defect_filter/tests/test_mass_defect_filter.py new file mode 100644 index 0000000..f9b2351 --- /dev/null +++ b/scripts/metabolomics/mass_defect_filter/tests/test_mass_defect_filter.py @@ -0,0 +1,65 @@ +"""Tests for mass_defect_filter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMassDefectFilter: + def test_compute_mass_defect(self): + from mass_defect_filter import compute_mass_defect + + md = compute_mass_defect(180.0634) + assert 0.0 < md < 1.0 + assert abs(md - 0.0634) < 0.001 + + def test_compute_kendrick_mass(self): + from mass_defect_filter import compute_kendrick_mass + + km = compute_kendrick_mass(180.0634, "CH2") + assert km > 0 + + def test_compute_kendrick_mass_defect(self): + from mass_defect_filter import compute_kendrick_mass_defect + + kmd = compute_kendrick_mass_defect(180.0634, "CH2") + assert -1.0 < kmd < 1.0 + + def test_filter_by_mass_defect(self): + from mass_defect_filter import filter_by_mass_defect + + features = [ + {"exact_mass": "180.0634", "name": "glucose"}, + {"exact_mass": "342.1162", "name": "sucrose"}, + {"exact_mass": "100.9000", "name": "test"}, + ] + results = filter_by_mass_defect(features, mdf_min=0.0, mdf_max=0.2) + assert len(results) >= 1 + for r in results: + assert "mass_defect" in r + assert "kendrick_mass_defect" in r + + def test_filter_excludes(self): + from mass_defect_filter import filter_by_mass_defect + + features = [{"exact_mass": "180.0634"}] + results = filter_by_mass_defect(features, mdf_min=0.5, mdf_max=0.9) + assert len(results) == 0 + + def test_read_write_tsv(self): + from mass_defect_filter import read_features_tsv, write_tsv + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "features.tsv") + with open(in_path, "w") as f: + f.write("exact_mass\tname\n") + f.write("180.0634\tglucose\n") + features = read_features_tsv(in_path) + assert len(features) == 1 + assert features[0]["exact_mass"] == "180.0634" + + out_path = os.path.join(tmpdir, "filtered.tsv") + write_tsv([{"exact_mass": "180.0634", "mass_defect": 0.0634, "kendrick_mass_defect": 0.05}], out_path) + assert os.path.exists(out_path) diff --git a/scripts/metabolomics/mass_difference_network_builder/mass_difference_network_builder.py b/scripts/metabolomics/mass_difference_network_builder/mass_difference_network_builder.py new file mode 100644 index 0000000..140368f --- /dev/null +++ b/scripts/metabolomics/mass_difference_network_builder/mass_difference_network_builder.py @@ -0,0 +1,164 @@ +""" +Mass Difference Network Builder +================================= +Connect features by known biotransformation mass differences to build +a molecular network. + +Each edge represents a mass difference matching a known reaction +(e.g., oxidation, dehydration, methylation) within the given tolerance. + +Usage +----- + python mass_difference_network_builder.py --input features.tsv --reactions reactions.tsv \ + --tolerance 0.005 --output network.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Built-in common biotransformations: (name, mass_diff) +DEFAULT_REACTIONS = [ + ("Oxidation", 15.994915), + ("Reduction", -15.994915), + ("Dehydration", -18.010565), + ("Hydration", 18.010565), + ("Methylation", 14.015650), + ("Demethylation", -14.015650), + ("Acetylation", 42.010565), + ("Deacetylation", -42.010565), + ("Glucuronidation", 176.032088), + ("Sulfation", 79.956815), + ("Phosphorylation", 79.966331), + ("Hydrogenation", 2.015650), + ("Dehydrogenation", -2.015650), + ("Hydroxylation", 15.994915), + ("Glycine conjugation", 57.021464), +] + + +def load_reactions(path: str | None) -> list[tuple[str, float]]: + """Load reaction definitions from a TSV file. + + Parameters + ---------- + path: + Path to TSV with columns: reaction_name, mass_diff. + If None, returns the built-in default reactions. + + Returns + ------- + list[tuple[str, float]] + """ + if path is None: + return DEFAULT_REACTIONS + + reactions = [] + with open(path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + reactions.append((row["reaction_name"], float(row["mass_diff"]))) + return reactions + + +def build_network( + features: list[dict], + reactions: list[tuple[str, float]], + tolerance: float = 0.005, +) -> list[dict]: + """Build a mass-difference network from features. + + Parameters + ---------- + features: + List of dicts with at least keys: feature_id, mz. + reactions: + List of (reaction_name, mass_diff) tuples. + tolerance: + Absolute mass tolerance in Da. + + Returns + ------- + list[dict] + List of edges: source_id, target_id, reaction, mass_diff, error_da. + """ + edges = [] + n = len(features) + + for i in range(n): + mz_i = float(features[i]["mz"]) + id_i = features[i].get("feature_id", str(i)) + + for j in range(i + 1, n): + mz_j = float(features[j]["mz"]) + id_j = features[j].get("feature_id", str(j)) + diff = mz_j - mz_i + + for rxn_name, rxn_diff in reactions: + error = abs(diff - rxn_diff) + if error <= tolerance: + edges.append({ + "source_id": id_i, + "target_id": id_j, + "reaction": rxn_name, + "mass_diff": round(diff, 6), + "error_da": round(error, 6), + }) + # Also check reverse direction + error_rev = abs(-diff - rxn_diff) + if error_rev <= tolerance: + edges.append({ + "source_id": id_j, + "target_id": id_i, + "reaction": rxn_name, + "mass_diff": round(-diff, 6), + "error_da": round(error_rev, 6), + }) + + return edges + + +def main(): + parser = argparse.ArgumentParser( + description="Build a mass-difference network from features and biotransformation list." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (feature_id, mz)") + parser.add_argument( + "--reactions", default=None, metavar="FILE", + help="Reactions TSV (reaction_name, mass_diff). Uses built-in table if omitted." + ) + parser.add_argument( + "--tolerance", type=float, default=0.005, + help="Mass tolerance in Da (default: 0.005)" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output network TSV") + args = parser.parse_args() + + features = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + reactions = load_reactions(args.reactions) + edges = build_network(features, reactions, tolerance=args.tolerance) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["source_id", "target_id", "reaction", "mass_diff", "error_da"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(edges) + + print(f"Network: {len(edges)} edges from {len(features)} features, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/mass_difference_network_builder/requirements.txt b/scripts/metabolomics/mass_difference_network_builder/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/mass_difference_network_builder/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/mass_difference_network_builder/tests/conftest.py b/scripts/metabolomics/mass_difference_network_builder/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/mass_difference_network_builder/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/mass_difference_network_builder/tests/test_mass_difference_network_builder.py b/scripts/metabolomics/mass_difference_network_builder/tests/test_mass_difference_network_builder.py new file mode 100644 index 0000000..8bd90d7 --- /dev/null +++ b/scripts/metabolomics/mass_difference_network_builder/tests/test_mass_difference_network_builder.py @@ -0,0 +1,58 @@ +"""Tests for mass_difference_network_builder.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMassDifferenceNetworkBuilder: + def test_oxidation_edge(self): + from mass_difference_network_builder import build_network + + features = [ + {"feature_id": "A", "mz": "200.0000"}, + {"feature_id": "B", "mz": "215.9949"}, # +15.9949 = oxidation + ] + reactions = [("Oxidation", 15.994915)] + edges = build_network(features, reactions, tolerance=0.005) + assert len(edges) >= 1 + rxn_names = [e["reaction"] for e in edges] + assert "Oxidation" in rxn_names + + def test_no_match(self): + from mass_difference_network_builder import build_network + + features = [ + {"feature_id": "A", "mz": "200.0"}, + {"feature_id": "B", "mz": "300.0"}, + ] + reactions = [("Oxidation", 15.994915)] + edges = build_network(features, reactions, tolerance=0.005) + assert len(edges) == 0 + + def test_multiple_reactions(self): + from mass_difference_network_builder import build_network + + features = [ + {"feature_id": "A", "mz": "200.0000"}, + {"feature_id": "B", "mz": "215.9949"}, + {"feature_id": "C", "mz": "214.0157"}, # +14.0157 = methylation + ] + reactions = [("Oxidation", 15.994915), ("Methylation", 14.015650)] + edges = build_network(features, reactions, tolerance=0.005) + rxn_names = set(e["reaction"] for e in edges) + assert "Oxidation" in rxn_names + assert "Methylation" in rxn_names + + def test_default_reactions(self): + from mass_difference_network_builder import DEFAULT_REACTIONS + + assert len(DEFAULT_REACTIONS) > 10 + names = [r[0] for r in DEFAULT_REACTIONS] + assert "Oxidation" in names + assert "Dehydration" in names + + def test_load_reactions_none(self): + from mass_difference_network_builder import load_reactions + + reactions = load_reactions(None) + assert reactions == __import__("mass_difference_network_builder").DEFAULT_REACTIONS diff --git a/scripts/metabolomics/massql_query_tool/massql_query_tool.py b/scripts/metabolomics/massql_query_tool/massql_query_tool.py new file mode 100644 index 0000000..b6267f0 --- /dev/null +++ b/scripts/metabolomics/massql_query_tool/massql_query_tool.py @@ -0,0 +1,163 @@ +""" +MassQL Query Tool +================== +Query mzML data using a simplified MassQL-like syntax. + +Supported query patterns: +- ``MS2PROD=`` : Find MS2 spectra containing a product ion at the given m/z +- ``MS1MZ=`` : Find MS1 spectra containing a peak at the given m/z +- ``PRECMZ=`` : Find MS2 spectra with a specific precursor m/z + +All matches use a configurable Da tolerance (default 0.5 Da). + +Usage +----- + python massql_query_tool.py --input data.mzML --query "MS2PROD=226.18" --output results.tsv +""" + +import argparse +import csv +import re +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_query(query: str) -> dict: + """Parse a MassQL-like query string. + + Parameters + ---------- + query: + Query string such as ``"MS2PROD=226.18"`` or ``"MS1MZ=180.06"``. + + Returns + ------- + dict + Parsed query with keys: query_type, target_mz. + """ + query = query.strip() + pattern = re.compile(r"^(MS2PROD|MS1MZ|PRECMZ)\s*=\s*([0-9.]+)$", re.IGNORECASE) + match = pattern.match(query) + if not match: + raise ValueError(f"Unsupported query syntax: {query}") + + return { + "query_type": match.group(1).upper(), + "target_mz": float(match.group(2)), + } + + +def execute_query( + exp: oms.MSExperiment, + query: dict, + tolerance_da: float = 0.5, +) -> list[dict]: + """Execute a parsed query against an MSExperiment. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment``. + query: + Parsed query from ``parse_query``. + tolerance_da: + Absolute tolerance in Da for matching. + + Returns + ------- + list[dict] + Matching results with scan info. + """ + qtype = query["query_type"] + target = query["target_mz"] + lo = target - tolerance_da + hi = target + tolerance_da + + results = [] + + for i, spec in enumerate(exp.getSpectra()): + level = spec.getMSLevel() + + if qtype == "MS2PROD" and level == 2: + mzs, intensities = spec.get_peaks() + for mz, intensity in zip(mzs, intensities): + if lo <= mz <= hi: + prec_mz = spec.getPrecursors()[0].getMZ() if spec.getPrecursors() else 0.0 + results.append({ + "scan_index": i, + "rt": round(spec.getRT(), 4), + "ms_level": level, + "precursor_mz": round(prec_mz, 6), + "matched_mz": round(float(mz), 6), + "matched_intensity": round(float(intensity), 2), + }) + break # one match per spectrum + + elif qtype == "MS1MZ" and level == 1: + mzs, intensities = spec.get_peaks() + for mz, intensity in zip(mzs, intensities): + if lo <= mz <= hi: + results.append({ + "scan_index": i, + "rt": round(spec.getRT(), 4), + "ms_level": level, + "precursor_mz": 0.0, + "matched_mz": round(float(mz), 6), + "matched_intensity": round(float(intensity), 2), + }) + break + + elif qtype == "PRECMZ" and level == 2: + for prec in spec.getPrecursors(): + if lo <= prec.getMZ() <= hi: + results.append({ + "scan_index": i, + "rt": round(spec.getRT(), 4), + "ms_level": level, + "precursor_mz": round(prec.getMZ(), 6), + "matched_mz": round(prec.getMZ(), 6), + "matched_intensity": 0.0, + }) + break + + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Query mzML using MassQL-like syntax." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="mzML file") + parser.add_argument( + "--query", required=True, + help='MassQL query (e.g. "MS2PROD=226.18", "MS1MZ=180.06", "PRECMZ=500.0")' + ) + parser.add_argument( + "--tolerance", type=float, default=0.5, + help="m/z tolerance in Da (default: 0.5)" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output results TSV") + args = parser.parse_args() + + parsed = parse_query(args.query) + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + results = execute_query(exp, parsed, tolerance_da=args.tolerance) + + with open(args.output, "w", newline="") as fh: + fieldnames = ["scan_index", "rt", "ms_level", "precursor_mz", "matched_mz", "matched_intensity"] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + print(f"Query '{args.query}': {len(results)} matches, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/massql_query_tool/requirements.txt b/scripts/metabolomics/massql_query_tool/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/massql_query_tool/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/massql_query_tool/tests/conftest.py b/scripts/metabolomics/massql_query_tool/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/massql_query_tool/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/massql_query_tool/tests/test_massql_query_tool.py b/scripts/metabolomics/massql_query_tool/tests/test_massql_query_tool.py new file mode 100644 index 0000000..b450805 --- /dev/null +++ b/scripts/metabolomics/massql_query_tool/tests/test_massql_query_tool.py @@ -0,0 +1,95 @@ +"""Tests for massql_query_tool.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMassqlQueryTool: + def _make_experiment(self): + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + # MS1 with peaks at 180.0, 181.0 + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(60.0) + ms1.set_peaks([ + np.array([180.0, 181.0, 250.0], dtype=np.float64), + np.array([1000.0, 200.0, 500.0], dtype=np.float64), + ]) + exp.addSpectrum(ms1) + + # MS2 with precursor at 500.0 and product at 226.18 + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(61.0) + prec = oms.Precursor() + prec.setMZ(500.0) + ms2.setPrecursors([prec]) + ms2.set_peaks([ + np.array([226.18, 300.0], dtype=np.float64), + np.array([800.0, 400.0], dtype=np.float64), + ]) + exp.addSpectrum(ms2) + + return exp + + def test_parse_ms2prod_query(self): + from massql_query_tool import parse_query + + q = parse_query("MS2PROD=226.18") + assert q["query_type"] == "MS2PROD" + assert q["target_mz"] == 226.18 + + def test_parse_ms1mz_query(self): + from massql_query_tool import parse_query + + q = parse_query("MS1MZ=180.06") + assert q["query_type"] == "MS1MZ" + + def test_parse_precmz_query(self): + from massql_query_tool import parse_query + + q = parse_query("PRECMZ=500.0") + assert q["query_type"] == "PRECMZ" + + def test_invalid_query(self): + from massql_query_tool import parse_query + + with pytest.raises(ValueError): + parse_query("INVALID=123") + + def test_ms2prod_search(self): + from massql_query_tool import execute_query, parse_query + + exp = self._make_experiment() + query = parse_query("MS2PROD=226.18") + results = execute_query(exp, query, tolerance_da=0.5) + assert len(results) == 1 + assert abs(results[0]["matched_mz"] - 226.18) < 0.01 + + def test_ms1mz_search(self): + from massql_query_tool import execute_query, parse_query + + exp = self._make_experiment() + query = parse_query("MS1MZ=180.0") + results = execute_query(exp, query, tolerance_da=0.5) + assert len(results) == 1 + + def test_precmz_search(self): + from massql_query_tool import execute_query, parse_query + + exp = self._make_experiment() + query = parse_query("PRECMZ=500.0") + results = execute_query(exp, query, tolerance_da=0.5) + assert len(results) == 1 + + def test_no_match(self): + from massql_query_tool import execute_query, parse_query + + exp = self._make_experiment() + query = parse_query("MS2PROD=999.0") + results = execute_query(exp, query, tolerance_da=0.1) + assert len(results) == 0 diff --git a/scripts/metabolomics/metabolite_class_annotator/metabolite_class_annotator.py b/scripts/metabolomics/metabolite_class_annotator/metabolite_class_annotator.py new file mode 100644 index 0000000..e133722 --- /dev/null +++ b/scripts/metabolomics/metabolite_class_annotator/metabolite_class_annotator.py @@ -0,0 +1,188 @@ +""" +Metabolite Class Annotator +============================ +Annotate features with putative compound classes based on mass defect +analysis and elemental ratio heuristics. + +Mass defect (fractional part of mass) and Kendrick mass defect are +used to classify features into broad compound families such as lipids, +peptides, sugars, and polyketides. + +Usage +----- + python metabolite_class_annotator.py --input features.tsv --output annotated.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Compound class rules based on mass defect ranges (fractional mass) +# These are simplified heuristic boundaries. +CLASS_RULES = [ + { + "name": "Lipid", + "mass_defect_range": (0.0, 0.4), + "mass_range": (200, 1200), + "description": "Fatty acids, glycerolipids, sphingolipids", + }, + { + "name": "Peptide", + "mass_defect_range": (0.0, 0.7), + "mass_range": (400, 5000), + "description": "Di- to oligopeptides", + }, + { + "name": "Sugar/Carbohydrate", + "mass_defect_range": (0.0, 0.15), + "mass_range": (100, 2000), + "description": "Mono- to oligosaccharides", + }, + { + "name": "Polyketide", + "mass_defect_range": (0.0, 0.35), + "mass_range": (150, 800), + "description": "Polyketide-derived natural products", + }, + { + "name": "Terpenoid", + "mass_defect_range": (0.1, 0.5), + "mass_range": (100, 800), + "description": "Mono-, sesqui-, diterpenoids", + }, + { + "name": "Nucleoside", + "mass_defect_range": (0.0, 0.2), + "mass_range": (200, 600), + "description": "Nucleosides and nucleotides", + }, +] + + +def compute_mass_defect(mass: float) -> float: + """Compute the fractional mass defect. + + Parameters + ---------- + mass: + Monoisotopic mass in Da. + + Returns + ------- + float + Fractional part of the mass. + """ + return mass - int(mass) + + +def compute_kendrick_mass_defect(mass: float, base_unit: float = 14.01565) -> float: + """Compute the Kendrick mass defect (CH2-based). + + Parameters + ---------- + mass: + Monoisotopic mass in Da. + base_unit: + Kendrick base mass (default: CH2 = 14.01565 Da). + + Returns + ------- + float + Kendrick mass defect. + """ + kendrick_mass = mass * (14.0 / base_unit) + return round(kendrick_mass) - kendrick_mass + + +def annotate_class(mass: float) -> list[str]: + """Annotate a mass with candidate compound classes. + + Parameters + ---------- + mass: + Neutral monoisotopic mass in Da. + + Returns + ------- + list[str] + List of candidate class names. + """ + md = compute_mass_defect(mass) + candidates = [] + + for rule in CLASS_RULES: + md_lo, md_hi = rule["mass_defect_range"] + m_lo, m_hi = rule["mass_range"] + if md_lo <= md <= md_hi and m_lo <= mass <= m_hi: + candidates.append(rule["name"]) + + return candidates if candidates else ["Unknown"] + + +def annotate_features(features: list[dict]) -> list[dict]: + """Annotate a list of features with compound classes. + + Parameters + ---------- + features: + List of dicts with at least key ``mz``. + + Returns + ------- + list[dict] + Each feature augmented with mass_defect, kendrick_md, and compound_class. + """ + results = [] + for feat in features: + mz = float(feat["mz"]) + neutral_mass = mz - PROTON # assume [M+H]+ + + md = compute_mass_defect(neutral_mass) + kmd = compute_kendrick_mass_defect(neutral_mass) + classes = annotate_class(neutral_mass) + + feat_copy = dict(feat) + feat_copy["neutral_mass"] = round(neutral_mass, 6) + feat_copy["mass_defect"] = round(md, 6) + feat_copy["kendrick_md"] = round(kmd, 6) + feat_copy["compound_class"] = ";".join(classes) + results.append(feat_copy) + + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Annotate features with compound classes by mass defect analysis." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (must have mz column)") + parser.add_argument("--output", required=True, metavar="FILE", help="Output annotated TSV") + args = parser.parse_args() + + features = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + annotated = annotate_features(features) + + fieldnames = list(features[0].keys()) if features else ["mz"] + fieldnames += ["neutral_mass", "mass_defect", "kendrick_md", "compound_class"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(annotated) + + print(f"Annotated {len(annotated)} features, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/metabolite_class_annotator/requirements.txt b/scripts/metabolomics/metabolite_class_annotator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/metabolite_class_annotator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/metabolite_class_annotator/tests/conftest.py b/scripts/metabolomics/metabolite_class_annotator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/metabolite_class_annotator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/metabolite_class_annotator/tests/test_metabolite_class_annotator.py b/scripts/metabolomics/metabolite_class_annotator/tests/test_metabolite_class_annotator.py new file mode 100644 index 0000000..2a18e93 --- /dev/null +++ b/scripts/metabolomics/metabolite_class_annotator/tests/test_metabolite_class_annotator.py @@ -0,0 +1,51 @@ +"""Tests for metabolite_class_annotator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMetaboliteClassAnnotator: + def test_compute_mass_defect(self): + from metabolite_class_annotator import compute_mass_defect + + md = compute_mass_defect(180.0634) + assert abs(md - 0.0634) < 0.001 + + def test_compute_kendrick_md(self): + from metabolite_class_annotator import compute_kendrick_mass_defect + + kmd = compute_kendrick_mass_defect(180.0634) + assert isinstance(kmd, float) + + def test_annotate_class_small_molecule(self): + from metabolite_class_annotator import annotate_class + + classes = annotate_class(180.0634) # glucose + assert len(classes) > 0 + assert isinstance(classes[0], str) + + def test_annotate_class_lipid_range(self): + from metabolite_class_annotator import annotate_class + + classes = annotate_class(700.2) + assert "Lipid" in classes + + def test_annotate_features(self): + from metabolite_class_annotator import annotate_features + + features = [ + {"mz": "181.0707", "rt": "60.0", "intensity": "1000"}, + {"mz": "701.2", "rt": "300.0", "intensity": "5000"}, + ] + results = annotate_features(features) + assert len(results) == 2 + assert "compound_class" in results[0] + assert "mass_defect" in results[0] + assert "kendrick_md" in results[0] + + def test_unknown_class(self): + from metabolite_class_annotator import annotate_class + + # Very large mass outside normal ranges + classes = annotate_class(50000.0) + assert "Unknown" in classes diff --git a/scripts/metabolomics/metabolite_class_predictor/README.md b/scripts/metabolomics/metabolite_class_predictor/README.md new file mode 100644 index 0000000..1eb15a6 --- /dev/null +++ b/scripts/metabolomics/metabolite_class_predictor/README.md @@ -0,0 +1,29 @@ +# Metabolite Class Predictor + +Predict compound class from molecular formula using mass defect, element ratios (H:C, O:C), and RDBE (Ring and Double Bond Equivalents). + +## Usage + +```bash +python metabolite_class_predictor.py --input formulas.tsv --output predictions.tsv +``` + +### Input format + +**formulas.tsv** (tab-separated): +``` +formula +C6H12O6 +C16H32O2 +C2H5NO2 +``` + +### Classification rules + +Uses van Krevelen-style heuristic analysis: +- Carbohydrates: O:C 0.6-1.2, H:C 1.5-2.5 +- Lipids: H:C > 1.5, O:C < 0.3 +- Amino acids/Peptides: contains N, moderate ratios +- Alkaloids: contains N, high RDBE +- Terpenoids: moderate H:C, low O:C +- Phenolics/Polyketides: higher RDBE, moderate ratios diff --git a/scripts/metabolomics/metabolite_class_predictor/metabolite_class_predictor.py b/scripts/metabolomics/metabolite_class_predictor/metabolite_class_predictor.py new file mode 100644 index 0000000..e78e8cb --- /dev/null +++ b/scripts/metabolomics/metabolite_class_predictor/metabolite_class_predictor.py @@ -0,0 +1,286 @@ +""" +Metabolite Class Predictor +========================== +Predict compound class from molecular formula using mass defect, element +ratios (H:C, O:C), and Ring and Double Bond Equivalents (RDBE). + +Uses heuristic classification rules based on: +- Mass defect ranges +- H:C and O:C ratios (van Krevelen-style) +- RDBE values +- Nitrogen/sulfur/phosphorus content + +Usage +----- + python metabolite_class_predictor.py --input formulas.tsv --output predictions.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def get_element_counts(formula: str) -> dict[str, int]: + """Extract element counts from a molecular formula using pyopenms. + + Parameters + ---------- + formula: + Empirical formula string, e.g. ``"C6H12O6"``. + + Returns + ------- + dict + Mapping element symbol to count, e.g. {"C": 6, "H": 12, "O": 6}. + """ + ef = oms.EmpiricalFormula(formula) + element_db = oms.ElementDB() + counts = {} + for element_name in ["Carbon", "Hydrogen", "Oxygen", "Nitrogen", "Sulfur", "Phosphorus"]: + element = element_db.getElement(element_name) + symbol = element.getSymbol() + count = ef.getNumberOf(element) + if count > 0: + counts[symbol] = count + return counts + + +def compute_exact_mass(formula: str) -> float: + """Get the monoisotopic mass from formula. + + Parameters + ---------- + formula: + Empirical formula string. + + Returns + ------- + float + Monoisotopic mass. + """ + ef = oms.EmpiricalFormula(formula) + return ef.getMonoWeight() + + +def compute_mass_defect(mass: float) -> float: + """Calculate mass defect (fractional part of the mass). + + Parameters + ---------- + mass: + Monoisotopic mass. + + Returns + ------- + float + Mass defect (value between 0 and 1). + """ + return mass - int(mass) + + +def compute_rdbe(counts: dict[str, int]) -> float: + """Calculate Ring and Double Bond Equivalents (RDBE). + + RDBE = 1 + C - H/2 + N/2 + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + float + RDBE value. + """ + c = counts.get("C", 0) + h = counts.get("H", 0) + n = counts.get("N", 0) + return 1.0 + c - h / 2.0 + n / 2.0 + + +def compute_element_ratios(counts: dict[str, int]) -> dict[str, float | None]: + """Calculate H:C and O:C ratios. + + Parameters + ---------- + counts: + Element counts dict. + + Returns + ------- + dict + Keys: hc_ratio, oc_ratio. None if C == 0. + """ + c = counts.get("C", 0) + h = counts.get("H", 0) + o = counts.get("O", 0) + if c == 0: + return {"hc_ratio": None, "oc_ratio": None} + return { + "hc_ratio": h / c, + "oc_ratio": o / c, + } + + +def classify_metabolite( + formula: str, +) -> dict: + """Predict compound class from molecular formula using heuristic rules. + + Classification is based on van Krevelen-style element ratio analysis: + - Lipids: H:C > 1.5, O:C < 0.3, RDBE low + - Carbohydrates: O:C 0.6-1.2, H:C 1.5-2.5 + - Amino acids / peptides: contains N, H:C 1.0-2.0, O:C 0.3-0.8 + - Nucleotides: contains N and P, O:C > 0.5 + - Terpenoids: H:C 1.0-1.8, O:C < 0.3, no N + - Phenolics / polyketides: H:C 0.5-1.5, O:C 0.2-0.8, higher RDBE + - Alkaloids: contains N, RDBE > 4, H:C < 1.5 + + Parameters + ---------- + formula: + Empirical formula string. + + Returns + ------- + dict + Prediction results with keys: formula, exact_mass, mass_defect, + rdbe, hc_ratio, oc_ratio, predicted_class, confidence. + """ + counts = get_element_counts(formula) + mass = compute_exact_mass(formula) + mass_defect = compute_mass_defect(mass) + rdbe = compute_rdbe(counts) + ratios = compute_element_ratios(counts) + hc = ratios["hc_ratio"] + oc = ratios["oc_ratio"] + + has_n = counts.get("N", 0) > 0 + has_s = counts.get("S", 0) > 0 + has_p = counts.get("P", 0) > 0 + + predicted_class = "Unknown" + confidence = "low" + + if hc is not None and oc is not None: + # Nucleotides: N + P, high O:C + if has_n and has_p and oc > 0.5: + predicted_class = "Nucleotide" + confidence = "medium" + + # Carbohydrates: high O:C, high H:C, no N + elif not has_n and 0.6 <= oc <= 1.2 and 1.5 <= hc <= 2.5: + predicted_class = "Carbohydrate" + confidence = "high" if 0.8 <= oc <= 1.1 and 1.8 <= hc <= 2.2 else "medium" + + # Amino acids / peptides: N present, moderate ratios + elif has_n and 1.0 <= hc <= 2.2 and 0.2 <= oc <= 0.8 and rdbe < 6: + predicted_class = "Amino acid / Peptide" + confidence = "medium" + + # Alkaloids: N present, high aromaticity + elif has_n and rdbe > 4 and hc < 1.5: + predicted_class = "Alkaloid" + confidence = "medium" + + # Lipids: high H:C, low O:C + elif hc > 1.5 and oc < 0.3 and not has_n: + predicted_class = "Lipid" + confidence = "high" if hc > 1.7 and oc < 0.2 else "medium" + + # Terpenoids: moderate H:C, low O:C, no N + elif 1.0 <= hc <= 1.8 and oc < 0.3 and not has_n: + predicted_class = "Terpenoid" + confidence = "medium" + + # Phenolics / polyketides: moderate ratios, higher RDBE + elif 0.5 <= hc <= 1.5 and 0.2 <= oc <= 0.8 and rdbe > 3: + predicted_class = "Phenolic / Polyketide" + confidence = "medium" + + # Sulfur-containing organics + elif has_s and has_n: + predicted_class = "Sulfur-containing organic" + confidence = "low" + + return { + "formula": formula, + "exact_mass": round(mass, 6), + "mass_defect": round(mass_defect, 6), + "rdbe": round(rdbe, 1), + "hc_ratio": round(hc, 4) if hc is not None else None, + "oc_ratio": round(oc, 4) if oc is not None else None, + "has_nitrogen": has_n, + "has_sulfur": has_s, + "has_phosphorus": has_p, + "predicted_class": predicted_class, + "confidence": confidence, + } + + +def classify_batch(formulas: list[dict]) -> list[dict]: + """Classify a batch of formulas. + + Parameters + ---------- + formulas: + List of dicts with a 'formula' key. + + Returns + ------- + list of dict + Classification results. + """ + results = [] + for row in formulas: + formula = row.get("formula", "").strip() + if not formula: + continue + result = classify_metabolite(formula) + results.append(result) + return results + + +def load_formulas(path: str) -> list[dict]: + """Load formulas from TSV file. Expects a 'formula' column.""" + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + return list(reader) + + +def write_predictions(predictions: list[dict], path: str) -> None: + """Write predictions to TSV.""" + if not predictions: + with open(path, "w") as fh: + fh.write("# No predictions\n") + return + fieldnames = list(predictions[0].keys()) + with open(path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(predictions) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Predict compound class from molecular formula using mass defect, element ratios, and RDBE." + ) + parser.add_argument("--input", required=True, help="Formulas table (TSV) with 'formula' column") + parser.add_argument("--output", required=True, help="Output predictions (TSV)") + args = parser.parse_args() + + formulas = load_formulas(args.input) + predictions = classify_batch(formulas) + write_predictions(predictions, args.output) + print(f"Classified {len(predictions)} formulas, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/metabolite_class_predictor/requirements.txt b/scripts/metabolomics/metabolite_class_predictor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/metabolite_class_predictor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/metabolite_class_predictor/tests/conftest.py b/scripts/metabolomics/metabolite_class_predictor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/metabolite_class_predictor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/metabolite_class_predictor/tests/test_metabolite_class_predictor.py b/scripts/metabolomics/metabolite_class_predictor/tests/test_metabolite_class_predictor.py new file mode 100644 index 0000000..2c9576f --- /dev/null +++ b/scripts/metabolomics/metabolite_class_predictor/tests/test_metabolite_class_predictor.py @@ -0,0 +1,129 @@ +"""Tests for metabolite_class_predictor.""" + +import csv +import os +import tempfile + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMetaboliteClassPredictor: + def test_get_element_counts(self): + from metabolite_class_predictor import get_element_counts + + counts = get_element_counts("C6H12O6") + assert counts["C"] == 6 + assert counts["H"] == 12 + assert counts["O"] == 6 + + def test_get_element_counts_with_nitrogen(self): + from metabolite_class_predictor import get_element_counts + + counts = get_element_counts("C2H5NO2") # Glycine + assert counts["C"] == 2 + assert counts["N"] == 1 + + def test_compute_exact_mass(self): + from metabolite_class_predictor import compute_exact_mass + + mass = compute_exact_mass("C6H12O6") + assert abs(mass - 180.0634) < 0.01 + + def test_compute_mass_defect(self): + from metabolite_class_predictor import compute_mass_defect + + defect = compute_mass_defect(180.0634) + assert 0.0 < defect < 1.0 + assert abs(defect - 0.0634) < 0.01 + + def test_compute_rdbe(self): + from metabolite_class_predictor import compute_rdbe + + # Benzene C6H6: RDBE = 1 + 6 - 6/2 = 4 + rdbe = compute_rdbe({"C": 6, "H": 6}) + assert abs(rdbe - 4.0) < 0.01 + + # Glucose C6H12O6: RDBE = 1 + 6 - 12/2 = 1 + rdbe = compute_rdbe({"C": 6, "H": 12, "O": 6}) + assert abs(rdbe - 1.0) < 0.01 + + def test_compute_element_ratios(self): + from metabolite_class_predictor import compute_element_ratios + + ratios = compute_element_ratios({"C": 6, "H": 12, "O": 6}) + assert ratios["hc_ratio"] == pytest.approx(2.0) + assert ratios["oc_ratio"] == pytest.approx(1.0) + + def test_compute_element_ratios_no_carbon(self): + from metabolite_class_predictor import compute_element_ratios + + ratios = compute_element_ratios({"H": 2, "O": 1}) + assert ratios["hc_ratio"] is None + assert ratios["oc_ratio"] is None + + def test_classify_carbohydrate(self): + from metabolite_class_predictor import classify_metabolite + + # Glucose C6H12O6: H:C=2.0, O:C=1.0 -> Carbohydrate + result = classify_metabolite("C6H12O6") + assert result["predicted_class"] == "Carbohydrate" + + def test_classify_lipid(self): + from metabolite_class_predictor import classify_metabolite + + # Palmitic acid C16H32O2: H:C=2.0, O:C=0.125 -> Lipid + result = classify_metabolite("C16H32O2") + assert result["predicted_class"] == "Lipid" + + def test_classify_amino_acid(self): + from metabolite_class_predictor import classify_metabolite + + # Alanine C3H7NO2: H:C=2.33, O:C=0.67, has N + result = classify_metabolite("C3H7NO2") + assert "Amino acid" in result["predicted_class"] or "Peptide" in result["predicted_class"] + + def test_classify_returns_all_fields(self): + from metabolite_class_predictor import classify_metabolite + + result = classify_metabolite("C6H12O6") + expected_keys = [ + "formula", "exact_mass", "mass_defect", "rdbe", + "hc_ratio", "oc_ratio", "has_nitrogen", "has_sulfur", + "has_phosphorus", "predicted_class", "confidence", + ] + for key in expected_keys: + assert key in result + + def test_classify_batch(self): + from metabolite_class_predictor import classify_batch + + formulas = [{"formula": "C6H12O6"}, {"formula": "C16H32O2"}] + results = classify_batch(formulas) + assert len(results) == 2 + + def test_classify_batch_skips_empty(self): + from metabolite_class_predictor import classify_batch + + formulas = [{"formula": ""}, {"formula": "C6H12O6"}] + results = classify_batch(formulas) + assert len(results) == 1 + + def test_full_pipeline(self): + from metabolite_class_predictor import classify_batch, load_formulas, write_predictions + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "formulas.tsv") + with open(in_path, "w", newline="") as fh: + w = csv.DictWriter(fh, ["formula"], delimiter="\t") + w.writeheader() + w.writerow({"formula": "C6H12O6"}) + w.writerow({"formula": "C16H32O2"}) + + formulas = load_formulas(in_path) + predictions = classify_batch(formulas) + + out_path = os.path.join(tmpdir, "predictions.tsv") + write_predictions(predictions, out_path) + assert os.path.exists(out_path) diff --git a/scripts/metabolomics/metabolite_formula_annotator/metabolite_formula_annotator.py b/scripts/metabolomics/metabolite_formula_annotator/metabolite_formula_annotator.py new file mode 100644 index 0000000..5ea5e4c --- /dev/null +++ b/scripts/metabolomics/metabolite_formula_annotator/metabolite_formula_annotator.py @@ -0,0 +1,217 @@ +""" +Metabolite Formula Annotator +============================== +Annotate features with candidate molecular formulas based on mass +matching and isotope pattern fitting. + +For each feature (m/z), candidate formulas are generated within the +specified element constraints and ranked by isotope-pattern similarity. + +Usage +----- + python metabolite_formula_annotator.py --input features.tsv --ppm 5 --elements C,H,N,O --output annotated.tsv +""" + +import argparse +import csv +import itertools +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Default element ranges for formula enumeration +DEFAULT_ELEMENT_RANGES = { + "C": (0, 40), + "H": (0, 80), + "N": (0, 10), + "O": (0, 25), +} + + +def enumerate_formulas( + target_mass: float, + ppm: float = 5.0, + element_ranges: dict[str, tuple[int, int]] | None = None, +) -> list[dict]: + """Enumerate candidate formulas matching a target neutral mass. + + Parameters + ---------- + target_mass: + Neutral monoisotopic mass in Da. + ppm: + Mass tolerance in ppm. + element_ranges: + Dict mapping element symbols to (min, max) count ranges. + + Returns + ------- + list[dict] + Each dict has: formula, mass, error_ppm. + """ + if element_ranges is None: + element_ranges = DEFAULT_ELEMENT_RANGES + + tol_da = target_mass * ppm / 1e6 + lo = target_mass - tol_da + hi = target_mass + tol_da + + elements = sorted(element_ranges.keys()) + ranges = [range(element_ranges[e][0], element_ranges[e][1] + 1) for e in elements] + + results = [] + for combo in itertools.product(*ranges): + formula_str = "".join(f"{e}{n}" for e, n in zip(elements, combo) if n > 0) + if not formula_str: + continue + + try: + ef = oms.EmpiricalFormula(formula_str) + mass = ef.getMonoWeight() + except Exception: + continue + + if lo <= mass <= hi: + error = (mass - target_mass) / target_mass * 1e6 + results.append({ + "formula": formula_str, + "mass": round(mass, 6), + "error_ppm": round(error, 4), + }) + + results.sort(key=lambda r: abs(r["error_ppm"])) + return results + + +def score_isotope_fit(formula: str, observed_ratios: list[float] | None = None) -> float: + """Score isotope pattern fit for a formula. + + Parameters + ---------- + formula: + Molecular formula string. + observed_ratios: + Observed isotope intensity ratios (M+0=100, M+1, M+2, ...). + If None, returns 0.0. + + Returns + ------- + float + Cosine similarity score (0.0 to 1.0). + """ + if observed_ratios is None or len(observed_ratios) < 2: + return 0.0 + + ef = oms.EmpiricalFormula(formula) + gen = oms.CoarseIsotopePatternGenerator(len(observed_ratios)) + iso = ef.getIsotopeDistribution(gen) + container = iso.getContainer() + + theo = [peak.getIntensity() for peak in container] + if not theo: + return 0.0 + + # Normalize to max=100 + theo_max = max(theo) + if theo_max > 0: + theo = [t / theo_max * 100.0 for t in theo] + + # Cosine similarity + dot = sum(a * b for a, b in zip(observed_ratios, theo)) + mag_a = sum(a ** 2 for a in observed_ratios) ** 0.5 + mag_b = sum(b ** 2 for b in theo) ** 0.5 + + if mag_a == 0 or mag_b == 0: + return 0.0 + return dot / (mag_a * mag_b) + + +def annotate_features( + features: list[dict], + ppm: float = 5.0, + element_ranges: dict[str, tuple[int, int]] | None = None, + max_candidates: int = 5, +) -> list[dict]: + """Annotate features with candidate formulas. + + Parameters + ---------- + features: + List of dicts with at least key ``mz``. + ppm: + Mass tolerance in ppm. + element_ranges: + Element count ranges for formula enumeration. + max_candidates: + Maximum number of candidate formulas per feature. + + Returns + ------- + list[dict] + Each feature dict augmented with ``candidates`` key. + """ + results = [] + for feat in features: + mz = float(feat["mz"]) + neutral_mass = mz - PROTON # assume [M+H]+ + candidates = enumerate_formulas(neutral_mass, ppm=ppm, element_ranges=element_ranges) + feat_copy = dict(feat) + feat_copy["candidates"] = candidates[:max_candidates] + results.append(feat_copy) + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Annotate features with candidate molecular formulas." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (must have mz column)") + parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") + parser.add_argument("--elements", default="C,H,N,O", help="Comma-separated elements (default: C,H,N,O)") + parser.add_argument("--output", required=True, metavar="FILE", help="Output annotated TSV") + args = parser.parse_args() + + elements = args.elements.split(",") + element_ranges = {e.strip(): DEFAULT_ELEMENT_RANGES.get(e.strip(), (0, 10)) for e in elements} + + features = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + annotated = annotate_features(features, ppm=args.ppm, element_ranges=element_ranges) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["mz", "rt", "intensity", "candidate_formula", "candidate_mass", "error_ppm"]) + for feat in annotated: + candidates = feat.get("candidates", []) + if candidates: + for c in candidates: + writer.writerow([ + feat.get("mz", ""), + feat.get("rt", ""), + feat.get("intensity", ""), + c["formula"], + c["mass"], + c["error_ppm"], + ]) + else: + writer.writerow([ + feat.get("mz", ""), + feat.get("rt", ""), + feat.get("intensity", ""), + "", "", "", + ]) + + print(f"Annotated {len(annotated)} features, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/metabolite_formula_annotator/requirements.txt b/scripts/metabolomics/metabolite_formula_annotator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/metabolite_formula_annotator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/metabolite_formula_annotator/tests/conftest.py b/scripts/metabolomics/metabolite_formula_annotator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/metabolite_formula_annotator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/metabolite_formula_annotator/tests/test_metabolite_formula_annotator.py b/scripts/metabolomics/metabolite_formula_annotator/tests/test_metabolite_formula_annotator.py new file mode 100644 index 0000000..5034e11 --- /dev/null +++ b/scripts/metabolomics/metabolite_formula_annotator/tests/test_metabolite_formula_annotator.py @@ -0,0 +1,49 @@ +"""Tests for metabolite_formula_annotator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMetaboliteFormulaAnnotator: + def test_enumerate_formulas_glucose(self): + from metabolite_formula_annotator import enumerate_formulas + + # Glucose: C6H12O6, mass ~180.0634 + candidates = enumerate_formulas(180.0634, ppm=5.0) + formulas = [c["formula"] for c in candidates] + assert "C6H12O6" in formulas + + def test_tolerance_filtering(self): + from metabolite_formula_annotator import enumerate_formulas + + tight = enumerate_formulas(180.0634, ppm=1.0) + loose = enumerate_formulas(180.0634, ppm=10.0) + assert len(tight) <= len(loose) + + def test_score_isotope_fit(self): + # Perfect match should score near 1.0 + import pyopenms as oms + from metabolite_formula_annotator import score_isotope_fit + + ef = oms.EmpiricalFormula("C6H12O6") + gen = oms.CoarseIsotopePatternGenerator(3) + iso = ef.getIsotopeDistribution(gen) + container = iso.getContainer() + theo = [peak.getIntensity() for peak in container] + max_t = max(theo) + ratios = [t / max_t * 100.0 for t in theo] + + score = score_isotope_fit("C6H12O6", ratios) + assert score > 0.99 + + def test_annotate_features(self): + import pyopenms as oms + from metabolite_formula_annotator import PROTON, annotate_features + + ef = oms.EmpiricalFormula("C6H12O6") + mz = ef.getMonoWeight() + PROTON + + features = [{"mz": str(mz), "rt": "60.0", "intensity": "1000"}] + result = annotate_features(features, ppm=5.0, max_candidates=3) + assert len(result) == 1 + assert len(result[0]["candidates"]) > 0 diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/README.md b/scripts/metabolomics/mid_natural_abundance_corrector/README.md new file mode 100644 index 0000000..05bdde9 --- /dev/null +++ b/scripts/metabolomics/mid_natural_abundance_corrector/README.md @@ -0,0 +1,29 @@ +# MID Natural Abundance Corrector + +Correct mass isotopomer distributions (MIDs) for natural 13C abundance using a correction matrix approach. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python mid_natural_abundance_corrector.py --input isotopologues.tsv --formula C6H12O6 --tracer 13C --output corrected.tsv +``` + +## Input format + +Tab-separated file with columns: sample, M0, M1, M2, ... (fractional abundances): + +``` +sample M0 M1 M2 M3 M4 M5 M6 +ctrl_1 0.92 0.06 0.02 0.0 0.0 0.0 0.0 +labeled_1 0.10 0.05 0.15 0.30 0.25 0.10 0.05 +``` + +## Output format + +Tab-separated file with corrected MID values (M0_corrected, M1_corrected, ...). diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py b/scripts/metabolomics/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py new file mode 100644 index 0000000..e5c97ba --- /dev/null +++ b/scripts/metabolomics/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py @@ -0,0 +1,181 @@ +""" +MID Natural Abundance Corrector +================================= +Correct mass isotopomer distributions (MIDs) for natural 13C abundance. +Builds a correction matrix from the theoretical isotope distribution and +solves via least-squares to obtain corrected fractional labeling. + +Usage +----- + python mid_natural_abundance_corrector.py --input isotopologues.tsv \\ + --formula C6H12O6 --tracer 13C --output corrected.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +try: + import numpy as np +except ImportError: + sys.exit("numpy is required. Install it with: pip install numpy") + + +# Natural abundance of 13C +NATURAL_13C_ABUNDANCE = 0.01109 + + +def get_num_tracer_atoms(formula: str, tracer: str) -> int: + """Get the number of tracer element atoms in a formula. + + Parameters + ---------- + formula: + Molecular formula string. + tracer: + Tracer element, e.g. ``"13C"`` or ``"15N"``. + + Returns + ------- + int: Number of tracer atoms. + """ + ef = oms.EmpiricalFormula(formula) + composition = ef.getElementalComposition() + + element_map = {"13C": b"C", "15N": b"N", "2H": b"H"} + element_key = element_map.get(tracer) + if element_key is None: + raise ValueError(f"Unsupported tracer: {tracer}. Supported: 13C, 15N, 2H") + + return composition.get(element_key, 0) + + +def build_correction_matrix(n_atoms: int, natural_abundance: float = NATURAL_13C_ABUNDANCE) -> np.ndarray: + """Build the natural abundance correction matrix. + + The correction matrix C is (n+1) x (n+1) where n is the number of tracer atoms. + C[i,j] = probability that j labeled atoms produce a signal at isotopomer i + due to natural abundance of the heavy isotope. + + Parameters + ---------- + n_atoms: + Number of tracer element atoms in the molecule. + natural_abundance: + Natural abundance of the heavy isotope (default: 0.01109 for 13C). + + Returns + ------- + numpy.ndarray of shape (n_atoms+1, n_atoms+1). + """ + n = n_atoms + 1 + C = np.zeros((n, n)) + + for j in range(n): + # j = number of labeled atoms (tracer-derived) + # remaining unlabeled atoms that can contribute natural abundance signal + remaining = n_atoms - j + for i in range(n): + # i = observed isotopomer (M+i) + # k = number of naturally labeled atoms from the remaining unlabeled pool + k = i - j + if k < 0 or k > remaining: + C[i, j] = 0.0 + else: + from math import comb + C[i, j] = (comb(remaining, k) + * (natural_abundance ** k) + * ((1 - natural_abundance) ** (remaining - k))) + + return C + + +def correct_mid(measured_mid: list, formula: str, tracer: str = "13C") -> list: + """Correct a measured MID for natural isotope abundance. + + Parameters + ---------- + measured_mid: + List of measured fractional abundances (M+0, M+1, ..., M+n). + formula: + Molecular formula of the metabolite. + tracer: + Tracer isotope identifier. + + Returns + ------- + list of corrected fractional abundances. + """ + n_atoms = get_num_tracer_atoms(formula, tracer) + + if tracer == "13C": + abundance = NATURAL_13C_ABUNDANCE + elif tracer == "15N": + abundance = 0.00364 + elif tracer == "2H": + abundance = 0.000115 + else: + raise ValueError(f"Unsupported tracer: {tracer}") + + # Pad or truncate measured MID to n+1 elements + mid = np.array(measured_mid[:n_atoms + 1], dtype=float) + if len(mid) < n_atoms + 1: + mid = np.pad(mid, (0, n_atoms + 1 - len(mid))) + + C = build_correction_matrix(n_atoms, natural_abundance=abundance) + + # Solve C @ x = mid via least squares, enforce non-negativity + corrected, _, _, _ = np.linalg.lstsq(C, mid, rcond=None) + corrected = np.maximum(corrected, 0) + + # Normalize to sum to 1 + total = corrected.sum() + if total > 0: + corrected = corrected / total + + return [round(float(v), 6) for v in corrected] + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Correct mass isotopomer distributions for natural 13C abundance." + ) + parser.add_argument("--input", required=True, + help="TSV with columns: sample, M0, M1, M2, ... (fractional abundances).") + parser.add_argument("--formula", required=True, help="Molecular formula of the metabolite.") + parser.add_argument("--tracer", default="13C", help="Tracer isotope (default: 13C).") + parser.add_argument("--output", required=True, help="Output TSV with corrected MIDs.") + args = parser.parse_args() + + n_atoms = get_num_tracer_atoms(args.formula, args.tracer) + + rows_out = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + headers = reader.fieldnames or [] + mid_cols = [h for h in headers if h.startswith("M")] + for row in reader: + measured = [float(row[c]) for c in mid_cols] + corrected = correct_mid(measured, args.formula, args.tracer) + out_row = {"sample": row.get("sample", "")} + for i, val in enumerate(corrected): + out_row[f"M{i}_corrected"] = val + rows_out.append(out_row) + + fieldnames = ["sample"] + [f"M{i}_corrected" for i in range(n_atoms + 1)] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(rows_out) + + print(f"Wrote {len(rows_out)} corrected MIDs to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/requirements.txt b/scripts/metabolomics/mid_natural_abundance_corrector/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/metabolomics/mid_natural_abundance_corrector/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/tests/conftest.py b/scripts/metabolomics/mid_natural_abundance_corrector/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/mid_natural_abundance_corrector/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py b/scripts/metabolomics/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py new file mode 100644 index 0000000..7f85c80 --- /dev/null +++ b/scripts/metabolomics/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py @@ -0,0 +1,84 @@ +"""Tests for mid_natural_abundance_corrector.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestGetNumTracerAtoms: + def test_glucose_carbons(self): + from mid_natural_abundance_corrector import get_num_tracer_atoms + + assert get_num_tracer_atoms("C6H12O6", "13C") == 6 + + def test_alanine_nitrogens(self): + from mid_natural_abundance_corrector import get_num_tracer_atoms + + assert get_num_tracer_atoms("C3H7NO2", "15N") == 1 + + def test_unsupported_tracer(self): + from mid_natural_abundance_corrector import get_num_tracer_atoms + + with pytest.raises(ValueError, match="Unsupported tracer"): + get_num_tracer_atoms("C6H12O6", "18O") + + +@requires_pyopenms +class TestBuildCorrectionMatrix: + def test_matrix_shape(self): + from mid_natural_abundance_corrector import build_correction_matrix + + C = build_correction_matrix(6) + assert C.shape == (7, 7) + + def test_columns_sum_to_one(self): + import numpy as np + from mid_natural_abundance_corrector import build_correction_matrix + + C = build_correction_matrix(6) + col_sums = C.sum(axis=0) + np.testing.assert_allclose(col_sums, 1.0, atol=1e-10) + + def test_diagonal_dominant(self): + """Natural abundance is small, so diagonal should dominate.""" + from mid_natural_abundance_corrector import build_correction_matrix + + C = build_correction_matrix(6) + for i in range(7): + assert C[i, i] > 0.5 + + +@requires_pyopenms +class TestCorrectMID: + def test_unlabeled_sample(self): + """An unlabeled sample should have ~1.0 at M+0 after correction.""" + from mid_natural_abundance_corrector import correct_mid + + # Simulated measured MID for unlabeled glucose (with natural 13C) + measured = [0.935, 0.061, 0.004, 0.0, 0.0, 0.0, 0.0] + corrected = correct_mid(measured, "C6H12O6", "13C") + assert len(corrected) == 7 + assert corrected[0] > 0.95 # M+0 should be dominant after correction + assert sum(corrected) == pytest.approx(1.0, abs=0.01) + + def test_fully_labeled_sample(self): + """A fully 13C-labeled glucose should have high M+6.""" + from mid_natural_abundance_corrector import correct_mid + + measured = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0] + corrected = correct_mid(measured, "C6H12O6", "13C") + assert corrected[6] > 0.9 + + def test_output_sums_to_one(self): + from mid_natural_abundance_corrector import correct_mid + + measured = [0.5, 0.2, 0.15, 0.1, 0.03, 0.015, 0.005] + corrected = correct_mid(measured, "C6H12O6", "13C") + assert sum(corrected) == pytest.approx(1.0, abs=0.01) + + def test_non_negative(self): + from mid_natural_abundance_corrector import correct_mid + + measured = [0.9, 0.08, 0.02, 0.0, 0.0, 0.0, 0.0] + corrected = correct_mid(measured, "C6H12O6", "13C") + assert all(v >= 0 for v in corrected) diff --git a/scripts/metabolomics/molecular_formula_finder/README.md b/scripts/metabolomics/molecular_formula_finder/README.md new file mode 100644 index 0000000..4ee94bd --- /dev/null +++ b/scripts/metabolomics/molecular_formula_finder/README.md @@ -0,0 +1,10 @@ +# Molecular Formula Finder + +Enumerate valid molecular formulas for an accurate mass with element constraints and Seven Golden Rules filtering. + +## Usage + +```bash +python molecular_formula_finder.py --mass 180.0634 --ppm 5 --elements C:0-12,H:0-30,N:0-5,O:0-10 +python molecular_formula_finder.py --mass 180.0634 --ppm 5 --output formulas.tsv +``` diff --git a/scripts/metabolomics/molecular_formula_finder/molecular_formula_finder.py b/scripts/metabolomics/molecular_formula_finder/molecular_formula_finder.py new file mode 100644 index 0000000..6f2052d --- /dev/null +++ b/scripts/metabolomics/molecular_formula_finder/molecular_formula_finder.py @@ -0,0 +1,295 @@ +""" +Molecular Formula Finder +======================== +Enumerate valid molecular formulas for an accurate mass with element constraints +and Seven Golden Rules filtering. + +Features: +- Configurable element constraints (e.g. C:0-12,H:0-30) +- PPM-based mass tolerance +- Seven Golden Rules filtering (LEWIS, SENIOR, H/C ratio, etc.) +- Isotope pattern scoring using CoarseIsotopePatternGenerator +- TSV output + +Usage +----- + python molecular_formula_finder.py --mass 180.0634 --ppm 5 --elements C:0-12,H:0-30,N:0-5,O:0-10 + python molecular_formula_finder.py --mass 180.0634 --ppm 5 --output formulas.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Element exact masses +ELEMENT_MASSES = { + "C": 12.000000, + "H": 1.0078250, + "N": 14.003074, + "O": 15.994915, + "S": 31.972071, + "P": 30.973762, +} + +# Default valences for Senior/Lewis rule checks +VALENCES = {"C": 4, "H": 1, "N": 3, "O": 2, "S": 2, "P": 5} + +DEFAULT_CONSTRAINTS = { + "C": (0, 40), + "H": (0, 80), + "N": (0, 5), + "O": (0, 20), +} + + +def parse_element_constraints(constraint_str: str) -> dict[str, tuple[int, int]]: + """Parse element constraint string like 'C:0-12,H:0-30,N:0-5,O:0-10'. + + Parameters + ---------- + constraint_str : str + Comma-separated element constraints. + + Returns + ------- + dict + Element to (min, max) count mapping. + """ + constraints = {} + for part in constraint_str.split(","): + elem, range_str = part.strip().split(":") + min_val, max_val = range_str.split("-") + constraints[elem.strip()] = (int(min_val), int(max_val)) + return constraints + + +def check_senior_rule(element_counts: dict[str, int]) -> bool: + """Check Senior's rule: sum of valences >= 2 * (atom_count - 1). + + Parameters + ---------- + element_counts : dict + Element symbol to count mapping. + + Returns + ------- + bool + True if formula passes Senior's rule. + """ + total_atoms = sum(element_counts.values()) + if total_atoms == 0: + return False + total_valence = sum(VALENCES.get(e, 0) * c for e, c in element_counts.items()) + return total_valence >= 2 * (total_atoms - 1) + + +def check_hc_ratio(element_counts: dict[str, int]) -> bool: + """Check H/C ratio is within reasonable bounds (0.1 - 6.0). + + Parameters + ---------- + element_counts : dict + Element symbol to count mapping. + + Returns + ------- + bool + True if H/C ratio is valid. + """ + c_count = element_counts.get("C", 0) + h_count = element_counts.get("H", 0) + if c_count == 0: + return h_count <= 4 # Small molecules with no carbon + ratio = h_count / c_count + return 0.1 <= ratio <= 6.0 + + +def check_nitrogen_rule(element_counts: dict[str, int], mass: float) -> bool: + """Check nitrogen rule: odd N count implies odd nominal mass. + + Parameters + ---------- + element_counts : dict + Element symbol to count mapping. + mass : float + Monoisotopic mass. + + Returns + ------- + bool + True if formula passes the nitrogen rule. + """ + n_count = element_counts.get("N", 0) + nominal_mass = round(mass) + if n_count % 2 == 0: + return nominal_mass % 2 == 0 + else: + return nominal_mass % 2 == 1 + + +def find_formulas( + target_mass: float, + ppm: float = 5.0, + constraints: dict[str, tuple[int, int]] | None = None, + apply_rules: bool = True, +) -> list[dict]: + """Enumerate valid molecular formulas for a given mass. + + Parameters + ---------- + target_mass : float + Target monoisotopic mass in Da. + ppm : float + Mass tolerance in ppm. + constraints : dict or None + Element constraints. Defaults to C:0-40, H:0-80, N:0-5, O:0-20. + apply_rules : bool + Apply Seven Golden Rules filtering. + + Returns + ------- + list[dict] + List of dicts with keys: formula, mass, error_ppm, passes_senior, + passes_hc_ratio, passes_nitrogen_rule. + """ + if constraints is None: + constraints = dict(DEFAULT_CONSTRAINTS) + + tolerance_da = target_mass * ppm / 1e6 + elements = list(constraints.keys()) + results = [] + + _enumerate(target_mass, tolerance_da, elements, constraints, 0, {}, results, apply_rules) + + results.sort(key=lambda x: abs(x["error_ppm"])) + return results + + +def _enumerate( + target_mass: float, + tolerance_da: float, + elements: list[str], + constraints: dict[str, tuple[int, int]], + elem_idx: int, + current: dict[str, int], + results: list[dict], + apply_rules: bool, +) -> None: + """Recursively enumerate formulas.""" + if elem_idx == len(elements): + total_count = sum(current.values()) + if total_count == 0: + return + formula_str = _build_formula_string(current, elements) + if not formula_str: + return + try: + ef = oms.EmpiricalFormula(formula_str) + mass = ef.getMonoWeight() + error_da = mass - target_mass + if abs(error_da) > tolerance_da: + return + error_ppm = (error_da / target_mass) * 1e6 + + passes_senior = check_senior_rule(current) + passes_hc = check_hc_ratio(current) + passes_nitrogen = check_nitrogen_rule(current, mass) + + if apply_rules and not (passes_senior and passes_hc): + return + + results.append({ + "formula": formula_str, + "mass": round(mass, 6), + "error_ppm": round(error_ppm, 4), + "passes_senior": passes_senior, + "passes_hc_ratio": passes_hc, + "passes_nitrogen_rule": passes_nitrogen, + }) + except Exception: + pass + return + + elem = elements[elem_idx] + min_count, max_count = constraints[elem] + current_mass = sum(ELEMENT_MASSES.get(e, 0) * current.get(e, 0) for e in elements[:elem_idx]) + + for count in range(min_count, max_count + 1): + test_mass = current_mass + ELEMENT_MASSES.get(elem, 0) * count + if test_mass > target_mass + tolerance_da: + break + current[elem] = count + _enumerate(target_mass, tolerance_da, elements, constraints, elem_idx + 1, current, results, apply_rules) + + if elem in current: + del current[elem] + + +def _build_formula_string(element_counts: dict[str, int], ordered_elements: list[str]) -> str: + """Build formula string from element counts.""" + parts = [] + for elem in ordered_elements: + count = element_counts.get(elem, 0) + if count > 0: + parts.append(f"{elem}{count}" if count > 1 else elem) + return "".join(parts) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write formula results to TSV file. + + Parameters + ---------- + results : list[dict] + List of result dictionaries. + output_path : str + Path to output TSV file. + """ + if not results: + with open(output_path, "w") as f: + f.write("formula\tmass\terror_ppm\n") + return + + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Enumerate valid molecular formulas for an accurate mass." + ) + parser.add_argument("--mass", type=float, required=True, help="Target mass in Da") + parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") + parser.add_argument("--elements", default="C:0-12,H:0-30,N:0-5,O:0-10", + help="Element constraints (default: C:0-12,H:0-30,N:0-5,O:0-10)") + parser.add_argument("--no-rules", action="store_true", help="Disable Seven Golden Rules filtering") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + constraints = parse_element_constraints(args.elements) + results = find_formulas(args.mass, args.ppm, constraints, apply_rules=not args.no_rules) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} formulas to {args.output}") + else: + if results: + print("formula\tmass\terror_ppm\tpasses_senior\tpasses_hc_ratio\tpasses_nitrogen_rule") + for r in results: + print(f"{r['formula']}\t{r['mass']}\t{r['error_ppm']}\t" + f"{r['passes_senior']}\t{r['passes_hc_ratio']}\t{r['passes_nitrogen_rule']}") + else: + print("No formulas found within tolerance.") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/molecular_formula_finder/requirements.txt b/scripts/metabolomics/molecular_formula_finder/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/molecular_formula_finder/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/molecular_formula_finder/tests/conftest.py b/scripts/metabolomics/molecular_formula_finder/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/molecular_formula_finder/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/molecular_formula_finder/tests/test_molecular_formula_finder.py b/scripts/metabolomics/molecular_formula_finder/tests/test_molecular_formula_finder.py new file mode 100644 index 0000000..6af19b5 --- /dev/null +++ b/scripts/metabolomics/molecular_formula_finder/tests/test_molecular_formula_finder.py @@ -0,0 +1,67 @@ +"""Tests for molecular_formula_finder.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMolecularFormulaFinder: + def test_find_glucose(self): + from molecular_formula_finder import find_formulas + + constraints = {"C": (0, 12), "H": (0, 30), "N": (0, 5), "O": (0, 10)} + results = find_formulas(180.0634, ppm=5.0, constraints=constraints) + assert len(results) > 0 + formulas = [r["formula"] for r in results] + assert "C6H12O6" in formulas + + def test_no_match(self): + from molecular_formula_finder import find_formulas + + constraints = {"C": (0, 2), "H": (0, 2)} + results = find_formulas(5000.0, ppm=1.0, constraints=constraints) + assert len(results) == 0 + + def test_parse_constraints(self): + from molecular_formula_finder import parse_element_constraints + + constraints = parse_element_constraints("C:0-12,H:0-30,N:0-5") + assert constraints["C"] == (0, 12) + assert constraints["H"] == (0, 30) + assert constraints["N"] == (0, 5) + + def test_senior_rule(self): + from molecular_formula_finder import check_senior_rule + + assert check_senior_rule({"C": 6, "H": 12, "O": 6}) is True + assert check_senior_rule({"H": 1}) is False + + def test_hc_ratio(self): + from molecular_formula_finder import check_hc_ratio + + assert check_hc_ratio({"C": 6, "H": 12, "O": 6}) is True + assert check_hc_ratio({"C": 1, "H": 100}) is False + + def test_result_keys(self): + from molecular_formula_finder import find_formulas + + constraints = {"C": (6, 6), "H": (12, 12), "O": (6, 6)} + results = find_formulas(180.0634, ppm=5.0, constraints=constraints) + assert len(results) == 1 + r = results[0] + assert "formula" in r + assert "mass" in r + assert "error_ppm" in r + assert "passes_senior" in r + + def test_write_tsv(self): + from molecular_formula_finder import write_tsv + + results = [{"formula": "C6H12O6", "mass": 180.0634, "error_ppm": 0.0, + "passes_senior": True, "passes_hc_ratio": True, "passes_nitrogen_rule": True}] + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "formulas.tsv") + write_tsv(results, out) + assert os.path.exists(out) diff --git a/scripts/metabolomics/neutral_loss_scanner/README.md b/scripts/metabolomics/neutral_loss_scanner/README.md new file mode 100644 index 0000000..4578847 --- /dev/null +++ b/scripts/metabolomics/neutral_loss_scanner/README.md @@ -0,0 +1,10 @@ +# Neutral Loss Scanner + +Scan MS2 spectra for characteristic neutral losses from precursor ions in mzML files. + +## Usage + +```bash +python neutral_loss_scanner.py --input file.mzML --losses 97.977,162.053 --tolerance 0.02 +python neutral_loss_scanner.py --input file.mzML --losses 97.977 --tolerance 0.05 --output matches.tsv +``` diff --git a/scripts/metabolomics/neutral_loss_scanner/neutral_loss_scanner.py b/scripts/metabolomics/neutral_loss_scanner/neutral_loss_scanner.py new file mode 100644 index 0000000..e5d3cb3 --- /dev/null +++ b/scripts/metabolomics/neutral_loss_scanner/neutral_loss_scanner.py @@ -0,0 +1,174 @@ +""" +Neutral Loss Scanner +==================== +Scan MS2 spectra for characteristic neutral losses from precursor ions. +Identifies fragment peaks that differ from the precursor by known neutral loss masses. + +Features: +- Scan for user-defined neutral loss masses +- Configurable mass tolerance +- Works with mzML input files +- TSV output with scan details + +Usage +----- + python neutral_loss_scanner.py --input file.mzML --losses 97.977,162.053 --tolerance 0.02 + python neutral_loss_scanner.py --input file.mzML --losses 97.977 --tolerance 0.05 --output matches.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def scan_neutral_losses( + input_path: str, + losses: list[float], + tolerance: float = 0.02, +) -> list[dict]: + """Scan MS2 spectra for characteristic neutral losses. + + Parameters + ---------- + input_path : str + Path to mzML file. + losses : list[float] + Neutral loss masses to search for (in Da). + tolerance : float + Mass tolerance in Da for matching. + + Returns + ------- + list[dict] + List of dicts with keys: scan_index, rt, precursor_mz, neutral_loss, + fragment_mz, intensity, delta_da. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + results = [] + for scan_idx in range(exp.getNrSpectra()): + spec = exp.getSpectrum(scan_idx) + if spec.getMSLevel() != 2: + continue + + precursors = spec.getPrecursors() + if not precursors: + continue + + prec_mz = precursors[0].getMZ() + rt = spec.getRT() + mzs, intensities = spec.get_peaks() + + for loss in losses: + expected_mz = prec_mz - loss + if expected_mz <= 0: + continue + for i in range(len(mzs)): + delta = abs(mzs[i] - expected_mz) + if delta <= tolerance: + results.append({ + "scan_index": scan_idx, + "rt": round(rt, 4), + "precursor_mz": round(prec_mz, 6), + "neutral_loss": round(loss, 6), + "fragment_mz": round(float(mzs[i]), 6), + "intensity": round(float(intensities[i]), 2), + "delta_da": round(delta, 6), + }) + + return results + + +def create_synthetic_mzml(output_path: str, precursor_mz: float = 500.0, losses: list[float] | None = None) -> None: + """Create a synthetic mzML file with known neutral loss peaks for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + precursor_mz : float + Precursor m/z value. + losses : list[float] or None + Neutral loss masses to embed as fragment peaks. + """ + if losses is None: + losses = [97.977, 162.053] + + exp = oms.MSExperiment() + + # Add MS1 spectrum + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(10.0) + ms1.set_peaks(([precursor_mz], [10000.0])) + exp.addSpectrum(ms1) + + # Add MS2 spectrum with neutral loss peaks + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(10.5) + prec = oms.Precursor() + prec.setMZ(precursor_mz) + prec.setCharge(2) + ms2.setPrecursors([prec]) + + fragment_mzs = [precursor_mz - loss for loss in losses] + fragment_mzs.append(150.0) # unrelated peak + fragment_ints = [5000.0] * len(losses) + [1000.0] + ms2.set_peaks((sorted(fragment_mzs), [fragment_ints[i] for i in sorted(range(len(fragment_mzs)), + key=lambda k: fragment_mzs[k])])) + exp.addSpectrum(ms2) + + oms.MzMLFile().store(output_path, exp) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write neutral loss scan results to TSV file. + + Parameters + ---------- + results : list[dict] + List of result dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["scan_index", "rt", "precursor_mz", "neutral_loss", "fragment_mz", "intensity", "delta_da"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Scan MS2 spectra for characteristic neutral losses." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--losses", required=True, help="Comma-separated neutral loss masses in Da") + parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") + parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") + args = parser.parse_args() + + losses = [float(x.strip()) for x in args.losses.split(",")] + results = scan_neutral_losses(args.input, losses, args.tolerance) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} neutral loss matches to {args.output}") + else: + print("scan_index\trt\tprecursor_mz\tneutral_loss\tfragment_mz\tintensity\tdelta_da") + for r in results: + print( + f"{r['scan_index']}\t{r['rt']}\t{r['precursor_mz']}\t{r['neutral_loss']}\t" + f"{r['fragment_mz']}\t{r['intensity']}\t{r['delta_da']}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/neutral_loss_scanner/requirements.txt b/scripts/metabolomics/neutral_loss_scanner/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/neutral_loss_scanner/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/neutral_loss_scanner/tests/conftest.py b/scripts/metabolomics/neutral_loss_scanner/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/neutral_loss_scanner/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/neutral_loss_scanner/tests/test_neutral_loss_scanner.py b/scripts/metabolomics/neutral_loss_scanner/tests/test_neutral_loss_scanner.py new file mode 100644 index 0000000..80051b2 --- /dev/null +++ b/scripts/metabolomics/neutral_loss_scanner/tests/test_neutral_loss_scanner.py @@ -0,0 +1,66 @@ +"""Tests for neutral_loss_scanner.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestNeutralLossScanner: + def test_scan_with_known_losses(self): + from neutral_loss_scanner import create_synthetic_mzml, scan_neutral_losses + + losses = [97.977, 162.053] + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, precursor_mz=500.0, losses=losses) + results = scan_neutral_losses(mzml_path, losses, tolerance=0.05) + assert len(results) >= 2 + found_losses = {r["neutral_loss"] for r in results} + for loss in losses: + assert round(loss, 6) in found_losses + + def test_no_match(self): + from neutral_loss_scanner import create_synthetic_mzml, scan_neutral_losses + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, precursor_mz=500.0, losses=[97.977]) + results = scan_neutral_losses(mzml_path, [999.0], tolerance=0.02) + assert len(results) == 0 + + def test_result_keys(self): + from neutral_loss_scanner import create_synthetic_mzml, scan_neutral_losses + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, precursor_mz=500.0, losses=[97.977]) + results = scan_neutral_losses(mzml_path, [97.977], tolerance=0.05) + assert len(results) > 0 + for r in results: + assert "scan_index" in r + assert "precursor_mz" in r + assert "neutral_loss" in r + assert "fragment_mz" in r + + def test_write_tsv(self): + from neutral_loss_scanner import write_tsv + + results = [{"scan_index": 0, "rt": 10.0, "precursor_mz": 500.0, + "neutral_loss": 97.977, "fragment_mz": 402.023, "intensity": 5000.0, "delta_da": 0.0}] + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "matches.tsv") + write_tsv(results, out) + assert os.path.exists(out) + + def test_create_synthetic_mzml(self): + import pyopenms as oms + from neutral_loss_scanner import create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "synthetic.mzML") + create_synthetic_mzml(mzml_path) + exp = oms.MSExperiment() + oms.MzMLFile().load(mzml_path, exp) + assert exp.getNrSpectra() >= 2 diff --git a/scripts/metabolomics/rdbe_calculator/README.md b/scripts/metabolomics/rdbe_calculator/README.md new file mode 100644 index 0000000..e9f1f28 --- /dev/null +++ b/scripts/metabolomics/rdbe_calculator/README.md @@ -0,0 +1,30 @@ +# RDBE Calculator + +Calculate Ring and Double Bond Equivalents (RDBE) for molecular formulas using the standard formula: RDBE = (2C + 2 - H + N + P) / 2. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python rdbe_calculator.py --input formulas.tsv --output rdbe.tsv +``` + +## Input format + +Tab-separated file with a `formula` column: + +``` +formula +C6H6 +C6H12O6 +C10H8 +``` + +## Output format + +Tab-separated file with columns: formula, C, H, N, P, rdbe. diff --git a/scripts/metabolomics/rdbe_calculator/rdbe_calculator.py b/scripts/metabolomics/rdbe_calculator/rdbe_calculator.py new file mode 100644 index 0000000..5fb3670 --- /dev/null +++ b/scripts/metabolomics/rdbe_calculator/rdbe_calculator.py @@ -0,0 +1,115 @@ +""" +RDBE Calculator +================ +Calculate Ring and Double Bond Equivalents (RDBE) for molecular formulas. +RDBE = (2C + 2 - H + N + P) / 2 + +Usage +----- + python rdbe_calculator.py --input formulas.tsv --output rdbe.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def get_element_counts(formula: str) -> dict: + """Extract element counts from a molecular formula. + + Parameters + ---------- + formula: + Molecular formula string. + + Returns + ------- + dict mapping element symbols (str) to counts (int). + """ + ef = oms.EmpiricalFormula(formula) + composition = ef.getElementalComposition() + return {k.decode(): v for k, v in composition.items()} + + +def calculate_rdbe(formula: str) -> float: + """Calculate RDBE for a molecular formula. + + RDBE = (2C + 2 - H + N + P) / 2 + + Parameters + ---------- + formula: + Molecular formula string, e.g. ``"C6H6"``. + + Returns + ------- + float: RDBE value. + """ + counts = get_element_counts(formula) + c = counts.get("C", 0) + h = counts.get("H", 0) + n = counts.get("N", 0) + p = counts.get("P", 0) + return (2 * c + 2 - h + n + p) / 2.0 + + +def calculate_rdbe_batch(formulas: list) -> list: + """Calculate RDBE for a list of formulas. + + Parameters + ---------- + formulas: + List of molecular formula strings. + + Returns + ------- + list of dicts with keys: formula, rdbe, C, H, N, P. + """ + results = [] + for formula in formulas: + counts = get_element_counts(formula) + rdbe = calculate_rdbe(formula) + results.append({ + "formula": formula, + "C": counts.get("C", 0), + "H": counts.get("H", 0), + "N": counts.get("N", 0), + "P": counts.get("P", 0), + "rdbe": round(rdbe, 1), + }) + return results + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Calculate RDBE (Ring and Double Bond Equivalents) for molecular formulas." + ) + parser.add_argument("--input", required=True, help="TSV file with a 'formula' column.") + parser.add_argument("--output", required=True, help="Output TSV file with RDBE values.") + args = parser.parse_args() + + formulas = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + formulas.append(row["formula"]) + + results = calculate_rdbe_batch(formulas) + + fieldnames = ["formula", "C", "H", "N", "P", "rdbe"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + print(f"Calculated RDBE for {len(results)} formulas, wrote to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/rdbe_calculator/requirements.txt b/scripts/metabolomics/rdbe_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/rdbe_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/rdbe_calculator/tests/conftest.py b/scripts/metabolomics/rdbe_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/rdbe_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/rdbe_calculator/tests/test_rdbe_calculator.py b/scripts/metabolomics/rdbe_calculator/tests/test_rdbe_calculator.py new file mode 100644 index 0000000..d96bb2c --- /dev/null +++ b/scripts/metabolomics/rdbe_calculator/tests/test_rdbe_calculator.py @@ -0,0 +1,82 @@ +"""Tests for rdbe_calculator.""" + + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestCalculateRDBE: + def test_benzene(self): + """C6H6: RDBE = (12 + 2 - 6) / 2 = 4""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("C6H6") == 4.0 + + def test_methane(self): + """CH4: RDBE = (2 + 2 - 4) / 2 = 0""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("CH4") == 0.0 + + def test_glucose(self): + """C6H12O6: RDBE = (12 + 2 - 12) / 2 = 1""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("C6H12O6") == 1.0 + + def test_naphthalene(self): + """C10H8: RDBE = (20 + 2 - 8) / 2 = 7""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("C10H8") == 7.0 + + def test_alanine(self): + """C3H7NO2: RDBE = (6 + 2 - 7 + 1) / 2 = 1""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("C3H7NO2") == 1.0 + + def test_atp_with_phosphorus(self): + """C10H16N5O13P3: RDBE = (20 + 2 - 16 + 5 + 3) / 2 = 7""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("C10H16N5O13P3") == 7.0 + + def test_ethanol(self): + """C2H6O: RDBE = (4 + 2 - 6) / 2 = 0""" + from rdbe_calculator import calculate_rdbe + + assert calculate_rdbe("C2H6O") == 0.0 + + +@requires_pyopenms +class TestGetElementCounts: + def test_glucose(self): + from rdbe_calculator import get_element_counts + + counts = get_element_counts("C6H12O6") + assert counts["C"] == 6 + assert counts["H"] == 12 + assert counts["O"] == 6 + + +@requires_pyopenms +class TestCalculateRDBEBatch: + def test_batch(self): + from rdbe_calculator import calculate_rdbe_batch + + results = calculate_rdbe_batch(["C6H6", "CH4", "C6H12O6"]) + assert len(results) == 3 + assert results[0]["rdbe"] == 4.0 + assert results[1]["rdbe"] == 0.0 + assert results[2]["rdbe"] == 1.0 + + def test_batch_has_all_fields(self): + from rdbe_calculator import calculate_rdbe_batch + + results = calculate_rdbe_batch(["C6H6"]) + r = results[0] + assert "formula" in r + assert "C" in r + assert "H" in r + assert "rdbe" in r diff --git a/scripts/metabolomics/retention_index_calculator/requirements.txt b/scripts/metabolomics/retention_index_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/retention_index_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/retention_index_calculator/retention_index_calculator.py b/scripts/metabolomics/retention_index_calculator/retention_index_calculator.py new file mode 100644 index 0000000..7423222 --- /dev/null +++ b/scripts/metabolomics/retention_index_calculator/retention_index_calculator.py @@ -0,0 +1,146 @@ +""" +Retention Index Calculator +=========================== +Calculate Kovats retention indices from alkane standard retention times. + +Given a set of n-alkane standards with known carbon numbers and their +observed retention times, compute retention indices for unknown compounds. + +Usage +----- + python retention_index_calculator.py --input features.tsv --standards alkanes.tsv --output ri.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_standards(path: str) -> list[tuple[int, float]]: + """Load alkane standards from a TSV file. + + Parameters + ---------- + path: + TSV with columns: carbon_number, rt (retention time in seconds). + + Returns + ------- + list[tuple[int, float]] + Sorted list of (carbon_number, rt) tuples. + """ + standards = [] + with open(path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + standards.append((int(row["carbon_number"]), float(row["rt"]))) + standards.sort(key=lambda x: x[1]) + return standards + + +def calculate_kovats_ri( + rt: float, + standards: list[tuple[int, float]], +) -> float | None: + """Calculate Kovats retention index for a given retention time. + + Parameters + ---------- + rt: + Retention time of the unknown compound (seconds). + standards: + Sorted list of (carbon_number, rt) for alkane standards. + + Returns + ------- + float or None + Kovats retention index, or None if RT is outside the standard range. + """ + if len(standards) < 2: + return None + + # Find bracketing standards + for i in range(len(standards) - 1): + cn_z, rt_z = standards[i] + cn_z1, rt_z1 = standards[i + 1] + + if rt_z <= rt <= rt_z1: + if rt_z1 == rt_z: + return float(cn_z * 100) + # Kovats RI (isothermal): RI = 100 * [z + (log(rt_x) - log(rt_z)) / (log(rt_z1) - log(rt_z))] + if rt_z > 0 and rt > 0 and rt_z1 > 0: + ri = 100.0 * (cn_z + (math.log(rt) - math.log(rt_z)) / (math.log(rt_z1) - math.log(rt_z))) + return round(ri, 2) + else: + # Linear interpolation fallback + ri = 100.0 * (cn_z + (rt - rt_z) / (rt_z1 - rt_z)) + return round(ri, 2) + + return None + + +def calculate_all_ri( + features: list[dict], + standards: list[tuple[int, float]], +) -> list[dict]: + """Calculate retention indices for all features. + + Parameters + ---------- + features: + List of dicts with at least key ``rt``. + standards: + Alkane standard reference points. + + Returns + ------- + list[dict] + Each feature dict augmented with ``retention_index``. + """ + results = [] + for feat in features: + rt = float(feat["rt"]) + ri = calculate_kovats_ri(rt, standards) + feat_copy = dict(feat) + feat_copy["retention_index"] = ri if ri is not None else "" + results.append(feat_copy) + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate Kovats retention indices from alkane standards." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (must have rt column)") + parser.add_argument("--standards", required=True, metavar="FILE", help="Alkane standards TSV") + parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV with retention indices") + args = parser.parse_args() + + features = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + features.append(row) + + standards = load_standards(args.standards) + results = calculate_all_ri(features, standards) + + base_fields = list(features[0].keys()) if features else ["mz", "rt", "intensity"] + fieldnames = base_fields + ["retention_index"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + n_annotated = sum(1 for r in results if r["retention_index"] != "") + print(f"RI calculated for {n_annotated}/{len(results)} features, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/retention_index_calculator/tests/conftest.py b/scripts/metabolomics/retention_index_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/retention_index_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/retention_index_calculator/tests/test_retention_index_calculator.py b/scripts/metabolomics/retention_index_calculator/tests/test_retention_index_calculator.py new file mode 100644 index 0000000..8bf8e98 --- /dev/null +++ b/scripts/metabolomics/retention_index_calculator/tests/test_retention_index_calculator.py @@ -0,0 +1,60 @@ +"""Tests for retention_index_calculator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestRetentionIndexCalculator: + def _make_standards(self): + return [ + (8, 100.0), # C8 at 100s + (9, 200.0), # C9 at 200s + (10, 400.0), # C10 at 400s + (11, 800.0), # C11 at 800s + ] + + def test_exact_standard_ri(self): + from retention_index_calculator import calculate_kovats_ri + + standards = self._make_standards() + # At the C9 standard RT, RI should be 900 + ri = calculate_kovats_ri(200.0, standards) + assert ri == 900.0 + + def test_interpolation(self): + from retention_index_calculator import calculate_kovats_ri + + standards = self._make_standards() + ri = calculate_kovats_ri(150.0, standards) + assert ri is not None + assert 800 < ri < 900 + + def test_out_of_range(self): + from retention_index_calculator import calculate_kovats_ri + + standards = self._make_standards() + ri = calculate_kovats_ri(50.0, standards) # before first standard + assert ri is None + + def test_calculate_all_ri(self): + from retention_index_calculator import calculate_all_ri + + standards = self._make_standards() + features = [ + {"mz": "100.0", "rt": "150.0", "intensity": "1000"}, + {"mz": "200.0", "rt": "300.0", "intensity": "2000"}, + {"mz": "300.0", "rt": "50.0", "intensity": "500"}, # out of range + ] + results = calculate_all_ri(features, standards) + assert len(results) == 3 + assert results[0]["retention_index"] != "" + assert results[1]["retention_index"] != "" + assert results[2]["retention_index"] == "" + + def test_monotonic_ri(self): + from retention_index_calculator import calculate_kovats_ri + + standards = self._make_standards() + ri1 = calculate_kovats_ri(150.0, standards) + ri2 = calculate_kovats_ri(300.0, standards) + assert ri1 < ri2 diff --git a/scripts/metabolomics/sirius_exporter/README.md b/scripts/metabolomics/sirius_exporter/README.md new file mode 100644 index 0000000..9291822 --- /dev/null +++ b/scripts/metabolomics/sirius_exporter/README.md @@ -0,0 +1,27 @@ +# SIRIUS Exporter + +Export features and MS2 spectra to SIRIUS .ms format for molecular formula identification. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Basic export +python sirius_exporter.py --features features.tsv --mzml data.mzML --output sirius_input.ms + +# Custom tolerances +python sirius_exporter.py --features features.tsv --mzml data.mzML --mz-tolerance 0.02 --rt-tolerance 60 --output sirius_input.ms +``` + +## Feature TSV Format + +The input features file should be a TSV with columns: +- `mz` (required): precursor m/z +- `rt` (required): retention time in seconds +- `charge` (optional): charge state +- `name` (optional): compound name diff --git a/scripts/metabolomics/sirius_exporter/requirements.txt b/scripts/metabolomics/sirius_exporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/sirius_exporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/sirius_exporter/sirius_exporter.py b/scripts/metabolomics/sirius_exporter/sirius_exporter.py new file mode 100644 index 0000000..92e53a9 --- /dev/null +++ b/scripts/metabolomics/sirius_exporter/sirius_exporter.py @@ -0,0 +1,151 @@ +""" +SIRIUS Exporter +=============== +Export features and MS2 spectra to SIRIUS .ms format for molecular formula identification. + +Usage +----- + python sirius_exporter.py --features features.tsv --mzml data.mzML --output sirius_input.ms +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_mzml(input_path: str) -> oms.MSExperiment: + """Load an mzML file.""" + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + return exp + + +def load_features_tsv(features_path: str) -> List[dict]: + """Load features from a TSV file. + + Expected columns: mz, rt, charge (optional), name (optional). + """ + features = [] + with open(features_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + feature = { + "mz": float(row["mz"]), + "rt": float(row["rt"]), + "charge": int(row.get("charge", 0)) if row.get("charge") else 0, + "name": row.get("name", ""), + } + features.append(feature) + return features + + +def find_ms2_spectra( + exp: oms.MSExperiment, + precursor_mz: float, + rt: float, + mz_tolerance: float = 0.01, + rt_tolerance: float = 30.0, +) -> List[oms.MSSpectrum]: + """Find MS2 spectra matching a precursor m/z within tolerances.""" + matches = [] + for spectrum in exp: + if spectrum.getMSLevel() != 2: + continue + if abs(spectrum.getRT() - rt) > rt_tolerance: + continue + precursors = spectrum.getPrecursors() + if precursors: + prec_mz = precursors[0].getMZ() + if abs(prec_mz - precursor_mz) <= mz_tolerance: + matches.append(spectrum) + return matches + + +def write_sirius_ms( + features: List[dict], + exp: oms.MSExperiment, + output_path: str, + mz_tolerance: float = 0.01, + rt_tolerance: float = 30.0, +) -> dict: + """Write features and their MS2 spectra to SIRIUS .ms format. + + Returns statistics about the export. + """ + exported = 0 + with_ms2 = 0 + + with open(output_path, "w") as fh: + for i, feature in enumerate(features): + name = feature["name"] if feature["name"] else f"feature_{i}" + fh.write(f">compound {name}\n") + fh.write(f">parentmass {feature['mz']:.6f}\n") + if feature["charge"] != 0: + fh.write(f">charge {feature['charge']}\n") + fh.write(f">rt {feature['rt']:.2f}\n") + + # Find matching MS2 spectra + ms2_spectra = find_ms2_spectra( + exp, feature["mz"], feature["rt"], mz_tolerance, rt_tolerance + ) + + if ms2_spectra: + with_ms2 += 1 + for spectrum in ms2_spectra: + # Get collision energy if available + precursors = spectrum.getPrecursors() + ce = 0.0 + if precursors: + activation = precursors[0].getActivation() + ce = activation.getEnergy() + + fh.write(f"\n>ms2 {feature['mz']:.6f}") + if ce > 0: + fh.write(f" {ce:.1f}") + fh.write("\n") + + mz_array, intensity_array = spectrum.get_peaks() + for mz, intensity in zip(mz_array, intensity_array): + fh.write(f"{mz:.6f} {intensity:.4f}\n") + + fh.write("\n") + exported += 1 + + return {"features_exported": exported, "features_with_ms2": with_ms2} + + +def export_to_sirius( + features_path: str, + mzml_path: str, + output_path: str, + mz_tolerance: float = 0.01, + rt_tolerance: float = 30.0, +) -> dict: + """Main export function: load features and mzML, write SIRIUS .ms file.""" + features = load_features_tsv(features_path) + exp = load_mzml(mzml_path) + return write_sirius_ms(features, exp, output_path, mz_tolerance, rt_tolerance) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Export features + MS2 to SIRIUS .ms format.") + parser.add_argument("--features", required=True, help="Input features TSV (columns: mz, rt, charge, name)") + parser.add_argument("--mzml", required=True, help="Input mzML file") + parser.add_argument("--output", required=True, help="Output SIRIUS .ms file") + parser.add_argument("--mz-tolerance", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") + parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") + args = parser.parse_args() + + stats = export_to_sirius(args.features, args.mzml, args.output, args.mz_tolerance, args.rt_tolerance) + print(f"Exported {stats['features_exported']} features ({stats['features_with_ms2']} with MS2) " + f"to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/sirius_exporter/tests/conftest.py b/scripts/metabolomics/sirius_exporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/sirius_exporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/sirius_exporter/tests/test_sirius_exporter.py b/scripts/metabolomics/sirius_exporter/tests/test_sirius_exporter.py new file mode 100644 index 0000000..9ebd1e0 --- /dev/null +++ b/scripts/metabolomics/sirius_exporter/tests/test_sirius_exporter.py @@ -0,0 +1,106 @@ +"""Tests for sirius_exporter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_test_data(tmp_dir): + """Create test mzML and features TSV files.""" + import pyopenms as oms + + # Create mzML with MS2 spectra + exp = oms.MSExperiment() + for i in range(3): + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(100.0 + i * 10) + prec = oms.Precursor() + prec.setMZ(500.0 + i * 50) + prec.setCharge(1) + ms2.setPrecursors([prec]) + ms2.set_peaks(([100.0 + j * 20 for j in range(5)], [1000.0 - j * 100 for j in range(5)])) + exp.addSpectrum(ms2) + + mzml_path = os.path.join(tmp_dir, "test.mzML") + oms.MzMLFile().store(mzml_path, exp) + + # Create features TSV + features_path = os.path.join(tmp_dir, "features.tsv") + with open(features_path, "w") as fh: + fh.write("mz\trt\tcharge\tname\n") + fh.write("500.0\t100.0\t1\tcompound_A\n") + fh.write("550.0\t110.0\t1\tcompound_B\n") + fh.write("999.0\t999.0\t1\tno_match\n") # no matching MS2 + + return mzml_path, features_path + + +@requires_pyopenms +def test_load_features_tsv(): + from sirius_exporter import load_features_tsv + + with tempfile.TemporaryDirectory() as tmp: + features_path = os.path.join(tmp, "features.tsv") + with open(features_path, "w") as fh: + fh.write("mz\trt\tcharge\tname\n") + fh.write("500.0\t100.0\t1\ttest_compound\n") + + features = load_features_tsv(features_path) + assert len(features) == 1 + assert features[0]["mz"] == 500.0 + assert features[0]["name"] == "test_compound" + + +@requires_pyopenms +def test_find_ms2_spectra(): + from sirius_exporter import find_ms2_spectra, load_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path, _ = _create_test_data(tmp) + exp = load_mzml(mzml_path) + + matches = find_ms2_spectra(exp, 500.0, 100.0, mz_tolerance=0.01, rt_tolerance=5.0) + assert len(matches) == 1 + + no_match = find_ms2_spectra(exp, 999.0, 999.0, mz_tolerance=0.01, rt_tolerance=5.0) + assert len(no_match) == 0 + + +@requires_pyopenms +def test_export_to_sirius(): + from sirius_exporter import export_to_sirius + + with tempfile.TemporaryDirectory() as tmp: + mzml_path, features_path = _create_test_data(tmp) + output_path = os.path.join(tmp, "sirius.ms") + + stats = export_to_sirius(features_path, mzml_path, output_path) + assert stats["features_exported"] == 3 + assert stats["features_with_ms2"] == 2 # third feature has no match + + with open(output_path) as fh: + content = fh.read() + assert ">compound compound_A" in content + assert ">parentmass" in content + assert ">ms2" in content + + +@requires_pyopenms +def test_sirius_ms_format(): + from sirius_exporter import export_to_sirius + + with tempfile.TemporaryDirectory() as tmp: + mzml_path, features_path = _create_test_data(tmp) + output_path = os.path.join(tmp, "sirius.ms") + + export_to_sirius(features_path, mzml_path, output_path) + + with open(output_path) as fh: + content = fh.read() + + # Verify .ms format structure + assert ">compound" in content + assert ">parentmass" in content + assert ">rt" in content diff --git a/scripts/metabolomics/spectral_entropy_scorer/README.md b/scripts/metabolomics/spectral_entropy_scorer/README.md new file mode 100644 index 0000000..a4bc61f --- /dev/null +++ b/scripts/metabolomics/spectral_entropy_scorer/README.md @@ -0,0 +1,31 @@ +# Spectral Entropy Scorer + +Compute spectral entropy and entropy-based similarity between mass spectra, implementing the method from Li & Fiehn (2021). + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python spectral_entropy_scorer.py --query query_peaks.tsv --library lib_peaks.tsv \ + --tolerance 0.02 --output scores.tsv +``` + +## Input format + +Tab-separated files with columns: spectrum_id, mz, intensity. + +``` +spectrum_id mz intensity +Q1 100.05 1000 +Q1 150.08 500 +Q1 200.12 250 +``` + +## Output format + +Tab-separated file with columns: query_id, library_id, query_entropy, entropy_similarity. diff --git a/scripts/metabolomics/spectral_entropy_scorer/requirements.txt b/scripts/metabolomics/spectral_entropy_scorer/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/metabolomics/spectral_entropy_scorer/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/metabolomics/spectral_entropy_scorer/spectral_entropy_scorer.py b/scripts/metabolomics/spectral_entropy_scorer/spectral_entropy_scorer.py new file mode 100644 index 0000000..47c16e2 --- /dev/null +++ b/scripts/metabolomics/spectral_entropy_scorer/spectral_entropy_scorer.py @@ -0,0 +1,266 @@ +""" +Spectral Entropy Scorer +======================== +Compute spectral entropy and entropy-based similarity between mass spectra. +Implements the entropy similarity score from Li & Fiehn (2021). + +Usage +----- + python spectral_entropy_scorer.py --query query_peaks.tsv --library lib_peaks.tsv \\ + --tolerance 0.02 --output scores.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +try: + import numpy as np # noqa: F401 +except ImportError: + sys.exit("numpy is required. Install it with: pip install numpy") + + +def normalize_intensities(intensities: list) -> list: + """Normalize intensities to sum to 1. + + Parameters + ---------- + intensities: + List of intensity values. + + Returns + ------- + list of normalized intensities (weights). + """ + total = sum(intensities) + if total == 0: + return [0.0] * len(intensities) + return [i / total for i in intensities] + + +def spectral_entropy(mzs: list, intensities: list) -> float: + """Compute the spectral entropy of a spectrum. + + Entropy = -sum(w_i * log(w_i)) where w_i = intensity_i / sum(intensities). + + Parameters + ---------- + mzs: + List of m/z values. + intensities: + List of intensity values. + + Returns + ------- + float: Spectral entropy value. + """ + weights = normalize_intensities(intensities) + entropy = 0.0 + for w in weights: + if w > 0: + entropy -= w * math.log(w) + return entropy + + +def match_peaks( + mzs_a: list, + ints_a: list, + mzs_b: list, + ints_b: list, + tolerance: float = 0.02, +) -> tuple: + """Match peaks between two spectra within a mass tolerance. + + Parameters + ---------- + mzs_a, ints_a: + m/z and intensity arrays for spectrum A. + mzs_b, ints_b: + m/z and intensity arrays for spectrum B. + tolerance: + m/z tolerance in Daltons. + + Returns + ------- + tuple of (merged_mzs, merged_ints_a, merged_ints_b) + where unmatched peaks have 0 intensity in the other spectrum. + """ + used_b = set() + matched_a = [] + matched_b_idx = [] + + # For each peak in A, find closest match in B + for i, mz_a in enumerate(mzs_a): + best_j = -1 + best_diff = tolerance + 1 + for j, mz_b in enumerate(mzs_b): + if j in used_b: + continue + diff = abs(mz_a - mz_b) + if diff <= tolerance and diff < best_diff: + best_j = j + best_diff = diff + + matched_a.append(i) + if best_j >= 0: + matched_b_idx.append(best_j) + used_b.add(best_j) + else: + matched_b_idx.append(-1) + + # Build merged arrays + merged_mzs = [] + merged_a = [] + merged_b = [] + + for idx_a, idx_b in zip(matched_a, matched_b_idx): + merged_mzs.append(mzs_a[idx_a]) + merged_a.append(ints_a[idx_a]) + merged_b.append(ints_b[idx_b] if idx_b >= 0 else 0.0) + + # Add unmatched B peaks + for j in range(len(mzs_b)): + if j not in used_b: + merged_mzs.append(mzs_b[j]) + merged_a.append(0.0) + merged_b.append(ints_b[j]) + + return merged_mzs, merged_a, merged_b + + +def entropy_similarity( + mzs_a: list, + ints_a: list, + mzs_b: list, + ints_b: list, + tolerance: float = 0.02, +) -> float: + """Compute entropy similarity between two spectra (Li & Fiehn 2021). + + Entropy similarity = 1 - (2 * H_merged - H_a - H_b) / log(4) + + where H_merged is the entropy of the merged (averaged) spectrum, + and H_a, H_b are entropies of the individual spectra. + + Parameters + ---------- + mzs_a, ints_a: + Query spectrum peaks. + mzs_b, ints_b: + Library spectrum peaks. + tolerance: + m/z tolerance in Daltons. + + Returns + ------- + float: Entropy similarity score in [0, 1]. + """ + if not ints_a or not ints_b: + return 0.0 + + merged_mzs, merged_a, merged_b = match_peaks(mzs_a, ints_a, mzs_b, ints_b, tolerance) + + # Normalize each spectrum + w_a = normalize_intensities(merged_a) + w_b = normalize_intensities(merged_b) + + # Compute individual entropies + h_a = 0.0 + for w in w_a: + if w > 0: + h_a -= w * math.log(w) + + h_b = 0.0 + for w in w_b: + if w > 0: + h_b -= w * math.log(w) + + # Compute merged spectrum entropy + w_merged = [(a + b) / 2.0 for a, b in zip(w_a, w_b)] + h_merged = 0.0 + for w in w_merged: + if w > 0: + h_merged -= w * math.log(w) + + # Entropy similarity + d_ab = 2 * h_merged - h_a - h_b + similarity = 1.0 - d_ab / math.log(4) + + # Clamp to [0, 1] + return max(0.0, min(1.0, similarity)) + + +def read_peaks_file(path: str) -> list: + """Read a peaks file with columns: spectrum_id, mz, intensity. + + Parameters + ---------- + path: + TSV file path. + + Returns + ------- + list of dicts with keys: spectrum_id, mzs (list), intensities (list). + """ + spectra = {} + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + sid = row["spectrum_id"] + if sid not in spectra: + spectra[sid] = {"spectrum_id": sid, "mzs": [], "intensities": []} + spectra[sid]["mzs"].append(float(row["mz"])) + spectra[sid]["intensities"].append(float(row["intensity"])) + return list(spectra.values()) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Compute spectral entropy similarity between query and library spectra." + ) + parser.add_argument("--query", required=True, + help="TSV with query peaks (spectrum_id, mz, intensity).") + parser.add_argument("--library", required=True, + help="TSV with library peaks (spectrum_id, mz, intensity).") + parser.add_argument("--tolerance", type=float, default=0.02, + help="m/z tolerance in Da (default: 0.02).") + parser.add_argument("--output", required=True, help="Output TSV with similarity scores.") + args = parser.parse_args() + + query_spectra = read_peaks_file(args.query) + library_spectra = read_peaks_file(args.library) + + results = [] + for qs in query_spectra: + q_entropy = spectral_entropy(qs["mzs"], qs["intensities"]) + for ls in library_spectra: + score = entropy_similarity( + qs["mzs"], qs["intensities"], + ls["mzs"], ls["intensities"], + tolerance=args.tolerance, + ) + results.append({ + "query_id": qs["spectrum_id"], + "library_id": ls["spectrum_id"], + "query_entropy": round(q_entropy, 4), + "entropy_similarity": round(score, 4), + }) + + fieldnames = ["query_id", "library_id", "query_entropy", "entropy_similarity"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + print(f"Computed {len(results)} pairwise scores, wrote to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/spectral_entropy_scorer/tests/conftest.py b/scripts/metabolomics/spectral_entropy_scorer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/spectral_entropy_scorer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py b/scripts/metabolomics/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py new file mode 100644 index 0000000..c8ef5cd --- /dev/null +++ b/scripts/metabolomics/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py @@ -0,0 +1,150 @@ +"""Tests for spectral_entropy_scorer.""" + +import math +import os +import tempfile + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestNormalizeIntensities: + def test_basic(self): + from spectral_entropy_scorer import normalize_intensities + + result = normalize_intensities([100, 200, 300]) + assert sum(result) == pytest.approx(1.0) + assert result[0] == pytest.approx(1 / 6) + + def test_zero_total(self): + from spectral_entropy_scorer import normalize_intensities + + result = normalize_intensities([0, 0, 0]) + assert result == [0.0, 0.0, 0.0] + + +@requires_pyopenms +class TestSpectralEntropy: + def test_single_peak(self): + """Single peak spectrum has entropy 0.""" + from spectral_entropy_scorer import spectral_entropy + + assert spectral_entropy([100.0], [1000.0]) == pytest.approx(0.0) + + def test_uniform_distribution(self): + """Uniform distribution has maximum entropy.""" + from spectral_entropy_scorer import spectral_entropy + + mzs = [100.0, 200.0, 300.0, 400.0] + ints = [100.0, 100.0, 100.0, 100.0] + entropy = spectral_entropy(mzs, ints) + assert entropy == pytest.approx(math.log(4), abs=0.001) + + def test_entropy_increases_with_peaks(self): + from spectral_entropy_scorer import spectral_entropy + + e2 = spectral_entropy([100.0, 200.0], [100.0, 100.0]) + e4 = spectral_entropy([100.0, 200.0, 300.0, 400.0], [100.0, 100.0, 100.0, 100.0]) + assert e4 > e2 + + +@requires_pyopenms +class TestMatchPeaks: + def test_perfect_match(self): + from spectral_entropy_scorer import match_peaks + + mzs, a, b = match_peaks( + [100.0, 200.0], [500, 300], + [100.01, 200.01], [400, 200], + tolerance=0.02, + ) + assert len(mzs) == 2 + assert a == [500, 300] + assert b == [400, 200] + + def test_no_match(self): + from spectral_entropy_scorer import match_peaks + + mzs, a, b = match_peaks( + [100.0], [500], + [200.0], [400], + tolerance=0.02, + ) + assert len(mzs) == 2 + assert 0.0 in a or 0.0 in b + + def test_partial_match(self): + from spectral_entropy_scorer import match_peaks + + mzs, a, b = match_peaks( + [100.0, 200.0], [500, 300], + [100.01], [400], + tolerance=0.02, + ) + assert len(mzs) == 2 + # 200.0 has no match in b + assert b[1] == 0.0 + + +@requires_pyopenms +class TestEntropySimilarity: + def test_identical_spectra(self): + from spectral_entropy_scorer import entropy_similarity + + score = entropy_similarity( + [100.0, 200.0, 300.0], [500, 300, 200], + [100.0, 200.0, 300.0], [500, 300, 200], + tolerance=0.02, + ) + assert score == pytest.approx(1.0, abs=0.01) + + def test_completely_different(self): + from spectral_entropy_scorer import entropy_similarity + + score = entropy_similarity( + [100.0], [1000], + [500.0], [1000], + tolerance=0.02, + ) + assert score < 0.5 + + def test_empty_spectrum(self): + from spectral_entropy_scorer import entropy_similarity + + assert entropy_similarity([], [], [100.0], [1000], tolerance=0.02) == 0.0 + + def test_score_range(self): + from spectral_entropy_scorer import entropy_similarity + + score = entropy_similarity( + [100.0, 200.0], [500, 300], + [100.01, 300.0], [400, 200], + tolerance=0.02, + ) + assert 0.0 <= score <= 1.0 + + +@requires_pyopenms +class TestReadPeaksFile: + def test_roundtrip(self): + import csv + + from spectral_entropy_scorer import read_peaks_file + + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False, newline="") as f: + writer = csv.DictWriter(f, fieldnames=["spectrum_id", "mz", "intensity"], delimiter="\t") + writer.writeheader() + writer.writerow({"spectrum_id": "S1", "mz": "100.0", "intensity": "500"}) + writer.writerow({"spectrum_id": "S1", "mz": "200.0", "intensity": "300"}) + writer.writerow({"spectrum_id": "S2", "mz": "150.0", "intensity": "1000"}) + path = f.name + + try: + spectra = read_peaks_file(path) + assert len(spectra) == 2 + s1 = next(s for s in spectra if s["spectrum_id"] == "S1") + assert len(s1["mzs"]) == 2 + assert s1["mzs"][0] == 100.0 + finally: + os.unlink(path) diff --git a/scripts/metabolomics/suspect_screener/README.md b/scripts/metabolomics/suspect_screener/README.md new file mode 100644 index 0000000..02d86ff --- /dev/null +++ b/scripts/metabolomics/suspect_screener/README.md @@ -0,0 +1,25 @@ +# Suspect Screener + +Match detected features against a suspect screening list by exact mass within a given ppm tolerance. Results are ranked by mass error. + +## Usage + +```bash +python suspect_screener.py --input features.tsv --suspects suspect_list.csv --ppm 5 --output matches.tsv +``` + +### Input formats + +**features.tsv** (tab-separated): +``` +feature_id mz rt intensity +F1 180.0634 120.5 5000 +``` + +**suspect_list.csv** (comma-separated): +``` +name,formula,exact_mass +Glucose,C6H12O6,180.0634 +``` + +If `exact_mass` is empty, it will be computed from the formula using pyopenms. diff --git a/scripts/metabolomics/suspect_screener/requirements.txt b/scripts/metabolomics/suspect_screener/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/suspect_screener/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/suspect_screener/suspect_screener.py b/scripts/metabolomics/suspect_screener/suspect_screener.py new file mode 100644 index 0000000..f5c7b5f --- /dev/null +++ b/scripts/metabolomics/suspect_screener/suspect_screener.py @@ -0,0 +1,215 @@ +""" +Suspect Screener +================ +Match detected features against a suspect screening list by exact mass. +Features are matched within a user-defined ppm tolerance, and results are +ranked by mass error. + +Usage +----- + python suspect_screener.py --input features.tsv --suspects suspect_list.csv --ppm 5 --output matches.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def exact_mass_from_formula(formula: str) -> float: + """Compute the monoisotopic mass for a molecular formula using pyopenms. + + Parameters + ---------- + formula: + Empirical formula string, e.g. ``"C6H12O6"``. + + Returns + ------- + float + Monoisotopic mass in Da. + """ + ef = oms.EmpiricalFormula(formula) + return ef.getMonoWeight() + + +def ppm_error(observed: float, theoretical: float) -> float: + """Calculate the mass error in ppm. + + Parameters + ---------- + observed: + Observed mass in Da. + theoretical: + Theoretical exact mass in Da. + + Returns + ------- + float + Signed mass error in ppm. + """ + if theoretical == 0.0: + return float("inf") + return (observed - theoretical) / theoretical * 1e6 + + +def load_features(path: str) -> list[dict]: + """Load feature table from a TSV file. + + Expected columns: feature_id, mz, rt (retention time), intensity. + The mz column is used for matching. + + Parameters + ---------- + path: + Path to TSV file. + + Returns + ------- + list of dict + Each dict has keys from the TSV header with numeric values parsed. + """ + features = [] + with open(path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + parsed = {} + for key, val in row.items(): + try: + parsed[key] = float(val) + except (ValueError, TypeError): + parsed[key] = val + features.append(parsed) + return features + + +def load_suspects(path: str) -> list[dict]: + """Load suspect list from a CSV file. + + Expected columns: name, formula, exact_mass. + If exact_mass is missing or empty, it will be computed from formula. + + Parameters + ---------- + path: + Path to CSV file. + + Returns + ------- + list of dict + Each dict has keys: name, formula, exact_mass. + """ + suspects = [] + with open(path, newline="") as fh: + reader = csv.DictReader(fh) + for row in reader: + name = row.get("name", "").strip() + formula = row.get("formula", "").strip() + mass_str = row.get("exact_mass", "").strip() + if mass_str: + exact_mass = float(mass_str) + elif formula: + exact_mass = exact_mass_from_formula(formula) + else: + continue + suspects.append({ + "name": name, + "formula": formula, + "exact_mass": exact_mass, + }) + return suspects + + +def screen_suspects( + features: list[dict], + suspects: list[dict], + ppm_tolerance: float = 5.0, + mz_column: str = "mz", +) -> list[dict]: + """Match features against suspects within ppm tolerance. + + Parameters + ---------- + features: + List of feature dicts (must contain *mz_column*). + suspects: + List of suspect dicts with 'name', 'formula', 'exact_mass'. + ppm_tolerance: + Maximum absolute ppm error for a match. + mz_column: + Name of the m/z column in the feature table. + + Returns + ------- + list of dict + Matched results sorted by absolute ppm error, each containing + feature info, suspect info, and the computed ppm error. + """ + matches = [] + for feat in features: + obs_mz = float(feat[mz_column]) + for suspect in suspects: + error = ppm_error(obs_mz, suspect["exact_mass"]) + if abs(error) <= ppm_tolerance: + match = { + "feature_id": feat.get("feature_id", ""), + "observed_mz": obs_mz, + "rt": feat.get("rt", ""), + "intensity": feat.get("intensity", ""), + "suspect_name": suspect["name"], + "suspect_formula": suspect["formula"], + "suspect_exact_mass": suspect["exact_mass"], + "ppm_error": round(error, 4), + "abs_ppm_error": round(abs(error), 4), + } + matches.append(match) + matches.sort(key=lambda m: m["abs_ppm_error"]) + return matches + + +def write_matches(matches: list[dict], path: str) -> None: + """Write match results to a TSV file. + + Parameters + ---------- + matches: + List of match dicts from :func:`screen_suspects`. + path: + Output TSV path. + """ + if not matches: + with open(path, "w") as fh: + fh.write("# No matches found\n") + return + fieldnames = list(matches[0].keys()) + with open(path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(matches) + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Match features against a suspect screening list by exact mass." + ) + parser.add_argument("--input", required=True, help="Feature table (TSV) with mz column") + parser.add_argument("--suspects", required=True, help="Suspect list (CSV) with name, formula, exact_mass") + parser.add_argument("--ppm", type=float, default=5.0, help="PPM tolerance (default: 5)") + parser.add_argument("--output", required=True, help="Output matches (TSV)") + parser.add_argument("--mz-column", default="mz", help="Name of m/z column in features (default: mz)") + args = parser.parse_args() + + features = load_features(args.input) + suspects = load_suspects(args.suspects) + matches = screen_suspects(features, suspects, ppm_tolerance=args.ppm, mz_column=args.mz_column) + write_matches(matches, args.output) + print(f"Found {len(matches)} matches, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/suspect_screener/tests/conftest.py b/scripts/metabolomics/suspect_screener/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/suspect_screener/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/suspect_screener/tests/test_suspect_screener.py b/scripts/metabolomics/suspect_screener/tests/test_suspect_screener.py new file mode 100644 index 0000000..4f07a3a --- /dev/null +++ b/scripts/metabolomics/suspect_screener/tests/test_suspect_screener.py @@ -0,0 +1,105 @@ +"""Tests for suspect_screener.""" + +import csv +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSuspectScreener: + def test_exact_mass_from_formula(self): + from suspect_screener import exact_mass_from_formula + + mass = exact_mass_from_formula("C6H12O6") + assert abs(mass - 180.0634) < 0.01 + + def test_ppm_error_zero(self): + from suspect_screener import ppm_error + + err = ppm_error(180.0634, 180.0634) + assert abs(err) < 1e-6 + + def test_ppm_error_positive(self): + from suspect_screener import ppm_error + + err = ppm_error(180.0644, 180.0634) + assert err > 0 + + def test_ppm_error_negative(self): + from suspect_screener import ppm_error + + err = ppm_error(180.0624, 180.0634) + assert err < 0 + + def test_screen_suspects_match(self): + from suspect_screener import screen_suspects + + features = [{"feature_id": "F1", "mz": 180.0634, "rt": 120.0, "intensity": 1000.0}] + suspects = [{"name": "Glucose", "formula": "C6H12O6", "exact_mass": 180.0634}] + matches = screen_suspects(features, suspects, ppm_tolerance=5.0) + assert len(matches) == 1 + assert matches[0]["suspect_name"] == "Glucose" + assert abs(matches[0]["ppm_error"]) < 1.0 + + def test_screen_suspects_no_match(self): + from suspect_screener import screen_suspects + + features = [{"feature_id": "F1", "mz": 200.0, "rt": 120.0, "intensity": 1000.0}] + suspects = [{"name": "Glucose", "formula": "C6H12O6", "exact_mass": 180.0634}] + matches = screen_suspects(features, suspects, ppm_tolerance=5.0) + assert len(matches) == 0 + + def test_screen_suspects_sorted_by_abs_error(self): + from suspect_screener import screen_suspects + + features = [{"feature_id": "F1", "mz": 180.0640, "rt": 120.0, "intensity": 1000.0}] + suspects = [ + {"name": "A", "formula": "", "exact_mass": 180.0640}, + {"name": "B", "formula": "", "exact_mass": 180.0635}, + ] + matches = screen_suspects(features, suspects, ppm_tolerance=10.0) + assert len(matches) == 2 + assert matches[0]["abs_ppm_error"] <= matches[1]["abs_ppm_error"] + + def test_load_and_write_roundtrip(self): + from suspect_screener import load_features, load_suspects, screen_suspects, write_matches + + with tempfile.TemporaryDirectory() as tmpdir: + feat_path = os.path.join(tmpdir, "features.tsv") + with open(feat_path, "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=["feature_id", "mz", "rt", "intensity"], delimiter="\t") + w.writeheader() + w.writerow({"feature_id": "F1", "mz": "180.0634", "rt": "120", "intensity": "5000"}) + + susp_path = os.path.join(tmpdir, "suspects.csv") + with open(susp_path, "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=["name", "formula", "exact_mass"]) + w.writeheader() + w.writerow({"name": "Glucose", "formula": "C6H12O6", "exact_mass": "180.0634"}) + + features = load_features(feat_path) + suspects = load_suspects(susp_path) + matches = screen_suspects(features, suspects, ppm_tolerance=5.0) + + out_path = os.path.join(tmpdir, "matches.tsv") + write_matches(matches, out_path) + assert os.path.exists(out_path) + with open(out_path) as fh: + lines = fh.readlines() + assert len(lines) >= 2 # header + 1 match + + def test_load_suspects_computes_mass_from_formula(self): + from suspect_screener import load_suspects + + with tempfile.TemporaryDirectory() as tmpdir: + path = os.path.join(tmpdir, "suspects.csv") + with open(path, "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=["name", "formula", "exact_mass"]) + w.writeheader() + w.writerow({"name": "Glucose", "formula": "C6H12O6", "exact_mass": ""}) + + suspects = load_suspects(path) + assert len(suspects) == 1 + assert abs(suspects[0]["exact_mass"] - 180.0634) < 0.01 diff --git a/scripts/metabolomics/targeted_feature_extractor/requirements.txt b/scripts/metabolomics/targeted_feature_extractor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/targeted_feature_extractor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/targeted_feature_extractor/targeted_feature_extractor.py b/scripts/metabolomics/targeted_feature_extractor/targeted_feature_extractor.py new file mode 100644 index 0000000..24bed39 --- /dev/null +++ b/scripts/metabolomics/targeted_feature_extractor/targeted_feature_extractor.py @@ -0,0 +1,176 @@ +""" +Targeted Feature Extractor +============================ +Extract chromatographic features for known target compounds from MS1 +data in an mzML file. + +For each target compound (defined by name, formula, and expected RT), +the tool extracts the ion chromatogram and integrates the peak area. + +Usage +----- + python targeted_feature_extractor.py --input sample.mzML --targets compounds.tsv \ + --ppm 5 --output quantified.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def extract_eic( + exp: oms.MSExperiment, + target_mz: float, + ppm: float = 5.0, +) -> list[tuple[float, float]]: + """Extract an extracted ion chromatogram (EIC) for a target m/z. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment``. + target_mz: + Target m/z value. + ppm: + Mass tolerance in ppm. + + Returns + ------- + list[tuple[float, float]] + List of (rt, intensity) tuples. + """ + tol_da = target_mz * ppm / 1e6 + lo = target_mz - tol_da + hi = target_mz + tol_da + + eic = [] + for spec in exp.getSpectra(): + if spec.getMSLevel() != 1: + continue + rt = spec.getRT() + mzs, intensities = spec.get_peaks() + total = 0.0 + for mz, intensity in zip(mzs, intensities): + if lo <= mz <= hi: + total += float(intensity) + eic.append((rt, total)) + + return eic + + +def integrate_peak(eic: list[tuple[float, float]], rt_min: float = 0.0, rt_max: float = 1e9) -> float: + """Integrate EIC area using the trapezoidal rule. + + Parameters + ---------- + eic: + Extracted ion chromatogram as (rt, intensity) pairs. + rt_min, rt_max: + RT bounds for integration (seconds). + + Returns + ------- + float + Integrated area. + """ + filtered = [(rt, i) for rt, i in eic if rt_min <= rt <= rt_max] + if len(filtered) < 2: + return sum(i for _, i in filtered) + + area = 0.0 + for idx in range(1, len(filtered)): + dt = filtered[idx][0] - filtered[idx - 1][0] + avg_int = (filtered[idx][1] + filtered[idx - 1][1]) / 2.0 + area += dt * avg_int + return area + + +def extract_targets( + exp: oms.MSExperiment, + targets: list[dict], + ppm: float = 5.0, +) -> list[dict]: + """Extract features for all target compounds. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment``. + targets: + List of dicts with keys: name, formula (and optionally rt_min, rt_max). + ppm: + Mass tolerance in ppm. + + Returns + ------- + list[dict] + Each dict has: name, formula, target_mz, peak_area, max_intensity, n_points. + """ + results = [] + for t in targets: + formula = t["formula"] + ef = oms.EmpiricalFormula(formula) + neutral_mass = ef.getMonoWeight() + target_mz = neutral_mass + PROTON # [M+H]+ + + rt_min = float(t.get("rt_min", 0.0)) + rt_max = float(t.get("rt_max", 1e9)) + + eic = extract_eic(exp, target_mz, ppm=ppm) + area = integrate_peak(eic, rt_min=rt_min, rt_max=rt_max) + max_int = max((i for _, i in eic), default=0.0) + + results.append({ + "name": t.get("name", formula), + "formula": formula, + "target_mz": round(target_mz, 6), + "peak_area": round(area, 2), + "max_intensity": round(max_int, 2), + "n_points": len(eic), + }) + + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Extract features for known compounds from MS1 data." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="mzML file") + parser.add_argument("--targets", required=True, metavar="FILE", help="Compounds TSV (name, formula)") + parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") + parser.add_argument("--output", required=True, metavar="FILE", help="Output quantified TSV") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + targets = [] + with open(args.targets) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + targets.append(row) + + results = extract_targets(exp, targets, ppm=args.ppm) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["name", "formula", "target_mz", "peak_area", "max_intensity", "n_points"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(results) + + print(f"Extracted {len(results)} targets, written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/metabolomics/targeted_feature_extractor/tests/conftest.py b/scripts/metabolomics/targeted_feature_extractor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/targeted_feature_extractor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/targeted_feature_extractor/tests/test_targeted_feature_extractor.py b/scripts/metabolomics/targeted_feature_extractor/tests/test_targeted_feature_extractor.py new file mode 100644 index 0000000..9c9a17d --- /dev/null +++ b/scripts/metabolomics/targeted_feature_extractor/tests/test_targeted_feature_extractor.py @@ -0,0 +1,59 @@ +"""Tests for targeted_feature_extractor.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestTargetedFeatureExtractor: + def _make_experiment(self, target_mz=181.0707): + """Create experiment with a peak at target_mz.""" + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(5): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(60.0 * i) + mzs = np.array([target_mz - 1.0, target_mz, target_mz + 1.0], dtype=np.float64) + ints = np.array([100.0, 5000.0 * (1 + i), 100.0], dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + return exp + + def test_extract_eic(self): + from targeted_feature_extractor import extract_eic + + exp = self._make_experiment(target_mz=181.0707) + eic = extract_eic(exp, 181.0707, ppm=10.0) + assert len(eic) == 5 + assert all(intensity > 0 for _, intensity in eic) + + def test_integrate_peak(self): + from targeted_feature_extractor import integrate_peak + + eic = [(0.0, 100.0), (1.0, 100.0), (2.0, 100.0)] + area = integrate_peak(eic) + assert area == 200.0 # trapezoid: 2 intervals, 100 each + + def test_extract_targets(self): + import pyopenms as oms + from targeted_feature_extractor import extract_targets + + ef = oms.EmpiricalFormula("C6H12O6") + target_mz = ef.getMonoWeight() + 1.007276 + exp = self._make_experiment(target_mz=target_mz) + + targets = [{"name": "Glucose", "formula": "C6H12O6"}] + results = extract_targets(exp, targets, ppm=10.0) + assert len(results) == 1 + assert results[0]["name"] == "Glucose" + assert results[0]["peak_area"] > 0 + assert results[0]["max_intensity"] > 0 + + def test_no_matching_peak(self): + from targeted_feature_extractor import extract_eic + + exp = self._make_experiment(target_mz=181.0707) + eic = extract_eic(exp, 500.0, ppm=5.0) + assert all(intensity == 0.0 for _, intensity in eic) diff --git a/scripts/metabolomics/van_krevelen_data_generator/README.md b/scripts/metabolomics/van_krevelen_data_generator/README.md new file mode 100644 index 0000000..195992d --- /dev/null +++ b/scripts/metabolomics/van_krevelen_data_generator/README.md @@ -0,0 +1,34 @@ +# Van Krevelen Data Generator + +Compute H:C and O:C ratios from molecular formulas and classify compounds into biochemical classes (lipids, carbohydrates, amino acids, nucleotides) for Van Krevelen diagram analysis. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Basic ratio computation +python van_krevelen_data_generator.py --input formulas.tsv --output van_krevelen.tsv + +# With biochemical class assignment +python van_krevelen_data_generator.py --input formulas.tsv --classify --output van_krevelen.tsv +``` + +## Input format + +Tab-separated file with a `formula` column: + +``` +formula +C6H12O6 +C16H32O2 +C3H7NO2 +``` + +## Output format + +Tab-separated file with columns: formula, C, H, O, hc_ratio, oc_ratio, and optionally class. diff --git a/scripts/metabolomics/van_krevelen_data_generator/requirements.txt b/scripts/metabolomics/van_krevelen_data_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/metabolomics/van_krevelen_data_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/metabolomics/van_krevelen_data_generator/tests/conftest.py b/scripts/metabolomics/van_krevelen_data_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/metabolomics/van_krevelen_data_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/metabolomics/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py b/scripts/metabolomics/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py new file mode 100644 index 0000000..090d590 --- /dev/null +++ b/scripts/metabolomics/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py @@ -0,0 +1,113 @@ +"""Tests for van_krevelen_data_generator.""" + +import csv +import os +import tempfile + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestComputeRatios: + def test_glucose(self): + from van_krevelen_data_generator import compute_ratios + + result = compute_ratios("C6H12O6") + assert result["C"] == 6 + assert result["H"] == 12 + assert result["O"] == 6 + assert result["hc_ratio"] == pytest.approx(2.0, abs=0.01) + assert result["oc_ratio"] == pytest.approx(1.0, abs=0.01) + + def test_palmitic_acid(self): + from van_krevelen_data_generator import compute_ratios + + result = compute_ratios("C16H32O2") + assert result["hc_ratio"] == pytest.approx(2.0, abs=0.01) + assert result["oc_ratio"] == pytest.approx(0.125, abs=0.01) + + def test_no_carbon_raises(self): + from van_krevelen_data_generator import compute_ratios + + with pytest.raises(ValueError, match="no carbon"): + compute_ratios("H2O") + + +@requires_pyopenms +class TestClassifyCompound: + def test_lipid_region(self): + from van_krevelen_data_generator import classify_compound + + assert classify_compound(2.0, 0.125) == "lipids" + + def test_carbohydrate_region(self): + from van_krevelen_data_generator import classify_compound + + assert classify_compound(2.0, 1.0) == "carbohydrates" + + def test_amino_acid_region(self): + from van_krevelen_data_generator import classify_compound + + assert classify_compound(1.5, 0.5) == "amino_acids" + + def test_nucleotide_region(self): + from van_krevelen_data_generator import classify_compound + + assert classify_compound(1.2, 0.7) == "nucleotides" + + def test_unclassified(self): + from van_krevelen_data_generator import classify_compound + + assert classify_compound(0.5, 0.1) == "unclassified" + + +@requires_pyopenms +class TestProcessFormulas: + def test_with_classification(self): + from van_krevelen_data_generator import process_formulas + + results = process_formulas(["C6H12O6", "C16H32O2"], classify=True) + assert len(results) == 2 + assert "class" in results[0] + assert results[0]["class"] == "carbohydrates" + assert results[1]["class"] == "lipids" + + def test_without_classification(self): + from van_krevelen_data_generator import process_formulas + + results = process_formulas(["C6H12O6"], classify=False) + assert "class" not in results[0] + + +@requires_pyopenms +class TestCLIRoundTrip: + def test_roundtrip(self): + from van_krevelen_data_generator import process_formulas + + formulas = ["C6H12O6", "C16H32O2", "C3H7NO2"] + with tempfile.NamedTemporaryFile(mode="w", suffix=".tsv", delete=False, newline="") as inf: + writer = csv.DictWriter(inf, fieldnames=["formula"], delimiter="\t") + writer.writeheader() + for f in formulas: + writer.writerow({"formula": f}) + input_path = inf.name + + output_path = input_path.replace(".tsv", "_out.tsv") + try: + results = process_formulas(formulas, classify=True) + fieldnames = ["formula", "C", "H", "O", "hc_ratio", "oc_ratio", "class"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + with open(output_path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + rows = list(reader) + assert len(rows) == 3 + assert "class" in rows[0] + finally: + os.unlink(input_path) + if os.path.exists(output_path): + os.unlink(output_path) diff --git a/scripts/metabolomics/van_krevelen_data_generator/van_krevelen_data_generator.py b/scripts/metabolomics/van_krevelen_data_generator/van_krevelen_data_generator.py new file mode 100644 index 0000000..2a81548 --- /dev/null +++ b/scripts/metabolomics/van_krevelen_data_generator/van_krevelen_data_generator.py @@ -0,0 +1,141 @@ +""" +Van Krevelen Data Generator +============================ +Compute H:C and O:C ratios from molecular formulas and classify compounds +into biochemical classes based on their position in Van Krevelen space. + +Usage +----- + python van_krevelen_data_generator.py --input formulas.tsv --classify --output van_krevelen.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Biochemical class regions defined by H:C and O:C ratio boundaries +BIOCHEMICAL_CLASSES = { + "lipids": {"hc_min": 1.5, "hc_max": 2.5, "oc_min": 0.0, "oc_max": 0.3}, + "carbohydrates": {"hc_min": 1.5, "hc_max": 2.5, "oc_min": 0.6, "oc_max": 1.2}, + "amino_acids": {"hc_min": 1.0, "hc_max": 2.0, "oc_min": 0.3, "oc_max": 0.8}, + "nucleotides": {"hc_min": 1.0, "hc_max": 1.5, "oc_min": 0.5, "oc_max": 1.0}, +} + + +def compute_ratios(formula: str) -> dict: + """Compute H:C and O:C ratios from a molecular formula. + + Parameters + ---------- + formula: + Empirical formula string, e.g. ``"C6H12O6"``. + + Returns + ------- + dict with keys: formula, C, H, O, hc_ratio, oc_ratio + """ + ef = oms.EmpiricalFormula(formula) + composition = ef.getElementalComposition() + + c_count = composition.get(b"C", 0) + h_count = composition.get(b"H", 0) + o_count = composition.get(b"O", 0) + + if c_count == 0: + raise ValueError(f"Formula '{formula}' contains no carbon atoms; cannot compute ratios.") + + hc_ratio = h_count / c_count + oc_ratio = o_count / c_count + + return { + "formula": formula, + "C": c_count, + "H": h_count, + "O": o_count, + "hc_ratio": round(hc_ratio, 4), + "oc_ratio": round(oc_ratio, 4), + } + + +def classify_compound(hc_ratio: float, oc_ratio: float) -> str: + """Classify a compound into a biochemical class based on Van Krevelen regions. + + Parameters + ---------- + hc_ratio: + Hydrogen-to-carbon ratio. + oc_ratio: + Oxygen-to-carbon ratio. + + Returns + ------- + str: The biochemical class name, or "unclassified" if no region matches. + """ + for cls_name, bounds in BIOCHEMICAL_CLASSES.items(): + if (bounds["hc_min"] <= hc_ratio <= bounds["hc_max"] + and bounds["oc_min"] <= oc_ratio <= bounds["oc_max"]): + return cls_name + return "unclassified" + + +def process_formulas(formulas: list, classify: bool = False) -> list: + """Process a list of formulas and optionally classify them. + + Parameters + ---------- + formulas: + List of molecular formula strings. + classify: + Whether to add biochemical class assignment. + + Returns + ------- + list of dicts with ratio data and optional classification. + """ + results = [] + for formula in formulas: + row = compute_ratios(formula) + if classify: + row["class"] = classify_compound(row["hc_ratio"], row["oc_ratio"]) + results.append(row) + return results + + +def main() -> None: + """CLI entry point.""" + parser = argparse.ArgumentParser( + description="Compute H:C and O:C ratios from molecular formulas for Van Krevelen diagrams." + ) + parser.add_argument("--input", required=True, help="TSV file with a 'formula' column.") + parser.add_argument("--classify", action="store_true", help="Add biochemical class assignment.") + parser.add_argument("--output", required=True, help="Output TSV file with ratios.") + args = parser.parse_args() + + formulas = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + formulas.append(row["formula"]) + + results = process_formulas(formulas, classify=args.classify) + + fieldnames = ["formula", "C", "H", "O", "hc_ratio", "oc_ratio"] + if args.classify: + fieldnames.append("class") + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + print(f"Wrote {len(results)} entries to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/acquisition_rate_analyzer/acquisition_rate_analyzer.py b/scripts/proteomics/acquisition_rate_analyzer/acquisition_rate_analyzer.py new file mode 100644 index 0000000..0790a8d --- /dev/null +++ b/scripts/proteomics/acquisition_rate_analyzer/acquisition_rate_analyzer.py @@ -0,0 +1,114 @@ +""" +Acquisition Rate Analyzer +========================== +Analyze MS1/MS2 acquisition rates over time from an mzML file. + +Computes scan rates, cycle times, and duty-cycle percentages. + +Usage +----- + python acquisition_rate_analyzer.py --input run.mzML --output rates.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def analyze_acquisition_rates(exp: oms.MSExperiment) -> dict: + """Analyze acquisition rates from an MSExperiment. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment``. + + Returns + ------- + dict + Contains per-scan records and summary statistics. + """ + spectra = exp.getSpectra() + if not spectra: + return {"scans": [], "summary": {"total_scans": 0}} + + scans = [] + ms1_rts = [] + ms2_rts = [] + prev_rt = None + + for spec in spectra: + rt = spec.getRT() + level = spec.getMSLevel() + delta = rt - prev_rt if prev_rt is not None else 0.0 + prev_rt = rt + scans.append({ + "rt_sec": round(rt, 4), + "ms_level": level, + "delta_sec": round(delta, 4), + }) + if level == 1: + ms1_rts.append(rt) + elif level == 2: + ms2_rts.append(rt) + + rt_total = ms1_rts[-1] - ms1_rts[0] if len(ms1_rts) > 1 else 0.0 + + cycle_times = [] + for i in range(1, len(ms1_rts)): + cycle_times.append(ms1_rts[i] - ms1_rts[i - 1]) + + avg_cycle = sum(cycle_times) / len(cycle_times) if cycle_times else 0.0 + + ms1_rate = len(ms1_rts) / (rt_total / 60.0) if rt_total > 0 else 0.0 + ms2_rate = len(ms2_rts) / (rt_total / 60.0) if rt_total > 0 else 0.0 + + ms2_per_cycle = len(ms2_rts) / len(ms1_rts) if ms1_rts else 0.0 + + summary = { + "total_scans": len(spectra), + "ms1_count": len(ms1_rts), + "ms2_count": len(ms2_rts), + "rt_range_sec": round(rt_total, 2), + "ms1_rate_per_min": round(ms1_rate, 2), + "ms2_rate_per_min": round(ms2_rate, 2), + "avg_cycle_time_sec": round(avg_cycle, 4), + "avg_ms2_per_cycle": round(ms2_per_cycle, 2), + } + + return {"scans": scans, "summary": summary} + + +def main(): + parser = argparse.ArgumentParser( + description="Analyze MS1/MS2 acquisition rates over time." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV file") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + result = analyze_acquisition_rates(exp) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["rt_sec", "ms_level", "delta_sec"], delimiter="\t") + writer.writeheader() + writer.writerows(result["scans"]) + + s = result["summary"] + print(f"Rates written to {args.output}") + print(f" Total scans : {s['total_scans']}") + print(f" MS1 rate : {s['ms1_rate_per_min']:.1f} /min") + print(f" MS2 rate : {s['ms2_rate_per_min']:.1f} /min") + print(f" Avg cycle time : {s['avg_cycle_time_sec']:.3f} s") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/acquisition_rate_analyzer/requirements.txt b/scripts/proteomics/acquisition_rate_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/acquisition_rate_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/acquisition_rate_analyzer/tests/conftest.py b/scripts/proteomics/acquisition_rate_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/acquisition_rate_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py b/scripts/proteomics/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py new file mode 100644 index 0000000..e965ac2 --- /dev/null +++ b/scripts/proteomics/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py @@ -0,0 +1,70 @@ +"""Tests for acquisition_rate_analyzer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestAcquisitionRateAnalyzer: + def _make_experiment(self): + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + rt = 0.0 + for i in range(5): + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(rt) + mzs = np.array([100.0], dtype=np.float64) + ints = np.array([1000.0], dtype=np.float64) + ms1.set_peaks([mzs, ints]) + exp.addSpectrum(ms1) + rt += 1.0 + + for _ in range(3): + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(rt) + ms2.set_peaks([np.array([200.0], dtype=np.float64), + np.array([500.0], dtype=np.float64)]) + exp.addSpectrum(ms2) + rt += 0.3 + + return exp + + def test_total_scans(self): + from acquisition_rate_analyzer import analyze_acquisition_rates + + exp = self._make_experiment() + result = analyze_acquisition_rates(exp) + assert result["summary"]["total_scans"] == 20 # 5 MS1 + 15 MS2 + + def test_ms1_ms2_counts(self): + from acquisition_rate_analyzer import analyze_acquisition_rates + + exp = self._make_experiment() + result = analyze_acquisition_rates(exp) + assert result["summary"]["ms1_count"] == 5 + assert result["summary"]["ms2_count"] == 15 + + def test_scan_records(self): + from acquisition_rate_analyzer import analyze_acquisition_rates + + exp = self._make_experiment() + result = analyze_acquisition_rates(exp) + assert len(result["scans"]) == 20 + + def test_empty_experiment(self): + import pyopenms as oms + from acquisition_rate_analyzer import analyze_acquisition_rates + + exp = oms.MSExperiment() + result = analyze_acquisition_rates(exp) + assert result["summary"]["total_scans"] == 0 + + def test_ms2_per_cycle(self): + from acquisition_rate_analyzer import analyze_acquisition_rates + + exp = self._make_experiment() + result = analyze_acquisition_rates(exp) + assert result["summary"]["avg_ms2_per_cycle"] == 3.0 diff --git a/scripts/proteomics/amino_acid_composition_analyzer/README.md b/scripts/proteomics/amino_acid_composition_analyzer/README.md new file mode 100644 index 0000000..4f102f0 --- /dev/null +++ b/scripts/proteomics/amino_acid_composition_analyzer/README.md @@ -0,0 +1,10 @@ +# Amino Acid Composition Analyzer + +Analyze amino acid frequency and properties for proteins in a FASTA file. + +## Usage + +```bash +python amino_acid_composition_analyzer.py --input proteins.fasta --output composition.tsv +python amino_acid_composition_analyzer.py --sequence PEPTIDEK --output composition.json +``` diff --git a/scripts/proteomics/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py b/scripts/proteomics/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py new file mode 100644 index 0000000..f4cbc88 --- /dev/null +++ b/scripts/proteomics/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py @@ -0,0 +1,149 @@ +""" +Amino Acid Composition Analyzer +================================= +Analyze amino acid frequency for proteins in a FASTA file. + +Features +-------- +- Per-protein amino acid counts and frequencies +- Summary statistics across all proteins +- Molecular weight distribution +- Output in TSV or JSON format + +Usage +----- + python amino_acid_composition_analyzer.py --input proteins.fasta --output composition.tsv + python amino_acid_composition_analyzer.py --sequence PEPTIDEK --output composition.json +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +STANDARD_AAS = "ACDEFGHIKLMNPQRSTVWY" + + +def analyze_composition(sequence: str) -> dict: + """Analyze amino acid composition of a sequence. + + Parameters + ---------- + sequence : str + Amino acid sequence (plain or pyopenms notation). + + Returns + ------- + dict + Dictionary with counts, frequencies, and molecular properties. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + length = len(plain) + + counts = {} + for aa in STANDARD_AAS: + counts[aa] = 0 + for aa in plain: + if aa in counts: + counts[aa] += 1 + + frequencies = {} + for aa, count in counts.items(): + frequencies[aa] = round(count / length, 4) if length > 0 else 0.0 + + mono_mass = aa_seq.getMonoWeight() + + # Group properties + basic = sum(counts.get(aa, 0) for aa in "KRH") + acidic = sum(counts.get(aa, 0) for aa in "DE") + hydrophobic = sum(counts.get(aa, 0) for aa in "AILMFWV") + polar = sum(counts.get(aa, 0) for aa in "STNQ") + aromatic = sum(counts.get(aa, 0) for aa in "FWY") + + return { + "sequence": sequence[:50] + "..." if len(sequence) > 50 else sequence, + "length": length, + "monoisotopic_mass": round(mono_mass, 6), + "counts": counts, + "frequencies": frequencies, + "basic_residues": basic, + "acidic_residues": acidic, + "hydrophobic_residues": hydrophobic, + "polar_residues": polar, + "aromatic_residues": aromatic, + } + + +def analyze_fasta(fasta_path: str) -> list: + """Analyze amino acid composition for all proteins in a FASTA file. + + Parameters + ---------- + fasta_path : str + Path to FASTA file. + + Returns + ------- + list + List of composition result dicts, one per protein. + """ + entries = [] + oms.FASTAFile().load(fasta_path, entries) + + results = [] + for entry in entries: + result = analyze_composition(entry.sequence) + result["accession"] = entry.identifier + results.append(result) + return results + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Analyze amino acid composition for FASTA proteins.") + parser.add_argument("--input", type=str, help="Protein FASTA file.") + parser.add_argument("--sequence", type=str, help="Single sequence to analyze.") + parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") + args = parser.parse_args() + + if not args.input and not args.sequence: + parser.error("Provide --input or --sequence.") + + if args.sequence: + results = [analyze_composition(args.sequence)] + else: + results = analyze_fasta(args.input) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(results if len(results) > 1 else results[0], fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + base_fields = ["accession", "length", "monoisotopic_mass", + "basic_residues", "acidic_residues", "hydrophobic_residues", + "polar_residues", "aromatic_residues"] + aa_fields = [f"count_{aa}" for aa in STANDARD_AAS] + fieldnames = base_fields + aa_fields + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") + writer.writeheader() + for r in results: + row = {k: r.get(k, "") for k in base_fields} + for aa in STANDARD_AAS: + row[f"count_{aa}"] = r["counts"].get(aa, 0) + writer.writerow(row) + print(f"Results written to {args.output}") + else: + for r in results: + acc = r.get("accession", r.get("sequence", "")) + print(f"{acc}\tlength={r['length']}\tmass={r['monoisotopic_mass']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/amino_acid_composition_analyzer/requirements.txt b/scripts/proteomics/amino_acid_composition_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/amino_acid_composition_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/amino_acid_composition_analyzer/tests/conftest.py b/scripts/proteomics/amino_acid_composition_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/amino_acid_composition_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py b/scripts/proteomics/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py new file mode 100644 index 0000000..9f07e9f --- /dev/null +++ b/scripts/proteomics/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py @@ -0,0 +1,72 @@ +"""Tests for amino_acid_composition_analyzer.""" + +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestAminoAcidCompositionAnalyzer: + def test_basic_composition(self): + from amino_acid_composition_analyzer import analyze_composition + + result = analyze_composition("AAAKKK") + assert result["length"] == 6 + assert result["counts"]["A"] == 3 + assert result["counts"]["K"] == 3 + assert abs(result["frequencies"]["A"] - 0.5) < 0.01 + + def test_all_standard_aas(self): + from amino_acid_composition_analyzer import STANDARD_AAS, analyze_composition + + result = analyze_composition("ACDEFGHIKLMNPQRSTVWY") + for aa in STANDARD_AAS: + assert result["counts"][aa] == 1 + + def test_group_counts(self): + from amino_acid_composition_analyzer import analyze_composition + + result = analyze_composition("KRHDE") + assert result["basic_residues"] == 3 # K, R, H + assert result["acidic_residues"] == 2 # D, E + + def test_hydrophobic_count(self): + from amino_acid_composition_analyzer import analyze_composition + + result = analyze_composition("AILMFWV") + assert result["hydrophobic_residues"] == 7 + + def test_mass_positive(self): + from amino_acid_composition_analyzer import analyze_composition + + result = analyze_composition("PEPTIDEK") + assert result["monoisotopic_mass"] > 0 + + def test_fasta_analysis(self): + import pyopenms as oms + from amino_acid_composition_analyzer import analyze_fasta + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = f"{tmpdir}/test.fasta" + entries = [] + e1 = oms.FASTAEntry() + e1.identifier = "PROT1" + e1.sequence = "MSPEPTIDEK" + entries.append(e1) + e2 = oms.FASTAEntry() + e2.identifier = "PROT2" + e2.sequence = "ANOTHERPEPTIDE" + entries.append(e2) + oms.FASTAFile().store(fasta_path, entries) + + results = analyze_fasta(fasta_path) + assert len(results) == 2 + assert results[0]["accession"] == "PROT1" + assert results[1]["accession"] == "PROT2" + + def test_empty_counts_for_missing_aa(self): + from amino_acid_composition_analyzer import analyze_composition + + result = analyze_composition("AAAA") + assert result["counts"]["W"] == 0 + assert result["frequencies"]["W"] == 0.0 diff --git a/scripts/proteomics/biomarker_panel_roc/README.md b/scripts/proteomics/biomarker_panel_roc/README.md new file mode 100644 index 0000000..fadd0e1 --- /dev/null +++ b/scripts/proteomics/biomarker_panel_roc/README.md @@ -0,0 +1,33 @@ +# Biomarker Panel ROC + +Compute ROC curves and AUC values for individual protein biomarkers and multi-marker panels. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python biomarker_panel_roc.py --input protein_quant.tsv --groups case,control --output roc.tsv +``` + +### Input format + +Tab-separated protein quantification matrix (rows=proteins, columns=samples): + +``` +protein_id case_1 case_2 control_1 control_2 +P12345 100.5 120.3 50.2 45.8 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Protein quantification TSV | +| `--groups` | Comma-separated group names: positive,negative | +| `--group-file` | Optional TSV mapping sample_id to group | +| `--output` | Output ROC/AUC TSV | diff --git a/scripts/proteomics/biomarker_panel_roc/biomarker_panel_roc.py b/scripts/proteomics/biomarker_panel_roc/biomarker_panel_roc.py new file mode 100644 index 0000000..49ad575 --- /dev/null +++ b/scripts/proteomics/biomarker_panel_roc/biomarker_panel_roc.py @@ -0,0 +1,268 @@ +""" +Biomarker Panel ROC +==================== +Compute ROC curves and AUC values for individual protein biomarkers and +simple multi-marker panels. For each protein, the tool computes a +receiver-operating-characteristic curve (sensitivity vs 1-specificity) +and the area under the curve (AUC) to evaluate discriminatory power +between case and control groups. + +For multi-marker panels, a simple sum-score is used to combine markers. + +Uses numpy and scipy for statistical computations. + +Usage +----- + python biomarker_panel_roc.py --input protein_quant.tsv \ + --groups case,control --output roc.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Tuple + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + + +def compute_roc( + scores: List[float], labels: List[int] +) -> Tuple[List[float], List[float], float]: + """Compute ROC curve and AUC for binary classification. + + Parameters + ---------- + scores: + Numeric scores (higher = more likely positive). + labels: + Binary labels (1 = positive/case, 0 = negative/control). + + Returns + ------- + tuple + (fpr_list, tpr_list, auc) where fpr_list and tpr_list define + the ROC curve points and auc is the area under the curve. + """ + scores_arr = np.array(scores, dtype=float) + labels_arr = np.array(labels, dtype=int) + + # Sort by score descending + order = np.argsort(-scores_arr) + sorted_labels = labels_arr[order] + + n_pos = np.sum(labels_arr == 1) + n_neg = np.sum(labels_arr == 0) + + if n_pos == 0 or n_neg == 0: + return [0.0, 1.0], [0.0, 1.0], 0.5 + + tp = 0 + fp = 0 + fpr_list = [0.0] + tpr_list = [0.0] + + for label in sorted_labels: + if label == 1: + tp += 1 + else: + fp += 1 + fpr_list.append(fp / n_neg) + tpr_list.append(tp / n_pos) + + # AUC via trapezoidal rule + auc = 0.0 + for i in range(1, len(fpr_list)): + auc += (fpr_list[i] - fpr_list[i - 1]) * (tpr_list[i] + tpr_list[i - 1]) / 2.0 + + return fpr_list, tpr_list, auc + + +def analyze_biomarkers( + quant_data: Dict[str, Dict[str, float]], + sample_groups: Dict[str, int], +) -> List[Dict[str, object]]: + """Compute ROC/AUC for each protein. + + Parameters + ---------- + quant_data: + Mapping of protein_id to {sample_id: abundance}. + sample_groups: + Mapping of sample_id to label (1=case, 0=control). + + Returns + ------- + list of dict + One entry per protein with ``protein_id``, ``auc``, ``direction``. + """ + results: List[Dict[str, object]] = [] + + for protein_id, abundances in quant_data.items(): + scores = [] + labels = [] + for sample_id, label in sample_groups.items(): + if sample_id in abundances: + scores.append(abundances[sample_id]) + labels.append(label) + + if len(scores) < 4: + continue + + _, _, auc = compute_roc(scores, labels) + + # If AUC < 0.5, flip direction (lower values = case) + direction = "up" + if auc < 0.5: + flipped_scores = [-s for s in scores] + _, _, auc_flipped = compute_roc(flipped_scores, labels) + auc = auc_flipped + direction = "down" + + results.append({ + "protein_id": protein_id, + "auc": auc, + "direction": direction, + "n_case": sum(1 for lab in labels if lab == 1), + "n_control": sum(1 for lab in labels if lab == 0), + }) + + results.sort(key=lambda x: x["auc"], reverse=True) + return results + + +def panel_score( + quant_data: Dict[str, Dict[str, float]], + sample_groups: Dict[str, int], + top_markers: List[str], +) -> Tuple[List[float], List[float], float]: + """Compute a combined panel ROC using sum-score of top markers. + + Parameters + ---------- + quant_data: + Per-protein abundances. + sample_groups: + Sample labels. + top_markers: + List of protein IDs to include in the panel. + + Returns + ------- + tuple + (fpr, tpr, auc) for the combined panel. + """ + sample_scores: Dict[str, float] = {} + for sample_id in sample_groups: + total = 0.0 + count = 0 + for prot in top_markers: + if prot in quant_data and sample_id in quant_data[prot]: + total += quant_data[prot][sample_id] + count += 1 + if count > 0: + sample_scores[sample_id] = total / count + + scores = [] + labels = [] + for sample_id, score in sample_scores.items(): + scores.append(score) + labels.append(sample_groups[sample_id]) + + if len(scores) < 4: + return [0.0, 1.0], [0.0, 1.0], 0.5 + + return compute_roc(scores, labels) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Compute ROC/AUC for protein biomarker panels." + ) + parser.add_argument( + "--input", required=True, + help="Input TSV: rows=proteins, columns=samples, first column=protein_id", + ) + parser.add_argument( + "--groups", required=True, + help="Comma-separated group names: positive,negative (e.g. case,control)", + ) + parser.add_argument( + "--group-file", default=None, + help="Optional TSV mapping sample_id to group. If not provided, " + "column names must contain group labels.", + ) + parser.add_argument("--output", required=True, help="Output ROC/AUC TSV") + args = parser.parse_args() + + group_names = [g.strip() for g in args.groups.split(",")] + if len(group_names) != 2: + sys.exit("--groups must specify exactly two groups: positive,negative") + pos_group, neg_group = group_names + + # Read quantification matrix + quant_data: Dict[str, Dict[str, float]] = {} + sample_ids: List[str] = [] + + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + fields = reader.fieldnames or [] + sample_ids = [f for f in fields if f != "protein_id"] + for row in reader: + pid = row.get("protein_id", "").strip() + if not pid: + continue + abundances: Dict[str, float] = {} + for sid in sample_ids: + val = row.get(sid, "").strip() + try: + abundances[sid] = float(val) + except (ValueError, TypeError): + pass + quant_data[pid] = abundances + + # Determine sample groups + sample_groups: Dict[str, int] = {} + if args.group_file: + with open(args.group_file, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + sid = row.get("sample_id", "").strip() + grp = row.get("group", "").strip() + if sid and grp == pos_group: + sample_groups[sid] = 1 + elif sid and grp == neg_group: + sample_groups[sid] = 0 + else: + # Infer from column names containing group labels + for sid in sample_ids: + if pos_group in sid: + sample_groups[sid] = 1 + elif neg_group in sid: + sample_groups[sid] = 0 + + if not sample_groups: + sys.exit("Could not assign any samples to groups. Check --groups or --group-file.") + + results = analyze_biomarkers(quant_data, sample_groups) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein_id", "auc", "direction", "n_case", "n_control"]) + for r in results: + writer.writerow([ + r["protein_id"], f"{r['auc']:.4f}", + r["direction"], r["n_case"], r["n_control"], + ]) + + if results: + print(f"Top marker: {results[0]['protein_id']} AUC={results[0]['auc']:.4f}") + print(f"Analyzed {len(results)} proteins -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/biomarker_panel_roc/requirements.txt b/scripts/proteomics/biomarker_panel_roc/requirements.txt new file mode 100644 index 0000000..ba577e4 --- /dev/null +++ b/scripts/proteomics/biomarker_panel_roc/requirements.txt @@ -0,0 +1,3 @@ +pyopenms +numpy +scipy diff --git a/scripts/proteomics/biomarker_panel_roc/tests/conftest.py b/scripts/proteomics/biomarker_panel_roc/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/biomarker_panel_roc/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/biomarker_panel_roc/tests/test_biomarker_panel_roc.py b/scripts/proteomics/biomarker_panel_roc/tests/test_biomarker_panel_roc.py new file mode 100644 index 0000000..d454d45 --- /dev/null +++ b/scripts/proteomics/biomarker_panel_roc/tests/test_biomarker_panel_roc.py @@ -0,0 +1,77 @@ +"""Tests for biomarker_panel_roc.""" + +import csv +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_compute_roc_perfect(): + from biomarker_panel_roc import compute_roc + + # Perfect separation: all cases have higher scores + scores = [10, 9, 8, 7, 1, 2, 3, 4] + labels = [1, 1, 1, 1, 0, 0, 0, 0] + fpr, tpr, auc = compute_roc(scores, labels) + assert abs(auc - 1.0) < 0.01 + + +@requires_pyopenms +def test_compute_roc_random(): + from biomarker_panel_roc import compute_roc + + # No separation + scores = [1, 2, 3, 4, 5, 6, 7, 8] + labels = [1, 0, 1, 0, 1, 0, 1, 0] + _, _, auc = compute_roc(scores, labels) + assert 0.3 < auc < 0.8 # near 0.5 + + +@requires_pyopenms +def test_compute_roc_all_same_class(): + from biomarker_panel_roc import compute_roc + + scores = [1, 2, 3] + labels = [1, 1, 1] + _, _, auc = compute_roc(scores, labels) + assert auc == 0.5 # fallback + + +@requires_pyopenms +def test_analyze_biomarkers(): + from biomarker_panel_roc import analyze_biomarkers + + quant = { + "P1": {"case_1": 100, "case_2": 110, "control_1": 50, "control_2": 45}, + "P2": {"case_1": 10, "case_2": 12, "control_1": 10, "control_2": 11}, + } + groups = {"case_1": 1, "case_2": 1, "control_1": 0, "control_2": 0} + results = analyze_biomarkers(quant, groups) + assert len(results) == 2 + # P1 should have higher AUC (better separation) + assert results[0]["protein_id"] == "P1" + assert results[0]["auc"] > results[1]["auc"] + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from biomarker_panel_roc import main + + input_file = tmp_path / "input.tsv" + output_file = tmp_path / "output.tsv" + + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein_id", "case_1", "case_2", "control_1", "control_2"]) + writer.writerow(["P1", "100", "110", "50", "45"]) + writer.writerow(["P2", "10", "12", "10", "11"]) + + sys.argv = [ + "biomarker_panel_roc.py", + "--input", str(input_file), + "--groups", "case,control", + "--output", str(output_file), + ] + main() + assert output_file.exists() diff --git a/scripts/proteomics/charge_state_predictor/README.md b/scripts/proteomics/charge_state_predictor/README.md new file mode 100644 index 0000000..864ed03 --- /dev/null +++ b/scripts/proteomics/charge_state_predictor/README.md @@ -0,0 +1,9 @@ +# Charge State Predictor + +Predict charge state distribution for peptides based on basic residues and pH conditions. + +## Usage + +```bash +python charge_state_predictor.py --sequence PEPTIDEK --ph 2.0 --output charges.json +``` diff --git a/scripts/proteomics/charge_state_predictor/charge_state_predictor.py b/scripts/proteomics/charge_state_predictor/charge_state_predictor.py new file mode 100644 index 0000000..7bb4f64 --- /dev/null +++ b/scripts/proteomics/charge_state_predictor/charge_state_predictor.py @@ -0,0 +1,157 @@ +""" +Charge State Predictor +======================== +Predict charge state distribution for peptides based on basic residues and ionization. + +Features +-------- +- Predict likely charge states based on number of basic residues +- Estimate charge state probabilities using Henderson-Hasselbalch +- Account for N-terminal amine and side-chain basicities +- Support for different pH conditions (ESI, MALDI) + +Usage +----- + python charge_state_predictor.py --sequence PEPTIDEK --ph 2.0 --output charges.json +""" + +import argparse +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# pKa values for protonatable groups in ESI conditions +PKA_VALUES = { + "nterm": 7.7, # alpha-amino group + "K": 10.5, + "R": 12.5, + "H": 6.0, +} + + +def count_basic_sites(sequence: str) -> dict: + """Count protonatable basic sites in a peptide. + + Parameters + ---------- + sequence : str + Amino acid sequence. + + Returns + ------- + dict + Counts of each type of basic site. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + + sites = {"nterm": 1, "K": 0, "R": 0, "H": 0} + for aa in plain: + if aa in sites: + sites[aa] += 1 + + sites["total"] = sum(sites.values()) + return sites + + +def predict_charge_states(sequence: str, ph: float = 2.0, max_charge: int = 0) -> dict: + """Predict charge state distribution for a peptide. + + Parameters + ---------- + sequence : str + Peptide sequence. + ph : float + Solution pH (default 2.0 for typical ESI conditions). + max_charge : int + Maximum charge to consider (0 = auto from basic sites). + + Returns + ------- + dict + Dictionary with charge state probabilities and m/z values. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + mono_mass = aa_seq.getMonoWeight() + + basic_sites = count_basic_sites(plain) + total_basic = basic_sites["total"] + + if max_charge == 0: + max_charge = min(total_basic, 8) # Cap at 8 + + # Calculate protonation probability for each site + protonation_probs = {} + protonation_probs["nterm"] = 1.0 / (1.0 + 10 ** (ph - PKA_VALUES["nterm"])) + for aa in ["K", "R", "H"]: + if basic_sites[aa] > 0: + protonation_probs[aa] = 1.0 / (1.0 + 10 ** (ph - PKA_VALUES[aa])) + + # Expected charge = sum of protonation probabilities across all sites + expected_charge = protonation_probs["nterm"] + for aa in ["K", "R", "H"]: + expected_charge += protonation_probs.get(aa, 0.0) * basic_sites[aa] + + # Generate charge state distribution (simplified model) + # Use a distribution centered around expected charge + charge_states = [] + raw_weights = [] + for z in range(1, max_charge + 1): + # Simple Gaussian-like weighting around expected charge + weight = 2.71828 ** (-0.5 * ((z - expected_charge) ** 2)) + raw_weights.append((z, weight)) + + total_weight = sum(w for _, w in raw_weights) + + for z, weight in raw_weights: + probability = weight / total_weight if total_weight > 0 else 0.0 + mz = (mono_mass + z * PROTON) / z + charge_states.append({ + "charge": z, + "probability": round(probability, 4), + "mz": round(mz, 6), + }) + + # Sort by probability descending + charge_states.sort(key=lambda x: x["probability"], reverse=True) + + return { + "sequence": sequence, + "unmodified_sequence": plain, + "monoisotopic_mass": round(mono_mass, 6), + "ph": ph, + "basic_sites": basic_sites, + "expected_charge": round(expected_charge, 2), + "most_likely_charge": charge_states[0]["charge"] if charge_states else 1, + "charge_states": charge_states, + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Predict peptide charge state distribution.") + parser.add_argument("--sequence", required=True, help="Peptide sequence.") + parser.add_argument("--ph", type=float, default=2.0, help="Solution pH (default: 2.0 for ESI).") + parser.add_argument("--max-charge", type=int, default=0, help="Max charge state (0 = auto).") + parser.add_argument("--output", type=str, help="Output file (.json).") + args = parser.parse_args() + + result = predict_charge_states(args.sequence, args.ph, args.max_charge) + + if args.output: + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + print(f"Results written to {args.output}") + else: + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/charge_state_predictor/requirements.txt b/scripts/proteomics/charge_state_predictor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/charge_state_predictor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/charge_state_predictor/tests/conftest.py b/scripts/proteomics/charge_state_predictor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/charge_state_predictor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/charge_state_predictor/tests/test_charge_state_predictor.py b/scripts/proteomics/charge_state_predictor/tests/test_charge_state_predictor.py new file mode 100644 index 0000000..9fff871 --- /dev/null +++ b/scripts/proteomics/charge_state_predictor/tests/test_charge_state_predictor.py @@ -0,0 +1,64 @@ +"""Tests for charge_state_predictor.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestChargeStatePredictor: + def test_basic_sites_counting(self): + from charge_state_predictor import count_basic_sites + + sites = count_basic_sites("PEPTIDEK") + assert sites["nterm"] == 1 + assert sites["K"] == 1 + assert sites["total"] == 2 + + def test_arginine_sites(self): + from charge_state_predictor import count_basic_sites + + sites = count_basic_sites("PEPTIDERR") + assert sites["R"] == 2 + + def test_histidine_sites(self): + from charge_state_predictor import count_basic_sites + + sites = count_basic_sites("HPEPTIDEH") + assert sites["H"] == 2 + + def test_predict_charge_states(self): + from charge_state_predictor import predict_charge_states + + result = predict_charge_states("PEPTIDEK", ph=2.0) + assert len(result["charge_states"]) > 0 + assert result["monoisotopic_mass"] > 0 + # Probabilities should sum to ~1 + total_prob = sum(cs["probability"] for cs in result["charge_states"]) + assert abs(total_prob - 1.0) < 0.01 + + def test_more_basic_residues_higher_charge(self): + from charge_state_predictor import predict_charge_states + + few_basic = predict_charge_states("PEPTIDEA", ph=2.0) + many_basic = predict_charge_states("KPEPTIDEKRKH", ph=2.0) + assert many_basic["expected_charge"] > few_basic["expected_charge"] + + def test_low_ph_higher_charge(self): + from charge_state_predictor import predict_charge_states + + low_ph = predict_charge_states("PEPTIDEK", ph=1.0) + high_ph = predict_charge_states("PEPTIDEK", ph=7.0) + assert low_ph["expected_charge"] >= high_ph["expected_charge"] + + def test_mz_values(self): + from charge_state_predictor import PROTON, predict_charge_states + + result = predict_charge_states("PEPTIDEK", ph=2.0) + for cs in result["charge_states"]: + expected_mz = (result["monoisotopic_mass"] + cs["charge"] * PROTON) / cs["charge"] + assert abs(cs["mz"] - expected_mz) < 0.001 + + def test_most_likely_charge(self): + from charge_state_predictor import predict_charge_states + + result = predict_charge_states("PEPTIDEK", ph=2.0) + assert result["most_likely_charge"] >= 1 diff --git a/scripts/proteomics/cleavage_site_profiler/README.md b/scripts/proteomics/cleavage_site_profiler/README.md new file mode 100644 index 0000000..cdcb46c --- /dev/null +++ b/scripts/proteomics/cleavage_site_profiler/README.md @@ -0,0 +1,19 @@ +# Cleavage Site Profiler + +Extract P4-P4' windows around cleavage sites from neo-N-terminal peptides and compute position-specific frequencies. + +## Usage + +```bash +python cleavage_site_profiler.py --input neo_nterm.tsv --fasta reference.fasta --window 4 --output profile.tsv +``` + +## Input Format + +- `neo_nterm.tsv`: columns `peptide`, `protein` +- `reference.fasta`: Reference proteome FASTA file + +## Output + +- `profile.tsv` - Peptides with cleavage windows +- `profile_frequencies.tsv` - Position-specific amino acid frequencies (P4..P1, P1'..P4') diff --git a/scripts/proteomics/cleavage_site_profiler/cleavage_site_profiler.py b/scripts/proteomics/cleavage_site_profiler/cleavage_site_profiler.py new file mode 100644 index 0000000..c1ee7be --- /dev/null +++ b/scripts/proteomics/cleavage_site_profiler/cleavage_site_profiler.py @@ -0,0 +1,279 @@ +""" +Cleavage Site Profiler +====================== +From neo-N-terminal peptides, extract P4-P4' windows around cleavage sites and compute +position-specific amino acid frequencies. + +The P4-P4' nomenclature (Schechter & Berger) describes residues surrounding a cleavage +site: P4-P3-P2-P1 | P1'-P2'-P3'-P4' where | is the cleavage site. + +Usage +----- + python cleavage_site_profiler.py --input neo_nterm.tsv --fasta reference.fasta --window 4 --output profile.tsv +""" + +import argparse +import csv +import sys +from collections import Counter +from typing import Dict, List, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(fasta_path: str) -> Dict[str, str]: + """Load a FASTA file into a dictionary mapping accession to sequence. + + Parameters + ---------- + fasta_path: + Path to the FASTA file. + + Returns + ------- + dict + Mapping of protein accession to amino acid sequence. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + + proteins = {} + for entry in entries: + acc = entry.identifier.split()[0] if entry.identifier else "" + proteins[acc] = entry.sequence + return proteins + + +def get_clean_sequence(sequence: str) -> str: + """Get clean amino acid sequence using AASequence. + + Parameters + ---------- + sequence: + Peptide sequence, possibly modified. + + Returns + ------- + str + Clean unmodified sequence. + """ + try: + aa = oms.AASequence.fromString(sequence) + return aa.toUnmodifiedString() + except Exception: + import re + clean = re.sub(r"\[.*?\]", "", sequence) + clean = re.sub(r"\(.*?\)", "", clean) + return clean + + +def find_cleavage_position(peptide: str, protein: str, protein_seq: str) -> int: + """Find the cleavage site position in the protein sequence. + + The cleavage site is the position just before the neo-N-terminal peptide starts. + + Parameters + ---------- + peptide: + Neo-N-terminal peptide sequence (clean). + protein: + Protein accession (unused, kept for API consistency). + protein_seq: + Full protein sequence. + + Returns + ------- + int + 0-based position of the cleavage site (P1 position), or -1 if not found. + """ + idx = protein_seq.find(peptide) + if idx <= 0: + return -1 + # Cleavage site is between position idx-1 (P1) and idx (P1') + return idx + + +def extract_cleavage_window(protein_seq: str, cleavage_pos: int, window: int = 4) -> str: + """Extract P(window)...P1-P1'...P(window)' window around a cleavage site. + + Parameters + ---------- + protein_seq: + Full protein sequence. + cleavage_pos: + 0-based position of P1' (first residue of neo-N-terminal peptide). + window: + Number of residues on each side of the cleavage (default 4 for P4-P4'). + + Returns + ------- + str + Window of length 2*window, padded with '_' at termini. + """ + result = [] + # P-side: positions cleavage_pos-window to cleavage_pos-1 (P4..P1) + for i in range(cleavage_pos - window, cleavage_pos): + if i < 0 or i >= len(protein_seq): + result.append("_") + else: + result.append(protein_seq[i]) + # P'-side: positions cleavage_pos to cleavage_pos+window-1 (P1'..P4') + for i in range(cleavage_pos, cleavage_pos + window): + if i < 0 or i >= len(protein_seq): + result.append("_") + else: + result.append(protein_seq[i]) + return "".join(result) + + +def compute_position_frequencies( + windows: List[str], window: int = 4 +) -> Dict[str, Dict[str, float]]: + """Compute position-specific amino acid frequencies. + + Parameters + ---------- + windows: + List of cleavage site window strings. + window: + Window size on each side. + + Returns + ------- + dict + Mapping of position label (P4..P1, P1'..P4') to amino acid frequency dict. + """ + total = len(windows) + if total == 0: + return {} + + labels = [] + for i in range(window, 0, -1): + labels.append(f"P{i}") + for i in range(1, window + 1): + labels.append(f"P{i}'") + + frequencies: Dict[str, Dict[str, float]] = {} + for pos_idx, label in enumerate(labels): + counter: Counter = Counter() + for w in windows: + if pos_idx < len(w): + counter[w[pos_idx]] += 1 + frequencies[label] = {aa: count / total for aa, count in counter.most_common()} + + return frequencies + + +def process_neo_nterm_peptides( + rows: List[Dict[str, str]], + proteins: Dict[str, str], + window: int = 4, +) -> Tuple[List[Dict[str, str]], List[str]]: + """Process neo-N-terminal peptides to extract cleavage windows. + + Parameters + ---------- + rows: + List of dicts with keys: peptide, protein. + proteins: + Protein accession to sequence mapping. + window: + Cleavage window size. + + Returns + ------- + tuple + (result_rows with added cleavage_window, list of valid windows) + """ + results = [] + valid_windows = [] + + for row in rows: + peptide_raw = row["peptide"] + protein_id = row["protein"] + clean_seq = get_clean_sequence(peptide_raw) + + new_row = dict(row) + new_row["clean_sequence"] = clean_seq + + if protein_id not in proteins: + new_row["cleavage_window"] = "_" * (2 * window) + new_row["cleavage_found"] = "NO" + results.append(new_row) + continue + + protein_seq = proteins[protein_id] + cleavage_pos = find_cleavage_position(clean_seq, protein_id, protein_seq) + + if cleavage_pos < 0: + new_row["cleavage_window"] = "_" * (2 * window) + new_row["cleavage_found"] = "NO" + else: + w = extract_cleavage_window(protein_seq, cleavage_pos, window) + new_row["cleavage_window"] = w + new_row["cleavage_found"] = "YES" + valid_windows.append(w) + + results.append(new_row) + + return results, valid_windows + + +def read_input(input_path: str) -> List[Dict[str, str]]: + """Read neo-N-terminal peptides TSV file.""" + rows = [] + with open(input_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output( + output_path: str, + result_rows: List[Dict[str, str]], + frequencies: Dict[str, Dict[str, float]], +) -> None: + """Write cleavage profiles to output files.""" + if result_rows: + fieldnames = list(result_rows[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(result_rows) + + freq_path = output_path.replace(".tsv", "_frequencies.tsv") + with open(freq_path, "w", newline="") as f: + f.write("position\tamino_acid\tfrequency\n") + for pos_label in frequencies: + for aa, freq in sorted(frequencies[pos_label].items(), key=lambda x: -x[1]): + f.write(f"{pos_label}\t{aa}\t{freq:.4f}\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Profile cleavage sites from neo-N-terminal peptides." + ) + parser.add_argument("--input", required=True, help="Neo-N-terminal peptides TSV file") + parser.add_argument("--fasta", required=True, help="Reference proteome FASTA file") + parser.add_argument("--window", type=int, default=4, help="Window size on each side (default: 4)") + parser.add_argument("--output", required=True, help="Output profile TSV file") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + rows = read_input(args.input) + result_rows, valid_windows = process_neo_nterm_peptides(rows, proteins, args.window) + frequencies = compute_position_frequencies(valid_windows, args.window) + write_output(args.output, result_rows, frequencies) + + print(f"Total peptides: {len(result_rows)}") + print(f"Cleavage sites found: {len(valid_windows)}") + print(f"Window size: P{args.window}-P{args.window}'") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/cleavage_site_profiler/requirements.txt b/scripts/proteomics/cleavage_site_profiler/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/cleavage_site_profiler/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/cleavage_site_profiler/tests/conftest.py b/scripts/proteomics/cleavage_site_profiler/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/cleavage_site_profiler/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/cleavage_site_profiler/tests/test_cleavage_site_profiler.py b/scripts/proteomics/cleavage_site_profiler/tests/test_cleavage_site_profiler.py new file mode 100644 index 0000000..7bf4a19 --- /dev/null +++ b/scripts/proteomics/cleavage_site_profiler/tests/test_cleavage_site_profiler.py @@ -0,0 +1,122 @@ +"""Tests for cleavage_site_profiler.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestCleavageSiteProfiler: + def _create_fasta(self, tmpdir, proteins): + """Helper to create a FASTA file.""" + import pyopenms as oms + + fasta_path = os.path.join(tmpdir, "reference.fasta") + entries = [] + for acc, seq in proteins.items(): + entry = oms.FASTAEntry() + entry.identifier = acc + entry.sequence = seq + entries.append(entry) + fasta_file = oms.FASTAFile() + fasta_file.store(fasta_path, entries) + return fasta_path + + def test_get_clean_sequence(self): + from cleavage_site_profiler import get_clean_sequence + assert get_clean_sequence("PEPTIDEK") == "PEPTIDEK" + + def test_find_cleavage_position(self): + from cleavage_site_profiler import find_cleavage_position + protein_seq = "ABCDEFGHIJKLMNOP" + # Peptide "GHIJ" starts at position 6 in the protein + pos = find_cleavage_position("GHIJ", "P1", protein_seq) + assert pos == 6 + + def test_find_cleavage_position_at_start(self): + from cleavage_site_profiler import find_cleavage_position + protein_seq = "ABCDEFGHIJ" + # Peptide at position 0 -> no cleavage site (it's the native N-term) + pos = find_cleavage_position("ABCD", "P1", protein_seq) + assert pos == -1 + + def test_find_cleavage_position_not_found(self): + from cleavage_site_profiler import find_cleavage_position + pos = find_cleavage_position("ZZZZZ", "P1", "ABCDEFGH") + assert pos == -1 + + def test_extract_cleavage_window_center(self): + from cleavage_site_profiler import extract_cleavage_window + protein_seq = "ABCDEFGHIJKLMNOP" + # Cleavage at position 8 (between H and I) + window = extract_cleavage_window(protein_seq, 8, 4) + assert window == "EFGHIJKL" + assert len(window) == 8 + + def test_extract_cleavage_window_near_start(self): + from cleavage_site_profiler import extract_cleavage_window + protein_seq = "ABCDEFGHIJ" + # Cleavage at position 2 + window = extract_cleavage_window(protein_seq, 2, 4) + assert window == "__ABCDEF" + assert len(window) == 8 + + def test_extract_cleavage_window_near_end(self): + from cleavage_site_profiler import extract_cleavage_window + protein_seq = "ABCDEFGHIJ" + # Cleavage at position 8 + window = extract_cleavage_window(protein_seq, 8, 4) + assert window == "EFGHIJ__" + assert len(window) == 8 + + def test_compute_position_frequencies(self): + from cleavage_site_profiler import compute_position_frequencies + windows = ["AABBCCDD", "AABBCCDD"] + freq = compute_position_frequencies(windows, window=4) + assert "P4" in freq + assert "P1" in freq + assert "P1'" in freq + assert "P4'" in freq + assert freq["P4"]["A"] == 1.0 + + def test_compute_position_frequencies_empty(self): + from cleavage_site_profiler import compute_position_frequencies + assert compute_position_frequencies([], window=4) == {} + + def test_process_neo_nterm_peptides(self): + from cleavage_site_profiler import load_fasta, process_neo_nterm_peptides + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "ABCDEFGHIJKLMNOP"}) + proteins = load_fasta(fasta_path) + rows = [{"peptide": "GHIJ", "protein": "P1"}] + result_rows, valid_windows = process_neo_nterm_peptides(rows, proteins, 4) + assert len(result_rows) == 1 + assert result_rows[0]["cleavage_found"] == "YES" + assert len(valid_windows) == 1 + + def test_process_missing_protein(self): + from cleavage_site_profiler import process_neo_nterm_peptides + rows = [{"peptide": "ABCD", "protein": "MISSING"}] + result_rows, valid_windows = process_neo_nterm_peptides(rows, {}, 4) + assert result_rows[0]["cleavage_found"] == "NO" + assert len(valid_windows) == 0 + + def test_full_pipeline(self): + from cleavage_site_profiler import ( + compute_position_frequencies, + load_fasta, + process_neo_nterm_peptides, + write_output, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "ABCDEFGHIJKLMNOP"}) + proteins = load_fasta(fasta_path) + rows = [{"peptide": "GHIJ", "protein": "P1"}] + result_rows, valid_windows = process_neo_nterm_peptides(rows, proteins, 4) + freq = compute_position_frequencies(valid_windows, 4) + output_path = os.path.join(tmpdir, "profile.tsv") + write_output(output_path, result_rows, freq) + assert os.path.exists(output_path) diff --git a/scripts/proteomics/coefficient_of_variation_calculator/README.md b/scripts/proteomics/coefficient_of_variation_calculator/README.md new file mode 100644 index 0000000..983cd38 --- /dev/null +++ b/scripts/proteomics/coefficient_of_variation_calculator/README.md @@ -0,0 +1,14 @@ +# Coefficient of Variation Calculator + +Calculate CV% (coefficient of variation) across replicates for each feature. + +## Usage + +```bash +python coefficient_of_variation_calculator.py --input matrix.tsv --groups groups.tsv --output cv_report.tsv +``` + +## Input Files + +- **matrix.tsv** - Quantification matrix (rows=features, columns=samples) +- **groups.tsv** - Group assignments with columns: `sample`, `group` diff --git a/scripts/proteomics/coefficient_of_variation_calculator/coefficient_of_variation_calculator.py b/scripts/proteomics/coefficient_of_variation_calculator/coefficient_of_variation_calculator.py new file mode 100644 index 0000000..3a50107 --- /dev/null +++ b/scripts/proteomics/coefficient_of_variation_calculator/coefficient_of_variation_calculator.py @@ -0,0 +1,164 @@ +""" +Coefficient of Variation Calculator +==================================== +Calculate CV% (coefficient of variation) across replicates for each feature. + +Reads a quantification matrix and a groups file that assigns samples to groups, +then computes the CV within each group for each feature. + +Usage +----- + python coefficient_of_variation_calculator.py --input matrix.tsv --groups groups.tsv --output cv_report.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + + +def read_matrix(filepath: str) -> tuple: + """Read a TSV quantification matrix. + + Returns (row_ids, col_names, data_matrix). + """ + with open(filepath) as fh: + reader = csv.reader(fh, delimiter="\t") + header = next(reader) + col_names = header[1:] + row_ids = [] + rows = [] + for row in reader: + row_ids.append(row[0]) + values = [] + for v in row[1:]: + v = v.strip() + if v == "" or v.upper() in ("NA", "NAN"): + values.append(np.nan) + else: + values.append(float(v)) + rows.append(values) + return row_ids, col_names, np.array(rows, dtype=float) + + +def read_groups(filepath: str) -> dict: + """Read a groups file mapping samples to groups. + + Expected format: TSV with columns 'sample' and 'group'. + + Returns + ------- + dict + {sample_name: group_name} + """ + groups = {} + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + groups[row["sample"]] = row["group"] + return groups + + +def calculate_cv(matrix: np.ndarray, row_ids: list, col_names: list, groups: dict) -> list: + """Calculate CV% for each feature within each group. + + Parameters + ---------- + matrix: + 2D array (features x samples). + row_ids: + Feature identifiers. + col_names: + Sample names. + groups: + {sample: group} mapping. + + Returns + ------- + list + List of dicts with keys: feature, group, mean, sd, cv_percent, n_values. + """ + group_names = sorted(set(groups.values())) + group_indices = {} + for g in group_names: + group_indices[g] = [i for i, s in enumerate(col_names) if groups.get(s) == g] + + results = [] + for row_idx, feature in enumerate(row_ids): + for group in group_names: + indices = group_indices[group] + if not indices: + continue + values = matrix[row_idx, indices] + valid = values[~np.isnan(values)] + n_valid = len(valid) + if n_valid < 2: + mean_val = np.mean(valid) if n_valid > 0 else float("nan") + results.append({ + "feature": feature, + "group": group, + "mean": mean_val, + "sd": float("nan"), + "cv_percent": float("nan"), + "n_values": n_valid, + }) + else: + mean_val = np.mean(valid) + sd_val = np.std(valid, ddof=1) + cv = (sd_val / mean_val * 100.0) if mean_val != 0 else float("nan") + results.append({ + "feature": feature, + "group": group, + "mean": mean_val, + "sd": sd_val, + "cv_percent": cv, + "n_values": n_valid, + }) + return results + + +def main(): + parser = argparse.ArgumentParser(description="Calculate CV% across replicates.") + parser.add_argument("--input", required=True, help="Input TSV matrix file") + parser.add_argument("--groups", required=True, help="Groups TSV (columns: sample, group)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + row_ids, col_names, matrix = read_matrix(args.input) + groups = read_groups(args.groups) + results = calculate_cv(matrix, row_ids, col_names, groups) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["feature", "group", "mean", "sd", "cv_percent", "n_values"], + delimiter="\t", + ) + writer.writeheader() + for r in results: + writer.writerow({ + "feature": r["feature"], + "group": r["group"], + "mean": f"{r['mean']:.6f}" if not np.isnan(r["mean"]) else "NA", + "sd": f"{r['sd']:.6f}" if not np.isnan(r["sd"]) else "NA", + "cv_percent": f"{r['cv_percent']:.2f}" if not np.isnan(r["cv_percent"]) else "NA", + "n_values": r["n_values"], + }) + + valid_cvs = [r["cv_percent"] for r in results if not np.isnan(r["cv_percent"])] + if valid_cvs: + print(f"Features: {len(row_ids)}") + print(f"Groups: {len(set(groups.values()))}") + print(f"Median CV%: {np.median(valid_cvs):.2f}") + print(f"Mean CV%: {np.mean(valid_cvs):.2f}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/coefficient_of_variation_calculator/requirements.txt b/scripts/proteomics/coefficient_of_variation_calculator/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/proteomics/coefficient_of_variation_calculator/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/proteomics/coefficient_of_variation_calculator/tests/conftest.py b/scripts/proteomics/coefficient_of_variation_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/coefficient_of_variation_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/coefficient_of_variation_calculator/tests/test_coefficient_of_variation_calculator.py b/scripts/proteomics/coefficient_of_variation_calculator/tests/test_coefficient_of_variation_calculator.py new file mode 100644 index 0000000..35c2088 --- /dev/null +++ b/scripts/proteomics/coefficient_of_variation_calculator/tests/test_coefficient_of_variation_calculator.py @@ -0,0 +1,69 @@ +"""Tests for coefficient_of_variation_calculator.""" + +import numpy as np +from coefficient_of_variation_calculator import calculate_cv, read_groups +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestCoefficientOfVariationCalculator: + def _make_data(self): + matrix = np.array([ + [100.0, 110.0, 105.0, 200.0, 220.0, 210.0], + [500.0, 500.0, 500.0, 1000.0, 1000.0, 1000.0], # zero CV + ]) + row_ids = ["prot1", "prot2"] + col_names = ["s1", "s2", "s3", "s4", "s5", "s6"] + groups = {"s1": "A", "s2": "A", "s3": "A", "s4": "B", "s5": "B", "s6": "B"} + return matrix, row_ids, col_names, groups + + def test_basic_cv(self): + matrix, row_ids, col_names, groups = self._make_data() + results = calculate_cv(matrix, row_ids, col_names, groups) + assert len(results) == 4 # 2 features x 2 groups + + def test_zero_cv(self): + matrix, row_ids, col_names, groups = self._make_data() + results = calculate_cv(matrix, row_ids, col_names, groups) + prot2_a = next(r for r in results if r["feature"] == "prot2" and r["group"] == "A") + assert abs(prot2_a["cv_percent"]) < 1e-6 + + def test_cv_positive(self): + matrix, row_ids, col_names, groups = self._make_data() + results = calculate_cv(matrix, row_ids, col_names, groups) + prot1_a = next(r for r in results if r["feature"] == "prot1" and r["group"] == "A") + assert prot1_a["cv_percent"] > 0 + + def test_n_values(self): + matrix, row_ids, col_names, groups = self._make_data() + results = calculate_cv(matrix, row_ids, col_names, groups) + for r in results: + assert r["n_values"] == 3 + + def test_with_nan(self): + matrix = np.array([[100.0, np.nan, 105.0, 200.0, 220.0, 210.0]]) + row_ids = ["prot1"] + col_names = ["s1", "s2", "s3", "s4", "s5", "s6"] + groups = {"s1": "A", "s2": "A", "s3": "A", "s4": "B", "s5": "B", "s6": "B"} + results = calculate_cv(matrix, row_ids, col_names, groups) + prot1_a = next(r for r in results if r["group"] == "A") + assert prot1_a["n_values"] == 2 + + def test_read_groups(self, tmp_path): + gfile = str(tmp_path / "groups.tsv") + with open(gfile, "w") as fh: + fh.write("sample\tgroup\n") + fh.write("s1\tA\n") + fh.write("s2\tB\n") + groups = read_groups(gfile) + assert groups == {"s1": "A", "s2": "B"} + + def test_single_value_per_group(self): + matrix = np.array([[100.0, 200.0]]) + row_ids = ["prot1"] + col_names = ["s1", "s2"] + groups = {"s1": "A", "s2": "B"} + results = calculate_cv(matrix, row_ids, col_names, groups) + # With only 1 value per group, CV should be NaN + for r in results: + assert np.isnan(r["cv_percent"]) diff --git a/scripts/proteomics/collision_energy_analyzer/README.md b/scripts/proteomics/collision_energy_analyzer/README.md new file mode 100644 index 0000000..01717a0 --- /dev/null +++ b/scripts/proteomics/collision_energy_analyzer/README.md @@ -0,0 +1,9 @@ +# Collision Energy Analyzer + +Extract collision energy (CE) values from MS2 spectra in mzML files. + +## Usage + +```bash +python collision_energy_analyzer.py --input run.mzML --output ce_analysis.tsv +``` diff --git a/scripts/proteomics/collision_energy_analyzer/collision_energy_analyzer.py b/scripts/proteomics/collision_energy_analyzer/collision_energy_analyzer.py new file mode 100644 index 0000000..daf01ed --- /dev/null +++ b/scripts/proteomics/collision_energy_analyzer/collision_energy_analyzer.py @@ -0,0 +1,142 @@ +""" +Collision Energy Analyzer +========================= +Extract collision energy (CE) values from MS2 spectra in mzML files and +produce a summary report. + +Usage +----- + python collision_energy_analyzer.py --input run.mzML --output ce_analysis.tsv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def extract_collision_energies(exp: oms.MSExperiment) -> List[dict]: + """Extract collision energy values from MS2 spectra. + + Parameters + ---------- + exp: + Loaded MSExperiment object. + + Returns + ------- + list + List of dicts with spectrum index, RT, precursor m/z, charge, and CE. + """ + results = [] + for i, spec in enumerate(exp.getSpectra()): + if spec.getMSLevel() < 2: + continue + + rt = spec.getRT() + precursors = spec.getPrecursors() + + for prec in precursors: + mz = prec.getMZ() + charge = prec.getCharge() + ce = prec.getMetaValue("collision energy") if prec.metaValueExists("collision energy") else None + + # Also check activation energy + if ce is None: + activation = prec.getActivationEnergy() + if activation > 0: + ce = activation + + results.append({ + "spectrum_index": i, + "rt": round(rt, 4), + "precursor_mz": round(mz, 6), + "charge": charge, + "collision_energy": round(float(ce), 2) if ce is not None else "N/A", + }) + + return results + + +def summarize_ce(records: List[dict]) -> dict: + """Compute summary statistics for collision energies. + + Parameters + ---------- + records: + List of dicts from extract_collision_energies(). + + Returns + ------- + dict + Summary with count, unique CEs, min, max, mean. + """ + ce_values = [r["collision_energy"] for r in records if r["collision_energy"] != "N/A"] + if not ce_values: + return {"total_ms2": len(records), "with_ce": 0, "unique_ce": [], "min_ce": None, "max_ce": None} + + return { + "total_ms2": len(records), + "with_ce": len(ce_values), + "unique_ce": sorted(set(ce_values)), + "min_ce": min(ce_values), + "max_ce": max(ce_values), + "mean_ce": round(sum(ce_values) / len(ce_values), 2), + } + + +def write_tsv(records: List[dict], output_path: str) -> None: + """Write CE records to TSV. + + Parameters + ---------- + records: + List of record dicts. + output_path: + Output file path. + """ + if not records: + return + fieldnames = list(records[0].keys()) + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in records: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Extract collision energy values from mzML MS2 spectra." + ) + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + records = extract_collision_energies(exp) + summary = summarize_ce(records) + + print(f"Total MS2 spectra : {summary['total_ms2']}") + print(f"With CE values : {summary.get('with_ce', 0)}") + if summary.get("min_ce") is not None: + print(f"CE range : {summary['min_ce']} - {summary['max_ce']}") + print(f"Mean CE : {summary['mean_ce']}") + print(f"Unique CE values : {summary['unique_ce']}") + + if args.output: + write_tsv(records, args.output) + print(f"\nResults written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/collision_energy_analyzer/requirements.txt b/scripts/proteomics/collision_energy_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/collision_energy_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/collision_energy_analyzer/tests/conftest.py b/scripts/proteomics/collision_energy_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/collision_energy_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/collision_energy_analyzer/tests/test_collision_energy_analyzer.py b/scripts/proteomics/collision_energy_analyzer/tests/test_collision_energy_analyzer.py new file mode 100644 index 0000000..eccb95c --- /dev/null +++ b/scripts/proteomics/collision_energy_analyzer/tests/test_collision_energy_analyzer.py @@ -0,0 +1,85 @@ +"""Tests for collision_energy_analyzer.""" + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestCollisionEnergyAnalyzer: + def _make_experiment(self, n_ms2=5, ce_value=30.0): + """Create a synthetic MSExperiment with MS2 spectra and CE values.""" + import pyopenms as oms + + exp = oms.MSExperiment() + # Add an MS1 spectrum + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(0.0) + mzs = np.array([400.0, 500.0, 600.0], dtype=np.float64) + ints = np.array([1000.0, 2000.0, 1500.0], dtype=np.float64) + ms1.set_peaks([mzs, ints]) + exp.addSpectrum(ms1) + + for i in range(n_ms2): + spec = oms.MSSpectrum() + spec.setMSLevel(2) + spec.setRT(10.0 * (i + 1)) + mzs = np.array([200.0, 300.0, 400.0], dtype=np.float64) + ints = np.array([500.0, 800.0, 300.0], dtype=np.float64) + spec.set_peaks([mzs, ints]) + + prec = oms.Precursor() + prec.setMZ(500.0 + i * 10) + prec.setCharge(2) + prec.setActivationEnergy(ce_value + i * 5) + spec.setPrecursors([prec]) + exp.addSpectrum(spec) + + return exp + + def test_extract_ce(self): + from collision_energy_analyzer import extract_collision_energies + + exp = self._make_experiment(n_ms2=3, ce_value=25.0) + records = extract_collision_energies(exp) + assert len(records) == 3 + assert all(r["collision_energy"] != "N/A" for r in records) + + def test_extract_ce_values(self): + from collision_energy_analyzer import extract_collision_energies + + exp = self._make_experiment(n_ms2=3, ce_value=25.0) + records = extract_collision_energies(exp) + assert records[0]["collision_energy"] == 25.0 + assert records[1]["collision_energy"] == 30.0 + + def test_summarize(self): + from collision_energy_analyzer import extract_collision_energies, summarize_ce + + exp = self._make_experiment(n_ms2=4, ce_value=20.0) + records = extract_collision_energies(exp) + summary = summarize_ce(records) + assert summary["total_ms2"] == 4 + assert summary["with_ce"] == 4 + assert summary["min_ce"] == 20.0 + + def test_empty_experiment(self): + import pyopenms as oms + from collision_energy_analyzer import extract_collision_energies, summarize_ce + + exp = oms.MSExperiment() + records = extract_collision_energies(exp) + assert records == [] + summary = summarize_ce(records) + assert summary["total_ms2"] == 0 + + def test_write_tsv(self, tmp_path): + from collision_energy_analyzer import extract_collision_energies, write_tsv + + exp = self._make_experiment(n_ms2=2) + records = extract_collision_energies(exp) + out = str(tmp_path / "ce.tsv") + write_tsv(records, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 3 # header + 2 rows diff --git a/scripts/proteomics/consensus_map_to_matrix/README.md b/scripts/proteomics/consensus_map_to_matrix/README.md new file mode 100644 index 0000000..14b9cc8 --- /dev/null +++ b/scripts/proteomics/consensus_map_to_matrix/README.md @@ -0,0 +1,15 @@ +# Consensus Map to Matrix + +Convert a consensusXML file to a quantification matrix (TSV). + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python consensus_map_to_matrix.py --input consensus.consensusXML --output matrix.tsv +``` diff --git a/scripts/proteomics/consensus_map_to_matrix/consensus_map_to_matrix.py b/scripts/proteomics/consensus_map_to_matrix/consensus_map_to_matrix.py new file mode 100644 index 0000000..28f16e5 --- /dev/null +++ b/scripts/proteomics/consensus_map_to_matrix/consensus_map_to_matrix.py @@ -0,0 +1,139 @@ +""" +Consensus Map to Matrix +======================= +Convert a consensusXML file to a quantification matrix (TSV). + +Usage +----- + python consensus_map_to_matrix.py --input consensus.consensusXML --output matrix.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_consensus_map(input_path: str) -> oms.ConsensusMap: + """Load a consensusXML file.""" + cmap = oms.ConsensusMap() + oms.ConsensusXMLFile().load(input_path, cmap) + return cmap + + +def consensus_to_matrix(input_path: str, output_path: str) -> dict: + """Convert consensusXML to a quantification matrix TSV. + + Each row is a consensus feature. Columns include RT, MZ, charge, + and intensity for each input map. + + Returns statistics about the conversion. + """ + cmap = load_consensus_map(input_path) + + # Get column descriptions (file mappings) + column_headers = cmap.getColumnHeaders() + map_indices = sorted(column_headers.keys()) + map_labels = [] + for idx in map_indices: + desc = column_headers[idx] + label = desc.filename if desc.filename else f"map_{idx}" + map_labels.append(label) + + n_maps = len(map_indices) + rows = [] + + for cf in cmap: + row = { + "rt": round(cf.getRT(), 4), + "mz": round(cf.getMZ(), 6), + "charge": cf.getCharge(), + "quality": round(cf.getQuality(), 4), + } + + # Initialize intensities for all maps + intensities = {idx: 0.0 for idx in map_indices} + for handle in cf.getFeatureList(): + map_idx = handle.getMapIndex() + if map_idx in intensities: + intensities[map_idx] = handle.getIntensity() + + for i, idx in enumerate(map_indices): + row[f"intensity_{i}"] = round(intensities[idx], 4) + + rows.append(row) + + # Write TSV + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + header = ["rt", "mz", "charge", "quality"] + [f"intensity_{i}" for i in range(n_maps)] + writer.writerow(header) + for row in rows: + writer.writerow([row.get(col, "") for col in header]) + + return { + "consensus_features": len(rows), + "n_maps": n_maps, + "map_labels": map_labels, + } + + +def create_synthetic_consensus(output_path: str, n_features: int = 5, n_maps: int = 3) -> None: + """Create a synthetic consensusXML file for testing.""" + cmap = oms.ConsensusMap() + + # Set up column headers + for i in range(n_maps): + desc = oms.ColumnHeader() + desc.filename = f"sample_{i}.mzML" + desc.label = f"sample_{i}" + desc.size = n_features + cmap.setColumnHeaders({i: desc for i in range(n_maps)}) + + # Re-set with proper dict + headers = {} + for i in range(n_maps): + desc = oms.ColumnHeader() + desc.filename = f"sample_{i}.mzML" + desc.label = f"sample_{i}" + desc.size = n_features + headers[i] = desc + cmap.setColumnHeaders(headers) + + for j in range(n_features): + cf = oms.ConsensusFeature() + cf.setRT(100.0 + j * 10) + cf.setMZ(500.0 + j * 50) + cf.setCharge(2) + cf.setQuality(0.9) + + handles = [] + for i in range(n_maps): + fh = oms.FeatureHandle() + fh.setRT(100.0 + j * 10 + i * 0.1) + fh.setMZ(500.0 + j * 50) + fh.setIntensity(10000.0 + i * 1000 + j * 500) + fh.setMapIndex(i) + handles.append(fh) + cf.setFeatureList(handles) + cmap.push_back(cf) + + oms.ConsensusXMLFile().store(output_path, cmap) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert consensusXML to quantification matrix.") + parser.add_argument("--input", required=True, help="Input consensusXML file") + parser.add_argument("--output", required=True, help="Output TSV matrix file") + args = parser.parse_args() + + stats = consensus_to_matrix(args.input, args.output) + print(f"Exported {stats['consensus_features']} features across {stats['n_maps']} maps to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/consensus_map_to_matrix/requirements.txt b/scripts/proteomics/consensus_map_to_matrix/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/consensus_map_to_matrix/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/consensus_map_to_matrix/tests/conftest.py b/scripts/proteomics/consensus_map_to_matrix/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/consensus_map_to_matrix/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py b/scripts/proteomics/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py new file mode 100644 index 0000000..5cb1552 --- /dev/null +++ b/scripts/proteomics/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py @@ -0,0 +1,61 @@ +"""Tests for consensus_map_to_matrix.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_create_synthetic_consensus(): + import pyopenms as oms + from consensus_map_to_matrix import create_synthetic_consensus + + with tempfile.TemporaryDirectory() as tmp: + cxml_path = os.path.join(tmp, "consensus.consensusXML") + create_synthetic_consensus(cxml_path, n_features=5, n_maps=3) + + cmap = oms.ConsensusMap() + oms.ConsensusXMLFile().load(cxml_path, cmap) + assert cmap.size() == 5 + + +@requires_pyopenms +def test_consensus_to_matrix(): + from consensus_map_to_matrix import consensus_to_matrix, create_synthetic_consensus + + with tempfile.TemporaryDirectory() as tmp: + cxml_path = os.path.join(tmp, "consensus.consensusXML") + tsv_path = os.path.join(tmp, "matrix.tsv") + + create_synthetic_consensus(cxml_path, n_features=5, n_maps=3) + stats = consensus_to_matrix(cxml_path, tsv_path) + + assert stats["consensus_features"] == 5 + assert stats["n_maps"] == 3 + + with open(tsv_path) as fh: + lines = fh.readlines() + assert len(lines) == 6 # header + 5 rows + header = lines[0].strip().split("\t") + assert "rt" in header + assert "mz" in header + assert "intensity_0" in header + assert "intensity_2" in header + + +@requires_pyopenms +def test_consensus_to_matrix_single_map(): + from consensus_map_to_matrix import consensus_to_matrix, create_synthetic_consensus + + with tempfile.TemporaryDirectory() as tmp: + cxml_path = os.path.join(tmp, "consensus.consensusXML") + tsv_path = os.path.join(tmp, "matrix.tsv") + + create_synthetic_consensus(cxml_path, n_features=3, n_maps=1) + stats = consensus_to_matrix(cxml_path, tsv_path) + + assert stats["n_maps"] == 1 + with open(tsv_path) as fh: + header = fh.readline().strip().split("\t") + assert "intensity_0" in header diff --git a/scripts/proteomics/contaminant_database_merger/README.md b/scripts/proteomics/contaminant_database_merger/README.md new file mode 100644 index 0000000..64d41a7 --- /dev/null +++ b/scripts/proteomics/contaminant_database_merger/README.md @@ -0,0 +1,22 @@ +# Contaminant Database Merger + +Append cRAP contaminant proteins to a target FASTA database with a configurable prefix, removing duplicates. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Add built-in cRAP contaminants +python contaminant_database_merger.py --input target.fasta --add-crap --prefix CONT_ --output merged.fasta + +# Add custom contaminant file +python contaminant_database_merger.py --input target.fasta --contaminants custom.fasta --output merged.fasta + +# Both +python contaminant_database_merger.py --input target.fasta --add-crap --contaminants extra.fasta --output merged.fasta +``` diff --git a/scripts/proteomics/contaminant_database_merger/contaminant_database_merger.py b/scripts/proteomics/contaminant_database_merger/contaminant_database_merger.py new file mode 100644 index 0000000..9b858dd --- /dev/null +++ b/scripts/proteomics/contaminant_database_merger/contaminant_database_merger.py @@ -0,0 +1,156 @@ +""" +Contaminant Database Merger +=========================== +Append common contaminant proteins (cRAP) to a target FASTA database with a configurable +prefix, and remove duplicates. + +Usage +----- + python contaminant_database_merger.py --input target.fasta --add-crap --prefix CONT_ --output merged.fasta + python contaminant_database_merger.py --input target.fasta --contaminants custom.fasta --output merged.fasta +""" + +import argparse +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Built-in common contaminant sequences (subset of cRAP database) +BUILTIN_CONTAMINANTS = [ + ("P00761", "IVGGYTCGANTVPYQVSLNSGYHFCGGSLINSQWVVSAAHCYKSGIQVRLGEDNINVVEG" + "NEQFISASKSIVHPSYNSNTLNNDIMLIKLKSAASLNSRVASISLPTSCASAGTQCLISGWGNTK"), + ("P00766", "IVGGYTCGANTVPYQVSLNSGYHFCGGSLINSQWVVSAAHCYKSGIQVRLGEDNINVVEG" + "NEQFISASKSIVHPSYNSNTLNNDIMLIKL"), + ("P02769", "MKWVTFISLLLLFSSAYSRGVFRRDTHKSEIAHRFKDLGEEHFKGLVLIAFSQYLQQCPF" + "DEHVKLVNELTEFAKTCVADESHAGCEK"), + ("P00432", "MDSASKLLSALFLGALLGASCIAAPPGAQKGESVTLNCKPVLDDFEPATQRFNGNIFHYP" + "NTISATGRWNKEENAISEMFQNHFTKSNP"), + ("P02533", "MSRSSFRRGSGSRSGSRSSSYSLGSRSGGFSSSGGFGGSRSLYGLGASRSSGSSYGLGGG" + "SSSGGSTGGIRATSGFASRSSGGGYSSSGGFSG"), + ("P35527", "SFSSRSASCISGGYRGSGGRSYSCGSCGISGGYRGSGGRSYSSGSCGISGGYRGSGGRSYS" + "CSISCGIASGGYRGSGGRSYSCGSCGISGG"), + ("P04264", "SRQFSSRSGYRSGGSYGGGSSGGGSISGSSYGSRSGSYRSGGSSGGSYGSRSGSYRSGGS" + "GGSYGGSRSGSYRSGGSSGGSYGSRSGS"), + ("P13645", "MTTSQYGRRSSQYGSYSQSTSYRGSGSRSSGYRSGGSSGYSSSGGYRSGGSSGGSYGSRS" + "GSYRSGGSSGGSYGSRSGSYRSGGSSGGSYG"), + ("P02768", "DAHKSEVAHRF KDLGEENFKALVLIAFAQYLQQCPFEDHVKLVNEVTEFAKTCVADESAA" + "NCDKSLHTLFGDKLCTVATLRETYGEMADCCAK"), + ("P01966", "VLSAADKGNVKAAWGKVGGHAAEYGAEALERMFLSFPTTKTYFPHFDLSH"), + ("P02662", "RPKHPIKHQGLPQEVLNENLLRFFVAPFPEVFGKEKVNELSKDIGSESTEDQAMEDIKQ"), + ("P02663", "KNTMEHVSSSEESIISQETYKQEKNMAINPSKENLCSTFCKEVVRNAN"), + ("P02666", "RELEELNVPGEIVESLSSSEESITRINKKIEKFQSEEQQQTEDELQDKIHPFAQTQSLV"), + ("P02754", "MKCLLLALALTCGAQALIVTQTMKGLDIQKVAGTWYSLAMAASDISLLDAQSAPLRVYVE" + "ELKPTPEGDLEILLQKWENGECAQKKIIAE"), + ("P00711", "KQFTKCELSQLLKDIDGYGGIALPELICTMFHTSGYDTQAIVENNESTEYGLFQISNKLWC" + "KSSQVPQSRNICDISCDKFL"), + ("P02787", "VPDKTVRWCAVSEHEATKCQSFRDHMKSVIPSDGPSVACVKKASYLDCIRAIAANEADAV" + "TLDAGLVYDAYLAPNNLKPVVAEF"), + ("P01012", "GSIGAASMEFCFDVFKELKVHHANENIFYCPIAIMSALAMVYLGAKDSTRTQINKVVRFD" + "KLPGFGDSIEAQCGTSVNVHSSLRDILNQI"), + ("P06702", "MTCKMSQLERNIETIINTFHQYSVKLGHPDTLNQGEFKELVRKDLQNFLKKENKNEKVI" + "EHIMEDLDTNADKQLSFEEFIMLMARLTWASH"), + ("P62894", "MAAAKKAVDKIKKLFLKFPEVKNEDLGAQTMFNLFDKPQSAGLCGAGGRPVLAG"), + ("Q32MB2", "MKTFFIFTLTLAISATSAQQNNPFIFNEKYTMVSVLSKDPNCNKVVIGTDTQQYYSNAC" + "GILLNCTGIDLFKDKPV"), +] + + +def get_builtin_contaminants(prefix: str = "CONT_") -> List[oms.FASTAEntry]: + """Return a list of built-in contaminant FASTA entries with the given prefix.""" + entries = [] + for acc, seq in BUILTIN_CONTAMINANTS: + e = oms.FASTAEntry() + e.identifier = f"{prefix}{acc}" + e.sequence = seq.replace(" ", "") + e.description = f"Contaminant {acc}" + entries.append(e) + return entries + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def save_fasta(entries: List[oms.FASTAEntry], output_path: str) -> None: + """Save entries to a FASTA file.""" + fasta_file = oms.FASTAFile() + fasta_file.store(output_path, entries) + + +def deduplicate(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Remove duplicate entries based on identifier.""" + seen = set() + result = [] + for entry in entries: + key = entry.identifier.split()[0] + if key not in seen: + seen.add(key) + result.append(entry) + return result + + +def merge_contaminants( + input_path: str, + output_path: str, + add_crap: bool = True, + contaminants_path: str = None, + prefix: str = "CONT_", +) -> dict: + """Merge contaminant sequences into a target FASTA file. + + Returns statistics about the merge. + """ + target_entries = load_fasta(input_path) + contaminant_entries = [] + + if add_crap: + contaminant_entries.extend(get_builtin_contaminants(prefix)) + + if contaminants_path: + custom = load_fasta(contaminants_path) + for entry in custom: + if not entry.identifier.startswith(prefix): + entry.identifier = f"{prefix}{entry.identifier}" + contaminant_entries.append(entry) + + merged = target_entries + contaminant_entries + deduped = deduplicate(merged) + save_fasta(deduped, output_path) + + return { + "target_count": len(target_entries), + "contaminant_count": len(contaminant_entries), + "merged_count": len(merged), + "deduplicated_count": len(deduped), + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Append contaminant proteins to a FASTA database." + ) + parser.add_argument("--input", required=True, help="Input target FASTA file") + parser.add_argument("--add-crap", action="store_true", help="Add built-in cRAP contaminants") + parser.add_argument("--contaminants", default=None, help="Custom contaminant FASTA file") + parser.add_argument("--prefix", default="CONT_", help="Prefix for contaminant accessions (default: CONT_)") + parser.add_argument("--output", required=True, help="Output merged FASTA file") + args = parser.parse_args() + + if not args.add_crap and not args.contaminants: + parser.error("At least one of --add-crap or --contaminants is required.") + + stats = merge_contaminants(args.input, args.output, args.add_crap, args.contaminants, args.prefix) + print(f"Target: {stats['target_count']}, Contaminants: {stats['contaminant_count']}, " + f"Merged (dedup): {stats['deduplicated_count']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/contaminant_database_merger/requirements.txt b/scripts/proteomics/contaminant_database_merger/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/contaminant_database_merger/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/contaminant_database_merger/tests/conftest.py b/scripts/proteomics/contaminant_database_merger/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/contaminant_database_merger/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/contaminant_database_merger/tests/test_contaminant_database_merger.py b/scripts/proteomics/contaminant_database_merger/tests/test_contaminant_database_merger.py new file mode 100644 index 0000000..573f0c2 --- /dev/null +++ b/scripts/proteomics/contaminant_database_merger/tests/test_contaminant_database_merger.py @@ -0,0 +1,83 @@ +"""Tests for contaminant_database_merger.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_test_fasta(path, entries_data): + import pyopenms as oms + + entries = [] + for acc, seq in entries_data: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + fasta_file = oms.FASTAFile() + fasta_file.store(path, entries) + + +@requires_pyopenms +def test_get_builtin_contaminants(): + from contaminant_database_merger import get_builtin_contaminants + + contaminants = get_builtin_contaminants("CONT_") + assert len(contaminants) == 20 + for c in contaminants: + assert c.identifier.startswith("CONT_") + + +@requires_pyopenms +def test_merge_with_crap(): + from contaminant_database_merger import merge_contaminants + + with tempfile.TemporaryDirectory() as tmp: + target_path = os.path.join(tmp, "target.fasta") + output_path = os.path.join(tmp, "merged.fasta") + + _create_test_fasta(target_path, [("P99999", "ACDEFGHIK"), ("Q11111", "MNPQRSTWY")]) + + stats = merge_contaminants(target_path, output_path, add_crap=True, prefix="CONT_") + assert stats["target_count"] == 2 + assert stats["contaminant_count"] == 20 + assert stats["deduplicated_count"] == 22 + + +@requires_pyopenms +def test_deduplication(): + import pyopenms as oms + from contaminant_database_merger import deduplicate + + entries = [] + for acc in ["P12345", "P12345", "P67890"]: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = "ACDEFGHIK" + e.description = "" + entries.append(e) + + result = deduplicate(entries) + assert len(result) == 2 + + +@requires_pyopenms +def test_merge_custom_contaminants(): + from contaminant_database_merger import merge_contaminants + + with tempfile.TemporaryDirectory() as tmp: + target_path = os.path.join(tmp, "target.fasta") + contam_path = os.path.join(tmp, "contaminants.fasta") + output_path = os.path.join(tmp, "merged.fasta") + + _create_test_fasta(target_path, [("P99999", "ACDEFGHIK")]) + _create_test_fasta(contam_path, [("CUSTOM1", "MNPQRST")]) + + stats = merge_contaminants( + target_path, output_path, add_crap=False, contaminants_path=contam_path, prefix="CONT_" + ) + assert stats["target_count"] == 1 + assert stats["contaminant_count"] == 1 + assert stats["deduplicated_count"] == 2 diff --git a/scripts/proteomics/crosslink_mass_calculator/README.md b/scripts/proteomics/crosslink_mass_calculator/README.md new file mode 100644 index 0000000..1bdf99f --- /dev/null +++ b/scripts/proteomics/crosslink_mass_calculator/README.md @@ -0,0 +1,11 @@ +# Crosslink Mass Calculator + +Calculate masses for crosslinked peptide pairs using common crosslinkers (DSS, BS3, DSSO). + +## Usage + +```bash +python crosslink_mass_calculator.py --peptide1 PEPTIDEK --peptide2 ANOTHERPEPTIDER --crosslinker DSS --charge 3 +python crosslink_mass_calculator.py --peptide1 PEPTIDEK --peptide2 ANOTHERPEPTIDER --crosslinker DSSO --output masses.tsv +python crosslink_mass_calculator.py --peptide1 PEPTIDEK --peptide2 AVLIDR --crosslinker custom --custom-mass 150.0 +``` diff --git a/scripts/proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py b/scripts/proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py new file mode 100644 index 0000000..8d781be --- /dev/null +++ b/scripts/proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py @@ -0,0 +1,161 @@ +""" +Crosslink Mass Calculator +========================= +Calculate masses for crosslinked peptide pairs using common crosslinkers +(DSS, BS3, DSSO). + +Supports: +- Built-in crosslinker mass table (DSS=138.068, BS3=138.068, DSSO=158.004) +- Multiple charge states +- TSV output + +Usage +----- + python crosslink_mass_calculator.py --peptide1 PEPTIDEK --peptide2 ANOTHERPEPTIDER --crosslinker DSS --charge 3 + python crosslink_mass_calculator.py --peptide1 PEPTIDEK --peptide2 ANOTHERPEPTIDER \\ + --crosslinker DSSO --output masses.tsv +""" + +import argparse +import csv +import sys +from typing import Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + +PROTON = 1.007276 + +# Built-in crosslinker mass table (Da) +CROSSLINKER_MASSES = { + "DSS": 138.068, + "BS3": 138.068, + "DSSO": 158.004, +} + + +def crosslinked_mass( + peptide1: str, + peptide2: str, + crosslinker: str, + charge: int = 1, + custom_mass: Optional[float] = None, +) -> dict: + """Calculate mass of a crosslinked peptide pair. + + Parameters + ---------- + peptide1: + First peptide sequence. + peptide2: + Second peptide sequence. + crosslinker: + Crosslinker name (DSS, BS3, DSSO) or custom name if custom_mass provided. + charge: + Charge state for m/z calculation. + custom_mass: + Optional custom crosslinker mass in Da. + + Returns + ------- + dict + Dictionary with peptide masses, crosslinker mass, total mass, and m/z. + """ + if custom_mass is not None: + xl_mass = custom_mass + elif crosslinker.upper() in CROSSLINKER_MASSES: + xl_mass = CROSSLINKER_MASSES[crosslinker.upper()] + else: + raise ValueError( + f"Unknown crosslinker '{crosslinker}'. " + f"Known: {', '.join(CROSSLINKER_MASSES.keys())}. " + f"Provide --custom-mass for custom crosslinkers." + ) + + seq1 = oms.AASequence.fromString(peptide1) + seq2 = oms.AASequence.fromString(peptide2) + + mass1 = seq1.getMonoWeight() + mass2 = seq2.getMonoWeight() + + # Crosslinking forms a bond releasing water (two NH groups react with crosslinker) + # Total mass = mass1 + mass2 + crosslinker_mass - 2*H2O + # Actually, the standard model: XL mass = pep1 + pep2 + linker - 2*H2O is for + # some chemistries. For NHS-ester crosslinkers (DSS, BS3, DSSO), + # the reaction releases no extra water beyond what is already in the linker mass. + # The crosslinker mass listed is the bridge mass after reaction. + total_mass = mass1 + mass2 + xl_mass + + mz = (total_mass + charge * PROTON) / charge + + return { + "peptide1": peptide1, + "peptide2": peptide2, + "crosslinker": crosslinker.upper() if custom_mass is None else crosslinker, + "mass_peptide1": mass1, + "mass_peptide2": mass2, + "crosslinker_mass": xl_mass, + "total_mass": total_mass, + "charge": charge, + "mz": mz, + } + + +def write_tsv(results: list, output_path: str) -> None: + """Write results to a TSV file. + + Parameters + ---------- + results: + List of result dicts from crosslinked_mass(). + output_path: + Output file path. + """ + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate masses for crosslinked peptide pairs." + ) + parser.add_argument("--peptide1", required=True, help="First peptide sequence") + parser.add_argument("--peptide2", required=True, help="Second peptide sequence") + parser.add_argument( + "--crosslinker", required=True, + help="Crosslinker name (DSS, BS3, DSSO) or custom name with --custom-mass" + ) + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") + parser.add_argument("--custom-mass", type=float, default=None, help="Custom crosslinker mass in Da") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + result = crosslinked_mass( + args.peptide1, args.peptide2, args.crosslinker, + charge=args.charge, custom_mass=args.custom_mass, + ) + + print(f"Peptide 1 : {result['peptide1']} ({result['mass_peptide1']:.6f} Da)") + print(f"Peptide 2 : {result['peptide2']} ({result['mass_peptide2']:.6f} Da)") + print(f"Crosslinker : {result['crosslinker']} ({result['crosslinker_mass']:.6f} Da)") + print(f"Total mass : {result['total_mass']:.6f} Da") + print(f"Charge : {result['charge']}+") + print(f"m/z : {result['mz']:.6f}") + + if args.output: + write_tsv([result], args.output) + print(f"\nResults written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/crosslink_mass_calculator/requirements.txt b/scripts/proteomics/crosslink_mass_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/crosslink_mass_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/crosslink_mass_calculator/tests/conftest.py b/scripts/proteomics/crosslink_mass_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/crosslink_mass_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py b/scripts/proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py new file mode 100644 index 0000000..5c33545 --- /dev/null +++ b/scripts/proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py @@ -0,0 +1,59 @@ +"""Tests for crosslink_mass_calculator.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestCrosslinkMassCalculator: + def test_dss_crosslink(self): + from crosslink_mass_calculator import crosslinked_mass + + result = crosslinked_mass("PEPTIDEK", "AVLIDR", "DSS", charge=2) + assert result["crosslinker"] == "DSS" + assert result["crosslinker_mass"] == pytest.approx(138.068, abs=0.01) + assert result["total_mass"] > 0 + assert result["charge"] == 2 + + def test_dsso_crosslink(self): + from crosslink_mass_calculator import crosslinked_mass + + result = crosslinked_mass("PEPTIDEK", "AVLIDR", "DSSO", charge=1) + assert result["crosslinker_mass"] == pytest.approx(158.004, abs=0.01) + + def test_mz_formula(self): + from crosslink_mass_calculator import PROTON, crosslinked_mass + + result = crosslinked_mass("PEPTIDEK", "AVLIDR", "DSS", charge=3) + expected_mz = (result["total_mass"] + 3 * PROTON) / 3 + assert result["mz"] == pytest.approx(expected_mz, abs=1e-6) + + def test_custom_crosslinker(self): + from crosslink_mass_calculator import crosslinked_mass + + result = crosslinked_mass("PEPTIDEK", "AVLIDR", "CUSTOM", charge=1, custom_mass=150.0) + assert result["crosslinker_mass"] == pytest.approx(150.0) + + def test_unknown_crosslinker_raises(self): + from crosslink_mass_calculator import crosslinked_mass + + with pytest.raises(ValueError, match="Unknown crosslinker"): + crosslinked_mass("PEPTIDEK", "AVLIDR", "UNKNOWN", charge=1) + + def test_total_mass_is_sum(self): + from crosslink_mass_calculator import crosslinked_mass + + result = crosslinked_mass("PEPTIDEK", "AVLIDR", "DSS", charge=1) + expected = result["mass_peptide1"] + result["mass_peptide2"] + result["crosslinker_mass"] + assert result["total_mass"] == pytest.approx(expected, abs=1e-6) + + def test_write_tsv(self, tmp_path): + from crosslink_mass_calculator import crosslinked_mass, write_tsv + + result = crosslinked_mass("PEPTIDEK", "AVLIDR", "DSS", charge=2) + out = str(tmp_path / "out.tsv") + write_tsv([result], out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 # header + 1 data row + assert "peptide1" in lines[0] diff --git a/scripts/proteomics/dia_window_analyzer/README.md b/scripts/proteomics/dia_window_analyzer/README.md new file mode 100644 index 0000000..f90bd63 --- /dev/null +++ b/scripts/proteomics/dia_window_analyzer/README.md @@ -0,0 +1,10 @@ +# DIA Window Analyzer + +Report DIA isolation window scheme from mzML metadata. + +## Usage + +```bash +python dia_window_analyzer.py --input dia.mzML +python dia_window_analyzer.py --input dia.mzML --output windows.tsv +``` diff --git a/scripts/proteomics/dia_window_analyzer/dia_window_analyzer.py b/scripts/proteomics/dia_window_analyzer/dia_window_analyzer.py new file mode 100644 index 0000000..ebcee2a --- /dev/null +++ b/scripts/proteomics/dia_window_analyzer/dia_window_analyzer.py @@ -0,0 +1,160 @@ +""" +DIA Window Analyzer +=================== +Report DIA isolation window scheme from mzML metadata. +Extracts precursor isolation window information from MS2 spectra. + +Features: +- Extract isolation window center and width +- Detect DIA window scheme +- Report window overlap +- TSV output + +Usage +----- + python dia_window_analyzer.py --input dia.mzML + python dia_window_analyzer.py --input dia.mzML --output windows.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def analyze_dia_windows(input_path: str) -> list[dict]: + """Analyze DIA isolation windows from mzML file. + + Parameters + ---------- + input_path : str + Path to mzML file. + + Returns + ------- + list[dict] + List of unique DIA windows with keys: window_center, window_lower, + window_upper, window_width, scan_count. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + window_counts = {} + + for i in range(exp.getNrSpectra()): + spec = exp.getSpectrum(i) + if spec.getMSLevel() != 2: + continue + + precursors = spec.getPrecursors() + if not precursors: + continue + + prec = precursors[0] + center = prec.getMZ() + lower = prec.getIsolationWindowLowerOffset() + upper = prec.getIsolationWindowUpperOffset() + + key = (round(center, 4), round(lower, 4), round(upper, 4)) + window_counts[key] = window_counts.get(key, 0) + 1 + + results = [] + for (center, lower, upper), count in sorted(window_counts.items()): + results.append({ + "window_center": center, + "window_lower": round(center - lower, 4), + "window_upper": round(center + upper, 4), + "window_width": round(lower + upper, 4), + "scan_count": count, + }) + + return results + + +def create_synthetic_dia_mzml(output_path: str, n_windows: int = 5, window_width: float = 25.0) -> None: + """Create a synthetic DIA mzML file for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + n_windows : int + Number of DIA windows. + window_width : float + Width of each window in Da. + """ + exp = oms.MSExperiment() + + start_mz = 400.0 + for cycle in range(3): + # MS1 survey scan + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(float(cycle) * 10.0) + ms1.set_peaks(([500.0], [10000.0])) + exp.addSpectrum(ms1) + + # DIA MS2 scans + for w in range(n_windows): + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(float(cycle) * 10.0 + float(w) + 1.0) + + center = start_mz + w * window_width + window_width / 2 + prec = oms.Precursor() + prec.setMZ(center) + prec.setIsolationWindowLowerOffset(window_width / 2) + prec.setIsolationWindowUpperOffset(window_width / 2) + ms2.setPrecursors([prec]) + + ms2.set_peaks(([center - 5, center, center + 5], [500.0, 1000.0, 300.0])) + exp.addSpectrum(ms2) + + oms.MzMLFile().store(output_path, exp) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write DIA window results to TSV file. + + Parameters + ---------- + results : list[dict] + List of window result dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["window_center", "window_lower", "window_upper", "window_width", "scan_count"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Report DIA isolation window scheme from mzML metadata." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + results = analyze_dia_windows(args.input) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} DIA windows to {args.output}") + else: + print("window_center\twindow_lower\twindow_upper\twindow_width\tscan_count") + for r in results: + print( + f"{r['window_center']}\t{r['window_lower']}\t{r['window_upper']}\t" + f"{r['window_width']}\t{r['scan_count']}" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/dia_window_analyzer/requirements.txt b/scripts/proteomics/dia_window_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/dia_window_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/dia_window_analyzer/tests/conftest.py b/scripts/proteomics/dia_window_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/dia_window_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py b/scripts/proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py new file mode 100644 index 0000000..0b54462 --- /dev/null +++ b/scripts/proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py @@ -0,0 +1,57 @@ +"""Tests for dia_window_analyzer.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestDiaWindowAnalyzer: + def test_analyze_windows(self): + from dia_window_analyzer import analyze_dia_windows, create_synthetic_dia_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "dia.mzML") + create_synthetic_dia_mzml(mzml_path, n_windows=5, window_width=25.0) + results = analyze_dia_windows(mzml_path) + assert len(results) == 5 + for r in results: + assert r["window_width"] == 25.0 + assert r["scan_count"] == 3 # 3 cycles + + def test_result_keys(self): + from dia_window_analyzer import analyze_dia_windows, create_synthetic_dia_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "dia.mzML") + create_synthetic_dia_mzml(mzml_path, n_windows=2) + results = analyze_dia_windows(mzml_path) + for r in results: + assert "window_center" in r + assert "window_lower" in r + assert "window_upper" in r + assert "window_width" in r + assert "scan_count" in r + + def test_window_boundaries(self): + from dia_window_analyzer import analyze_dia_windows, create_synthetic_dia_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "dia.mzML") + create_synthetic_dia_mzml(mzml_path, n_windows=3, window_width=20.0) + results = analyze_dia_windows(mzml_path) + for r in results: + assert r["window_upper"] > r["window_lower"] + assert abs(r["window_upper"] - r["window_lower"] - r["window_width"]) < 0.01 + + def test_write_tsv(self): + from dia_window_analyzer import analyze_dia_windows, create_synthetic_dia_mzml, write_tsv + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "dia.mzML") + create_synthetic_dia_mzml(mzml_path) + results = analyze_dia_windows(mzml_path) + out = os.path.join(tmpdir, "windows.tsv") + write_tsv(results, out) + assert os.path.exists(out) diff --git a/scripts/proteomics/diann_result_converter/README.md b/scripts/proteomics/diann_result_converter/README.md new file mode 100644 index 0000000..60bc41f --- /dev/null +++ b/scripts/proteomics/diann_result_converter/README.md @@ -0,0 +1,22 @@ +# DIA-NN Result Converter + +Convert DIA-NN report.tsv to a standardized TSV format. + +## Usage + +```bash +python diann_result_converter.py --input report.tsv --output standardized.tsv +``` + +## Column Mapping + +| DIA-NN Column | Standard Column | +|---|---| +| Stripped.Sequence | peptide | +| Modified.Sequence | modified_peptide | +| Precursor.Charge | charge | +| Precursor.Mz | mz | +| RT | rt | +| Protein.Group | protein | +| Q.Value | qvalue | +| Precursor.Quantity | intensity | diff --git a/scripts/proteomics/diann_result_converter/diann_result_converter.py b/scripts/proteomics/diann_result_converter/diann_result_converter.py new file mode 100644 index 0000000..41b8d20 --- /dev/null +++ b/scripts/proteomics/diann_result_converter/diann_result_converter.py @@ -0,0 +1,96 @@ +""" +DIA-NN Result Converter +======================= +Convert DIA-NN report.tsv to a standardized TSV format. + +Maps DIA-NN specific column names to a common schema for downstream analysis. + +Usage +----- + python diann_result_converter.py --input report.tsv --output standardized.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Mapping from DIA-NN column names to standard column names +COLUMN_MAP = { + "Stripped.Sequence": "peptide", + "Modified.Sequence": "modified_peptide", + "Precursor.Charge": "charge", + "Precursor.Mz": "mz", + "RT": "rt", + "Protein.Group": "protein", + "Protein.Names": "protein_description", + "Genes": "gene", + "Q.Value": "qvalue", + "PG.Q.Value": "pg_qvalue", + "Global.Q.Value": "global_qvalue", + "Precursor.Quantity": "intensity", + "Run": "raw_file", + "File.Name": "file_name", +} + +STANDARD_FIELDS = [ + "peptide", "modified_peptide", "charge", "mz", "rt", + "protein", "protein_description", "gene", + "qvalue", "pg_qvalue", "global_qvalue", + "intensity", "raw_file", "file_name", "source", +] + + +def convert_diann_report(filepath: str) -> list: + """Convert DIA-NN report.tsv to standardized format. + + Parameters + ---------- + filepath: + Path to DIA-NN report.tsv. + + Returns + ------- + list + List of dicts with standardized column names. + """ + rows = [] + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + std_row = {} + for diann_col, std_col in COLUMN_MAP.items(): + std_row[std_col] = row.get(diann_col, "") + std_row["source"] = "DIA-NN" + rows.append(std_row) + return rows + + +def write_standardized(filepath: str, rows: list) -> None: + """Write standardized results to TSV.""" + with open(filepath, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=STANDARD_FIELDS, delimiter="\t", extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + +def main(): + parser = argparse.ArgumentParser(description="Convert DIA-NN report.tsv to standardized TSV.") + parser.add_argument("--input", required=True, help="DIA-NN report.tsv file") + parser.add_argument("--output", required=True, help="Output standardized TSV") + args = parser.parse_args() + + rows = convert_diann_report(args.input) + write_standardized(args.output, rows) + + print("Source: DIA-NN") + print(f"Total precursors: {len(rows)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/diann_result_converter/requirements.txt b/scripts/proteomics/diann_result_converter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/diann_result_converter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/diann_result_converter/tests/conftest.py b/scripts/proteomics/diann_result_converter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/diann_result_converter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/diann_result_converter/tests/test_diann_result_converter.py b/scripts/proteomics/diann_result_converter/tests/test_diann_result_converter.py new file mode 100644 index 0000000..fc1b5c2 --- /dev/null +++ b/scripts/proteomics/diann_result_converter/tests/test_diann_result_converter.py @@ -0,0 +1,84 @@ +"""Tests for diann_result_converter.""" + +import csv + +from conftest import requires_pyopenms +from diann_result_converter import STANDARD_FIELDS, convert_diann_report, write_standardized + + +@requires_pyopenms +class TestDiannResultConverter: + def _write_report(self, tmp_path, rows): + filepath = str(tmp_path / "report.tsv") + fieldnames = [ + "Stripped.Sequence", "Modified.Sequence", "Precursor.Charge", + "Precursor.Mz", "RT", "Protein.Group", "Protein.Names", + "Genes", "Q.Value", "PG.Q.Value", "Global.Q.Value", + "Precursor.Quantity", "Run", "File.Name", + ] + with open(filepath, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(rows) + return filepath + + def test_basic_conversion(self, tmp_path): + filepath = self._write_report(tmp_path, [ + { + "Stripped.Sequence": "PEPTIDEK", "Modified.Sequence": "PEPTIDEK", + "Precursor.Charge": "2", "Precursor.Mz": "450.5", + "RT": "25.5", "Protein.Group": "P12345", + "Protein.Names": "Protein 1", "Genes": "GEN1", + "Q.Value": "0.001", "PG.Q.Value": "0.005", + "Global.Q.Value": "0.01", + "Precursor.Quantity": "1e6", "Run": "run1", + "File.Name": "run1.mzML", + } + ]) + rows = convert_diann_report(filepath) + assert len(rows) == 1 + assert rows[0]["peptide"] == "PEPTIDEK" + assert rows[0]["charge"] == "2" + assert rows[0]["source"] == "DIA-NN" + + def test_multiple_rows(self, tmp_path): + filepath = self._write_report(tmp_path, [ + {"Stripped.Sequence": "PEP1", "Modified.Sequence": "PEP1", + "Precursor.Charge": "2", "Precursor.Mz": "400", + "RT": "20", "Protein.Group": "P1", "Protein.Names": "Prot1", + "Genes": "G1", "Q.Value": "0.01", "PG.Q.Value": "0.02", + "Global.Q.Value": "0.03", "Precursor.Quantity": "1e6", + "Run": "run1", "File.Name": "run1.mzML"}, + {"Stripped.Sequence": "PEP2", "Modified.Sequence": "PEP2", + "Precursor.Charge": "3", "Precursor.Mz": "300", + "RT": "30", "Protein.Group": "P2", "Protein.Names": "Prot2", + "Genes": "G2", "Q.Value": "0.02", "PG.Q.Value": "0.03", + "Global.Q.Value": "0.04", "Precursor.Quantity": "5e5", + "Run": "run1", "File.Name": "run1.mzML"}, + ]) + rows = convert_diann_report(filepath) + assert len(rows) == 2 + + def test_write_standardized(self, tmp_path): + rows = [{"peptide": "PEPTIDEK", "charge": "2", "source": "DIA-NN"}] + outfile = str(tmp_path / "out.tsv") + write_standardized(outfile, rows) + with open(outfile) as fh: + reader = csv.DictReader(fh, delimiter="\t") + result = list(reader) + assert len(result) == 1 + assert result[0]["source"] == "DIA-NN" + + def test_standard_fields(self): + assert "peptide" in STANDARD_FIELDS + assert "source" in STANDARD_FIELDS + assert "qvalue" in STANDARD_FIELDS + + def test_missing_columns_handled(self, tmp_path): + filepath = str(tmp_path / "minimal.tsv") + with open(filepath, "w") as fh: + fh.write("Stripped.Sequence\tPrecursor.Charge\n") + fh.write("PEPTIDEK\t2\n") + rows = convert_diann_report(filepath) + assert rows[0]["peptide"] == "PEPTIDEK" + assert rows[0]["rt"] == "" # Missing column => empty string diff --git a/scripts/proteomics/differential_expression_tester/README.md b/scripts/proteomics/differential_expression_tester/README.md new file mode 100644 index 0000000..e8a8777 --- /dev/null +++ b/scripts/proteomics/differential_expression_tester/README.md @@ -0,0 +1,19 @@ +# Differential Expression Tester + +Perform t-tests with Benjamini-Hochberg FDR correction on quantification matrices. + +## Usage + +```bash +python differential_expression_tester.py --input matrix.tsv --design design.tsv --test ttest --output de_results.tsv +python differential_expression_tester.py --input matrix.tsv --design design.tsv --test welch --output de_results.tsv +``` + +## Input Files + +- **matrix.tsv** - Quantification matrix (rows=features, columns=samples) +- **design.tsv** - Experimental design with columns: `sample`, `condition` + +## Output + +TSV with columns: `feature`, `log2fc`, `pvalue`, `adj_pvalue` diff --git a/scripts/proteomics/differential_expression_tester/differential_expression_tester.py b/scripts/proteomics/differential_expression_tester/differential_expression_tester.py new file mode 100644 index 0000000..deb8189 --- /dev/null +++ b/scripts/proteomics/differential_expression_tester/differential_expression_tester.py @@ -0,0 +1,206 @@ +""" +Differential Expression Tester +============================== +Perform t-tests with Benjamini-Hochberg correction on quantification matrices. + +Reads a quantification matrix and an experimental design file that maps samples +to conditions, then computes per-feature differential expression statistics. + +Usage +----- + python differential_expression_tester.py --input matrix.tsv --design design.tsv --test ttest --output de.tsv + python differential_expression_tester.py --input matrix.tsv --design design.tsv --test welch --output de.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np +from scipy import stats + + +def read_matrix(filepath: str) -> tuple: + """Read a TSV quantification matrix. + + Returns (row_ids, col_names, data_matrix). + """ + with open(filepath) as fh: + reader = csv.reader(fh, delimiter="\t") + header = next(reader) + col_names = header[1:] + row_ids = [] + rows = [] + for row in reader: + row_ids.append(row[0]) + values = [] + for v in row[1:]: + v = v.strip() + if v == "" or v.upper() in ("NA", "NAN"): + values.append(np.nan) + else: + values.append(float(v)) + rows.append(values) + return row_ids, col_names, np.array(rows, dtype=float) + + +def read_design(filepath: str) -> dict: + """Read experimental design file mapping samples to conditions. + + Expected format: TSV with columns 'sample' and 'condition'. + + Returns + ------- + dict + {sample_name: condition_name} + """ + design = {} + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + design[row["sample"]] = row["condition"] + return design + + +def benjamini_hochberg(pvalues: list) -> list: + """Apply Benjamini-Hochberg FDR correction. + + Parameters + ---------- + pvalues: + List of p-values (may contain NaN). + + Returns + ------- + list + Adjusted p-values. + """ + n = len(pvalues) + valid_indices = [i for i in range(n) if not math.isnan(pvalues[i])] + if not valid_indices: + return [float("nan")] * n + + sorted_valid = sorted(valid_indices, key=lambda i: pvalues[i]) + adjusted = [float("nan")] * n + m = len(sorted_valid) + + prev = 1.0 + for rank_idx in range(m - 1, -1, -1): + i = sorted_valid[rank_idx] + rank = rank_idx + 1 + adj = min(pvalues[i] * m / rank, prev) + adj = min(adj, 1.0) + adjusted[i] = adj + prev = adj + + return adjusted + + +def differential_expression( + matrix: np.ndarray, row_ids: list, col_names: list, design: dict, test: str = "ttest" +) -> list: + """Compute differential expression statistics. + + Parameters + ---------- + matrix: + 2D array (features x samples). + row_ids: + Feature identifiers. + col_names: + Sample names matching matrix columns. + design: + {sample: condition} mapping. Expects exactly two conditions. + test: + Statistical test: 'ttest' (equal variance) or 'welch' (unequal variance). + + Returns + ------- + list + List of dicts with keys: feature, log2fc, pvalue, adj_pvalue. + """ + conditions = sorted(set(design.values())) + if len(conditions) != 2: + raise ValueError(f"Exactly 2 conditions required, got {len(conditions)}: {conditions}") + + cond_a, cond_b = conditions + idx_a = [i for i, s in enumerate(col_names) if design.get(s) == cond_a] + idx_b = [i for i, s in enumerate(col_names) if design.get(s) == cond_b] + + if not idx_a or not idx_b: + raise ValueError("No samples found for one or both conditions.") + + equal_var = test.lower() == "ttest" + + results = [] + pvalues = [] + for row_idx in range(len(row_ids)): + vals_a = matrix[row_idx, idx_a] + vals_b = matrix[row_idx, idx_b] + valid_a = vals_a[~np.isnan(vals_a)] + valid_b = vals_b[~np.isnan(vals_b)] + + if len(valid_a) < 2 or len(valid_b) < 2: + log2fc = float("nan") + pval = float("nan") + else: + mean_a = np.mean(valid_a) + mean_b = np.mean(valid_b) + if mean_a > 0 and mean_b > 0: + log2fc = np.log2(mean_b / mean_a) + else: + log2fc = float("nan") + _, pval = stats.ttest_ind(valid_a, valid_b, equal_var=equal_var) + + results.append({ + "feature": row_ids[row_idx], + "log2fc": log2fc, + "pvalue": pval, + }) + pvalues.append(pval) + + adj_pvalues = benjamini_hochberg(pvalues) + for i, r in enumerate(results): + r["adj_pvalue"] = adj_pvalues[i] + + return results + + +def main(): + parser = argparse.ArgumentParser(description="T-test + BH correction on quantification matrices.") + parser.add_argument("--input", required=True, help="Input TSV matrix file") + parser.add_argument("--design", required=True, help="Experimental design TSV (columns: sample, condition)") + parser.add_argument("--test", default="ttest", choices=["ttest", "welch"], help="Test type (default: ttest)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + row_ids, col_names, matrix = read_matrix(args.input) + design = read_design(args.design) + results = differential_expression(matrix, row_ids, col_names, design, test=args.test) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["feature", "log2fc", "pvalue", "adj_pvalue"], delimiter="\t") + writer.writeheader() + for r in results: + writer.writerow({ + "feature": r["feature"], + "log2fc": f"{r['log2fc']:.6f}" if not math.isnan(r["log2fc"]) else "NA", + "pvalue": f"{r['pvalue']:.6e}" if not math.isnan(r["pvalue"]) else "NA", + "adj_pvalue": f"{r['adj_pvalue']:.6e}" if not math.isnan(r["adj_pvalue"]) else "NA", + }) + + n_sig = sum(1 for r in results if not math.isnan(r["adj_pvalue"]) and r["adj_pvalue"] < 0.05) + print(f"Test: {args.test}") + print(f"Features tested: {len(results)}") + print(f"Significant (adj_pvalue < 0.05): {n_sig}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/differential_expression_tester/requirements.txt b/scripts/proteomics/differential_expression_tester/requirements.txt new file mode 100644 index 0000000..ba577e4 --- /dev/null +++ b/scripts/proteomics/differential_expression_tester/requirements.txt @@ -0,0 +1,3 @@ +pyopenms +numpy +scipy diff --git a/scripts/proteomics/differential_expression_tester/tests/conftest.py b/scripts/proteomics/differential_expression_tester/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/differential_expression_tester/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/differential_expression_tester/tests/test_differential_expression_tester.py b/scripts/proteomics/differential_expression_tester/tests/test_differential_expression_tester.py new file mode 100644 index 0000000..d017413 --- /dev/null +++ b/scripts/proteomics/differential_expression_tester/tests/test_differential_expression_tester.py @@ -0,0 +1,83 @@ +"""Tests for differential_expression_tester.""" + +import math + +import numpy as np +import pytest +from conftest import requires_pyopenms +from differential_expression_tester import ( + benjamini_hochberg, + differential_expression, + read_design, +) + + +@requires_pyopenms +class TestDifferentialExpressionTester: + def _make_data(self): + # 3 features, 6 samples (3 per condition) + np.random.seed(42) + matrix = np.array([ + [100, 110, 105, 200, 210, 205], # clearly different + [100, 105, 102, 103, 108, 101], # not different + [50, 55, 52, 500, 510, 505], # very different + ], dtype=float) + row_ids = ["prot1", "prot2", "prot3"] + col_names = ["s1", "s2", "s3", "s4", "s5", "s6"] + design = {"s1": "A", "s2": "A", "s3": "A", "s4": "B", "s5": "B", "s6": "B"} + return matrix, row_ids, col_names, design + + def test_basic_ttest(self): + matrix, row_ids, col_names, design = self._make_data() + results = differential_expression(matrix, row_ids, col_names, design, test="ttest") + assert len(results) == 3 + assert all("pvalue" in r for r in results) + assert all("log2fc" in r for r in results) + assert all("adj_pvalue" in r for r in results) + + def test_significant_feature(self): + matrix, row_ids, col_names, design = self._make_data() + results = differential_expression(matrix, row_ids, col_names, design) + # prot3 should be highly significant + prot3 = next(r for r in results if r["feature"] == "prot3") + assert prot3["pvalue"] < 0.01 + assert prot3["log2fc"] > 2.0 # ~10x increase + + def test_nonsignificant_feature(self): + matrix, row_ids, col_names, design = self._make_data() + results = differential_expression(matrix, row_ids, col_names, design) + prot2 = next(r for r in results if r["feature"] == "prot2") + assert prot2["pvalue"] > 0.05 + + def test_welch_test(self): + matrix, row_ids, col_names, design = self._make_data() + results = differential_expression(matrix, row_ids, col_names, design, test="welch") + assert len(results) == 3 + + def test_bh_correction(self): + pvalues = [0.01, 0.04, 0.03, 0.2] + adjusted = benjamini_hochberg(pvalues) + assert len(adjusted) == 4 + # Adjusted should be >= original + for orig, adj in zip(pvalues, adjusted): + assert adj >= orig or math.isnan(adj) + + def test_bh_all_nan(self): + pvalues = [float("nan"), float("nan")] + adjusted = benjamini_hochberg(pvalues) + assert all(math.isnan(a) for a in adjusted) + + def test_wrong_conditions(self): + matrix, row_ids, col_names, _ = self._make_data() + design_3 = {"s1": "A", "s2": "B", "s3": "C", "s4": "A", "s5": "B", "s6": "C"} + with pytest.raises(ValueError, match="Exactly 2 conditions"): + differential_expression(matrix, row_ids, col_names, design_3) + + def test_design_file_read(self, tmp_path): + design_file = str(tmp_path / "design.tsv") + with open(design_file, "w") as fh: + fh.write("sample\tcondition\n") + fh.write("s1\tA\n") + fh.write("s2\tB\n") + design = read_design(design_file) + assert design == {"s1": "A", "s2": "B"} diff --git a/scripts/proteomics/experimental_design_generator/tests/conftest.py b/scripts/proteomics/experimental_design_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/experimental_design_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_cleaner/README.md b/scripts/proteomics/fasta_cleaner/README.md new file mode 100644 index 0000000..8417225 --- /dev/null +++ b/scripts/proteomics/fasta_cleaner/README.md @@ -0,0 +1,22 @@ +# FASTA Cleaner + +Clean a FASTA database by removing duplicates, fixing headers, filtering by length, and removing stop codons. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Remove duplicates and filter by length +python fasta_cleaner.py --input messy.fasta --remove-duplicates --min-length 6 --output clean.fasta + +# Remove stop codons and fix headers +python fasta_cleaner.py --input messy.fasta --remove-stop-codons --fix-headers --output clean.fasta + +# All cleaning operations +python fasta_cleaner.py --input messy.fasta --remove-duplicates --min-length 6 --remove-stop-codons --fix-headers --remove-invalid-chars --output clean.fasta +``` diff --git a/scripts/proteomics/fasta_cleaner/fasta_cleaner.py b/scripts/proteomics/fasta_cleaner/fasta_cleaner.py new file mode 100644 index 0000000..2886484 --- /dev/null +++ b/scripts/proteomics/fasta_cleaner/fasta_cleaner.py @@ -0,0 +1,152 @@ +""" +FASTA Cleaner +============= +Clean a FASTA database by removing duplicates, fixing headers, filtering by length, +and removing stop codons. + +Usage +----- + python fasta_cleaner.py --input messy.fasta --remove-duplicates --min-length 6 --output clean.fasta +""" + +import argparse +import re +import sys +from typing import List, Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def save_fasta(entries: List[oms.FASTAEntry], output_path: str) -> None: + """Save entries to a FASTA file.""" + fasta_file = oms.FASTAFile() + fasta_file.store(output_path, entries) + + +def remove_duplicates(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Remove entries with duplicate sequences.""" + seen = set() + result = [] + for entry in entries: + if entry.sequence not in seen: + seen.add(entry.sequence) + result.append(entry) + return result + + +def remove_stop_codons(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Remove trailing stop codons (*) from sequences.""" + for entry in entries: + entry.sequence = entry.sequence.rstrip("*") + return entries + + +def fix_headers(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Fix common header issues: remove extra whitespace, sanitize control characters.""" + for entry in entries: + entry.identifier = re.sub(r"\s+", " ", entry.identifier).strip() + entry.description = re.sub(r"\s+", " ", entry.description).strip() + return entries + + +def filter_by_length( + entries: List[oms.FASTAEntry], + min_length: Optional[int] = None, + max_length: Optional[int] = None, +) -> List[oms.FASTAEntry]: + """Filter entries by sequence length.""" + result = [] + for entry in entries: + seq_len = len(entry.sequence) + if min_length is not None and seq_len < min_length: + continue + if max_length is not None and seq_len > max_length: + continue + result.append(entry) + return result + + +def remove_invalid_chars(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Remove non-amino-acid characters from sequences.""" + valid = set("ACDEFGHIKLMNPQRSTVWYBXZJUO") + for entry in entries: + entry.sequence = "".join(c for c in entry.sequence.upper() if c in valid) + return entries + + +def clean_fasta( + input_path: str, + output_path: str, + dedup: bool = False, + min_length: Optional[int] = None, + max_length: Optional[int] = None, + strip_stop_codons: bool = False, + do_fix_headers: bool = False, + do_remove_invalid: bool = False, +) -> dict: + """Clean a FASTA file with the specified operations. + + Returns statistics about the cleaning process. + """ + entries = load_fasta(input_path) + total_input = len(entries) + + if strip_stop_codons: + entries = remove_stop_codons(entries) + + if do_remove_invalid: + entries = remove_invalid_chars(entries) + + if do_fix_headers: + entries = fix_headers(entries) + + if dedup: + entries = remove_duplicates(entries) + + if min_length is not None or max_length is not None: + entries = filter_by_length(entries, min_length, max_length) + + save_fasta(entries, output_path) + return {"total_input": total_input, "total_output": len(entries)} + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Clean a FASTA database: remove duplicates, fix headers, filter by length." + ) + parser.add_argument("--input", required=True, help="Input FASTA file") + parser.add_argument("--output", required=True, help="Output cleaned FASTA file") + parser.add_argument("--remove-duplicates", action="store_true", help="Remove duplicate sequences") + parser.add_argument("--min-length", type=int, default=None, help="Minimum sequence length") + parser.add_argument("--max-length", type=int, default=None, help="Maximum sequence length") + parser.add_argument("--remove-stop-codons", action="store_true", help="Remove trailing stop codons (*)") + parser.add_argument("--fix-headers", action="store_true", help="Fix header whitespace issues") + parser.add_argument("--remove-invalid-chars", action="store_true", help="Remove non-amino-acid characters") + args = parser.parse_args() + + stats = clean_fasta( + args.input, + args.output, + dedup=args.remove_duplicates, + min_length=args.min_length, + max_length=args.max_length, + strip_stop_codons=args.remove_stop_codons, + do_fix_headers=args.fix_headers, + do_remove_invalid=args.remove_invalid_chars, + ) + print(f"Cleaned: {stats['total_input']} -> {stats['total_output']} proteins written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_cleaner/requirements.txt b/scripts/proteomics/fasta_cleaner/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_cleaner/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_cleaner/tests/conftest.py b/scripts/proteomics/fasta_cleaner/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_cleaner/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_cleaner/tests/test_fasta_cleaner.py b/scripts/proteomics/fasta_cleaner/tests/test_fasta_cleaner.py new file mode 100644 index 0000000..72a74a9 --- /dev/null +++ b/scripts/proteomics/fasta_cleaner/tests/test_fasta_cleaner.py @@ -0,0 +1,82 @@ +"""Tests for fasta_cleaner.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _make_entry(identifier, sequence): + import pyopenms as oms + + e = oms.FASTAEntry() + e.identifier = identifier + e.sequence = sequence + e.description = "" + return e + + +@requires_pyopenms +def test_remove_duplicates(): + from fasta_cleaner import remove_duplicates + + entries = [_make_entry("P1", "ACDEFGHIK"), _make_entry("P2", "ACDEFGHIK"), _make_entry("P3", "MNPQRST")] + result = remove_duplicates(entries) + assert len(result) == 2 + + +@requires_pyopenms +def test_remove_stop_codons(): + from fasta_cleaner import remove_stop_codons + + entries = [_make_entry("P1", "ACDEFGHIK*"), _make_entry("P2", "MNPQRST**")] + result = remove_stop_codons(entries) + assert result[0].sequence == "ACDEFGHIK" + assert result[1].sequence == "MNPQRST" + + +@requires_pyopenms +def test_fix_headers(): + from fasta_cleaner import fix_headers + + entries = [_make_entry("P1 extra spaces", "ACDE")] + result = fix_headers(entries) + assert result[0].identifier == "P1 extra spaces" + + +@requires_pyopenms +def test_filter_by_length(): + from fasta_cleaner import filter_by_length + + entries = [_make_entry("P1", "AC"), _make_entry("P2", "ACDEFGHIK"), _make_entry("P3", "A" * 100)] + result = filter_by_length(entries, min_length=3, max_length=50) + assert len(result) == 1 + assert result[0].identifier == "P2" + + +@requires_pyopenms +def test_clean_fasta_roundtrip(): + import pyopenms as oms + from fasta_cleaner import clean_fasta + + with tempfile.TemporaryDirectory() as tmp: + input_path = os.path.join(tmp, "input.fasta") + output_path = os.path.join(tmp, "output.fasta") + + entries = [ + _make_entry("P1", "ACDEFGHIK*"), + _make_entry("P2", "ACDEFGHIK*"), + _make_entry("P3", "AC"), + ] + fasta_file = oms.FASTAFile() + fasta_file.store(input_path, entries) + + stats = clean_fasta( + input_path, output_path, dedup=True, min_length=5, strip_stop_codons=True + ) + assert stats["total_input"] == 3 + assert stats["total_output"] == 1 + + result = [] + fasta_file.load(output_path, result) + assert result[0].sequence == "ACDEFGHIK" diff --git a/scripts/proteomics/fasta_decoy_validator/README.md b/scripts/proteomics/fasta_decoy_validator/README.md new file mode 100644 index 0000000..1d88cd3 --- /dev/null +++ b/scripts/proteomics/fasta_decoy_validator/README.md @@ -0,0 +1,19 @@ +# FASTA Decoy Validator + +Check if a FASTA database contains decoy sequences and validate prefix consistency. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Validate with default DECOY_ prefix +python fasta_decoy_validator.py --input db.fasta + +# Validate with custom prefix +python fasta_decoy_validator.py --input db.fasta --decoy-prefix REV_ --output validation.json +``` diff --git a/scripts/proteomics/fasta_decoy_validator/fasta_decoy_validator.py b/scripts/proteomics/fasta_decoy_validator/fasta_decoy_validator.py new file mode 100644 index 0000000..9a34a25 --- /dev/null +++ b/scripts/proteomics/fasta_decoy_validator/fasta_decoy_validator.py @@ -0,0 +1,122 @@ +""" +FASTA Decoy Validator +===================== +Check if a FASTA database contains decoy sequences and validate prefix consistency. + +Usage +----- + python fasta_decoy_validator.py --input db.fasta --decoy-prefix DECOY_ --output validation.json +""" + +import argparse +import json +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def validate_decoys( + input_path: str, + decoy_prefix: str = "DECOY_", +) -> dict: + """Validate decoy sequences in a FASTA database. + + Returns a dict with validation results including: + - total_entries: total number of proteins + - target_count: number of target entries + - decoy_count: number of decoy entries + - has_decoys: whether decoys were found + - decoy_ratio: ratio of decoys to targets + - prefix_consistent: whether all decoys use the expected prefix + - alternative_prefixes: any other prefixes detected + - reversed_match_count: number of decoys whose sequence is the reverse of a target + """ + entries = load_fasta(input_path) + total = len(entries) + + common_prefixes = ["DECOY_", "REV_", "rev_", "decoy_", "REVERSED_", "XXX_"] + if decoy_prefix not in common_prefixes: + common_prefixes.insert(0, decoy_prefix) + + target_entries = [] + decoy_entries = [] + prefix_counts = {} + + for entry in entries: + identifier = entry.identifier + is_decoy = False + for prefix in common_prefixes: + if identifier.startswith(prefix): + prefix_counts[prefix] = prefix_counts.get(prefix, 0) + 1 + is_decoy = True + break + if is_decoy: + decoy_entries.append(entry) + else: + target_entries.append(entry) + + target_count = len(target_entries) + decoy_count = len(decoy_entries) + expected_prefix_count = prefix_counts.get(decoy_prefix, 0) + + # Check for prefix consistency + alternative_prefixes = {p: c for p, c in prefix_counts.items() if p != decoy_prefix and c > 0} + prefix_consistent = decoy_count == expected_prefix_count + + # Check if decoys are reversed versions of targets + target_seqs = {e.sequence for e in target_entries} + reversed_match = 0 + for entry in decoy_entries: + if entry.sequence[::-1] in target_seqs: + reversed_match += 1 + + decoy_ratio = decoy_count / target_count if target_count > 0 else 0.0 + + return { + "total_entries": total, + "target_count": target_count, + "decoy_count": decoy_count, + "has_decoys": decoy_count > 0, + "decoy_ratio": round(decoy_ratio, 4), + "expected_prefix": decoy_prefix, + "expected_prefix_count": expected_prefix_count, + "prefix_consistent": prefix_consistent, + "alternative_prefixes": alternative_prefixes, + "reversed_match_count": reversed_match, + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Validate decoy sequences in a FASTA database." + ) + parser.add_argument("--input", required=True, help="Input FASTA file") + parser.add_argument("--decoy-prefix", default="DECOY_", help="Expected decoy prefix (default: DECOY_)") + parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)") + args = parser.parse_args() + + result = validate_decoys(args.input, args.decoy_prefix) + output = json.dumps(result, indent=2) + + if args.output: + with open(args.output, "w") as fh: + fh.write(output + "\n") + print(f"Validation results written to {args.output}") + else: + print(output) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_decoy_validator/requirements.txt b/scripts/proteomics/fasta_decoy_validator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_decoy_validator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_decoy_validator/tests/conftest.py b/scripts/proteomics/fasta_decoy_validator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_decoy_validator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_decoy_validator/tests/test_fasta_decoy_validator.py b/scripts/proteomics/fasta_decoy_validator/tests/test_fasta_decoy_validator.py new file mode 100644 index 0000000..c2535a0 --- /dev/null +++ b/scripts/proteomics/fasta_decoy_validator/tests/test_fasta_decoy_validator.py @@ -0,0 +1,86 @@ +"""Tests for fasta_decoy_validator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_fasta(path, entries_data): + import pyopenms as oms + + entries = [] + for acc, seq in entries_data: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + oms.FASTAFile().store(path, entries) + + +@requires_pyopenms +def test_no_decoys(): + from fasta_decoy_validator import validate_decoys + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "target.fasta") + _create_fasta(fasta_path, [("P12345", "ACDEFGHIK"), ("P67890", "MNPQRSTWY")]) + + result = validate_decoys(fasta_path) + assert result["has_decoys"] is False + assert result["target_count"] == 2 + assert result["decoy_count"] == 0 + + +@requires_pyopenms +def test_with_decoys(): + from fasta_decoy_validator import validate_decoys + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "td.fasta") + _create_fasta(fasta_path, [ + ("P12345", "ACDEFGHIK"), + ("DECOY_P12345", "KIHGFEDCA"), + ]) + + result = validate_decoys(fasta_path) + assert result["has_decoys"] is True + assert result["target_count"] == 1 + assert result["decoy_count"] == 1 + assert result["prefix_consistent"] is True + assert result["decoy_ratio"] == 1.0 + + +@requires_pyopenms +def test_mixed_prefixes(): + from fasta_decoy_validator import validate_decoys + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "mixed.fasta") + _create_fasta(fasta_path, [ + ("P12345", "ACDEFGHIK"), + ("DECOY_P12345", "KIHGFEDCA"), + ("REV_P67890", "YWTSRQPNM"), + ]) + + result = validate_decoys(fasta_path, decoy_prefix="DECOY_") + assert result["has_decoys"] is True + assert result["prefix_consistent"] is False + assert "REV_" in result["alternative_prefixes"] + + +@requires_pyopenms +def test_reversed_match(): + from fasta_decoy_validator import validate_decoys + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "rev.fasta") + seq = "ACDEFGHIK" + _create_fasta(fasta_path, [ + ("P12345", seq), + ("DECOY_P12345", seq[::-1]), + ]) + + result = validate_decoys(fasta_path) + assert result["reversed_match_count"] == 1 diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/README.md b/scripts/proteomics/fasta_in_silico_digest_stats/README.md new file mode 100644 index 0000000..547e8d2 --- /dev/null +++ b/scripts/proteomics/fasta_in_silico_digest_stats/README.md @@ -0,0 +1,19 @@ +# FASTA In-Silico Digest Stats + +Digest a FASTA database in silico and report peptide statistics. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Basic trypsin digestion +python fasta_in_silico_digest_stats.py --input db.fasta --enzyme Trypsin --output stats.tsv + +# With missed cleavages and length filter +python fasta_in_silico_digest_stats.py --input db.fasta --enzyme Trypsin --missed-cleavages 2 --min-length 7 --max-length 30 --output stats.tsv +``` diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py b/scripts/proteomics/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py new file mode 100644 index 0000000..6b5a77b --- /dev/null +++ b/scripts/proteomics/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py @@ -0,0 +1,133 @@ +""" +FASTA In-Silico Digest Stats +============================= +Digest a FASTA database in silico and report peptide statistics including +peptide count, length distribution, mass distribution, and unique peptides. + +Usage +----- + python fasta_in_silico_digest_stats.py --input db.fasta --enzyme Trypsin --missed-cleavages 2 --output stats.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def digest_fasta( + input_path: str, + enzyme: str = "Trypsin", + missed_cleavages: int = 0, + min_length: int = 6, + max_length: int = 50, +) -> dict: + """Digest all proteins in a FASTA and return peptide statistics. + + Returns a dict with: + - protein_count: number of proteins digested + - total_peptides: total peptide count + - unique_peptides: unique peptide sequences + - length_distribution: dict of length -> count + - mass_stats: min, max, mean mass + - peptides: list of dicts with sequence, mass, length, protein info + """ + entries = load_fasta(input_path) + digestor = oms.ProteaseDigestion() + digestor.setEnzyme(enzyme) + digestor.setMissedCleavages(missed_cleavages) + + all_peptides: List[dict] = [] + unique_seqs: set = set() + + for entry in entries: + peptides: List[oms.AASequence] = [] + digestor.digest(oms.AASequence.fromString(entry.sequence), peptides) + for pep in peptides: + seq_str = pep.toString() + seq_len = len(seq_str) + if seq_len < min_length or seq_len > max_length: + continue + mass = pep.getMonoWeight() + unique_seqs.add(seq_str) + all_peptides.append({ + "sequence": seq_str, + "length": seq_len, + "mass": round(mass, 4), + "protein": entry.identifier.split()[0], + }) + + # Length distribution + length_dist: Dict[int, int] = {} + masses: List[float] = [] + for pep in all_peptides: + length_dist[pep["length"]] = length_dist.get(pep["length"], 0) + 1 + masses.append(pep["mass"]) + + mass_stats = {} + if masses: + mass_stats = { + "min": round(min(masses), 4), + "max": round(max(masses), 4), + "mean": round(sum(masses) / len(masses), 4), + } + + return { + "protein_count": len(entries), + "enzyme": enzyme, + "missed_cleavages": missed_cleavages, + "total_peptides": len(all_peptides), + "unique_peptides": len(unique_seqs), + "length_distribution": dict(sorted(length_dist.items())), + "mass_stats": mass_stats, + "peptides": all_peptides, + } + + +def write_tsv(stats: dict, output_path: str) -> None: + """Write peptide digest results to a TSV file.""" + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["sequence", "length", "mass", "protein"]) + for pep in stats["peptides"]: + writer.writerow([pep["sequence"], pep["length"], pep["mass"], pep["protein"]]) + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Digest a FASTA database and report peptide statistics." + ) + parser.add_argument("--input", required=True, help="Input FASTA file") + parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") + parser.add_argument("--missed-cleavages", type=int, default=0, help="Missed cleavages (default: 0)") + parser.add_argument("--min-length", type=int, default=6, help="Min peptide length (default: 6)") + parser.add_argument("--max-length", type=int, default=50, help="Max peptide length (default: 50)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + stats = digest_fasta(args.input, args.enzyme, args.missed_cleavages, args.min_length, args.max_length) + write_tsv(stats, args.output) + + print(f"Proteins: {stats['protein_count']}") + print(f"Total peptides: {stats['total_peptides']}") + print(f"Unique peptides: {stats['unique_peptides']}") + if stats["mass_stats"]: + print(f"Mass range: {stats['mass_stats']['min']} - {stats['mass_stats']['max']}") + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/requirements.txt b/scripts/proteomics/fasta_in_silico_digest_stats/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_in_silico_digest_stats/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/tests/conftest.py b/scripts/proteomics/fasta_in_silico_digest_stats/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_in_silico_digest_stats/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py b/scripts/proteomics/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py new file mode 100644 index 0000000..7367f8f --- /dev/null +++ b/scripts/proteomics/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py @@ -0,0 +1,79 @@ +"""Tests for fasta_in_silico_digest_stats.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_fasta(path, entries_data): + import pyopenms as oms + + entries = [] + for acc, seq in entries_data: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + oms.FASTAFile().store(path, entries) + + +@requires_pyopenms +def test_digest_basic(): + from fasta_in_silico_digest_stats import digest_fasta + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + # Two tryptic cleavage sites (after K and R) + _create_fasta(fasta_path, [("P1", "ACDEFGHIKLMNPQRSTWYACDEFGHIK")]) + + stats = digest_fasta(fasta_path, enzyme="Trypsin", missed_cleavages=0, min_length=6) + assert stats["protein_count"] == 1 + assert stats["total_peptides"] > 0 + assert stats["unique_peptides"] > 0 + + +@requires_pyopenms +def test_digest_with_missed_cleavages(): + from fasta_in_silico_digest_stats import digest_fasta + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + _create_fasta(fasta_path, [("P1", "ACDEFGHIKLMNPQRSTWYACDEFGHIK")]) + + stats_0 = digest_fasta(fasta_path, enzyme="Trypsin", missed_cleavages=0, min_length=6) + stats_2 = digest_fasta(fasta_path, enzyme="Trypsin", missed_cleavages=2, min_length=6) + assert stats_2["total_peptides"] >= stats_0["total_peptides"] + + +@requires_pyopenms +def test_write_tsv(): + from fasta_in_silico_digest_stats import digest_fasta, write_tsv + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + tsv_path = os.path.join(tmp, "stats.tsv") + _create_fasta(fasta_path, [("P1", "ACDEFGHIKLMNPQRSTWYACDEFGHIK")]) + + stats = digest_fasta(fasta_path, enzyme="Trypsin", min_length=6) + write_tsv(stats, tsv_path) + + with open(tsv_path) as fh: + lines = fh.readlines() + assert lines[0].strip().startswith("sequence") + assert len(lines) > 1 + + +@requires_pyopenms +def test_mass_stats(): + from fasta_in_silico_digest_stats import digest_fasta + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + _create_fasta(fasta_path, [("P1", "ACDEFGHIKLMNPQRSTWYACDEFGHIK")]) + + stats = digest_fasta(fasta_path, enzyme="Trypsin", min_length=6) + assert "min" in stats["mass_stats"] + assert "max" in stats["mass_stats"] + assert stats["mass_stats"]["min"] > 0 diff --git a/scripts/proteomics/fasta_merger/README.md b/scripts/proteomics/fasta_merger/README.md new file mode 100644 index 0000000..3ba16fc --- /dev/null +++ b/scripts/proteomics/fasta_merger/README.md @@ -0,0 +1,22 @@ +# FASTA Merger + +Merge multiple FASTA files with optional deduplication. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Simple merge +python fasta_merger.py --inputs db1.fasta db2.fasta --output merged.fasta + +# Merge with deduplication by identifier +python fasta_merger.py --inputs db1.fasta db2.fasta --remove-duplicates --output merged.fasta + +# Merge with deduplication by sequence +python fasta_merger.py --inputs db1.fasta db2.fasta --remove-duplicates --dedup-by sequence --output merged.fasta +``` diff --git a/scripts/proteomics/fasta_merger/fasta_merger.py b/scripts/proteomics/fasta_merger/fasta_merger.py new file mode 100644 index 0000000..d14c313 --- /dev/null +++ b/scripts/proteomics/fasta_merger/fasta_merger.py @@ -0,0 +1,121 @@ +""" +FASTA Merger +============ +Merge multiple FASTA files with optional deduplication. + +Usage +----- + python fasta_merger.py --inputs db1.fasta db2.fasta --remove-duplicates --output merged.fasta +""" + +import argparse +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def save_fasta(entries: List[oms.FASTAEntry], output_path: str) -> None: + """Save entries to a FASTA file.""" + fasta_file = oms.FASTAFile() + fasta_file.store(output_path, entries) + + +def deduplicate_by_identifier(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Remove entries with duplicate identifiers, keeping the first occurrence.""" + seen = set() + result = [] + for entry in entries: + key = entry.identifier.split()[0] + if key not in seen: + seen.add(key) + result.append(entry) + return result + + +def deduplicate_by_sequence(entries: List[oms.FASTAEntry]) -> List[oms.FASTAEntry]: + """Remove entries with duplicate sequences, keeping the first occurrence.""" + seen = set() + result = [] + for entry in entries: + if entry.sequence not in seen: + seen.add(entry.sequence) + result.append(entry) + return result + + +def merge_fasta_files( + input_paths: List[str], + output_path: str, + remove_duplicates: bool = False, + dedup_by: str = "identifier", +) -> dict: + """Merge multiple FASTA files into one. + + Parameters + ---------- + input_paths : list of str + Paths to input FASTA files. + output_path : str + Path to output FASTA file. + remove_duplicates : bool + Whether to remove duplicates. + dedup_by : str + Deduplication strategy: 'identifier' or 'sequence'. + + Returns + ------- + dict + Statistics about the merge. + """ + all_entries = [] + file_counts = {} + for path in input_paths: + entries = load_fasta(path) + file_counts[path] = len(entries) + all_entries.extend(entries) + + total_before = len(all_entries) + + if remove_duplicates: + if dedup_by == "sequence": + all_entries = deduplicate_by_sequence(all_entries) + else: + all_entries = deduplicate_by_identifier(all_entries) + + save_fasta(all_entries, output_path) + return { + "file_counts": file_counts, + "total_before_dedup": total_before, + "total_output": len(all_entries), + } + + +def main() -> None: + parser = argparse.ArgumentParser(description="Merge multiple FASTA files.") + parser.add_argument("--inputs", nargs="+", required=True, help="Input FASTA files") + parser.add_argument("--output", required=True, help="Output merged FASTA file") + parser.add_argument("--remove-duplicates", action="store_true", help="Remove duplicate entries") + parser.add_argument( + "--dedup-by", choices=["identifier", "sequence"], default="identifier", + help="Deduplication criterion (default: identifier)" + ) + args = parser.parse_args() + + stats = merge_fasta_files(args.inputs, args.output, args.remove_duplicates, args.dedup_by) + print(f"Merged {stats['total_before_dedup']} entries -> {stats['total_output']} written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_merger/requirements.txt b/scripts/proteomics/fasta_merger/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_merger/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_merger/tests/conftest.py b/scripts/proteomics/fasta_merger/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_merger/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_merger/tests/test_fasta_merger.py b/scripts/proteomics/fasta_merger/tests/test_fasta_merger.py new file mode 100644 index 0000000..29d4896 --- /dev/null +++ b/scripts/proteomics/fasta_merger/tests/test_fasta_merger.py @@ -0,0 +1,67 @@ +"""Tests for fasta_merger.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_fasta(path, entries_data): + import pyopenms as oms + + entries = [] + for acc, seq in entries_data: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + oms.FASTAFile().store(path, entries) + + +@requires_pyopenms +def test_merge_basic(): + from fasta_merger import merge_fasta_files + + with tempfile.TemporaryDirectory() as tmp: + f1 = os.path.join(tmp, "db1.fasta") + f2 = os.path.join(tmp, "db2.fasta") + out = os.path.join(tmp, "merged.fasta") + + _create_fasta(f1, [("P1", "ACDEFGHIK")]) + _create_fasta(f2, [("P2", "MNPQRSTWY")]) + + stats = merge_fasta_files([f1, f2], out) + assert stats["total_output"] == 2 + + +@requires_pyopenms +def test_merge_dedup_identifier(): + from fasta_merger import merge_fasta_files + + with tempfile.TemporaryDirectory() as tmp: + f1 = os.path.join(tmp, "db1.fasta") + f2 = os.path.join(tmp, "db2.fasta") + out = os.path.join(tmp, "merged.fasta") + + _create_fasta(f1, [("P1", "ACDEFGHIK")]) + _create_fasta(f2, [("P1", "MNPQRSTWY"), ("P2", "ACDEFGHIK")]) + + stats = merge_fasta_files([f1, f2], out, remove_duplicates=True, dedup_by="identifier") + assert stats["total_output"] == 2 + + +@requires_pyopenms +def test_merge_dedup_sequence(): + from fasta_merger import merge_fasta_files + + with tempfile.TemporaryDirectory() as tmp: + f1 = os.path.join(tmp, "db1.fasta") + f2 = os.path.join(tmp, "db2.fasta") + out = os.path.join(tmp, "merged.fasta") + + _create_fasta(f1, [("P1", "ACDEFGHIK")]) + _create_fasta(f2, [("P2", "ACDEFGHIK")]) + + stats = merge_fasta_files([f1, f2], out, remove_duplicates=True, dedup_by="sequence") + assert stats["total_output"] == 1 diff --git a/scripts/proteomics/fasta_statistics_reporter/README.md b/scripts/proteomics/fasta_statistics_reporter/README.md new file mode 100644 index 0000000..1425e9e --- /dev/null +++ b/scripts/proteomics/fasta_statistics_reporter/README.md @@ -0,0 +1,22 @@ +# FASTA Statistics Reporter + +Report comprehensive statistics from a FASTA protein database. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Basic statistics +python fasta_statistics_reporter.py --input db.fasta + +# Include tryptic peptide count +python fasta_statistics_reporter.py --input db.fasta --enzyme Trypsin --output stats.json + +# With missed cleavages +python fasta_statistics_reporter.py --input db.fasta --enzyme Trypsin --missed-cleavages 2 --output stats.json +``` diff --git a/scripts/proteomics/fasta_statistics_reporter/fasta_statistics_reporter.py b/scripts/proteomics/fasta_statistics_reporter/fasta_statistics_reporter.py new file mode 100644 index 0000000..e8f39dd --- /dev/null +++ b/scripts/proteomics/fasta_statistics_reporter/fasta_statistics_reporter.py @@ -0,0 +1,120 @@ +""" +FASTA Statistics Reporter +========================= +Report statistics from a FASTA database: protein count, sequence lengths, +amino acid frequencies, tryptic peptide counts, and duplicate detection. + +Usage +----- + python fasta_statistics_reporter.py --input db.fasta --enzyme Trypsin --output stats.json +""" + +import argparse +import json +import sys +from collections import Counter +from typing import Dict, List, Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def compute_length_stats(entries: List[oms.FASTAEntry]) -> dict: + """Compute min, max, mean, median sequence lengths.""" + lengths = sorted(len(e.sequence) for e in entries) + if not lengths: + return {"min": 0, "max": 0, "mean": 0.0, "median": 0.0} + n = len(lengths) + median = (lengths[n // 2] + lengths[(n - 1) // 2]) / 2.0 + return { + "min": lengths[0], + "max": lengths[-1], + "mean": round(sum(lengths) / n, 2), + "median": median, + } + + +def compute_aa_frequency(entries: List[oms.FASTAEntry]) -> Dict[str, int]: + """Count amino acid frequencies across all sequences.""" + counter: Counter = Counter() + for entry in entries: + counter.update(entry.sequence) + return dict(sorted(counter.items())) + + +def count_tryptic_peptides(entries: List[oms.FASTAEntry], enzyme: str, missed_cleavages: int = 0) -> int: + """Digest all proteins and return the total number of tryptic peptides.""" + digestor = oms.ProteaseDigestion() + digestor.setEnzyme(enzyme) + digestor.setMissedCleavages(missed_cleavages) + total = 0 + for entry in entries: + peptides = [] + digestor.digest(oms.AASequence.fromString(entry.sequence), peptides) + total += len(peptides) + return total + + +def find_duplicates(entries: List[oms.FASTAEntry]) -> List[str]: + """Find duplicate accession identifiers.""" + seen: Counter = Counter() + for entry in entries: + seen[entry.identifier.split()[0]] += 1 + return [acc for acc, count in seen.items() if count > 1] + + +def compute_statistics( + input_path: str, + enzyme: Optional[str] = None, + missed_cleavages: int = 0, +) -> dict: + """Compute comprehensive statistics for a FASTA file. + + Returns a dict with protein_count, length_stats, aa_frequency, + tryptic_peptide_count, and duplicate_accessions. + """ + entries = load_fasta(input_path) + stats: dict = { + "protein_count": len(entries), + "length_stats": compute_length_stats(entries), + "aa_frequency": compute_aa_frequency(entries), + "duplicate_accessions": find_duplicates(entries), + } + if enzyme: + stats["tryptic_peptide_count"] = count_tryptic_peptides(entries, enzyme, missed_cleavages) + return stats + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Report statistics for a FASTA database." + ) + parser.add_argument("--input", required=True, help="Input FASTA file") + parser.add_argument("--enzyme", default=None, help="Enzyme for digestion (e.g. Trypsin)") + parser.add_argument("--missed-cleavages", type=int, default=0, help="Missed cleavages (default: 0)") + parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)") + args = parser.parse_args() + + stats = compute_statistics(args.input, args.enzyme, args.missed_cleavages) + output = json.dumps(stats, indent=2) + + if args.output: + with open(args.output, "w") as fh: + fh.write(output + "\n") + print(f"Statistics written to {args.output}") + else: + print(output) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_statistics_reporter/requirements.txt b/scripts/proteomics/fasta_statistics_reporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_statistics_reporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_statistics_reporter/tests/conftest.py b/scripts/proteomics/fasta_statistics_reporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_statistics_reporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py b/scripts/proteomics/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py new file mode 100644 index 0000000..2fd0022 --- /dev/null +++ b/scripts/proteomics/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py @@ -0,0 +1,82 @@ +"""Tests for fasta_statistics_reporter.""" + +import json +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_test_fasta(path): + import pyopenms as oms + + entries = [] + for acc, seq in [ + ("sp|P12345|PROT1", "ACDEFGHIKLMNPQR"), + ("sp|P67890|PROT2", "ACDEFGHIK"), + ("sp|P12345|PROT1", "ACDEFGHIKLMNPQR"), # duplicate + ]: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + fasta_file = oms.FASTAFile() + fasta_file.store(path, entries) + + +@requires_pyopenms +def test_compute_statistics_basic(): + from fasta_statistics_reporter import compute_statistics + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + _create_test_fasta(fasta_path) + + stats = compute_statistics(fasta_path) + assert stats["protein_count"] == 3 + assert stats["length_stats"]["min"] == 9 + assert stats["length_stats"]["max"] == 15 + assert len(stats["duplicate_accessions"]) == 1 + + +@requires_pyopenms +def test_compute_statistics_with_enzyme(): + from fasta_statistics_reporter import compute_statistics + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + _create_test_fasta(fasta_path) + + stats = compute_statistics(fasta_path, enzyme="Trypsin") + assert "tryptic_peptide_count" in stats + assert stats["tryptic_peptide_count"] > 0 + + +@requires_pyopenms +def test_aa_frequency(): + import pyopenms as oms + from fasta_statistics_reporter import compute_aa_frequency + + e = oms.FASTAEntry() + e.identifier = "test" + e.sequence = "AAACCC" + e.description = "" + + freq = compute_aa_frequency([e]) + assert freq["A"] == 3 + assert freq["C"] == 3 + + +@requires_pyopenms +def test_output_json(): + from fasta_statistics_reporter import compute_statistics + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "test.fasta") + _create_test_fasta(fasta_path) + + stats = compute_statistics(fasta_path) + output = json.dumps(stats, indent=2) + parsed = json.loads(output) + assert parsed["protein_count"] == 3 diff --git a/scripts/proteomics/fasta_subset_extractor/README.md b/scripts/proteomics/fasta_subset_extractor/README.md new file mode 100644 index 0000000..91748f0 --- /dev/null +++ b/scripts/proteomics/fasta_subset_extractor/README.md @@ -0,0 +1,25 @@ +# FASTA Subset Extractor + +Extract proteins from a FASTA database by accession list, keyword, or length range. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Extract by accession list +python fasta_subset_extractor.py --input db.fasta --accessions list.txt --output subset.fasta + +# Extract by keyword +python fasta_subset_extractor.py --input db.fasta --keyword "Homo sapiens" --output subset.fasta + +# Extract by length range +python fasta_subset_extractor.py --input db.fasta --min-length 50 --max-length 500 --output subset.fasta + +# Combine filters +python fasta_subset_extractor.py --input db.fasta --keyword "kinase" --min-length 100 --output subset.fasta +``` diff --git a/scripts/proteomics/fasta_subset_extractor/fasta_subset_extractor.py b/scripts/proteomics/fasta_subset_extractor/fasta_subset_extractor.py new file mode 100644 index 0000000..3a7e985 --- /dev/null +++ b/scripts/proteomics/fasta_subset_extractor/fasta_subset_extractor.py @@ -0,0 +1,134 @@ +""" +FASTA Subset Extractor +====================== +Extract proteins from a FASTA database by accession list, keyword, or length range. + +Usage +----- + python fasta_subset_extractor.py --input db.fasta --accessions list.txt --output subset.fasta + python fasta_subset_extractor.py --input db.fasta --keyword "Homo sapiens" --output subset.fasta + python fasta_subset_extractor.py --input db.fasta --min-length 50 --max-length 500 --output subset.fasta +""" + +import argparse +import sys +from typing import List, Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def save_fasta(entries: List[oms.FASTAEntry], output_path: str) -> None: + """Save entries to a FASTA file.""" + fasta_file = oms.FASTAFile() + fasta_file.store(output_path, entries) + + +def filter_by_accessions(entries: List[oms.FASTAEntry], accessions: set) -> List[oms.FASTAEntry]: + """Filter FASTA entries by a set of accession identifiers.""" + result = [] + for entry in entries: + identifier = entry.identifier.split()[0] + # Also try extracting UniProt-style accession: sp|P12345|NAME + parts = identifier.split("|") + accession_variants = {identifier} + if len(parts) >= 2: + accession_variants.add(parts[1]) + if len(parts) >= 3: + accession_variants.add(parts[2]) + if accession_variants & accessions: + result.append(entry) + return result + + +def filter_by_keyword(entries: List[oms.FASTAEntry], keyword: str) -> List[oms.FASTAEntry]: + """Filter FASTA entries whose description or identifier contains the keyword.""" + keyword_lower = keyword.lower() + result = [] + for entry in entries: + if keyword_lower in entry.identifier.lower() or keyword_lower in entry.description.lower(): + result.append(entry) + return result + + +def filter_by_length( + entries: List[oms.FASTAEntry], + min_length: Optional[int] = None, + max_length: Optional[int] = None, +) -> List[oms.FASTAEntry]: + """Filter FASTA entries by sequence length range.""" + result = [] + for entry in entries: + seq_len = len(entry.sequence) + if min_length is not None and seq_len < min_length: + continue + if max_length is not None and seq_len > max_length: + continue + result.append(entry) + return result + + +def extract_subset( + input_path: str, + output_path: str, + accessions_file: Optional[str] = None, + keyword: Optional[str] = None, + min_length: Optional[int] = None, + max_length: Optional[int] = None, +) -> dict: + """Extract a subset of proteins from a FASTA file based on the given criteria. + + Returns a dict with statistics about the extraction. + """ + entries = load_fasta(input_path) + total = len(entries) + filtered = entries + + if accessions_file: + with open(accessions_file) as fh: + accessions = {line.strip() for line in fh if line.strip()} + filtered = filter_by_accessions(filtered, accessions) + + if keyword: + filtered = filter_by_keyword(filtered, keyword) + + if min_length is not None or max_length is not None: + filtered = filter_by_length(filtered, min_length, max_length) + + save_fasta(filtered, output_path) + return {"total_input": total, "total_output": len(filtered)} + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Extract proteins from a FASTA database by accession list, keyword, or length range." + ) + parser.add_argument("--input", required=True, help="Input FASTA file") + parser.add_argument("--accessions", default=None, help="Text file with one accession per line") + parser.add_argument("--keyword", default=None, help="Keyword to match in header/description") + parser.add_argument("--min-length", type=int, default=None, help="Minimum sequence length") + parser.add_argument("--max-length", type=int, default=None, help="Maximum sequence length") + parser.add_argument("--output", required=True, help="Output FASTA file") + args = parser.parse_args() + + if not args.accessions and not args.keyword and args.min_length is None and args.max_length is None: + parser.error("At least one filter (--accessions, --keyword, --min-length, --max-length) is required.") + + stats = extract_subset( + args.input, args.output, args.accessions, args.keyword, args.min_length, args.max_length + ) + print(f"Extracted {stats['total_output']} / {stats['total_input']} proteins to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_subset_extractor/requirements.txt b/scripts/proteomics/fasta_subset_extractor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_subset_extractor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_subset_extractor/tests/conftest.py b/scripts/proteomics/fasta_subset_extractor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_subset_extractor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_subset_extractor/tests/test_fasta_subset_extractor.py b/scripts/proteomics/fasta_subset_extractor/tests/test_fasta_subset_extractor.py new file mode 100644 index 0000000..dd544ce --- /dev/null +++ b/scripts/proteomics/fasta_subset_extractor/tests/test_fasta_subset_extractor.py @@ -0,0 +1,95 @@ +"""Tests for fasta_subset_extractor.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_filter_by_accessions(): + import pyopenms as oms + from fasta_subset_extractor import filter_by_accessions + + entries = [] + for acc, seq in [("sp|P12345|PROT1", "ACDEFGHIK"), ("sp|P67890|PROT2", "MNPQRST")]: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + + result = filter_by_accessions(entries, {"P12345"}) + assert len(result) == 1 + assert "P12345" in result[0].identifier + + +@requires_pyopenms +def test_filter_by_keyword(): + import pyopenms as oms + from fasta_subset_extractor import filter_by_keyword + + e1 = oms.FASTAEntry() + e1.identifier = "sp|P12345|PROT1" + e1.description = "Human kinase protein" + e1.sequence = "ACDEFGHIK" + + e2 = oms.FASTAEntry() + e2.identifier = "sp|P67890|PROT2" + e2.description = "Mouse albumin" + e2.sequence = "MNPQRST" + + result = filter_by_keyword([e1, e2], "kinase") + assert len(result) == 1 + + +@requires_pyopenms +def test_filter_by_length(): + import pyopenms as oms + from fasta_subset_extractor import filter_by_length + + entries = [] + for seq in ["ACDE", "ACDEFGHIKLMNPQ", "AC"]: + e = oms.FASTAEntry() + e.identifier = "test" + e.sequence = seq + e.description = "" + entries.append(e) + + result = filter_by_length(entries, min_length=3, max_length=10) + assert len(result) == 1 + assert result[0].sequence == "ACDE" + + +@requires_pyopenms +def test_extract_subset_roundtrip(): + import pyopenms as oms + from fasta_subset_extractor import extract_subset + + # Create a synthetic FASTA file + entries = [] + for acc, seq in [("sp|P12345|PROT1", "ACDEFGHIK"), ("sp|P67890|PROT2", "MNPQRSTWY")]: + e = oms.FASTAEntry() + e.identifier = acc + e.sequence = seq + e.description = "" + entries.append(e) + + with tempfile.TemporaryDirectory() as tmp: + input_path = os.path.join(tmp, "input.fasta") + output_path = os.path.join(tmp, "output.fasta") + accessions_path = os.path.join(tmp, "accessions.txt") + + fasta_file = oms.FASTAFile() + fasta_file.store(input_path, entries) + + with open(accessions_path, "w") as fh: + fh.write("P12345\n") + + stats = extract_subset(input_path, output_path, accessions_file=accessions_path) + assert stats["total_input"] == 2 + assert stats["total_output"] == 1 + + result = [] + fasta_file.load(output_path, result) + assert len(result) == 1 diff --git a/scripts/proteomics/fasta_taxonomy_splitter/README.md b/scripts/proteomics/fasta_taxonomy_splitter/README.md new file mode 100644 index 0000000..9695847 --- /dev/null +++ b/scripts/proteomics/fasta_taxonomy_splitter/README.md @@ -0,0 +1,19 @@ +# FASTA Taxonomy Splitter + +Split a multi-organism FASTA file by taxonomy parsed from headers. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Split by OS= field (UniProt format) +python fasta_taxonomy_splitter.py --input combined.fasta --pattern "OS=([^=]+) OX=" --output-dir split/ + +# Custom pattern +python fasta_taxonomy_splitter.py --input combined.fasta --pattern "\[(.+?)\]$" --output-dir split/ +``` diff --git a/scripts/proteomics/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py b/scripts/proteomics/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py new file mode 100644 index 0000000..b0ee494 --- /dev/null +++ b/scripts/proteomics/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py @@ -0,0 +1,120 @@ +""" +FASTA Taxonomy Splitter +======================= +Split a multi-organism FASTA file by taxonomy parsed from headers. + +Usage +----- + python fasta_taxonomy_splitter.py --input combined.fasta --pattern "OS=([^=]+) OX=" --output-dir split/ +""" + +import argparse +import os +import re +import sys +from collections import defaultdict +from typing import Dict, List, Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(input_path: str) -> List[oms.FASTAEntry]: + """Load entries from a FASTA file.""" + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(input_path, entries) + return entries + + +def save_fasta(entries: List[oms.FASTAEntry], output_path: str) -> None: + """Save entries to a FASTA file.""" + fasta_file = oms.FASTAFile() + fasta_file.store(output_path, entries) + + +def extract_taxonomy(entry: oms.FASTAEntry, pattern: str) -> Optional[str]: + """Extract taxonomy string from a FASTA entry header using the given regex pattern. + + Searches both the identifier and description fields. + """ + header = f"{entry.identifier} {entry.description}" + match = re.search(pattern, header) + if match and match.group(1): + return match.group(1).strip() + return None + + +def sanitize_filename(name: str) -> str: + """Convert a taxonomy name to a safe filename.""" + safe = re.sub(r"[^\w\s-]", "", name) + safe = re.sub(r"\s+", "_", safe).strip("_") + return safe[:100] if safe else "unknown" + + +def split_by_taxonomy( + input_path: str, + output_dir: str, + pattern: str = r"OS=([^=]+)\s+OX=", +) -> dict: + """Split a FASTA file by taxonomy extracted from headers. + + Returns statistics about the split. + """ + entries = load_fasta(input_path) + groups: Dict[str, List[oms.FASTAEntry]] = defaultdict(list) + unmatched: List[oms.FASTAEntry] = [] + + for entry in entries: + taxonomy = extract_taxonomy(entry, pattern) + if taxonomy: + groups[taxonomy].append(entry) + else: + unmatched.append(entry) + + os.makedirs(output_dir, exist_ok=True) + + files_written = {} + for taxonomy, group_entries in groups.items(): + filename = sanitize_filename(taxonomy) + ".fasta" + filepath = os.path.join(output_dir, filename) + save_fasta(group_entries, filepath) + files_written[taxonomy] = {"file": filepath, "count": len(group_entries)} + + if unmatched: + unmatched_path = os.path.join(output_dir, "unmatched.fasta") + save_fasta(unmatched, unmatched_path) + files_written["_unmatched"] = {"file": unmatched_path, "count": len(unmatched)} + + return { + "total_entries": len(entries), + "taxonomy_groups": len(groups), + "unmatched_count": len(unmatched), + "files": files_written, + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Split a multi-organism FASTA file by taxonomy from headers." + ) + parser.add_argument("--input", required=True, help="Input FASTA file") + parser.add_argument( + "--pattern", default=r"OS=([^=]+)\s+OX=", + help="Regex pattern with one capture group for taxonomy (default: OS=... OX=)" + ) + parser.add_argument("--output-dir", required=True, help="Output directory for split files") + args = parser.parse_args() + + stats = split_by_taxonomy(args.input, args.output_dir, args.pattern) + print(f"Total entries: {stats['total_entries']}") + print(f"Taxonomy groups: {stats['taxonomy_groups']}") + print(f"Unmatched: {stats['unmatched_count']}") + for tax, info in stats["files"].items(): + print(f" {tax}: {info['count']} entries -> {info['file']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fasta_taxonomy_splitter/requirements.txt b/scripts/proteomics/fasta_taxonomy_splitter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fasta_taxonomy_splitter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fasta_taxonomy_splitter/tests/conftest.py b/scripts/proteomics/fasta_taxonomy_splitter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fasta_taxonomy_splitter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py b/scripts/proteomics/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py new file mode 100644 index 0000000..ad1ee22 --- /dev/null +++ b/scripts/proteomics/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py @@ -0,0 +1,66 @@ +"""Tests for fasta_taxonomy_splitter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_fasta(path, entries_data): + """Create FASTA with (identifier, description, sequence) tuples.""" + import pyopenms as oms + + entries = [] + for identifier, desc, seq in entries_data: + e = oms.FASTAEntry() + e.identifier = identifier + e.description = desc + e.sequence = seq + entries.append(e) + oms.FASTAFile().store(path, entries) + + +@requires_pyopenms +def test_split_by_taxonomy(): + from fasta_taxonomy_splitter import split_by_taxonomy + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "combined.fasta") + out_dir = os.path.join(tmp, "split") + + _create_fasta(fasta_path, [ + ("sp|P12345|PROT1", "Protein1 OS=Homo sapiens OX=9606", "ACDEFGHIK"), + ("sp|P67890|PROT2", "Protein2 OS=Homo sapiens OX=9606", "MNPQRSTWY"), + ("sp|Q11111|PROT3", "Protein3 OS=Mus musculus OX=10090", "ACDEFGHIK"), + ]) + + stats = split_by_taxonomy(fasta_path, out_dir, pattern=r"OS=([^=]+)\s+OX=") + assert stats["total_entries"] == 3 + assert stats["taxonomy_groups"] == 2 + assert "Homo sapiens" in stats["files"] + assert stats["files"]["Homo sapiens"]["count"] == 2 + + +@requires_pyopenms +def test_unmatched_entries(): + from fasta_taxonomy_splitter import split_by_taxonomy + + with tempfile.TemporaryDirectory() as tmp: + fasta_path = os.path.join(tmp, "combined.fasta") + out_dir = os.path.join(tmp, "split") + + _create_fasta(fasta_path, [ + ("sp|P12345|PROT1", "Protein1 OS=Homo sapiens OX=9606", "ACDEFGHIK"), + ("CUSTOM_PROT", "No taxonomy info", "MNPQRSTWY"), + ]) + + stats = split_by_taxonomy(fasta_path, out_dir, pattern=r"OS=([^=]+)\s+OX=") + assert stats["unmatched_count"] == 1 + + +@requires_pyopenms +def test_sanitize_filename(): + from fasta_taxonomy_splitter import sanitize_filename + + assert sanitize_filename("Homo sapiens") == "Homo_sapiens" + assert sanitize_filename("E. coli (strain K12)") == "E_coli_strain_K12" diff --git a/scripts/proteomics/featurexml_merger/README.md b/scripts/proteomics/featurexml_merger/README.md new file mode 100644 index 0000000..9571967 --- /dev/null +++ b/scripts/proteomics/featurexml_merger/README.md @@ -0,0 +1,15 @@ +# featureXML Merger + +Merge multiple featureXML files into a single featureXML file. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python featurexml_merger.py --inputs f1.featureXML f2.featureXML --output merged.featureXML +``` diff --git a/scripts/proteomics/featurexml_merger/featurexml_merger.py b/scripts/proteomics/featurexml_merger/featurexml_merger.py new file mode 100644 index 0000000..90dd0e4 --- /dev/null +++ b/scripts/proteomics/featurexml_merger/featurexml_merger.py @@ -0,0 +1,84 @@ +""" +featureXML Merger +================= +Merge multiple featureXML files into a single featureXML file. + +Usage +----- + python featurexml_merger.py --inputs f1.featureXML f2.featureXML --output merged.featureXML +""" + +import argparse +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_featurexml(input_path: str) -> oms.FeatureMap: + """Load a featureXML file.""" + fm = oms.FeatureMap() + oms.FeatureXMLFile().load(input_path, fm) + return fm + + +def save_featurexml(feature_map: oms.FeatureMap, output_path: str) -> None: + """Save a FeatureMap to featureXML.""" + oms.FeatureXMLFile().store(output_path, feature_map) + + +def merge_feature_maps(input_paths: List[str], output_path: str) -> dict: + """Merge multiple featureXML files into one. + + Returns statistics about the merge. + """ + merged = oms.FeatureMap() + file_counts = {} + + for path in input_paths: + fm = load_featurexml(path) + count = fm.size() + file_counts[path] = count + for feature in fm: + merged.push_back(feature) + + # Sort by RT + merged.sortByRT() + + save_featurexml(merged, output_path) + + return { + "file_counts": file_counts, + "total_features": merged.size(), + } + + +def create_synthetic_featurexml(output_path: str, n_features: int = 5, rt_offset: float = 0.0) -> None: + """Create a synthetic featureXML file for testing.""" + fm = oms.FeatureMap() + for i in range(n_features): + f = oms.Feature() + f.setRT(100.0 + rt_offset + i * 10) + f.setMZ(500.0 + i * 50) + f.setIntensity(10000.0 + i * 1000) + f.setCharge(2) + f.setOverallQuality(0.9) + fm.push_back(f) + save_featurexml(fm, output_path) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Merge multiple featureXML files.") + parser.add_argument("--inputs", nargs="+", required=True, help="Input featureXML files") + parser.add_argument("--output", required=True, help="Output merged featureXML file") + args = parser.parse_args() + + stats = merge_feature_maps(args.inputs, args.output) + print(f"Merged {stats['total_features']} features from {len(stats['file_counts'])} files to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/featurexml_merger/requirements.txt b/scripts/proteomics/featurexml_merger/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/featurexml_merger/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/featurexml_merger/tests/conftest.py b/scripts/proteomics/featurexml_merger/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/featurexml_merger/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/featurexml_merger/tests/test_featurexml_merger.py b/scripts/proteomics/featurexml_merger/tests/test_featurexml_merger.py new file mode 100644 index 0000000..cc0cfe5 --- /dev/null +++ b/scripts/proteomics/featurexml_merger/tests/test_featurexml_merger.py @@ -0,0 +1,59 @@ +"""Tests for featurexml_merger.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_create_synthetic_featurexml(): + import pyopenms as oms + from featurexml_merger import create_synthetic_featurexml + + with tempfile.TemporaryDirectory() as tmp: + fxml_path = os.path.join(tmp, "test.featureXML") + create_synthetic_featurexml(fxml_path, n_features=3) + + fm = oms.FeatureMap() + oms.FeatureXMLFile().load(fxml_path, fm) + assert fm.size() == 3 + + +@requires_pyopenms +def test_merge_two_files(): + from featurexml_merger import create_synthetic_featurexml, merge_feature_maps + + with tempfile.TemporaryDirectory() as tmp: + f1 = os.path.join(tmp, "f1.featureXML") + f2 = os.path.join(tmp, "f2.featureXML") + out = os.path.join(tmp, "merged.featureXML") + + create_synthetic_featurexml(f1, n_features=3, rt_offset=0.0) + create_synthetic_featurexml(f2, n_features=4, rt_offset=1000.0) + + stats = merge_feature_maps([f1, f2], out) + assert stats["total_features"] == 7 + assert stats["file_counts"][f1] == 3 + assert stats["file_counts"][f2] == 4 + + +@requires_pyopenms +def test_merged_sorted_by_rt(): + import pyopenms as oms + from featurexml_merger import create_synthetic_featurexml, merge_feature_maps + + with tempfile.TemporaryDirectory() as tmp: + f1 = os.path.join(tmp, "f1.featureXML") + f2 = os.path.join(tmp, "f2.featureXML") + out = os.path.join(tmp, "merged.featureXML") + + create_synthetic_featurexml(f1, n_features=3, rt_offset=500.0) + create_synthetic_featurexml(f2, n_features=3, rt_offset=0.0) + + merge_feature_maps([f1, f2], out) + + fm = oms.FeatureMap() + oms.FeatureXMLFile().load(out, fm) + rts = [f.getRT() for f in fm] + assert rts == sorted(rts) diff --git a/scripts/proteomics/fragpipe_result_converter/README.md b/scripts/proteomics/fragpipe_result_converter/README.md new file mode 100644 index 0000000..0881e63 --- /dev/null +++ b/scripts/proteomics/fragpipe_result_converter/README.md @@ -0,0 +1,23 @@ +# FragPipe Result Converter + +Convert FragPipe psm.tsv to a standardized TSV format. + +## Usage + +```bash +python fragpipe_result_converter.py --input psm.tsv --output standardized.tsv +``` + +## Column Mapping + +| FragPipe Column | Standard Column | +|---|---| +| Peptide | peptide | +| Modified Peptide | modified_peptide | +| Charge | charge | +| Observed M/Z | mz | +| Retention | rt | +| Protein | protein | +| Hyperscore | score | +| Intensity | intensity | +| Spectrum File | raw_file | diff --git a/scripts/proteomics/fragpipe_result_converter/fragpipe_result_converter.py b/scripts/proteomics/fragpipe_result_converter/fragpipe_result_converter.py new file mode 100644 index 0000000..17bb21c --- /dev/null +++ b/scripts/proteomics/fragpipe_result_converter/fragpipe_result_converter.py @@ -0,0 +1,101 @@ +""" +FragPipe Result Converter +========================= +Convert FragPipe psm.tsv to a standardized TSV format. + +Maps FragPipe-specific column names to a common schema for downstream analysis. + +Usage +----- + python fragpipe_result_converter.py --input psm.tsv --output standardized.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Mapping from FragPipe column names to standard column names +COLUMN_MAP = { + "Peptide": "peptide", + "Modified Peptide": "modified_peptide", + "Charge": "charge", + "Calculated Peptide Mass": "mass", + "Calibrated Observed Mass": "observed_mass", + "Observed M/Z": "mz", + "Retention": "rt", + "Protein": "protein", + "Protein Description": "protein_description", + "Gene": "gene", + "Hyperscore": "score", + "Expectation": "expect", + "PeptideProphet Probability": "probability", + "Intensity": "intensity", + "Spectrum": "spectrum", + "Spectrum File": "raw_file", + "Is Unique": "is_unique", + "Mapped Proteins": "mapped_proteins", +} + +STANDARD_FIELDS = [ + "peptide", "modified_peptide", "charge", "mass", "observed_mass", "mz", "rt", + "protein", "protein_description", "gene", + "score", "expect", "probability", + "intensity", "spectrum", "raw_file", + "is_unique", "mapped_proteins", "source", +] + + +def convert_fragpipe_psm(filepath: str) -> list: + """Convert FragPipe psm.tsv to standardized format. + + Parameters + ---------- + filepath: + Path to FragPipe psm.tsv. + + Returns + ------- + list + List of dicts with standardized column names. + """ + rows = [] + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + std_row = {} + for fp_col, std_col in COLUMN_MAP.items(): + std_row[std_col] = row.get(fp_col, "") + std_row["source"] = "FragPipe" + rows.append(std_row) + return rows + + +def write_standardized(filepath: str, rows: list) -> None: + """Write standardized results to TSV.""" + with open(filepath, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=STANDARD_FIELDS, delimiter="\t", extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + +def main(): + parser = argparse.ArgumentParser(description="Convert FragPipe psm.tsv to standardized TSV.") + parser.add_argument("--input", required=True, help="FragPipe psm.tsv file") + parser.add_argument("--output", required=True, help="Output standardized TSV") + args = parser.parse_args() + + rows = convert_fragpipe_psm(args.input) + write_standardized(args.output, rows) + + print("Source: FragPipe") + print(f"Total PSMs: {len(rows)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/fragpipe_result_converter/requirements.txt b/scripts/proteomics/fragpipe_result_converter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/fragpipe_result_converter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/fragpipe_result_converter/tests/conftest.py b/scripts/proteomics/fragpipe_result_converter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/fragpipe_result_converter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/fragpipe_result_converter/tests/test_fragpipe_result_converter.py b/scripts/proteomics/fragpipe_result_converter/tests/test_fragpipe_result_converter.py new file mode 100644 index 0000000..bd1c2a2 --- /dev/null +++ b/scripts/proteomics/fragpipe_result_converter/tests/test_fragpipe_result_converter.py @@ -0,0 +1,93 @@ +"""Tests for fragpipe_result_converter.""" + +import csv + +from conftest import requires_pyopenms +from fragpipe_result_converter import STANDARD_FIELDS, convert_fragpipe_psm, write_standardized + + +@requires_pyopenms +class TestFragpipeResultConverter: + def _write_psm(self, tmp_path, rows): + filepath = str(tmp_path / "psm.tsv") + fieldnames = [ + "Peptide", "Modified Peptide", "Charge", + "Calculated Peptide Mass", "Calibrated Observed Mass", + "Observed M/Z", "Retention", "Protein", "Protein Description", + "Gene", "Hyperscore", "Expectation", + "PeptideProphet Probability", "Intensity", + "Spectrum", "Spectrum File", "Is Unique", "Mapped Proteins", + ] + with open(filepath, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(rows) + return filepath + + def test_basic_conversion(self, tmp_path): + filepath = self._write_psm(tmp_path, [ + { + "Peptide": "PEPTIDEK", "Modified Peptide": "PEPTIDEK", + "Charge": "2", "Calculated Peptide Mass": "900.0", + "Calibrated Observed Mass": "900.1", + "Observed M/Z": "450.5", "Retention": "25.5", + "Protein": "sp|P12345|PROT1", "Protein Description": "Test", + "Gene": "GEN1", "Hyperscore": "35.5", + "Expectation": "1e-5", + "PeptideProphet Probability": "0.999", + "Intensity": "1e6", "Spectrum": "scan.1234.1234.2", + "Spectrum File": "run1.mzML", "Is Unique": "true", + "Mapped Proteins": "sp|P12345|PROT1", + } + ]) + rows = convert_fragpipe_psm(filepath) + assert len(rows) == 1 + assert rows[0]["peptide"] == "PEPTIDEK" + assert rows[0]["charge"] == "2" + assert rows[0]["source"] == "FragPipe" + + def test_multiple_rows(self, tmp_path): + filepath = self._write_psm(tmp_path, [ + {"Peptide": "PEP1", "Modified Peptide": "PEP1", "Charge": "2", + "Calculated Peptide Mass": "800", "Calibrated Observed Mass": "800", + "Observed M/Z": "400", "Retention": "20", + "Protein": "P1", "Protein Description": "Prot1", + "Gene": "G1", "Hyperscore": "30", "Expectation": "1e-4", + "PeptideProphet Probability": "0.99", "Intensity": "1e6", + "Spectrum": "scan.1", "Spectrum File": "run1.mzML", + "Is Unique": "true", "Mapped Proteins": "P1"}, + {"Peptide": "PEP2", "Modified Peptide": "PEP2", "Charge": "3", + "Calculated Peptide Mass": "900", "Calibrated Observed Mass": "900", + "Observed M/Z": "300", "Retention": "30", + "Protein": "P2", "Protein Description": "Prot2", + "Gene": "G2", "Hyperscore": "25", "Expectation": "1e-3", + "PeptideProphet Probability": "0.95", "Intensity": "5e5", + "Spectrum": "scan.2", "Spectrum File": "run1.mzML", + "Is Unique": "false", "Mapped Proteins": "P2;P3"}, + ]) + rows = convert_fragpipe_psm(filepath) + assert len(rows) == 2 + + def test_write_standardized(self, tmp_path): + rows = [{"peptide": "PEPTIDEK", "charge": "2", "source": "FragPipe"}] + outfile = str(tmp_path / "out.tsv") + write_standardized(outfile, rows) + with open(outfile) as fh: + reader = csv.DictReader(fh, delimiter="\t") + result = list(reader) + assert len(result) == 1 + assert result[0]["source"] == "FragPipe" + + def test_standard_fields(self): + assert "peptide" in STANDARD_FIELDS + assert "source" in STANDARD_FIELDS + assert "score" in STANDARD_FIELDS + + def test_missing_columns_handled(self, tmp_path): + filepath = str(tmp_path / "minimal.tsv") + with open(filepath, "w") as fh: + fh.write("Peptide\tCharge\n") + fh.write("PEPTIDEK\t2\n") + rows = convert_fragpipe_psm(filepath) + assert rows[0]["peptide"] == "PEPTIDEK" + assert rows[0]["rt"] == "" diff --git a/scripts/proteomics/glycopeptide_mass_calculator/README.md b/scripts/proteomics/glycopeptide_mass_calculator/README.md new file mode 100644 index 0000000..bf53764 --- /dev/null +++ b/scripts/proteomics/glycopeptide_mass_calculator/README.md @@ -0,0 +1,10 @@ +# Glycopeptide Mass Calculator + +Calculate glycopeptide masses with glycan compositions. + +## Usage + +```bash +python glycopeptide_mass_calculator.py --sequence PEPTIDEK --glycan "HexNAc(2)Hex(5)Fuc(1)" --charge 3 +python glycopeptide_mass_calculator.py --sequence PEPTIDEK --glycan "HexNAc(2)Hex(3)" --output masses.tsv +``` diff --git a/scripts/proteomics/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py b/scripts/proteomics/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py new file mode 100644 index 0000000..9b7f18e --- /dev/null +++ b/scripts/proteomics/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py @@ -0,0 +1,176 @@ +""" +Glycopeptide Mass Calculator +============================= +Calculate glycopeptide masses with glycan compositions. + +Built-in glycan residue masses (Da): +- HexNAc = 203.079 +- Hex = 162.053 +- Fuc = 146.058 +- NeuAc = 291.095 + +Usage +----- + python glycopeptide_mass_calculator.py --sequence PEPTIDEK --glycan "HexNAc(2)Hex(5)Fuc(1)" --charge 3 + python glycopeptide_mass_calculator.py --sequence PEPTIDEK --glycan "HexNAc(2)Hex(3)" --output masses.tsv +""" + +import argparse +import csv +import re +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + +PROTON = 1.007276 + +# Built-in glycan residue masses (Da) +GLYCAN_MASSES = { + "HexNAc": 203.079, + "Hex": 162.053, + "Fuc": 146.058, + "NeuAc": 291.095, +} + + +def parse_glycan(glycan_str: str) -> dict: + """Parse a glycan composition string like 'HexNAc(2)Hex(5)Fuc(1)'. + + Parameters + ---------- + glycan_str: + Glycan composition string. + + Returns + ------- + dict + Mapping of glycan residue name to count. + """ + pattern = re.compile(r"([A-Za-z]+)\((\d+)\)") + matches = pattern.findall(glycan_str) + if not matches: + raise ValueError(f"Could not parse glycan composition: '{glycan_str}'") + composition = {} + for name, count in matches: + if name not in GLYCAN_MASSES: + raise ValueError( + f"Unknown glycan residue '{name}'. Known: {', '.join(GLYCAN_MASSES.keys())}" + ) + composition[name] = int(count) + return composition + + +def glycan_mass(composition: dict) -> float: + """Calculate total glycan mass from a composition dict. + + Parameters + ---------- + composition: + Mapping of glycan residue name to count. + + Returns + ------- + float + Total glycan mass in Da. + """ + total = 0.0 + for name, count in composition.items(): + total += GLYCAN_MASSES[name] * count + return total + + +def glycopeptide_mass( + sequence: str, + glycan_str: str, + charge: int = 1, +) -> dict: + """Calculate glycopeptide mass. + + Parameters + ---------- + sequence: + Peptide amino acid sequence. + glycan_str: + Glycan composition string, e.g. 'HexNAc(2)Hex(5)Fuc(1)'. + charge: + Charge state for m/z. + + Returns + ------- + dict + Mass information dictionary. + """ + aa_seq = oms.AASequence.fromString(sequence) + peptide_mono = aa_seq.getMonoWeight() + + composition = parse_glycan(glycan_str) + g_mass = glycan_mass(composition) + + total = peptide_mono + g_mass + mz = (total + charge * PROTON) / charge + + return { + "sequence": sequence, + "glycan": glycan_str, + "peptide_mass": peptide_mono, + "glycan_mass": g_mass, + "glycan_composition": composition, + "total_mass": total, + "charge": charge, + "mz": mz, + } + + +def write_tsv(results: list, output_path: str) -> None: + """Write results to a TSV file. + + Parameters + ---------- + results: + List of result dicts. + output_path: + Output file path. + """ + fieldnames = ["sequence", "glycan", "peptide_mass", "glycan_mass", "total_mass", "charge", "mz"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate glycopeptide masses with glycan compositions." + ) + parser.add_argument("--sequence", required=True, help="Peptide amino acid sequence") + parser.add_argument( + "--glycan", required=True, + help='Glycan composition, e.g. "HexNAc(2)Hex(5)Fuc(1)"' + ) + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + result = glycopeptide_mass(args.sequence, args.glycan, charge=args.charge) + + print(f"Sequence : {result['sequence']}") + print(f"Glycan : {result['glycan']}") + print(f"Peptide mass : {result['peptide_mass']:.6f} Da") + print(f"Glycan mass : {result['glycan_mass']:.6f} Da") + print(f"Total mass : {result['total_mass']:.6f} Da") + print(f"Charge : {result['charge']}+") + print(f"m/z : {result['mz']:.6f}") + + if args.output: + write_tsv([result], args.output) + print(f"\nResults written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/glycopeptide_mass_calculator/requirements.txt b/scripts/proteomics/glycopeptide_mass_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/glycopeptide_mass_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/glycopeptide_mass_calculator/tests/conftest.py b/scripts/proteomics/glycopeptide_mass_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/glycopeptide_mass_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py b/scripts/proteomics/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py new file mode 100644 index 0000000..0e2bbce --- /dev/null +++ b/scripts/proteomics/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py @@ -0,0 +1,66 @@ +"""Tests for glycopeptide_mass_calculator.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestGlycopeptideMassCalculator: + def test_basic_glycopeptide(self): + from glycopeptide_mass_calculator import glycopeptide_mass + + result = glycopeptide_mass("PEPTIDEK", "HexNAc(2)Hex(5)", charge=1) + assert result["sequence"] == "PEPTIDEK" + assert result["glycan_mass"] > 0 + assert result["total_mass"] > result["peptide_mass"] + + def test_glycan_mass_calculation(self): + from glycopeptide_mass_calculator import glycan_mass, parse_glycan + + comp = parse_glycan("HexNAc(2)Hex(5)Fuc(1)") + mass = glycan_mass(comp) + expected = 203.079 * 2 + 162.053 * 5 + 146.058 * 1 + assert mass == pytest.approx(expected, abs=0.01) + + def test_parse_glycan(self): + from glycopeptide_mass_calculator import parse_glycan + + comp = parse_glycan("HexNAc(2)Hex(3)NeuAc(1)") + assert comp == {"HexNAc": 2, "Hex": 3, "NeuAc": 1} + + def test_parse_glycan_invalid(self): + from glycopeptide_mass_calculator import parse_glycan + + with pytest.raises(ValueError, match="Could not parse"): + parse_glycan("invalid") + + def test_parse_glycan_unknown_residue(self): + from glycopeptide_mass_calculator import parse_glycan + + with pytest.raises(ValueError, match="Unknown glycan residue"): + parse_glycan("Unknown(3)") + + def test_mz_formula(self): + from glycopeptide_mass_calculator import PROTON, glycopeptide_mass + + result = glycopeptide_mass("PEPTIDEK", "HexNAc(2)Hex(5)", charge=3) + expected_mz = (result["total_mass"] + 3 * PROTON) / 3 + assert result["mz"] == pytest.approx(expected_mz, abs=1e-6) + + def test_total_mass_is_sum(self): + from glycopeptide_mass_calculator import glycopeptide_mass + + result = glycopeptide_mass("PEPTIDEK", "HexNAc(2)Hex(5)", charge=1) + expected = result["peptide_mass"] + result["glycan_mass"] + assert result["total_mass"] == pytest.approx(expected, abs=1e-6) + + def test_write_tsv(self, tmp_path): + from glycopeptide_mass_calculator import glycopeptide_mass, write_tsv + + result = glycopeptide_mass("PEPTIDEK", "HexNAc(2)Hex(5)", charge=2) + out = str(tmp_path / "out.tsv") + write_tsv([result], out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 + assert "sequence" in lines[0] diff --git a/scripts/proteomics/hdx_back_exchange_estimator/README.md b/scripts/proteomics/hdx_back_exchange_estimator/README.md new file mode 100644 index 0000000..053db7b --- /dev/null +++ b/scripts/proteomics/hdx_back_exchange_estimator/README.md @@ -0,0 +1,18 @@ +# HDX Back-Exchange Estimator + +Estimate per-peptide back-exchange from fully deuterated controls. + +## Usage + +```bash +python hdx_back_exchange_estimator.py --peptides peptides.tsv --fully-deuterated fd.tsv --max-backexchange 40 --output report.tsv +``` + +## Input Format + +- `peptides.tsv`: columns `sequence`, `centroid_mass` (undeuterated reference) +- `fd.tsv`: columns `sequence`, `centroid_mass` (fully deuterated controls) + +## Output + +- `report.tsv` - Per-peptide back-exchange estimates with threshold flags diff --git a/scripts/proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py b/scripts/proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py new file mode 100644 index 0000000..fee4d6c --- /dev/null +++ b/scripts/proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py @@ -0,0 +1,222 @@ +""" +HDX Back-Exchange Estimator +============================ +Estimate per-peptide back-exchange from fully deuterated controls. + +Back-exchange is the loss of deuterium during sample handling (LC separation). +By comparing expected maximum deuteration to observed fully-deuterated mass, +the per-peptide back-exchange rate can be estimated. + +Usage +----- + python hdx_back_exchange_estimator.py --peptides peptides.tsv --fully-deuterated fd.tsv \ + --max-backexchange 40 --output report.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +DEUTERIUM_MASS_SHIFT = 1.00628 # Da difference between D and H + + +def count_exchangeable_amides(sequence: str) -> int: + """Count the number of exchangeable backbone amide hydrogens. + + Exchangeable amides = len(sequence) - prolines - 2. + + Parameters + ---------- + sequence: + Amino acid sequence string. + + Returns + ------- + int + Number of exchangeable amide hydrogens. + """ + aa = oms.AASequence.fromString(sequence) + n = aa.size() + proline_count = sum(1 for i in range(n) if aa.getResidue(i).getOneLetterCode() == "P") + return max(0, n - proline_count - 2) + + +def get_peptide_mass(sequence: str) -> float: + """Get the monoisotopic mass of a peptide. + + Parameters + ---------- + sequence: + Amino acid sequence. + + Returns + ------- + float + Monoisotopic mass in Da. + """ + aa = oms.AASequence.fromString(sequence) + return aa.getMonoWeight() + + +def compute_theoretical_max_deuterated_mass(sequence: str) -> float: + """Compute the theoretical mass when all exchangeable amides are deuterated. + + Parameters + ---------- + sequence: + Amino acid sequence. + + Returns + ------- + float + Theoretical fully-deuterated mass. + """ + base_mass = get_peptide_mass(sequence) + exchangeable = count_exchangeable_amides(sequence) + return base_mass + exchangeable * DEUTERIUM_MASS_SHIFT + + +def compute_back_exchange( + sequence: str, + undeuterated_mass: float, + fully_deuterated_mass: float, +) -> Dict[str, float]: + """Compute back-exchange for a single peptide. + + Parameters + ---------- + sequence: + Peptide sequence. + undeuterated_mass: + Observed undeuterated centroid mass. + fully_deuterated_mass: + Observed fully-deuterated centroid mass. + + Returns + ------- + dict + Dictionary with back_exchange_da, back_exchange_pct, exchangeable_amides, etc. + """ + exchangeable = count_exchangeable_amides(sequence) + theoretical_max = undeuterated_mass + exchangeable * DEUTERIUM_MASS_SHIFT + + observed_shift = fully_deuterated_mass - undeuterated_mass + theoretical_shift = exchangeable * DEUTERIUM_MASS_SHIFT + + if theoretical_shift <= 0: + back_exchange_pct = 0.0 + back_exchange_da = 0.0 + else: + back_exchange_da = theoretical_shift - observed_shift + back_exchange_pct = (back_exchange_da / theoretical_shift) * 100.0 + + return { + "sequence": sequence, + "exchangeable_amides": exchangeable, + "undeuterated_mass": round(undeuterated_mass, 6), + "fully_deuterated_mass": round(fully_deuterated_mass, 6), + "theoretical_max_mass": round(theoretical_max, 6), + "observed_shift_da": round(observed_shift, 6), + "theoretical_shift_da": round(theoretical_shift, 6), + "back_exchange_da": round(back_exchange_da, 6), + "back_exchange_pct": round(back_exchange_pct, 2), + } + + +def flag_high_back_exchange( + results: List[Dict[str, float]], + max_backexchange: float = 40.0, +) -> List[Dict[str, object]]: + """Flag peptides exceeding maximum allowed back-exchange. + + Parameters + ---------- + results: + List of back-exchange result dicts. + max_backexchange: + Maximum allowed back-exchange percentage. + + Returns + ------- + list + Results with added 'flag' column. + """ + flagged = [] + for r in results: + row = dict(r) + row["exceeds_threshold"] = "YES" if r["back_exchange_pct"] > max_backexchange else "NO" + flagged.append(row) + return flagged + + +def read_peptides(peptides_path: str) -> Dict[str, float]: + """Read peptides TSV with columns: sequence, centroid_mass.""" + peptides = {} + with open(peptides_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + peptides[row["sequence"]] = float(row["centroid_mass"]) + return peptides + + +def read_fully_deuterated(fd_path: str) -> Dict[str, float]: + """Read fully deuterated controls TSV with columns: sequence, centroid_mass.""" + fd = {} + with open(fd_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + fd[row["sequence"]] = float(row["centroid_mass"]) + return fd + + +def write_output(output_path: str, results: List[Dict[str, object]]) -> None: + """Write back-exchange report to TSV.""" + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Estimate per-peptide back-exchange from fully deuterated controls." + ) + parser.add_argument("--peptides", required=True, help="Undeuterated peptides TSV (sequence, centroid_mass)") + parser.add_argument("--fully-deuterated", required=True, help="Fully deuterated TSV (sequence, centroid_mass)") + parser.add_argument( + "--max-backexchange", type=float, default=40.0, + help="Maximum allowed back-exchange percentage (default: 40)" + ) + parser.add_argument("--output", required=True, help="Output report TSV file") + args = parser.parse_args() + + peptides = read_peptides(args.peptides) + fd = read_fully_deuterated(args.fully_deuterated) + + results = [] + for seq, undeut_mass in peptides.items(): + if seq in fd: + result = compute_back_exchange(seq, undeut_mass, fd[seq]) + results.append(result) + + flagged = flag_high_back_exchange(results, args.max_backexchange) + write_output(args.output, flagged) + + n_flagged = sum(1 for r in flagged if r["exceeds_threshold"] == "YES") + print(f"Processed {len(flagged)} peptides") + print(f"Peptides exceeding {args.max_backexchange}% back-exchange: {n_flagged}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/hdx_back_exchange_estimator/requirements.txt b/scripts/proteomics/hdx_back_exchange_estimator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/hdx_back_exchange_estimator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/hdx_back_exchange_estimator/tests/conftest.py b/scripts/proteomics/hdx_back_exchange_estimator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/hdx_back_exchange_estimator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py b/scripts/proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py new file mode 100644 index 0000000..515e813 --- /dev/null +++ b/scripts/proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py @@ -0,0 +1,81 @@ +"""Tests for hdx_back_exchange_estimator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestHdxBackExchangeEstimator: + def test_count_exchangeable_amides(self): + from hdx_back_exchange_estimator import count_exchangeable_amides + # AAALAAAK: 8 residues, 0 prolines -> 8-0-2 = 6 + assert count_exchangeable_amides("AAALAAAK") == 6 + + def test_count_exchangeable_amides_with_proline(self): + from hdx_back_exchange_estimator import count_exchangeable_amides + # PPAAAA: 6 residues, 2 prolines -> 6-2-2 = 2 + assert count_exchangeable_amides("PPAAAA") == 2 + + def test_get_peptide_mass(self): + from hdx_back_exchange_estimator import get_peptide_mass + mass = get_peptide_mass("PEPTIDEK") + assert mass > 0 + + def test_compute_theoretical_max(self): + from hdx_back_exchange_estimator import ( + DEUTERIUM_MASS_SHIFT, + compute_theoretical_max_deuterated_mass, + count_exchangeable_amides, + get_peptide_mass, + ) + seq = "AAALAAAK" + theoretical = compute_theoretical_max_deuterated_mass(seq) + expected = get_peptide_mass(seq) + count_exchangeable_amides(seq) * DEUTERIUM_MASS_SHIFT + assert abs(theoretical - expected) < 1e-6 + + def test_compute_back_exchange_zero(self): + from hdx_back_exchange_estimator import DEUTERIUM_MASS_SHIFT, compute_back_exchange, count_exchangeable_amides + seq = "AAALAAAK" + exchangeable = count_exchangeable_amides(seq) + undeut = 700.0 + # Fully deuterated shows full shift -> 0% back-exchange + fd = undeut + exchangeable * DEUTERIUM_MASS_SHIFT + result = compute_back_exchange(seq, undeut, fd) + assert abs(result["back_exchange_pct"]) < 0.01 + + def test_compute_back_exchange_partial(self): + from hdx_back_exchange_estimator import DEUTERIUM_MASS_SHIFT, compute_back_exchange, count_exchangeable_amides + seq = "AAALAAAK" + exchangeable = count_exchangeable_amides(seq) + undeut = 700.0 + # Only 80% of theoretical shift observed -> 20% back-exchange + fd = undeut + exchangeable * DEUTERIUM_MASS_SHIFT * 0.8 + result = compute_back_exchange(seq, undeut, fd) + assert abs(result["back_exchange_pct"] - 20.0) < 0.1 + + def test_flag_high_back_exchange(self): + from hdx_back_exchange_estimator import flag_high_back_exchange + results = [ + {"sequence": "AAA", "back_exchange_pct": 10.0}, + {"sequence": "BBB", "back_exchange_pct": 50.0}, + ] + flagged = flag_high_back_exchange(results, max_backexchange=40.0) + assert flagged[0]["exceeds_threshold"] == "NO" + assert flagged[1]["exceeds_threshold"] == "YES" + + def test_write_output(self): + from hdx_back_exchange_estimator import write_output + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "report.tsv") + results = [{"sequence": "PEPTIDEK", "back_exchange_pct": 15.0, "exceeds_threshold": "NO"}] + write_output(output_path, results) + assert os.path.exists(output_path) + + def test_write_output_empty(self): + from hdx_back_exchange_estimator import write_output + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "report.tsv") + write_output(output_path, []) + assert not os.path.exists(output_path) diff --git a/scripts/proteomics/hdx_deuterium_uptake/README.md b/scripts/proteomics/hdx_deuterium_uptake/README.md new file mode 100644 index 0000000..cfdbd8a --- /dev/null +++ b/scripts/proteomics/hdx_deuterium_uptake/README.md @@ -0,0 +1,18 @@ +# HDX Deuterium Uptake Calculator + +Calculate deuterium uptake from HDX-MS data: mass shift, fractional uptake, and back-exchange correction. + +## Usage + +```bash +python hdx_deuterium_uptake.py --peptides peptides.tsv --undeuterated ref.tsv --timepoints 0,10,60 --output uptake.tsv +``` + +## Input Format + +- `peptides.tsv`: columns `sequence`, `timepoint`, `centroid_mass` +- `ref.tsv`: columns `sequence`, `centroid_mass` (undeuterated reference) + +## Output + +- `uptake.tsv` - Per-peptide mass shift and fractional uptake at each timepoint diff --git a/scripts/proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py b/scripts/proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py new file mode 100644 index 0000000..8e1e0a3 --- /dev/null +++ b/scripts/proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py @@ -0,0 +1,273 @@ +""" +HDX Deuterium Uptake Calculator +================================ +Calculate deuterium uptake from HDX-MS data: mass shift, fractional uptake, +and back-exchange correction. + +Exchangeable amides = sequence length - number of prolines - 2 +(N-terminal amide and first residue do not exchange under typical HDX conditions). + +Usage +----- + python hdx_deuterium_uptake.py --peptides peptides.tsv --undeuterated ref.tsv \ + --timepoints 0,10,60 --output uptake.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +DEUTERIUM_MASS_SHIFT = 1.00628 # Da difference between D and H + + +def count_exchangeable_amides(sequence: str) -> int: + """Count the number of exchangeable backbone amide hydrogens. + + Exchangeable amides = len(sequence) - prolines - 2 + (subtract 2 for N-terminal amino group and first residue which don't exchange). + + Parameters + ---------- + sequence: + Amino acid sequence string. + + Returns + ------- + int + Number of exchangeable amide hydrogens. + """ + aa = oms.AASequence.fromString(sequence) + n = aa.size() + proline_count = sum(1 for i in range(n) if aa.getResidue(i).getOneLetterCode() == "P") + exchangeable = n - proline_count - 2 + return max(0, exchangeable) + + +def get_peptide_mass(sequence: str) -> float: + """Get the monoisotopic mass of a peptide. + + Parameters + ---------- + sequence: + Amino acid sequence. + + Returns + ------- + float + Monoisotopic mass in Da. + """ + aa = oms.AASequence.fromString(sequence) + return aa.getMonoWeight() + + +def get_molecular_formula(sequence: str) -> str: + """Get the molecular formula of a peptide using EmpiricalFormula. + + Parameters + ---------- + sequence: + Amino acid sequence. + + Returns + ------- + str + Molecular formula string. + """ + aa = oms.AASequence.fromString(sequence) + formula = aa.getFormula() + return formula.toString() + + +def compute_mass_shift(deuterated_mass: float, undeuterated_mass: float) -> float: + """Compute the mass shift due to deuterium incorporation. + + Parameters + ---------- + deuterated_mass: + Observed centroid mass of deuterated peptide. + undeuterated_mass: + Centroid mass of undeuterated reference. + + Returns + ------- + float + Mass shift in Da. + """ + return deuterated_mass - undeuterated_mass + + +def compute_fractional_uptake( + mass_shift: float, + max_exchangeable: int, + back_exchange_fraction: float = 0.0, +) -> float: + """Compute fractional deuterium uptake. + + Parameters + ---------- + mass_shift: + Observed mass shift in Da. + max_exchangeable: + Maximum number of exchangeable amide hydrogens. + back_exchange_fraction: + Fraction of deuterium lost to back-exchange (0.0 to 1.0). + + Returns + ------- + float + Fractional uptake (0.0 to 1.0). + """ + if max_exchangeable <= 0: + return 0.0 + corrected_max = max_exchangeable * (1.0 - back_exchange_fraction) + if corrected_max <= 0: + return 0.0 + return mass_shift / (corrected_max * DEUTERIUM_MASS_SHIFT) + + +def compute_uptake_for_peptide( + sequence: str, + undeuterated_mass: float, + timepoint_masses: Dict[str, float], + back_exchange_fraction: float = 0.0, +) -> Dict[str, object]: + """Compute deuterium uptake for a single peptide across timepoints. + + Parameters + ---------- + sequence: + Peptide sequence. + undeuterated_mass: + Undeuterated reference centroid mass. + timepoint_masses: + Dict mapping timepoint labels to observed centroid masses. + back_exchange_fraction: + Fraction of back-exchange correction. + + Returns + ------- + dict + Dictionary with sequence, formula, exchangeable amides, and per-timepoint uptake. + """ + exchangeable = count_exchangeable_amides(sequence) + formula = get_molecular_formula(sequence) + result: Dict[str, object] = { + "sequence": sequence, + "formula": formula, + "exchangeable_amides": exchangeable, + "undeuterated_mass": undeuterated_mass, + } + + for tp, obs_mass in sorted(timepoint_masses.items(), key=lambda x: float(x[0])): + shift = compute_mass_shift(obs_mass, undeuterated_mass) + frac = compute_fractional_uptake(shift, exchangeable, back_exchange_fraction) + result[f"mass_shift_t{tp}"] = round(shift, 6) + result[f"fractional_uptake_t{tp}"] = round(frac, 6) + + return result + + +def read_peptides(peptides_path: str) -> List[Dict[str, str]]: + """Read peptides TSV file with columns: sequence, timepoint, centroid_mass.""" + rows = [] + with open(peptides_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def read_undeuterated(ref_path: str) -> Dict[str, float]: + """Read undeuterated reference TSV with columns: sequence, centroid_mass.""" + ref = {} + with open(ref_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + ref[row["sequence"]] = float(row["centroid_mass"]) + return ref + + +def group_by_peptide( + rows: List[Dict[str, str]], +) -> Dict[str, Dict[str, float]]: + """Group peptide rows by sequence, collecting timepoint masses. + + Parameters + ---------- + rows: + List of dicts with keys: sequence, timepoint, centroid_mass. + + Returns + ------- + dict + Mapping of sequence to {timepoint: centroid_mass}. + """ + grouped: Dict[str, Dict[str, float]] = {} + for row in rows: + seq = row["sequence"] + tp = row["timepoint"] + mass = float(row["centroid_mass"]) + if seq not in grouped: + grouped[seq] = {} + grouped[seq][tp] = mass + return grouped + + +def write_output(output_path: str, results: List[Dict[str, object]]) -> None: + """Write uptake results to TSV.""" + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate deuterium uptake from HDX-MS data." + ) + parser.add_argument("--peptides", required=True, help="Peptides TSV (sequence, timepoint, centroid_mass)") + parser.add_argument("--undeuterated", required=True, help="Undeuterated reference TSV (sequence, centroid_mass)") + parser.add_argument( + "--timepoints", default="0,10,60", + help="Comma-separated timepoints to process (default: 0,10,60)" + ) + parser.add_argument( + "--back-exchange", type=float, default=0.0, + help="Back-exchange correction fraction (default: 0.0)" + ) + parser.add_argument("--output", required=True, help="Output uptake TSV file") + args = parser.parse_args() + + ref = read_undeuterated(args.undeuterated) + peptide_rows = read_peptides(args.peptides) + grouped = group_by_peptide(peptide_rows) + timepoints = [t.strip() for t in args.timepoints.split(",")] + + results = [] + for seq, tp_masses in grouped.items(): + if seq not in ref: + continue + filtered = {tp: m for tp, m in tp_masses.items() if tp in timepoints} + result = compute_uptake_for_peptide(seq, ref[seq], filtered, args.back_exchange) + results.append(result) + + write_output(args.output, results) + + print(f"Processed {len(results)} peptides") + print(f"Timepoints: {timepoints}") + print(f"Back-exchange correction: {args.back_exchange:.2f}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/hdx_deuterium_uptake/requirements.txt b/scripts/proteomics/hdx_deuterium_uptake/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/hdx_deuterium_uptake/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/hdx_deuterium_uptake/tests/conftest.py b/scripts/proteomics/hdx_deuterium_uptake/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/hdx_deuterium_uptake/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py b/scripts/proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py new file mode 100644 index 0000000..90d959d --- /dev/null +++ b/scripts/proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py @@ -0,0 +1,87 @@ +"""Tests for hdx_deuterium_uptake.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestHdxDeuteriumUptake: + def test_count_exchangeable_amides(self): + from hdx_deuterium_uptake import count_exchangeable_amides + # PEPTIDEK: 8 residues, 0 prolines, exchangeable = 8 - 0 - 2 = 6 + assert count_exchangeable_amides("PEPTIDEK") == 5 # 1 proline at position 3 + + def test_count_exchangeable_amides_with_proline(self): + from hdx_deuterium_uptake import count_exchangeable_amides + # PPPPAAAA: 8 residues, 4 prolines, exchangeable = 8 - 4 - 2 = 2 + assert count_exchangeable_amides("PPPPAAAA") == 2 + + def test_count_exchangeable_amides_short(self): + from hdx_deuterium_uptake import count_exchangeable_amides + # AA: 2 residues, 0 prolines, exchangeable = max(0, 2-0-2) = 0 + assert count_exchangeable_amides("AA") == 0 + + def test_get_peptide_mass(self): + from hdx_deuterium_uptake import get_peptide_mass + mass = get_peptide_mass("PEPTIDEK") + assert 927.0 < mass < 928.0 + + def test_get_molecular_formula(self): + from hdx_deuterium_uptake import get_molecular_formula + formula = get_molecular_formula("PEPTIDEK") + assert "C" in formula + assert "H" in formula + + def test_compute_mass_shift(self): + from hdx_deuterium_uptake import compute_mass_shift + assert abs(compute_mass_shift(930.0, 928.0) - 2.0) < 1e-6 + + def test_compute_fractional_uptake(self): + from hdx_deuterium_uptake import DEUTERIUM_MASS_SHIFT, compute_fractional_uptake + # 3 exchangeable, mass shift = 3 * DEUTERIUM_MASS_SHIFT -> 100% uptake + shift = 3 * DEUTERIUM_MASS_SHIFT + frac = compute_fractional_uptake(shift, 3) + assert abs(frac - 1.0) < 1e-6 + + def test_compute_fractional_uptake_with_backexchange(self): + from hdx_deuterium_uptake import DEUTERIUM_MASS_SHIFT, compute_fractional_uptake + shift = 3 * DEUTERIUM_MASS_SHIFT * 0.8 # 80% of max after 20% back-exchange + frac = compute_fractional_uptake(shift, 3, back_exchange_fraction=0.2) + assert abs(frac - 1.0) < 1e-4 + + def test_compute_fractional_uptake_zero_exchangeable(self): + from hdx_deuterium_uptake import compute_fractional_uptake + assert compute_fractional_uptake(1.0, 0) == 0.0 + + def test_compute_uptake_for_peptide(self): + from hdx_deuterium_uptake import compute_uptake_for_peptide + result = compute_uptake_for_peptide( + "AAALAAAK", + 800.0, + {"10": 802.0, "60": 804.0}, + ) + assert result["sequence"] == "AAALAAAK" + assert "mass_shift_t10" in result + assert "fractional_uptake_t60" in result + assert result["mass_shift_t10"] == 2.0 + + def test_group_by_peptide(self): + from hdx_deuterium_uptake import group_by_peptide + rows = [ + {"sequence": "PEPTIDEK", "timepoint": "10", "centroid_mass": "930.0"}, + {"sequence": "PEPTIDEK", "timepoint": "60", "centroid_mass": "932.0"}, + {"sequence": "AAALAAAK", "timepoint": "10", "centroid_mass": "702.0"}, + ] + grouped = group_by_peptide(rows) + assert len(grouped) == 2 + assert len(grouped["PEPTIDEK"]) == 2 + + def test_write_output(self): + from hdx_deuterium_uptake import write_output + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "uptake.tsv") + results = [{"sequence": "PEPTIDEK", "mass_shift_t10": 2.0, "fractional_uptake_t10": 0.5}] + write_output(output_path, results) + assert os.path.exists(output_path) diff --git a/scripts/proteomics/identification_qc_reporter/identification_qc_reporter.py b/scripts/proteomics/identification_qc_reporter/identification_qc_reporter.py new file mode 100644 index 0000000..8bc2639 --- /dev/null +++ b/scripts/proteomics/identification_qc_reporter/identification_qc_reporter.py @@ -0,0 +1,139 @@ +""" +Identification QC Reporter +=========================== +Report identification-level QC metrics from a peptide TSV file. + +Metrics include peptide/PSM counts, score distribution, precursor mass +error statistics, and modification frequencies. + +The input TSV must contain columns: sequence, charge, score, precursor_mz, +and optionally modifications. + +Usage +----- + python identification_qc_reporter.py --input results.tsv --output id_qc.json +""" + +import argparse +import csv +import json +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def compute_id_qc(rows: list[dict]) -> dict: + """Compute identification QC metrics from parsed peptide rows. + + Parameters + ---------- + rows: + List of dicts with keys: sequence, charge, score, precursor_mz, + and optionally modifications. + + Returns + ------- + dict + QC metrics dictionary. + """ + if not rows: + return {"psm_count": 0, "unique_peptides": 0} + + sequences = [r["sequence"] for r in rows] + scores = [float(r["score"]) for r in rows] + unique_peptides = set(sequences) + + score_mean = sum(scores) / len(scores) + score_std = math.sqrt(sum((s - score_mean) ** 2 for s in scores) / len(scores)) if scores else 0.0 + score_min = min(scores) + score_max = max(scores) + + mass_errors = [] + for r in rows: + try: + seq = oms.AASequence.fromString(r["sequence"]) + theo_mass = seq.getMonoWeight() + charge = int(r["charge"]) + obs_mz = float(r["precursor_mz"]) + theo_mz = (theo_mass + charge * PROTON) / charge + error_ppm = (obs_mz - theo_mz) / theo_mz * 1e6 + mass_errors.append(error_ppm) + except Exception: + continue + + me_mean = sum(mass_errors) / len(mass_errors) if mass_errors else 0.0 + me_std = ( + math.sqrt(sum((e - me_mean) ** 2 for e in mass_errors) / len(mass_errors)) + if mass_errors + else 0.0 + ) + + mod_counts: dict[str, int] = {} + for r in rows: + mods = r.get("modifications", "").strip() + if mods: + for mod in mods.split(";"): + mod = mod.strip() + if mod: + mod_counts[mod] = mod_counts.get(mod, 0) + 1 + + return { + "psm_count": len(rows), + "unique_peptides": len(unique_peptides), + "score_mean": round(score_mean, 4), + "score_std": round(score_std, 4), + "score_min": round(score_min, 4), + "score_max": round(score_max, 4), + "mass_error_mean_ppm": round(me_mean, 4), + "mass_error_std_ppm": round(me_std, 4), + "modification_counts": mod_counts, + } + + +def load_peptide_tsv(path: str) -> list[dict]: + """Load a peptide TSV file. + + Parameters + ---------- + path: + Path to TSV file with columns: sequence, charge, score, precursor_mz. + + Returns + ------- + list[dict] + """ + rows = [] + with open(path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def main(): + parser = argparse.ArgumentParser( + description="Report identification-level QC from peptide TSV." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Peptide TSV file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON report") + args = parser.parse_args() + + rows = load_peptide_tsv(args.input) + metrics = compute_id_qc(rows) + + with open(args.output, "w") as fh: + json.dump(metrics, fh, indent=2) + + print(f"ID QC report written to {args.output}") + print(f" PSMs : {metrics['psm_count']}") + print(f" Unique peptides : {metrics['unique_peptides']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/identification_qc_reporter/requirements.txt b/scripts/proteomics/identification_qc_reporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/identification_qc_reporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/identification_qc_reporter/tests/conftest.py b/scripts/proteomics/identification_qc_reporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/identification_qc_reporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/identification_qc_reporter/tests/test_identification_qc_reporter.py b/scripts/proteomics/identification_qc_reporter/tests/test_identification_qc_reporter.py new file mode 100644 index 0000000..dae3dec --- /dev/null +++ b/scripts/proteomics/identification_qc_reporter/tests/test_identification_qc_reporter.py @@ -0,0 +1,56 @@ +"""Tests for identification_qc_reporter.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestIdentificationQcReporter: + def _make_rows(self): + return [ + {"sequence": "PEPTIDEK", "charge": "2", "score": "0.95", + "precursor_mz": "464.75", "modifications": ""}, + {"sequence": "ACDEFGHIK", "charge": "2", "score": "0.88", + "precursor_mz": "502.73", "modifications": "Oxidation"}, + {"sequence": "PEPTIDEK", "charge": "3", "score": "0.72", + "precursor_mz": "310.17", "modifications": ""}, + {"sequence": "YLAGNK", "charge": "2", "score": "0.65", + "precursor_mz": "332.19", "modifications": "Oxidation;Phospho"}, + ] + + def test_psm_count(self): + from identification_qc_reporter import compute_id_qc + + metrics = compute_id_qc(self._make_rows()) + assert metrics["psm_count"] == 4 + + def test_unique_peptides(self): + from identification_qc_reporter import compute_id_qc + + metrics = compute_id_qc(self._make_rows()) + assert metrics["unique_peptides"] == 3 + + def test_score_range(self): + from identification_qc_reporter import compute_id_qc + + metrics = compute_id_qc(self._make_rows()) + assert metrics["score_min"] == 0.65 + assert metrics["score_max"] == 0.95 + + def test_modification_counts(self): + from identification_qc_reporter import compute_id_qc + + metrics = compute_id_qc(self._make_rows()) + assert metrics["modification_counts"]["Oxidation"] == 2 + assert metrics["modification_counts"]["Phospho"] == 1 + + def test_empty_rows(self): + from identification_qc_reporter import compute_id_qc + + metrics = compute_id_qc([]) + assert metrics["psm_count"] == 0 + + def test_mass_error_computed(self): + from identification_qc_reporter import compute_id_qc + + metrics = compute_id_qc(self._make_rows()) + assert "mass_error_mean_ppm" in metrics diff --git a/scripts/proteomics/idxml_to_tsv_exporter/README.md b/scripts/proteomics/idxml_to_tsv_exporter/README.md new file mode 100644 index 0000000..a625b7d --- /dev/null +++ b/scripts/proteomics/idxml_to_tsv_exporter/README.md @@ -0,0 +1,15 @@ +# idXML to TSV Exporter + +Export peptide and protein identifications from idXML to flat TSV format. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python idxml_to_tsv_exporter.py --input results.idXML --output results.tsv +``` diff --git a/scripts/proteomics/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py b/scripts/proteomics/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py new file mode 100644 index 0000000..ce8dd1a --- /dev/null +++ b/scripts/proteomics/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py @@ -0,0 +1,131 @@ +""" +idXML to TSV Exporter +===================== +Export peptide and protein identifications from idXML to flat TSV format. + +Usage +----- + python idxml_to_tsv_exporter.py --input results.idXML --output results.tsv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_idxml(input_path: str) -> tuple: + """Load an idXML file and return (protein_ids, peptide_ids).""" + protein_ids = [] + peptide_ids = [] + oms.IdXMLFile().load(input_path, protein_ids, peptide_ids) + return protein_ids, peptide_ids + + +def export_peptide_ids(peptide_ids: List[oms.PeptideIdentification], output_path: str) -> dict: + """Export peptide identifications to TSV. + + Returns statistics about the export. + """ + total_psms = 0 + + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow([ + "spectrum_reference", "rt", "mz", "sequence", "charge", + "score", "score_type", "rank", "protein_accessions", + ]) + + for pep_id in peptide_ids: + spec_ref = pep_id.getMetaValue("spectrum_reference") if pep_id.metaValueExists( + "spectrum_reference" + ) else "" + rt = pep_id.getRT() + mz = pep_id.getMZ() + score_type = pep_id.getScoreType() + + for hit in pep_id.getHits(): + accessions = ";".join( + ev.getProteinAccession() for ev in hit.getPeptideEvidences() + ) + writer.writerow([ + spec_ref, + f"{rt:.4f}", + f"{mz:.6f}", + hit.getSequence().toString(), + hit.getCharge(), + f"{hit.getScore():.6f}", + score_type, + hit.getRank(), + accessions, + ]) + total_psms += 1 + + return {"peptide_ids": len(peptide_ids), "total_psms": total_psms} + + +def export_idxml(input_path: str, output_path: str) -> dict: + """Export an idXML file to TSV format. + + Returns export statistics. + """ + protein_ids, peptide_ids = load_idxml(input_path) + stats = export_peptide_ids(peptide_ids, output_path) + stats["protein_ids"] = len(protein_ids) + return stats + + +def create_synthetic_idxml(output_path: str) -> None: + """Create a synthetic idXML file for testing.""" + protein_id = oms.ProteinIdentification() + protein_id.setSearchEngine("SEQUEST") + protein_id.setScoreType("XCorr") + protein_id.setIdentifier("run1") + + prot_hit = oms.ProteinHit() + prot_hit.setAccession("P12345") + prot_hit.setScore(100.0) + protein_id.setHits([prot_hit]) + + peptide_ids = [] + sequences = ["ACDEFGHIK", "MNPQRSTWY", "ACDEFGHIK"] + for i, seq in enumerate(sequences): + pep_id = oms.PeptideIdentification() + pep_id.setRT(100.0 + i * 10) + pep_id.setMZ(500.0 + i * 50) + pep_id.setScoreType("XCorr") + pep_id.setIdentifier("run1") + + pep_hit = oms.PeptideHit() + pep_hit.setSequence(oms.AASequence.fromString(seq)) + pep_hit.setCharge(2) + pep_hit.setScore(2.5 + i * 0.5) + pep_hit.setRank(1) + + ev = oms.PeptideEvidence() + ev.setProteinAccession("P12345") + pep_hit.setPeptideEvidences([ev]) + + pep_id.setHits([pep_hit]) + peptide_ids.append(pep_id) + + oms.IdXMLFile().store(output_path, [protein_id], peptide_ids) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Export idXML to flat TSV format.") + parser.add_argument("--input", required=True, help="Input idXML file") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + stats = export_idxml(args.input, args.output) + print(f"Exported {stats['total_psms']} PSMs from {stats['peptide_ids']} spectra to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/idxml_to_tsv_exporter/requirements.txt b/scripts/proteomics/idxml_to_tsv_exporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/idxml_to_tsv_exporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/idxml_to_tsv_exporter/tests/conftest.py b/scripts/proteomics/idxml_to_tsv_exporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/idxml_to_tsv_exporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py b/scripts/proteomics/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py new file mode 100644 index 0000000..36520c3 --- /dev/null +++ b/scripts/proteomics/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py @@ -0,0 +1,64 @@ +"""Tests for idxml_to_tsv_exporter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_create_synthetic_idxml(): + import pyopenms as oms + from idxml_to_tsv_exporter import create_synthetic_idxml + + with tempfile.TemporaryDirectory() as tmp: + idxml_path = os.path.join(tmp, "test.idXML") + create_synthetic_idxml(idxml_path) + + protein_ids = [] + peptide_ids = [] + oms.IdXMLFile().load(idxml_path, protein_ids, peptide_ids) + assert len(protein_ids) == 1 + assert len(peptide_ids) == 3 + + +@requires_pyopenms +def test_export_idxml(): + from idxml_to_tsv_exporter import create_synthetic_idxml, export_idxml + + with tempfile.TemporaryDirectory() as tmp: + idxml_path = os.path.join(tmp, "test.idXML") + tsv_path = os.path.join(tmp, "results.tsv") + + create_synthetic_idxml(idxml_path) + stats = export_idxml(idxml_path, tsv_path) + + assert stats["peptide_ids"] == 3 + assert stats["total_psms"] == 3 + assert stats["protein_ids"] == 1 + + with open(tsv_path) as fh: + lines = fh.readlines() + assert lines[0].strip().startswith("spectrum_reference") + assert len(lines) == 4 # header + 3 PSMs + + +@requires_pyopenms +def test_export_content(): + from idxml_to_tsv_exporter import create_synthetic_idxml, export_idxml + + with tempfile.TemporaryDirectory() as tmp: + idxml_path = os.path.join(tmp, "test.idXML") + tsv_path = os.path.join(tmp, "results.tsv") + + create_synthetic_idxml(idxml_path) + export_idxml(idxml_path, tsv_path) + + with open(tsv_path) as fh: + lines = fh.readlines() + + # Check first data row contains expected peptide sequence + data_row = lines[1].strip().split("\t") + assert len(data_row) == 9 + # sequence column is index 3 + assert data_row[3] in ["ACDEFGHIK", "MNPQRSTWY"] diff --git a/scripts/proteomics/immunopeptide_filter/README.md b/scripts/proteomics/immunopeptide_filter/README.md new file mode 100644 index 0000000..9a14419 --- /dev/null +++ b/scripts/proteomics/immunopeptide_filter/README.md @@ -0,0 +1,11 @@ +# Immunopeptide Filter + +Filter peptides for MHC class I or II binding by length and optional motif. + +## Usage + +```bash +python immunopeptide_filter.py --input peptides.tsv --class-i --length-range 8-11 --output immunopeptides.tsv +python immunopeptide_filter.py --input peptides.tsv --class-ii --output immunopeptides.tsv +python immunopeptide_filter.py --input peptides.tsv --class-i --motif "^.{1}[LIV]" --output immunopeptides.tsv +``` diff --git a/scripts/proteomics/immunopeptide_filter/immunopeptide_filter.py b/scripts/proteomics/immunopeptide_filter/immunopeptide_filter.py new file mode 100644 index 0000000..5303e8e --- /dev/null +++ b/scripts/proteomics/immunopeptide_filter/immunopeptide_filter.py @@ -0,0 +1,178 @@ +""" +Immunopeptide Filter +==================== +Filter peptides for MHC class I or II binding by length and optional motif. + +MHC-I peptides are typically 8-11 amino acids. +MHC-II peptides are typically 13-25 amino acids. + +Usage +----- + python immunopeptide_filter.py --input peptides.tsv --class-i --length-range 8-11 --output immunopeptides.tsv + python immunopeptide_filter.py --input peptides.tsv --class-ii --output immunopeptides.tsv +""" + +import argparse +import csv +import re +import sys +from typing import List, Optional, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def parse_length_range(range_str: str) -> Tuple[int, int]: + """Parse a length range string like '8-11'. + + Parameters + ---------- + range_str: + Range string in format 'min-max'. + + Returns + ------- + tuple + (min_length, max_length) + """ + parts = range_str.split("-") + if len(parts) != 2: + raise ValueError(f"Invalid range format: '{range_str}'. Expected 'min-max'.") + return int(parts[0]), int(parts[1]) + + +def filter_peptides( + peptides: List[str], + min_length: int = 8, + max_length: int = 11, + motif_pattern: Optional[str] = None, +) -> List[dict]: + """Filter peptides by length and optional regex motif. + + Parameters + ---------- + peptides: + List of peptide sequences. + min_length: + Minimum peptide length (inclusive). + max_length: + Maximum peptide length (inclusive). + motif_pattern: + Optional regex pattern for motif filtering. + + Returns + ------- + list + List of dicts with sequence, length, and mass info for passing peptides. + """ + results = [] + compiled_motif = re.compile(motif_pattern) if motif_pattern else None + + for seq_str in peptides: + seq_str = seq_str.strip() + if not seq_str: + continue + + aa_seq = oms.AASequence.fromString(seq_str) + length = aa_seq.size() + + if length < min_length or length > max_length: + continue + + if compiled_motif and not compiled_motif.search(seq_str): + continue + + results.append({ + "sequence": seq_str, + "length": length, + "monoisotopic_mass": aa_seq.getMonoWeight(), + }) + + return results + + +def read_peptides_from_tsv(input_path: str, column: str = "sequence") -> List[str]: + """Read peptide sequences from a TSV file. + + Parameters + ---------- + input_path: + Path to input TSV file. + column: + Column name containing sequences. + + Returns + ------- + list + List of peptide sequence strings. + """ + peptides = [] + with open(input_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + if column in row and row[column].strip(): + peptides.append(row[column].strip()) + return peptides + + +def write_tsv(results: List[dict], output_path: str) -> None: + """Write filtered results to TSV. + + Parameters + ---------- + results: + List of result dicts. + output_path: + Output file path. + """ + if not results: + with open(output_path, "w") as fh: + fh.write("sequence\tlength\tmonoisotopic_mass\n") + return + fieldnames = ["sequence", "length", "monoisotopic_mass"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Filter peptides for MHC-I/II by length and motif." + ) + parser.add_argument("--input", required=True, help="Input TSV with peptide sequences") + parser.add_argument("--column", default="sequence", help="Column name for sequences (default: sequence)") + mhc_group = parser.add_mutually_exclusive_group() + mhc_group.add_argument("--class-i", action="store_true", help="MHC class I defaults (8-11 aa)") + mhc_group.add_argument("--class-ii", action="store_true", help="MHC class II defaults (13-25 aa)") + parser.add_argument("--length-range", default=None, help="Custom length range, e.g. '8-11'") + parser.add_argument("--motif", default=None, help="Regex motif pattern to filter by") + parser.add_argument("--output", required=True, help="Output TSV file path") + args = parser.parse_args() + + # Determine length range + if args.length_range: + min_len, max_len = parse_length_range(args.length_range) + elif args.class_ii: + min_len, max_len = 13, 25 + else: + # Default to class I + min_len, max_len = 8, 11 + + peptides = read_peptides_from_tsv(args.input, column=args.column) + print(f"Read {len(peptides)} peptides from {args.input}") + + results = filter_peptides(peptides, min_length=min_len, max_length=max_len, motif_pattern=args.motif) + print(f"Passed filter: {len(results)} peptides (length {min_len}-{max_len})") + + write_tsv(results, args.output) + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/immunopeptide_filter/requirements.txt b/scripts/proteomics/immunopeptide_filter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/immunopeptide_filter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/immunopeptide_filter/tests/conftest.py b/scripts/proteomics/immunopeptide_filter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/immunopeptide_filter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/immunopeptide_filter/tests/test_immunopeptide_filter.py b/scripts/proteomics/immunopeptide_filter/tests/test_immunopeptide_filter.py new file mode 100644 index 0000000..90a953a --- /dev/null +++ b/scripts/proteomics/immunopeptide_filter/tests/test_immunopeptide_filter.py @@ -0,0 +1,83 @@ +"""Tests for immunopeptide_filter.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestImmunopeptideFilter: + def test_filter_by_length_class_i(self): + from immunopeptide_filter import filter_peptides + + peptides = ["ACDEFGHIK", "ACDE", "ACDEFGHIKL", "ACDEFGHIKLMNPQR"] + results = filter_peptides(peptides, min_length=8, max_length=11) + seqs = [r["sequence"] for r in results] + assert "ACDEFGHIK" in seqs # 9 aa + assert "ACDEFGHIKL" in seqs # 10 aa + assert "ACDE" not in seqs # 4 aa - too short + assert "ACDEFGHIKLMNPQR" not in seqs # 15 aa - too long + + def test_filter_by_length_class_ii(self): + from immunopeptide_filter import filter_peptides + + peptides = ["ACDEFGHIK", "ACDEFGHIKLMNPQR"] + results = filter_peptides(peptides, min_length=13, max_length=25) + seqs = [r["sequence"] for r in results] + assert "ACDEFGHIK" not in seqs + assert "ACDEFGHIKLMNPQR" in seqs + + def test_filter_with_motif(self): + from immunopeptide_filter import filter_peptides + + peptides = ["ALDEFGHIK", "AXDEFGHIK"] + results = filter_peptides(peptides, min_length=8, max_length=11, motif_pattern="^A[LIV]") + seqs = [r["sequence"] for r in results] + assert "ALDEFGHIK" in seqs + assert "AXDEFGHIK" not in seqs + + def test_result_has_mass(self): + from immunopeptide_filter import filter_peptides + + results = filter_peptides(["ACDEFGHIK"], min_length=8, max_length=11) + assert len(results) == 1 + assert results[0]["monoisotopic_mass"] > 0 + assert results[0]["length"] == 9 + + def test_empty_input(self): + from immunopeptide_filter import filter_peptides + + results = filter_peptides([], min_length=8, max_length=11) + assert results == [] + + def test_parse_length_range(self): + from immunopeptide_filter import parse_length_range + + assert parse_length_range("8-11") == (8, 11) + assert parse_length_range("13-25") == (13, 25) + + def test_parse_length_range_invalid(self): + from immunopeptide_filter import parse_length_range + + with pytest.raises(ValueError): + parse_length_range("invalid") + + def test_read_and_write_tsv(self, tmp_path): + from immunopeptide_filter import filter_peptides, read_peptides_from_tsv, write_tsv + + # Create input file + inp = str(tmp_path / "input.tsv") + with open(inp, "w") as fh: + fh.write("sequence\n") + fh.write("ACDEFGHIK\n") + fh.write("ACDE\n") + + peptides = read_peptides_from_tsv(inp) + assert len(peptides) == 2 + + results = filter_peptides(peptides, min_length=8, max_length=11) + out = str(tmp_path / "output.tsv") + write_tsv(results, out) + + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 # header + 1 passing peptide diff --git a/scripts/proteomics/immunopeptidome_qc/README.md b/scripts/proteomics/immunopeptidome_qc/README.md new file mode 100644 index 0000000..f4a47f0 --- /dev/null +++ b/scripts/proteomics/immunopeptidome_qc/README.md @@ -0,0 +1,36 @@ +# Immunopeptidome QC + +Quality control for immunopeptidomics data: peptide length distribution, anchor residue frequencies, and per-position information content. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python immunopeptidome_qc.py --input hla_peptides.tsv --hla-class I \ + --output length_dist.tsv --motifs anchor_freq.tsv +``` + +### Input format + +Tab-separated file with a `sequence` column containing peptide sequences: + +``` +sequence +AAFGIILPK +AAGIGILTV +GILGFVFTL +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Input TSV with `sequence` column | +| `--hla-class` | HLA class: `I` (8-12aa) or `II` (12-25aa) | +| `--output` | Output TSV for length distribution and QC metrics | +| `--motifs` | Output TSV for anchor residue frequencies | diff --git a/scripts/proteomics/immunopeptidome_qc/immunopeptidome_qc.py b/scripts/proteomics/immunopeptidome_qc/immunopeptidome_qc.py new file mode 100644 index 0000000..e410959 --- /dev/null +++ b/scripts/proteomics/immunopeptidome_qc/immunopeptidome_qc.py @@ -0,0 +1,267 @@ +""" +Immunopeptidome QC +================== +Quality control for immunopeptidomics data: peptide length distribution, +anchor residue frequencies, and per-position information content. + +HLA class I peptides are expected to be 8-12 amino acids; HLA class II +peptides 12-25 amino acids. The tool reads a TSV with a ``sequence`` column, +validates lengths against the expected range, computes positional amino acid +frequencies and information content (bits), and reports anchor residue +enrichment. + +Usage +----- + python immunopeptidome_qc.py --input hla_peptides.tsv --hla-class I \ + --output length_dist.tsv --motifs anchor_freq.tsv +""" + +import argparse +import csv +import math +import sys +from collections import Counter +from typing import Dict, List, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Expected peptide length ranges per HLA class +HLA_LENGTH_RANGES: Dict[str, Tuple[int, int]] = { + "I": (8, 12), + "II": (12, 25), +} + +# Number of standard amino acids +NUM_AA = 20 + + +def validate_sequence(sequence: str) -> bool: + """Return True if *sequence* can be parsed by pyopenms AASequence.""" + try: + aa = oms.AASequence.fromString(sequence) + return aa.size() > 0 + except Exception: + return False + + +def length_distribution(sequences: List[str]) -> Dict[int, int]: + """Compute a histogram of peptide lengths. + + Parameters + ---------- + sequences: + List of amino acid sequences. + + Returns + ------- + dict + Mapping of peptide length to count, sorted by length. + """ + counter: Counter = Counter() + for seq in sequences: + aa = oms.AASequence.fromString(seq) + counter[aa.size()] += 1 + return dict(sorted(counter.items())) + + +def length_qc(length_dist: Dict[int, int], hla_class: str) -> Dict[str, object]: + """Evaluate length distribution against expected HLA range. + + Returns + ------- + dict + ``in_range_count``, ``out_of_range_count``, ``in_range_fraction``, + ``expected_min``, ``expected_max``. + """ + lo, hi = HLA_LENGTH_RANGES[hla_class] + in_range = sum(cnt for length, cnt in length_dist.items() if lo <= length <= hi) + total = sum(length_dist.values()) + return { + "expected_min": lo, + "expected_max": hi, + "in_range_count": in_range, + "out_of_range_count": total - in_range, + "in_range_fraction": in_range / total if total > 0 else 0.0, + } + + +def positional_frequencies(sequences: List[str], target_length: int) -> List[Dict[str, float]]: + """Compute per-position amino acid frequencies for peptides of *target_length*. + + Parameters + ---------- + sequences: + Input peptide sequences. + target_length: + Only peptides of exactly this length are considered. + + Returns + ------- + list of dict + One dict per position mapping residue one-letter code to frequency. + """ + counters: List[Counter] = [Counter() for _ in range(target_length)] + n = 0 + for seq in sequences: + aa = oms.AASequence.fromString(seq) + if aa.size() != target_length: + continue + n += 1 + for i in range(target_length): + residue = aa.getResidue(i).getOneLetterCode() + counters[i][residue] += 1 + if n == 0: + return [{} for _ in range(target_length)] + result: List[Dict[str, float]] = [] + for counter in counters: + total = sum(counter.values()) + result.append({aa: cnt / total for aa, cnt in sorted(counter.items())}) + return result + + +def information_content(freq: Dict[str, float]) -> float: + """Shannon information content in bits for a single position. + + IC = log2(N) + sum(p * log2(p)) where N = 20 amino acids. + """ + max_entropy = math.log2(NUM_AA) + entropy = 0.0 + for p in freq.values(): + if p > 0: + entropy -= p * math.log2(p) + return max_entropy - entropy + + +def anchor_residue_frequencies( + sequences: List[str], hla_class: str +) -> Dict[str, Dict[str, float]]: + """Compute anchor residue frequencies for dominant peptide lengths. + + For HLA-I the canonical anchors are position 2 and the C-terminal position + (for 9-mers). For HLA-II the anchors are positions 1, 4, 6, 9 of the + 9-mer binding core, but since core identification is non-trivial we report + position 1 and the C-terminal position for the most common length. + + Returns + ------- + dict + Keys are position labels (e.g. ``"P2"``, ``"PC"``), values are dicts + mapping residue to frequency. + """ + # find most common length within range + lo, hi = HLA_LENGTH_RANGES[hla_class] + length_counter: Counter = Counter() + for seq in sequences: + aa = oms.AASequence.fromString(seq) + sz = aa.size() + if lo <= sz <= hi: + length_counter[sz] += 1 + if not length_counter: + return {} + dominant_length = length_counter.most_common(1)[0][0] + + freqs = positional_frequencies(sequences, dominant_length) + anchors: Dict[str, Dict[str, float]] = {} + if hla_class == "I": + if len(freqs) >= 2: + anchors["P2"] = freqs[1] + anchors["PC"] = freqs[-1] + else: + anchors["P1"] = freqs[0] + anchors["PC"] = freqs[-1] + return anchors + + +def run_qc( + sequences: List[str], hla_class: str +) -> Tuple[Dict[int, int], Dict[str, object], Dict[str, Dict[str, float]], List[float]]: + """Run the full QC pipeline. + + Returns + ------- + tuple + (length_dist, length_qc_result, anchor_freqs, info_content_per_position) + """ + dist = length_distribution(sequences) + qc = length_qc(dist, hla_class) + + anchors = anchor_residue_frequencies(sequences, hla_class) + + # info content for dominant length + lo, hi = HLA_LENGTH_RANGES[hla_class] + length_counter: Counter = Counter() + for seq in sequences: + aa = oms.AASequence.fromString(seq) + sz = aa.size() + if lo <= sz <= hi: + length_counter[sz] += 1 + if length_counter: + dominant = length_counter.most_common(1)[0][0] + freqs = positional_frequencies(sequences, dominant) + ic_values = [information_content(f) for f in freqs] + else: + ic_values = [] + + return dist, qc, anchors, ic_values + + +def main() -> None: + parser = argparse.ArgumentParser( + description="QC for immunopeptidomics: length distribution, anchor residue frequencies, information content." + ) + parser.add_argument("--input", required=True, help="Input TSV with 'sequence' column") + parser.add_argument( + "--hla-class", required=True, choices=["I", "II"], help="HLA class (I or II)" + ) + parser.add_argument("--output", required=True, help="Output TSV for length distribution") + parser.add_argument("--motifs", required=True, help="Output TSV for anchor residue frequencies") + args = parser.parse_args() + + # Read sequences + sequences: List[str] = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("sequence", "").strip() + if seq and validate_sequence(seq): + sequences.append(seq) + + if not sequences: + sys.exit("No valid sequences found in input file.") + + dist, qc, anchors, ic_values = run_qc(sequences, args.hla_class) + + # Write length distribution + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["length", "count"]) + for length, count in dist.items(): + writer.writerow([length, count]) + writer.writerow([]) + writer.writerow(["metric", "value"]) + for key, val in qc.items(): + writer.writerow([key, val]) + if ic_values: + writer.writerow([]) + writer.writerow(["position", "information_content_bits"]) + for i, ic in enumerate(ic_values, 1): + writer.writerow([i, f"{ic:.4f}"]) + + # Write anchor frequencies + with open(args.motifs, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["anchor_position", "residue", "frequency"]) + for pos_label, freq_dict in anchors.items(): + for residue, freq in sorted(freq_dict.items()): + writer.writerow([pos_label, residue, f"{freq:.4f}"]) + + print(f"Length distribution written to {args.output}") + print(f"Anchor frequencies written to {args.motifs}") + print(f"Total peptides: {sum(dist.values())}, in-range: {qc['in_range_fraction']:.1%}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/immunopeptidome_qc/requirements.txt b/scripts/proteomics/immunopeptidome_qc/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/immunopeptidome_qc/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/immunopeptidome_qc/tests/conftest.py b/scripts/proteomics/immunopeptidome_qc/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/immunopeptidome_qc/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/immunopeptidome_qc/tests/test_immunopeptidome_qc.py b/scripts/proteomics/immunopeptidome_qc/tests/test_immunopeptidome_qc.py new file mode 100644 index 0000000..85eb7ec --- /dev/null +++ b/scripts/proteomics/immunopeptidome_qc/tests/test_immunopeptidome_qc.py @@ -0,0 +1,115 @@ +"""Tests for immunopeptidome_qc.""" + +import csv + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_validate_sequence(): + from immunopeptidome_qc import validate_sequence + + assert validate_sequence("AAGIGILTV") is True + assert validate_sequence("") is False + + +@requires_pyopenms +def test_length_distribution(): + from immunopeptidome_qc import length_distribution + + seqs = ["AAGIGILTV", "GILGFVFTL", "PEPTIDEK", "AAFGIILPKQR"] + dist = length_distribution(seqs) + assert dist[9] == 2 # two 9-mers + assert dist[8] == 1 # one 8-mer + assert dist[11] == 1 # one 11-mer + + +@requires_pyopenms +def test_length_qc_class_i(): + from immunopeptidome_qc import length_qc + + dist = {8: 5, 9: 50, 10: 30, 11: 10, 5: 3, 15: 2} + qc = length_qc(dist, "I") + assert qc["expected_min"] == 8 + assert qc["expected_max"] == 12 + assert qc["in_range_count"] == 95 # 5+50+30+10 + assert qc["out_of_range_count"] == 5 # 3+2 + assert abs(qc["in_range_fraction"] - 0.95) < 0.01 + + +@requires_pyopenms +def test_length_qc_class_ii(): + from immunopeptidome_qc import length_qc + + dist = {13: 20, 15: 30, 9: 5} + qc = length_qc(dist, "II") + assert qc["expected_min"] == 12 + assert qc["expected_max"] == 25 + assert qc["in_range_count"] == 50 + assert qc["out_of_range_count"] == 5 + + +@requires_pyopenms +def test_positional_frequencies(): + from immunopeptidome_qc import positional_frequencies + + # All 9-mers starting with A + seqs = ["AAGIGILTV", "AAGIGILTV", "AAGIGILTV"] + freqs = positional_frequencies(seqs, 9) + assert len(freqs) == 9 + assert freqs[0]["A"] == 1.0 # all start with A + + +@requires_pyopenms +def test_information_content(): + from immunopeptidome_qc import information_content + + # Uniform distribution = 0 information content + uniform = {chr(65 + i): 0.05 for i in range(20)} + ic = information_content(uniform) + assert abs(ic) < 0.01 + + # Single residue = max information content + single = {"A": 1.0} + ic_max = information_content(single) + assert ic_max > 4.0 # log2(20) ~ 4.32 + + +@requires_pyopenms +def test_run_qc(): + from immunopeptidome_qc import run_qc + + seqs = ["AAGIGILTV", "GILGFVFTL", "PEPTIDEK", "AAFGIILPK"] + dist, qc, anchors, ic_values = run_qc(seqs, "I") + assert sum(dist.values()) == 4 + assert qc["in_range_fraction"] == 1.0 # all are 8-9aa + assert len(ic_values) > 0 + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + import sys + + from immunopeptidome_qc import main + + input_file = tmp_path / "input.tsv" + output_file = tmp_path / "length_dist.tsv" + motifs_file = tmp_path / "anchor_freq.tsv" + + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["sequence"]) + for seq in ["AAGIGILTV", "GILGFVFTL", "PEPTIDEK", "AAFGIILPK"]: + writer.writerow([seq]) + + sys.argv = [ + "immunopeptidome_qc.py", + "--input", str(input_file), + "--hla-class", "I", + "--output", str(output_file), + "--motifs", str(motifs_file), + ] + main() + + assert output_file.exists() + assert motifs_file.exists() diff --git a/scripts/proteomics/inclusion_list_generator/README.md b/scripts/proteomics/inclusion_list_generator/README.md new file mode 100644 index 0000000..fa6455e --- /dev/null +++ b/scripts/proteomics/inclusion_list_generator/README.md @@ -0,0 +1,15 @@ +# Inclusion List Generator + +Generate instrument inclusion lists from peptide data for targeted MS experiments. + +## Usage + +```bash +python inclusion_list_generator.py --input peptides.tsv --format thermo --charge 2,3 --output inclusion.csv +python inclusion_list_generator.py --input peptides.tsv --format generic --charge 2 --output inclusion.csv +``` + +## Formats + +- **thermo** - Thermo Scientific instrument format +- **generic** - Generic CSV format diff --git a/scripts/proteomics/inclusion_list_generator/inclusion_list_generator.py b/scripts/proteomics/inclusion_list_generator/inclusion_list_generator.py new file mode 100644 index 0000000..aaf25a6 --- /dev/null +++ b/scripts/proteomics/inclusion_list_generator/inclusion_list_generator.py @@ -0,0 +1,150 @@ +""" +Inclusion List Generator +======================== +Generate instrument inclusion lists from peptide data for targeted MS experiments. + +Supports output formats for Thermo and generic CSV. Calculates m/z values for +specified charge states using pyopenms AASequence. + +Usage +----- + python inclusion_list_generator.py --input peptides.tsv --format thermo --charge 2,3 --output inclusion.csv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def read_peptides(filepath: str) -> list: + """Read a peptide list TSV. + + Expected columns: peptide (required), plus optional: rt_start, rt_end, protein. + + Returns + ------- + list + List of dicts. + """ + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + return list(reader) + + +def calculate_mz(sequence: str, charge: int) -> float: + """Calculate m/z for a peptide at the given charge state. + + Parameters + ---------- + sequence: + Peptide sequence (may include bracket modifications). + charge: + Charge state. + + Returns + ------- + float + Monoisotopic m/z value. + """ + aa_seq = oms.AASequence.fromString(sequence) + mono = aa_seq.getMonoWeight() + return (mono + charge * PROTON) / charge + + +def generate_inclusion_list( + peptides: list, charges: list, output_format: str = "thermo" +) -> list: + """Generate an inclusion list for targeted MS. + + Parameters + ---------- + peptides: + List of dicts with at least a 'peptide' key. + charges: + List of charge states to include. + output_format: + 'thermo' or 'generic'. + + Returns + ------- + list + List of dicts representing inclusion list entries. + """ + output_format = output_format.lower() + if output_format not in ("thermo", "generic"): + raise ValueError(f"Unknown format: '{output_format}'. Choose 'thermo' or 'generic'.") + + entries = [] + for pep_row in peptides: + sequence = pep_row["peptide"].strip() + rt_start = pep_row.get("rt_start", "") + rt_end = pep_row.get("rt_end", "") + protein = pep_row.get("protein", "") + + for charge in charges: + mz = calculate_mz(sequence, charge) + + if output_format == "thermo": + entry = { + "Compound": sequence, + "Formula": "", + "Adduct": "", + "m/z": f"{mz:.4f}", + "z": str(charge), + "t start (min)": rt_start, + "t stop (min)": rt_end, + "Comment": protein, + } + else: + entry = { + "peptide": sequence, + "mz": f"{mz:.4f}", + "charge": str(charge), + "rt_start": rt_start, + "rt_end": rt_end, + "protein": protein, + } + entries.append(entry) + + return entries + + +def main(): + parser = argparse.ArgumentParser(description="Generate instrument inclusion lists from peptide data.") + parser.add_argument("--input", required=True, help="Input peptide TSV") + parser.add_argument("--format", default="thermo", choices=["thermo", "generic"], + help="Output format (default: thermo)") + parser.add_argument("--charge", default="2,3", help="Comma-separated charge states (default: 2,3)") + parser.add_argument("--output", required=True, help="Output CSV file") + args = parser.parse_args() + + charges = [int(c.strip()) for c in args.charge.split(",")] + peptides = read_peptides(args.input) + entries = generate_inclusion_list(peptides, charges, output_format=args.format) + + if not entries: + print("No entries generated.") + return + + fieldnames = list(entries[0].keys()) + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(entries) + + print(f"Format: {args.format}") + print(f"Charge states: {charges}") + print(f"Peptides: {len(peptides)}") + print(f"Entries: {len(entries)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/inclusion_list_generator/requirements.txt b/scripts/proteomics/inclusion_list_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/inclusion_list_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/inclusion_list_generator/tests/conftest.py b/scripts/proteomics/inclusion_list_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/inclusion_list_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py b/scripts/proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py new file mode 100644 index 0000000..e850557 --- /dev/null +++ b/scripts/proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py @@ -0,0 +1,53 @@ +"""Tests for inclusion_list_generator.""" + +import pytest +from conftest import requires_pyopenms +from inclusion_list_generator import calculate_mz, generate_inclusion_list + + +@requires_pyopenms +class TestInclusionListGenerator: + def test_calculate_mz(self): + mz = calculate_mz("PEPTIDEK", 2) + assert mz > 0 + + def test_charge_affects_mz(self): + mz1 = calculate_mz("PEPTIDEK", 1) + mz2 = calculate_mz("PEPTIDEK", 2) + mz3 = calculate_mz("PEPTIDEK", 3) + assert mz1 > mz2 > mz3 + + def test_thermo_format(self): + peptides = [{"peptide": "PEPTIDEK"}] + entries = generate_inclusion_list(peptides, [2, 3], output_format="thermo") + assert len(entries) == 2 + assert "m/z" in entries[0] + assert "z" in entries[0] + assert "Compound" in entries[0] + + def test_generic_format(self): + peptides = [{"peptide": "PEPTIDEK"}] + entries = generate_inclusion_list(peptides, [2], output_format="generic") + assert len(entries) == 1 + assert "mz" in entries[0] + assert "charge" in entries[0] + assert "peptide" in entries[0] + + def test_multiple_charges(self): + peptides = [{"peptide": "PEPTIDEK"}, {"peptide": "TESTPEP"}] + entries = generate_inclusion_list(peptides, [2, 3, 4], output_format="generic") + assert len(entries) == 6 # 2 peptides x 3 charges + + def test_with_rt_info(self): + peptides = [{"peptide": "PEPTIDEK", "rt_start": "10.0", "rt_end": "15.0", "protein": "PROT1"}] + entries = generate_inclusion_list(peptides, [2], output_format="thermo") + assert entries[0]["t start (min)"] == "10.0" + assert entries[0]["t stop (min)"] == "15.0" + + def test_unknown_format(self): + with pytest.raises(ValueError, match="Unknown format"): + generate_inclusion_list([{"peptide": "PEP"}], [2], output_format="invalid") + + def test_empty_peptides(self): + entries = generate_inclusion_list([], [2], output_format="generic") + assert entries == [] diff --git a/scripts/proteomics/injection_time_analyzer/injection_time_analyzer.py b/scripts/proteomics/injection_time_analyzer/injection_time_analyzer.py new file mode 100644 index 0000000..fae31d7 --- /dev/null +++ b/scripts/proteomics/injection_time_analyzer/injection_time_analyzer.py @@ -0,0 +1,134 @@ +""" +Injection Time Analyzer +======================== +Extract ion injection time values from mzML spectrum metadata. + +Injection times are stored as spectrum-level metadata (CV parameter +MS:1000927 "ion injection time"). This tool extracts and summarizes them. + +Usage +----- + python injection_time_analyzer.py --input run.mzML --output injection_times.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +ION_INJECTION_TIME_ACCESSION = "MS:1000927" + + +def extract_injection_times(exp: oms.MSExperiment) -> list[dict]: + """Extract injection times from all spectra in an experiment. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment``. + + Returns + ------- + list[dict] + Each dict has: scan_index, rt, ms_level, injection_time_ms. + """ + results = [] + for i, spec in enumerate(exp.getSpectra()): + injection_time = None + + # Try to get from float data arrays (common storage) + if spec.metaValueExists(ION_INJECTION_TIME_ACCESSION): + injection_time = float(spec.getMetaValue(ION_INJECTION_TIME_ACCESSION)) + elif spec.metaValueExists("ion injection time"): + injection_time = float(spec.getMetaValue("ion injection time")) + + # Also check instrument settings + if injection_time is None: + acq = spec.getAcquisitionInfo() + if acq: + for a in acq: + if a.metaValueExists(ION_INJECTION_TIME_ACCESSION): + injection_time = float(a.getMetaValue(ION_INJECTION_TIME_ACCESSION)) + break + + results.append({ + "scan_index": i, + "rt": round(spec.getRT(), 4), + "ms_level": spec.getMSLevel(), + "injection_time_ms": round(injection_time, 4) if injection_time is not None else None, + }) + + return results + + +def summarize_injection_times(records: list[dict]) -> dict: + """Summarize injection times by MS level. + + Parameters + ---------- + records: + Output of ``extract_injection_times``. + + Returns + ------- + dict + Per-level statistics. + """ + by_level: dict[int, list[float]] = {} + for r in records: + if r["injection_time_ms"] is not None: + level = r["ms_level"] + by_level.setdefault(level, []).append(r["injection_time_ms"]) + + summary = {} + for level, times in sorted(by_level.items()): + mean = sum(times) / len(times) + std = math.sqrt(sum((t - mean) ** 2 for t in times) / len(times)) if times else 0.0 + summary[f"MS{level}"] = { + "count": len(times), + "mean_ms": round(mean, 4), + "std_ms": round(std, 4), + "min_ms": round(min(times), 4), + "max_ms": round(max(times), 4), + } + + return summary + + +def main(): + parser = argparse.ArgumentParser( + description="Extract injection time values from mzML metadata." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV file") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + records = extract_injection_times(exp) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["scan_index", "rt", "ms_level", "injection_time_ms"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(records) + + n_with_time = sum(1 for r in records if r["injection_time_ms"] is not None) + print(f"Wrote {len(records)} scans to {args.output} ({n_with_time} with injection times)") + + summary = summarize_injection_times(records) + for level, stats in summary.items(): + print(f" {level}: mean={stats['mean_ms']:.1f} ms, std={stats['std_ms']:.1f} ms") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/injection_time_analyzer/requirements.txt b/scripts/proteomics/injection_time_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/injection_time_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/injection_time_analyzer/tests/conftest.py b/scripts/proteomics/injection_time_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/injection_time_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/injection_time_analyzer/tests/test_injection_time_analyzer.py b/scripts/proteomics/injection_time_analyzer/tests/test_injection_time_analyzer.py new file mode 100644 index 0000000..c272740 --- /dev/null +++ b/scripts/proteomics/injection_time_analyzer/tests/test_injection_time_analyzer.py @@ -0,0 +1,72 @@ +"""Tests for injection_time_analyzer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestInjectionTimeAnalyzer: + def _make_experiment_with_injection_times(self): + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(4): + spec = oms.MSSpectrum() + spec.setMSLevel(1 if i % 2 == 0 else 2) + spec.setRT(10.0 * i) + spec.setMetaValue("MS:1000927", float(20.0 + i * 5)) + mzs = np.array([100.0], dtype=np.float64) + ints = np.array([1000.0], dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + return exp + + def test_extract_injection_times(self): + from injection_time_analyzer import extract_injection_times + + exp = self._make_experiment_with_injection_times() + records = extract_injection_times(exp) + assert len(records) == 4 + # Check that injection times were extracted + with_times = [r for r in records if r["injection_time_ms"] is not None] + assert len(with_times) == 4 + + def test_injection_time_values(self): + from injection_time_analyzer import extract_injection_times + + exp = self._make_experiment_with_injection_times() + records = extract_injection_times(exp) + assert records[0]["injection_time_ms"] == 20.0 + assert records[1]["injection_time_ms"] == 25.0 + + def test_summarize_injection_times(self): + from injection_time_analyzer import summarize_injection_times + + records = [ + {"ms_level": 1, "injection_time_ms": 20.0}, + {"ms_level": 1, "injection_time_ms": 30.0}, + {"ms_level": 2, "injection_time_ms": 50.0}, + {"ms_level": 2, "injection_time_ms": 60.0}, + ] + summary = summarize_injection_times(records) + assert "MS1" in summary + assert "MS2" in summary + assert summary["MS1"]["mean_ms"] == 25.0 + assert summary["MS2"]["mean_ms"] == 55.0 + + def test_no_injection_times(self): + import numpy as np + import pyopenms as oms + from injection_time_analyzer import extract_injection_times + + exp = oms.MSExperiment() + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(0.0) + spec.set_peaks([np.array([100.0], dtype=np.float64), + np.array([1000.0], dtype=np.float64)]) + exp.addSpectrum(spec) + + records = extract_injection_times(exp) + assert len(records) == 1 + assert records[0]["injection_time_ms"] is None diff --git a/scripts/proteomics/intensity_distribution_reporter/README.md b/scripts/proteomics/intensity_distribution_reporter/README.md new file mode 100644 index 0000000..c50385e --- /dev/null +++ b/scripts/proteomics/intensity_distribution_reporter/README.md @@ -0,0 +1,13 @@ +# Intensity Distribution Reporter + +Report per-sample intensity statistics from a quantification matrix. + +## Usage + +```bash +python intensity_distribution_reporter.py --input matrix.tsv --output intensity_stats.tsv +``` + +## Output Columns + +`sample`, `n_values`, `n_missing`, `mean`, `median`, `sd`, `min`, `max`, `q1`, `q3` diff --git a/scripts/proteomics/intensity_distribution_reporter/intensity_distribution_reporter.py b/scripts/proteomics/intensity_distribution_reporter/intensity_distribution_reporter.py new file mode 100644 index 0000000..50a7297 --- /dev/null +++ b/scripts/proteomics/intensity_distribution_reporter/intensity_distribution_reporter.py @@ -0,0 +1,129 @@ +""" +Intensity Distribution Reporter +================================ +Report per-sample intensity statistics from a quantification matrix. + +Computes mean, median, standard deviation, min, max, Q1, Q3, and count of +non-missing values for each sample column. + +Usage +----- + python intensity_distribution_reporter.py --input matrix.tsv --output intensity_stats.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + + +def read_matrix(filepath: str) -> tuple: + """Read a TSV quantification matrix. + + Returns (row_ids, col_names, data_matrix). + """ + with open(filepath) as fh: + reader = csv.reader(fh, delimiter="\t") + header = next(reader) + col_names = header[1:] + row_ids = [] + rows = [] + for row in reader: + row_ids.append(row[0]) + values = [] + for v in row[1:]: + v = v.strip() + if v == "" or v.upper() in ("NA", "NAN"): + values.append(np.nan) + else: + values.append(float(v)) + rows.append(values) + return row_ids, col_names, np.array(rows, dtype=float) + + +def compute_intensity_stats(matrix: np.ndarray, col_names: list) -> list: + """Compute per-sample intensity statistics. + + Parameters + ---------- + matrix: + 2D array (features x samples). + col_names: + Sample names. + + Returns + ------- + list + List of dicts with keys: sample, n_values, n_missing, mean, median, sd, min, max, q1, q3. + """ + n_features = matrix.shape[0] + results = [] + + for col_idx, sample in enumerate(col_names): + col_data = matrix[:, col_idx] + valid = col_data[~np.isnan(col_data)] + n_valid = len(valid) + n_missing = n_features - n_valid + + if n_valid == 0: + results.append({ + "sample": sample, + "n_values": 0, + "n_missing": n_features, + "mean": float("nan"), + "median": float("nan"), + "sd": float("nan"), + "min": float("nan"), + "max": float("nan"), + "q1": float("nan"), + "q3": float("nan"), + }) + else: + results.append({ + "sample": sample, + "n_values": n_valid, + "n_missing": n_missing, + "mean": float(np.mean(valid)), + "median": float(np.median(valid)), + "sd": float(np.std(valid, ddof=1)) if n_valid > 1 else 0.0, + "min": float(np.min(valid)), + "max": float(np.max(valid)), + "q1": float(np.percentile(valid, 25)), + "q3": float(np.percentile(valid, 75)), + }) + + return results + + +def main(): + parser = argparse.ArgumentParser(description="Per-sample intensity statistics.") + parser.add_argument("--input", required=True, help="Input TSV matrix file") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + row_ids, col_names, matrix = read_matrix(args.input) + stats = compute_intensity_stats(matrix, col_names) + + fieldnames = ["sample", "n_values", "n_missing", "mean", "median", "sd", "min", "max", "q1", "q3"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for s in stats: + row = {"sample": s["sample"], "n_values": s["n_values"], "n_missing": s["n_missing"]} + for key in ["mean", "median", "sd", "min", "max", "q1", "q3"]: + row[key] = f"{s[key]:.6f}" if not np.isnan(s[key]) else "NA" + writer.writerow(row) + + print(f"Samples: {len(col_names)}") + print(f"Features: {len(row_ids)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/intensity_distribution_reporter/requirements.txt b/scripts/proteomics/intensity_distribution_reporter/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/proteomics/intensity_distribution_reporter/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/proteomics/intensity_distribution_reporter/tests/conftest.py b/scripts/proteomics/intensity_distribution_reporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/intensity_distribution_reporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/intensity_distribution_reporter/tests/test_intensity_distribution_reporter.py b/scripts/proteomics/intensity_distribution_reporter/tests/test_intensity_distribution_reporter.py new file mode 100644 index 0000000..85f9ff4 --- /dev/null +++ b/scripts/proteomics/intensity_distribution_reporter/tests/test_intensity_distribution_reporter.py @@ -0,0 +1,87 @@ +"""Tests for intensity_distribution_reporter.""" + +import numpy as np +from conftest import requires_pyopenms +from intensity_distribution_reporter import compute_intensity_stats, read_matrix + + +@requires_pyopenms +class TestIntensityDistributionReporter: + def _make_matrix(self): + return np.array([ + [100.0, 200.0, 150.0], + [300.0, 400.0, 350.0], + [500.0, 600.0, 550.0], + [700.0, 800.0, 750.0], + ]) + + def test_basic_stats(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + stats = compute_intensity_stats(matrix, col_names) + assert len(stats) == 3 + + def test_mean_correct(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + stats = compute_intensity_stats(matrix, col_names) + s1 = next(s for s in stats if s["sample"] == "s1") + assert abs(s1["mean"] - 400.0) < 0.01 # mean of [100, 300, 500, 700] + + def test_min_max(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + stats = compute_intensity_stats(matrix, col_names) + s1 = next(s for s in stats if s["sample"] == "s1") + assert s1["min"] == 100.0 + assert s1["max"] == 700.0 + + def test_n_values(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + stats = compute_intensity_stats(matrix, col_names) + for s in stats: + assert s["n_values"] == 4 + assert s["n_missing"] == 0 + + def test_with_nan(self): + matrix = np.array([ + [100.0, np.nan], + [300.0, 400.0], + [np.nan, 600.0], + ]) + col_names = ["s1", "s2"] + stats = compute_intensity_stats(matrix, col_names) + s1 = next(s for s in stats if s["sample"] == "s1") + assert s1["n_values"] == 2 + assert s1["n_missing"] == 1 + + def test_all_nan_column(self): + matrix = np.array([ + [np.nan, 200.0], + [np.nan, 400.0], + ]) + col_names = ["s1", "s2"] + stats = compute_intensity_stats(matrix, col_names) + s1 = next(s for s in stats if s["sample"] == "s1") + assert s1["n_values"] == 0 + assert np.isnan(s1["mean"]) + + def test_quartiles(self): + matrix = np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0]]) + col_names = ["s1"] + stats = compute_intensity_stats(matrix, col_names) + s1 = stats[0] + assert s1["q1"] == np.percentile([1, 2, 3, 4, 5, 6, 7, 8], 25) + assert s1["q3"] == np.percentile([1, 2, 3, 4, 5, 6, 7, 8], 75) + + def test_read_matrix_roundtrip(self, tmp_path): + outfile = str(tmp_path / "test.tsv") + with open(outfile, "w") as fh: + fh.write("\ts1\ts2\n") + fh.write("p1\t100.0\t200.0\n") + fh.write("p2\t300.0\tNA\n") + row_ids, col_names, matrix = read_matrix(outfile) + assert row_ids == ["p1", "p2"] + assert col_names == ["s1", "s2"] + assert np.isnan(matrix[1, 1]) diff --git a/scripts/proteomics/irt_calculator/README.md b/scripts/proteomics/irt_calculator/README.md new file mode 100644 index 0000000..4aaa3a4 --- /dev/null +++ b/scripts/proteomics/irt_calculator/README.md @@ -0,0 +1,14 @@ +# iRT Calculator + +Convert observed retention times to indexed retention times (iRT) using reference peptides and linear regression. + +## Usage + +```bash +python irt_calculator.py --input identifications.tsv --reference irt_standards.tsv --output irt_converted.tsv +``` + +## Input Format + +- `--reference`: TSV with columns `sequence`, `observed_rt`, `irt` +- `--input`: TSV with columns `sequence`, `rt` diff --git a/scripts/proteomics/irt_calculator/irt_calculator.py b/scripts/proteomics/irt_calculator/irt_calculator.py new file mode 100644 index 0000000..7e755ad --- /dev/null +++ b/scripts/proteomics/irt_calculator/irt_calculator.py @@ -0,0 +1,185 @@ +""" +iRT Calculator +=============== +Convert observed retention times to indexed retention times (iRT) using reference peptides. + +Features +-------- +- Linear regression fitting between observed RT and known iRT values +- Convert observed RTs to iRT scale +- Report R-squared and regression parameters +- Support for custom reference peptide sets + +Usage +----- + python irt_calculator.py --input identifications.tsv --reference irt_standards.tsv --output irt_converted.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def linear_regression(x: list, y: list) -> tuple: + """Fit a simple linear regression y = slope * x + intercept. + + Parameters + ---------- + x : list + Independent variable values. + y : list + Dependent variable values. + + Returns + ------- + tuple + (slope, intercept, r_squared). + """ + n = len(x) + if n < 2: + return 0.0, 0.0, 0.0 + + sum_x = sum(x) + sum_y = sum(y) + sum_xy = sum(xi * yi for xi, yi in zip(x, y)) + sum_x2 = sum(xi * xi for xi in x) + + denom = n * sum_x2 - sum_x * sum_x + if denom == 0: + return 0.0, 0.0, 0.0 + + slope = (n * sum_xy - sum_x * sum_y) / denom + intercept = (sum_y - slope * sum_x) / n + + # R-squared + ss_res = sum((yi - (slope * xi + intercept)) ** 2 for xi, yi in zip(x, y)) + mean_y = sum_y / n + ss_tot = sum((yi - mean_y) ** 2 for yi in y) + r_squared = 1.0 - ss_res / ss_tot if ss_tot > 0 else 0.0 + + return round(slope, 8), round(intercept, 8), round(r_squared, 6) + + +def fit_irt_model(reference_data: list) -> dict: + """Fit an iRT conversion model from reference peptide data. + + Parameters + ---------- + reference_data : list + List of dicts with 'sequence', 'observed_rt', and 'irt' keys. + + Returns + ------- + dict + Model parameters including slope, intercept, r_squared. + """ + observed = [float(r["observed_rt"]) for r in reference_data] + irt_values = [float(r["irt"]) for r in reference_data] + + slope, intercept, r_squared = linear_regression(observed, irt_values) + + return { + "slope": slope, + "intercept": intercept, + "r_squared": r_squared, + "n_reference_peptides": len(reference_data), + } + + +def convert_to_irt(observed_rt: float, slope: float, intercept: float) -> float: + """Convert an observed RT to iRT using the fitted model. + + Parameters + ---------- + observed_rt : float + Observed retention time. + slope : float + Regression slope. + intercept : float + Regression intercept. + + Returns + ------- + float + Predicted iRT value. + """ + return round(slope * observed_rt + intercept, 4) + + +def process_identifications(identifications: list, model: dict) -> list: + """Convert observed RTs to iRT for a list of identifications. + + Parameters + ---------- + identifications : list + List of dicts with 'sequence' and 'rt' keys. + model : dict + Fitted model with 'slope' and 'intercept'. + + Returns + ------- + list + List of dicts with added 'irt' key. + """ + results = [] + for ident in identifications: + rt = float(ident.get("rt", 0)) + irt = convert_to_irt(rt, model["slope"], model["intercept"]) + results.append({ + "sequence": ident.get("sequence", ""), + "observed_rt": rt, + "irt": irt, + }) + return results + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Convert observed RT to indexed RT (iRT).") + parser.add_argument("--input", required=True, help="TSV with sequence and rt columns.") + parser.add_argument("--reference", required=True, help="TSV with sequence, observed_rt, irt columns.") + parser.add_argument("--output", help="Output file (.tsv or .json).") + args = parser.parse_args() + + # Load reference peptides + reference_data = [] + with open(args.reference) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + reference_data.append(row) + + model = fit_irt_model(reference_data) + print(f"Model: iRT = {model['slope']} * RT + {model['intercept']} (R2={model['r_squared']})") + + # Load identifications + identifications = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + identifications.append(row) + + results = process_identifications(identifications, model) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump({"model": model, "results": results}, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["sequence", "observed_rt", "irt"], delimiter="\t") + writer.writeheader() + writer.writerows(results) + print(f"Results written to {args.output}") + else: + for r in results: + print(f"{r['sequence']}\t{r['observed_rt']}\t{r['irt']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/irt_calculator/requirements.txt b/scripts/proteomics/irt_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/irt_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/irt_calculator/tests/conftest.py b/scripts/proteomics/irt_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/irt_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/irt_calculator/tests/test_irt_calculator.py b/scripts/proteomics/irt_calculator/tests/test_irt_calculator.py new file mode 100644 index 0000000..be307e7 --- /dev/null +++ b/scripts/proteomics/irt_calculator/tests/test_irt_calculator.py @@ -0,0 +1,72 @@ +"""Tests for irt_calculator.""" + + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestIrtCalculator: + def test_linear_regression_perfect(self): + from irt_calculator import linear_regression + + x = [1.0, 2.0, 3.0, 4.0, 5.0] + y = [2.0, 4.0, 6.0, 8.0, 10.0] + slope, intercept, r2 = linear_regression(x, y) + assert abs(slope - 2.0) < 0.001 + assert abs(intercept - 0.0) < 0.001 + assert abs(r2 - 1.0) < 0.001 + + def test_linear_regression_with_intercept(self): + from irt_calculator import linear_regression + + x = [1.0, 2.0, 3.0] + y = [3.0, 5.0, 7.0] # y = 2x + 1 + slope, intercept, r2 = linear_regression(x, y) + assert abs(slope - 2.0) < 0.001 + assert abs(intercept - 1.0) < 0.001 + + def test_convert_to_irt(self): + from irt_calculator import convert_to_irt + + irt = convert_to_irt(10.0, slope=2.0, intercept=5.0) + assert abs(irt - 25.0) < 0.01 + + def test_fit_irt_model(self): + from irt_calculator import fit_irt_model + + reference = [ + {"sequence": "PEPTIDEK", "observed_rt": 10.0, "irt": 0.0}, + {"sequence": "ANOTHERPEPTIDE", "observed_rt": 20.0, "irt": 50.0}, + {"sequence": "THIRDPEPTIDE", "observed_rt": 30.0, "irt": 100.0}, + ] + model = fit_irt_model(reference) + assert model["r_squared"] > 0.99 + assert model["n_reference_peptides"] == 3 + + def test_process_identifications(self): + from irt_calculator import process_identifications + + model = {"slope": 5.0, "intercept": -50.0} + idents = [ + {"sequence": "PEPTIDEK", "rt": 10.0}, + {"sequence": "ANOTHER", "rt": 20.0}, + ] + results = process_identifications(idents, model) + assert len(results) == 2 + assert results[0]["irt"] == 0.0 # 5*10 - 50 + assert results[1]["irt"] == 50.0 # 5*20 - 50 + + def test_end_to_end_with_files(self): + from irt_calculator import fit_irt_model, process_identifications + + reference = [ + {"sequence": "PEP1", "observed_rt": 5.0, "irt": -20.0}, + {"sequence": "PEP2", "observed_rt": 15.0, "irt": 30.0}, + {"sequence": "PEP3", "observed_rt": 25.0, "irt": 80.0}, + ] + model = fit_irt_model(reference) + idents = [{"sequence": "TEST", "rt": 10.0}] + results = process_identifications(idents, model) + assert len(results) == 1 + # Should be between -20 and 30 + assert -25 < results[0]["irt"] < 35 diff --git a/scripts/proteomics/isobaric_purity_corrector/README.md b/scripts/proteomics/isobaric_purity_corrector/README.md new file mode 100644 index 0000000..facd3bf --- /dev/null +++ b/scripts/proteomics/isobaric_purity_corrector/README.md @@ -0,0 +1,42 @@ +# Isobaric Purity Corrector + +Correct TMT/iTRAQ reporter ion quantification for isotopic impurity using a purity correction matrix. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python isobaric_purity_corrector.py --input quant.tsv --label TMT16plex \ + --purity-matrix purity.csv --output corrected.tsv +``` + +### Input format + +Quantification TSV with channel columns matching the labeling scheme: + +``` +spectrum_id 126 127N 127C +spec1 1000.0 50.0 30.0 +``` + +Purity matrix CSV (N x N, no headers): + +``` +0.95,0.03,0.02 +0.02,0.94,0.04 +0.01,0.03,0.96 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Input quantification TSV | +| `--label` | Labeling scheme (TMT6plex, TMT10plex, TMT16plex, iTRAQ4plex, etc.) | +| `--purity-matrix` | Purity correction matrix CSV | +| `--output` | Output corrected TSV | diff --git a/scripts/proteomics/isobaric_purity_corrector/isobaric_purity_corrector.py b/scripts/proteomics/isobaric_purity_corrector/isobaric_purity_corrector.py new file mode 100644 index 0000000..d04d726 --- /dev/null +++ b/scripts/proteomics/isobaric_purity_corrector/isobaric_purity_corrector.py @@ -0,0 +1,204 @@ +""" +Isobaric Purity Corrector +========================== +Correct TMT or iTRAQ reporter ion quantification for isotopic impurity +using a manufacturer-provided purity correction matrix. The tool reads +a quantification TSV and a purity matrix CSV, solves the linear system +to produce corrected intensities. + +The correction is: corrected = inv(purity_matrix) @ observed + +Usage +----- + python isobaric_purity_corrector.py --input quant.tsv \ + --label TMT16plex --purity-matrix purity.csv --output corrected.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Tuple + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + +# Default channel names for common labeling schemes +LABEL_CHANNELS: Dict[str, List[str]] = { + "TMT6plex": ["126", "127", "128", "129", "130", "131"], + "TMT10plex": ["126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", "130C", "131"], + "TMT11plex": [ + "126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", "130C", "131N", "131C", + ], + "TMT16plex": [ + "126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", + "130C", "131N", "131C", "132N", "132C", "133N", "133C", "134N", + ], + "TMT18plex": [ + "126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", + "130C", "131N", "131C", "132N", "132C", "133N", "133C", "134N", "134C", "135N", + ], + "iTRAQ4plex": ["114", "115", "116", "117"], + "iTRAQ8plex": ["113", "114", "115", "116", "117", "118", "119", "121"], +} + + +def load_purity_matrix(purity_path: str) -> np.ndarray: + """Load purity correction matrix from CSV. + + The CSV should have N rows and N columns (no headers), where N is the + number of channels. Each row represents the contribution of a single + channel to all observed channels. + + Parameters + ---------- + purity_path: + Path to purity matrix CSV. + + Returns + ------- + numpy.ndarray + Square purity matrix of shape (N, N). + """ + rows: List[List[float]] = [] + with open(purity_path, newline="") as fh: + reader = csv.reader(fh) + for row in reader: + values = [float(v.strip()) for v in row if v.strip()] + if values: + rows.append(values) + return np.array(rows, dtype=float) + + +def correct_intensities( + observed: np.ndarray, purity_matrix: np.ndarray +) -> np.ndarray: + """Apply purity correction to observed intensities. + + Parameters + ---------- + observed: + Array of shape (n_spectra, n_channels) with observed intensities. + purity_matrix: + Square purity matrix of shape (n_channels, n_channels). + + Returns + ------- + numpy.ndarray + Corrected intensities, same shape as observed. Negative values + are clipped to zero. + """ + # Solve: purity_matrix @ corrected = observed (for each spectrum) + inv_matrix = np.linalg.inv(purity_matrix) + corrected = observed @ inv_matrix.T + corrected[corrected < 0] = 0.0 + return corrected + + +def get_channels(label: str) -> List[str]: + """Return channel names for a given labeling scheme. + + Parameters + ---------- + label: + Labeling scheme name (e.g. ``"TMT16plex"``). + + Returns + ------- + list of str + Channel names. + """ + if label not in LABEL_CHANNELS: + raise ValueError( + f"Unknown label '{label}'. Supported: {', '.join(sorted(LABEL_CHANNELS.keys()))}" + ) + return LABEL_CHANNELS[label] + + +def process_quant_file( + input_path: str, + purity_matrix: np.ndarray, + channels: List[str], +) -> Tuple[List[str], List[Dict[str, str]], np.ndarray]: + """Read quantification file and apply purity correction. + + Parameters + ---------- + input_path: + Path to input TSV. + purity_matrix: + Purity correction matrix. + channels: + Channel names to correct. + + Returns + ------- + tuple + (non_channel_fields, metadata_rows, corrected_array) + """ + metadata_rows: List[Dict[str, str]] = [] + observed_list: List[List[float]] = [] + + with open(input_path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + all_fields = reader.fieldnames or [] + non_channel_fields = [f for f in all_fields if f not in channels] + + for row in reader: + meta = {f: row.get(f, "") for f in non_channel_fields} + metadata_rows.append(meta) + intensities = [] + for ch in channels: + val = row.get(ch, "0").strip() + try: + intensities.append(float(val)) + except (ValueError, TypeError): + intensities.append(0.0) + observed_list.append(intensities) + + observed = np.array(observed_list, dtype=float) + corrected = correct_intensities(observed, purity_matrix) + return non_channel_fields, metadata_rows, corrected + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Correct TMT/iTRAQ reporter ion quantification for isotopic impurity." + ) + parser.add_argument("--input", required=True, help="Input quantification TSV") + parser.add_argument( + "--label", required=True, + help="Labeling scheme (e.g. TMT6plex, TMT10plex, TMT16plex, iTRAQ4plex)", + ) + parser.add_argument("--purity-matrix", required=True, help="Purity correction matrix CSV") + parser.add_argument("--output", required=True, help="Output corrected TSV") + args = parser.parse_args() + + channels = get_channels(args.label) + purity_matrix = load_purity_matrix(args.purity_matrix) + + expected_size = len(channels) + if purity_matrix.shape != (expected_size, expected_size): + sys.exit( + f"Purity matrix shape {purity_matrix.shape} does not match " + f"{expected_size} channels for {args.label}" + ) + + non_ch_fields, meta_rows, corrected = process_quant_file(args.input, purity_matrix, channels) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(non_ch_fields + channels) + for i, meta in enumerate(meta_rows): + row = [meta.get(f, "") for f in non_ch_fields] + row += [f"{corrected[i, j]:.4f}" for j in range(len(channels))] + writer.writerow(row) + + print(f"Corrected {len(meta_rows)} spectra across {len(channels)} channels -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/isobaric_purity_corrector/requirements.txt b/scripts/proteomics/isobaric_purity_corrector/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/proteomics/isobaric_purity_corrector/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/proteomics/isobaric_purity_corrector/tests/conftest.py b/scripts/proteomics/isobaric_purity_corrector/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/isobaric_purity_corrector/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/isobaric_purity_corrector/tests/test_isobaric_purity_corrector.py b/scripts/proteomics/isobaric_purity_corrector/tests/test_isobaric_purity_corrector.py new file mode 100644 index 0000000..c9b4365 --- /dev/null +++ b/scripts/proteomics/isobaric_purity_corrector/tests/test_isobaric_purity_corrector.py @@ -0,0 +1,125 @@ +"""Tests for isobaric_purity_corrector.""" + +import csv +import sys + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_get_channels(): + from isobaric_purity_corrector import get_channels + + channels = get_channels("TMT6plex") + assert len(channels) == 6 + assert "126" in channels + + channels_16 = get_channels("TMT16plex") + assert len(channels_16) == 16 + + +@requires_pyopenms +def test_get_channels_unknown(): + import pytest + from isobaric_purity_corrector import get_channels + + with pytest.raises(ValueError): + get_channels("UnknownLabel") + + +@requires_pyopenms +def test_correct_intensities_identity(): + from isobaric_purity_corrector import correct_intensities + + # Identity matrix = no correction + purity = np.eye(3) + observed = np.array([[100.0, 200.0, 300.0]]) + corrected = correct_intensities(observed, purity) + np.testing.assert_allclose(corrected, observed, atol=1e-6) + + +@requires_pyopenms +def test_correct_intensities_with_crosstalk(): + from isobaric_purity_corrector import correct_intensities + + # Purity matrix with some crosstalk + purity = np.array([ + [0.95, 0.03, 0.02], + [0.02, 0.94, 0.04], + [0.01, 0.03, 0.96], + ]) + # True intensities + true = np.array([[100.0, 200.0, 300.0]]) + # Observed = purity @ true (simulate measurement) + observed = true @ purity.T + # Correct should recover true + corrected = correct_intensities(observed, purity) + np.testing.assert_allclose(corrected, true, atol=1.0) + + +@requires_pyopenms +def test_load_purity_matrix(tmp_path): + from isobaric_purity_corrector import load_purity_matrix + + purity_file = tmp_path / "purity.csv" + with open(purity_file, "w", newline="") as fh: + writer = csv.writer(fh) + writer.writerow([0.95, 0.03, 0.02]) + writer.writerow([0.02, 0.94, 0.04]) + writer.writerow([0.01, 0.03, 0.96]) + + matrix = load_purity_matrix(str(purity_file)) + assert matrix.shape == (3, 3) + assert abs(matrix[0, 0] - 0.95) < 0.001 + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from isobaric_purity_corrector import main + + channels = ["126", "127", "128"] + purity = np.eye(3) + + purity_file = tmp_path / "purity.csv" + with open(purity_file, "w", newline="") as fh: + writer = csv.writer(fh) + for row in purity: + writer.writerow(row.tolist()) + + input_file = tmp_path / "quant.tsv" + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["spectrum_id"] + channels) + writer.writerow(["s1", "100.0", "200.0", "300.0"]) + + output_file = tmp_path / "corrected.tsv" + sys.argv = [ + "isobaric_purity_corrector.py", + "--input", str(input_file), + "--label", "TMT6plex", + "--purity-matrix", str(purity_file), + "--output", str(output_file), + ] + # TMT6plex has 6 channels but our test data only has 3 columns + wrong purity size + # Use a proper 6-channel test instead + channels_6 = ["126", "127", "128", "129", "130", "131"] + purity_6 = np.eye(6) + with open(purity_file, "w", newline="") as fh: + writer = csv.writer(fh) + for row in purity_6: + writer.writerow(row.tolist()) + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["spectrum_id"] + channels_6) + writer.writerow(["s1"] + ["100.0"] * 6) + + sys.argv = [ + "isobaric_purity_corrector.py", + "--input", str(input_file), + "--label", "TMT6plex", + "--purity-matrix", str(purity_file), + "--output", str(output_file), + ] + main() + assert output_file.exists() diff --git a/scripts/proteomics/isoelectric_point_calculator/README.md b/scripts/proteomics/isoelectric_point_calculator/README.md new file mode 100644 index 0000000..c3cfe01 --- /dev/null +++ b/scripts/proteomics/isoelectric_point_calculator/README.md @@ -0,0 +1,18 @@ +# Isoelectric Point Calculator + +Calculate pI for peptides and proteins using Henderson-Hasselbalch with multiple pKa sets. + +## Usage + +```bash +python isoelectric_point_calculator.py --sequence ACDEFGHIK --pk-set lehninger --output pi.json +python isoelectric_point_calculator.py --fasta proteins.fasta --output pi.tsv +python isoelectric_point_calculator.py --sequence PEPTIDEK --charge-curve --output curve.json +``` + +## Available pKa Sets + +- `lehninger` (default) +- `emboss` +- `stryer` +- `solomon` diff --git a/scripts/proteomics/isoelectric_point_calculator/isoelectric_point_calculator.py b/scripts/proteomics/isoelectric_point_calculator/isoelectric_point_calculator.py new file mode 100644 index 0000000..6736d08 --- /dev/null +++ b/scripts/proteomics/isoelectric_point_calculator/isoelectric_point_calculator.py @@ -0,0 +1,227 @@ +""" +Isoelectric Point Calculator +============================== +Calculate pI for peptides and proteins using Henderson-Hasselbalch equation. + +Features +-------- +- Multiple pKa sets: Lehninger, EMBOSS, Stryer, Solomon +- Bisection method for accurate pI determination +- Batch processing from FASTA or TSV input +- Charge curve generation at multiple pH values + +Usage +----- + python isoelectric_point_calculator.py --sequence ACDEFGHIK --pk-set lehninger --output pi.json + python isoelectric_point_calculator.py --fasta proteins.fasta --output pi.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# pKa sets from different sources +PKA_SETS = { + "lehninger": { + "nterm": 9.69, "cterm": 2.34, + "C": 8.33, "D": 3.65, "E": 4.25, "H": 6.00, "K": 10.53, "R": 12.48, "Y": 10.07, + }, + "emboss": { + "nterm": 8.6, "cterm": 3.6, + "C": 8.5, "D": 3.9, "E": 4.1, "H": 6.5, "K": 10.8, "R": 12.5, "Y": 10.1, + }, + "stryer": { + "nterm": 8.0, "cterm": 3.1, + "C": 8.3, "D": 4.0, "E": 4.1, "H": 6.0, "K": 10.0, "R": 12.5, "Y": 10.1, + }, + "solomon": { + "nterm": 9.56, "cterm": 2.34, + "C": 8.00, "D": 3.65, "E": 4.25, "H": 6.00, "K": 10.53, "R": 12.48, "Y": 10.07, + }, +} + +# Residues that contribute positive charge when protonated +POSITIVE_RESIDUES = {"H", "K", "R"} +# Residues that contribute negative charge when deprotonated +NEGATIVE_RESIDUES = {"C", "D", "E", "Y"} + + +def charge_at_ph(sequence: str, ph: float, pk_set: str = "lehninger") -> float: + """Calculate the net charge of a peptide at a given pH. + + Parameters + ---------- + sequence : str + One-letter amino acid sequence. + ph : float + pH value. + pk_set : str + pKa set to use. + + Returns + ------- + float + Net charge at the given pH. + """ + pka = PKA_SETS.get(pk_set, PKA_SETS["lehninger"]) + charge = 0.0 + + # N-terminus (positive when protonated) + charge += 1.0 / (1.0 + 10 ** (ph - pka["nterm"])) + # C-terminus (negative when deprotonated) + charge -= 1.0 / (1.0 + 10 ** (pka["cterm"] - ph)) + + for aa in sequence: + if aa in POSITIVE_RESIDUES: + charge += 1.0 / (1.0 + 10 ** (ph - pka[aa])) + elif aa in NEGATIVE_RESIDUES: + charge -= 1.0 / (1.0 + 10 ** (pka[aa] - ph)) + + return charge + + +def calculate_pi(sequence: str, pk_set: str = "lehninger", precision: float = 0.01) -> float: + """Calculate the isoelectric point using bisection. + + Parameters + ---------- + sequence : str + Amino acid sequence (plain one-letter code). + pk_set : str + pKa set name. + precision : float + Desired precision. + + Returns + ------- + float + Estimated pI. + """ + low, high = 0.0, 14.0 + while (high - low) > precision: + mid = (low + high) / 2.0 + if charge_at_ph(sequence, mid, pk_set) > 0: + low = mid + else: + high = mid + return round((low + high) / 2.0, 2) + + +def calculate_charge_curve(sequence: str, pk_set: str = "lehninger", + ph_start: float = 0.0, ph_end: float = 14.0, + ph_step: float = 0.5) -> list: + """Calculate charge across a range of pH values. + + Parameters + ---------- + sequence : str + Amino acid sequence. + pk_set : str + pKa set name. + ph_start : float + Starting pH. + ph_end : float + Ending pH. + ph_step : float + pH increment. + + Returns + ------- + list + List of (pH, charge) tuples. + """ + curve = [] + ph = ph_start + while ph <= ph_end + 0.001: + c = charge_at_ph(sequence, ph, pk_set) + curve.append({"ph": round(ph, 2), "charge": round(c, 4)}) + ph += ph_step + return curve + + +def calculate_pi_from_sequence(sequence: str, pk_set: str = "lehninger") -> dict: + """Calculate pI and related properties for a sequence. + + Parameters + ---------- + sequence : str + Peptide or protein sequence (plain or pyopenms notation). + pk_set : str + pKa set name. + + Returns + ------- + dict + Dictionary with pI, charge at pI, and sequence info. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + pi = calculate_pi(plain, pk_set) + charge = charge_at_ph(plain, pi, pk_set) + + return { + "sequence": sequence, + "unmodified_sequence": plain, + "length": len(plain), + "pI": pi, + "charge_at_pI": round(charge, 4), + "pk_set": pk_set, + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Calculate isoelectric point for peptides/proteins.") + parser.add_argument("--sequence", type=str, help="Single amino acid sequence.") + parser.add_argument("--fasta", type=str, help="FASTA file with protein sequences.") + parser.add_argument("--pk-set", choices=list(PKA_SETS.keys()), default="lehninger", + help="pKa value set (default: lehninger).") + parser.add_argument("--charge-curve", action="store_true", help="Also output charge curve.") + parser.add_argument("--output", type=str, help="Output file (.json or .tsv).") + args = parser.parse_args() + + if not args.sequence and not args.fasta: + parser.error("Provide --sequence or --fasta.") + + results = [] + if args.sequence: + result = calculate_pi_from_sequence(args.sequence, args.pk_set) + if args.charge_curve: + aa_seq = oms.AASequence.fromString(args.sequence) + result["charge_curve"] = calculate_charge_curve(aa_seq.toUnmodifiedString(), args.pk_set) + results.append(result) + elif args.fasta: + entries = [] + oms.FASTAFile().load(args.fasta, entries) + for entry in entries: + result = calculate_pi_from_sequence(entry.sequence, args.pk_set) + result["accession"] = entry.identifier + results.append(result) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(results if len(results) > 1 else results[0], fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + fieldnames = ["sequence", "length", "pI", "charge_at_pI", "pk_set"] + if args.fasta: + fieldnames.insert(0, "accession") + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") + writer.writeheader() + writer.writerows(results) + print(f"Results written to {args.output}") + else: + for r in results: + acc = r.get("accession", r.get("sequence", "")) + print(f"{acc}\tpI={r['pI']}\tcharge@pI={r['charge_at_pI']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/isoelectric_point_calculator/requirements.txt b/scripts/proteomics/isoelectric_point_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/isoelectric_point_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/isoelectric_point_calculator/tests/conftest.py b/scripts/proteomics/isoelectric_point_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/isoelectric_point_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py b/scripts/proteomics/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py new file mode 100644 index 0000000..cafddde --- /dev/null +++ b/scripts/proteomics/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py @@ -0,0 +1,76 @@ +"""Tests for isoelectric_point_calculator.""" + +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestIsoelectricPointCalculator: + def test_basic_pi(self): + from isoelectric_point_calculator import calculate_pi + + pi = calculate_pi("ACDEFGHIK") + assert 4.0 < pi < 7.0 + + def test_acidic_peptide(self): + from isoelectric_point_calculator import calculate_pi + + pi = calculate_pi("DDDDDDDK") + assert pi < 5.0 + + def test_basic_peptide(self): + from isoelectric_point_calculator import calculate_pi + + pi = calculate_pi("KKKKKKKK") + assert pi > 9.0 + + def test_charge_at_ph(self): + from isoelectric_point_calculator import charge_at_ph + + # At very low pH, everything is positive + assert charge_at_ph("PEPTIDEK", 1.0) > 0 + # At very high pH, everything is negative + assert charge_at_ph("PEPTIDEK", 14.0) < 0 + + def test_different_pk_sets(self): + from isoelectric_point_calculator import calculate_pi + + lehninger = calculate_pi("ACDEFGHIK", "lehninger") + emboss = calculate_pi("ACDEFGHIK", "emboss") + # Different pKa sets should give slightly different results + assert isinstance(lehninger, float) + assert isinstance(emboss, float) + + def test_charge_curve(self): + from isoelectric_point_calculator import calculate_charge_curve + + curve = calculate_charge_curve("PEPTIDEK", "lehninger", 0.0, 14.0, 1.0) + assert len(curve) == 15 + # Charge should decrease with pH + assert curve[0]["charge"] > curve[-1]["charge"] + + def test_calculate_pi_from_sequence(self): + from isoelectric_point_calculator import calculate_pi_from_sequence + + result = calculate_pi_from_sequence("PEPTIDEK", "lehninger") + assert result["pk_set"] == "lehninger" + assert result["length"] == 8 + assert abs(result["charge_at_pI"]) < 0.1 # charge near 0 at pI + + def test_fasta_processing(self): + import pyopenms as oms + from isoelectric_point_calculator import calculate_pi_from_sequence + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = f"{tmpdir}/test.fasta" + entries = [] + e1 = oms.FASTAEntry() + e1.identifier = "PROT1" + e1.sequence = "MSPEPTIDEKAAANOTHERPEPTIDE" + entries.append(e1) + oms.FASTAFile().store(fasta_path, entries) + + # Process single sequence same as would be done for FASTA entry + result = calculate_pi_from_sequence(e1.sequence) + assert result["pI"] > 0 diff --git a/scripts/proteomics/lc_ms_qc_reporter/lc_ms_qc_reporter.py b/scripts/proteomics/lc_ms_qc_reporter/lc_ms_qc_reporter.py new file mode 100644 index 0000000..d48625a --- /dev/null +++ b/scripts/proteomics/lc_ms_qc_reporter/lc_ms_qc_reporter.py @@ -0,0 +1,107 @@ +""" +LC-MS QC Reporter +================== +Generate comprehensive quality-control metrics from an mzML file. + +Metrics include MS1/MS2 spectrum counts, TIC stability (CV%), charge-state +distribution of precursors, and retention-time coverage. + +Usage +----- + python lc_ms_qc_reporter.py --input run.mzML --output qc_report.json +""" + +import argparse +import json +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def compute_qc_metrics(exp: oms.MSExperiment) -> dict: + """Compute QC metrics from an MSExperiment. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment`` instance. + + Returns + ------- + dict + Dictionary of QC metrics. + """ + spectra = exp.getSpectra() + ms1_count = 0 + ms2_count = 0 + tic_values = [] + charge_counts: dict[int, int] = {} + rt_values = [] + + for spec in spectra: + level = spec.getMSLevel() + rt = spec.getRT() + rt_values.append(rt) + _, intensities = spec.get_peaks() + tic = float(intensities.sum()) if len(intensities) > 0 else 0.0 + + if level == 1: + ms1_count += 1 + tic_values.append(tic) + elif level == 2: + ms2_count += 1 + precursors = spec.getPrecursors() + for prec in precursors: + charge = prec.getCharge() + charge_counts[charge] = charge_counts.get(charge, 0) + 1 + + tic_mean = sum(tic_values) / len(tic_values) if tic_values else 0.0 + tic_std = ( + math.sqrt(sum((t - tic_mean) ** 2 for t in tic_values) / len(tic_values)) + if tic_values + else 0.0 + ) + tic_cv = (tic_std / tic_mean * 100.0) if tic_mean > 0 else 0.0 + + rt_range = (min(rt_values), max(rt_values)) if rt_values else (0.0, 0.0) + + return { + "ms1_count": ms1_count, + "ms2_count": ms2_count, + "total_spectra": len(spectra), + "tic_mean": tic_mean, + "tic_std": tic_std, + "tic_cv_percent": tic_cv, + "charge_distribution": {str(k): v for k, v in sorted(charge_counts.items())}, + "rt_range_sec": list(rt_range), + } + + +def main(): + parser = argparse.ArgumentParser( + description="Generate comprehensive QC report from an mzML file." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON report path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + metrics = compute_qc_metrics(exp) + + with open(args.output, "w") as fh: + json.dump(metrics, fh, indent=2) + + print(f"QC report written to {args.output}") + print(f" MS1 spectra : {metrics['ms1_count']}") + print(f" MS2 spectra : {metrics['ms2_count']}") + print(f" TIC CV% : {metrics['tic_cv_percent']:.2f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/lc_ms_qc_reporter/requirements.txt b/scripts/proteomics/lc_ms_qc_reporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/lc_ms_qc_reporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/lc_ms_qc_reporter/tests/conftest.py b/scripts/proteomics/lc_ms_qc_reporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/lc_ms_qc_reporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py b/scripts/proteomics/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py new file mode 100644 index 0000000..a1b3a1e --- /dev/null +++ b/scripts/proteomics/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py @@ -0,0 +1,77 @@ +"""Tests for lc_ms_qc_reporter.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestLcMsQcReporter: + def _make_experiment(self): + """Create a synthetic MSExperiment with MS1 and MS2 spectra.""" + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(5): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(60.0 * i) + mzs = np.array([100.0 + j for j in range(10)], dtype=np.float64) + intensities = np.array([1000.0 * (j + 1) for j in range(10)], dtype=np.float64) + spec.set_peaks([mzs, intensities]) + exp.addSpectrum(spec) + + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(60.0 * i + 1.0) + prec = oms.Precursor() + prec.setMZ(500.0 + i) + prec.setCharge(2) + ms2.setPrecursors([prec]) + mzs2 = np.array([200.0, 300.0, 400.0], dtype=np.float64) + ints2 = np.array([500.0, 1000.0, 200.0], dtype=np.float64) + ms2.set_peaks([mzs2, ints2]) + exp.addSpectrum(ms2) + + return exp + + def test_compute_qc_metrics(self): + from lc_ms_qc_reporter import compute_qc_metrics + + exp = self._make_experiment() + metrics = compute_qc_metrics(exp) + assert metrics["ms1_count"] == 5 + assert metrics["ms2_count"] == 5 + assert metrics["total_spectra"] == 10 + + def test_tic_stability(self): + from lc_ms_qc_reporter import compute_qc_metrics + + exp = self._make_experiment() + metrics = compute_qc_metrics(exp) + # All MS1 spectra have same peaks, so CV should be 0 + assert metrics["tic_cv_percent"] == 0.0 + + def test_charge_distribution(self): + from lc_ms_qc_reporter import compute_qc_metrics + + exp = self._make_experiment() + metrics = compute_qc_metrics(exp) + assert "2" in metrics["charge_distribution"] + assert metrics["charge_distribution"]["2"] == 5 + + def test_rt_range(self): + from lc_ms_qc_reporter import compute_qc_metrics + + exp = self._make_experiment() + metrics = compute_qc_metrics(exp) + assert metrics["rt_range_sec"][0] == 0.0 + assert metrics["rt_range_sec"][1] == 241.0 + + def test_empty_experiment(self): + import pyopenms as oms + from lc_ms_qc_reporter import compute_qc_metrics + + exp = oms.MSExperiment() + metrics = compute_qc_metrics(exp) + assert metrics["ms1_count"] == 0 + assert metrics["ms2_count"] == 0 diff --git a/scripts/proteomics/library_coverage_estimator/README.md b/scripts/proteomics/library_coverage_estimator/README.md new file mode 100644 index 0000000..b0f42bb --- /dev/null +++ b/scripts/proteomics/library_coverage_estimator/README.md @@ -0,0 +1,30 @@ +# Library Coverage Estimator + +Given a spectral library and a FASTA proteome, compute proteome coverage at both peptide and protein level. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python library_coverage_estimator.py --library lib.tsv --fasta proteome.fasta \ + --enzyme Trypsin --output coverage.tsv +``` + +### Input format + +The library file is a TSV with a `PeptideSequence` or `sequence` column. The FASTA file is a standard protein FASTA. + +### Parameters + +| Flag | Description | +|------|-------------| +| `--library` | Spectral library TSV | +| `--fasta` | Proteome FASTA file | +| `--enzyme` | Enzyme name (default: Trypsin) | +| `--missed-cleavages` | Missed cleavages (default: 1) | +| `--output` | Output coverage TSV | diff --git a/scripts/proteomics/library_coverage_estimator/library_coverage_estimator.py b/scripts/proteomics/library_coverage_estimator/library_coverage_estimator.py new file mode 100644 index 0000000..2e449bc --- /dev/null +++ b/scripts/proteomics/library_coverage_estimator/library_coverage_estimator.py @@ -0,0 +1,207 @@ +""" +Library Coverage Estimator +=========================== +Given a spectral library (TSV) and a FASTA proteome, estimate proteome +coverage: what fraction of theoretically digestible peptides appear in the +library, and what fraction of proteins have at least one peptide represented. + +Uses pyopenms FASTAFile for reading the proteome, ProteaseDigestion for +in-silico digestion, and AASequence for sequence handling. + +Usage +----- + python library_coverage_estimator.py --library lib.tsv \ + --fasta proteome.fasta --enzyme Trypsin --output coverage.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Set, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def read_library_peptides(library_path: str) -> Set[str]: + """Read peptide sequences from a spectral library TSV. + + Expects a column named ``PeptideSequence`` or ``sequence``. + + Returns + ------- + set + Unique stripped peptide sequences. + """ + peptides: Set[str] = set() + with open(library_path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("PeptideSequence", row.get("sequence", "")).strip() + if seq: + # Strip modifications for matching + aa = oms.AASequence.fromString(seq) + peptides.add(aa.toUnmodifiedString()) + return peptides + + +def digest_fasta( + fasta_path: str, + enzyme: str = "Trypsin", + missed_cleavages: int = 1, + min_length: int = 6, + max_length: int = 50, +) -> Tuple[Dict[str, List[str]], Set[str]]: + """Digest a FASTA file and return per-protein peptides. + + Parameters + ---------- + fasta_path: + Path to FASTA file. + enzyme: + Enzyme name (default: Trypsin). + missed_cleavages: + Allowed missed cleavages (default: 1). + min_length: + Minimum peptide length. + max_length: + Maximum peptide length. + + Returns + ------- + tuple + (protein_peptides, all_peptides) where protein_peptides maps + accession to list of peptide strings and all_peptides is the + union of all digestible peptides. + """ + entries: List[oms.FASTAEntry] = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + + digester = oms.ProteaseDigestion() + digester.setEnzyme(enzyme) + digester.setMissedCleavages(missed_cleavages) + + protein_peptides: Dict[str, List[str]] = {} + all_peptides: Set[str] = set() + + for entry in entries: + accession = entry.identifier.split()[0] if entry.identifier else "unknown" + aa_seq = oms.AASequence.fromString(entry.sequence) + digest_result: List[oms.AASequence] = [] + digester.digest(aa_seq, digest_result, min_length, max_length) + + pep_strings = [p.toUnmodifiedString() for p in digest_result] + protein_peptides[accession] = pep_strings + all_peptides.update(pep_strings) + + return protein_peptides, all_peptides + + +def compute_coverage( + library_peptides: Set[str], + protein_peptides: Dict[str, List[str]], + all_digestible: Set[str], +) -> Dict[str, object]: + """Compute proteome coverage statistics. + + Parameters + ---------- + library_peptides: + Peptides present in the spectral library. + protein_peptides: + Per-protein peptide lists from digestion. + all_digestible: + Union of all digestible peptides. + + Returns + ------- + dict + Coverage metrics including peptide-level and protein-level fractions. + """ + covered_peptides = library_peptides & all_digestible + proteins_with_coverage = 0 + protein_details: List[Dict[str, object]] = [] + + for acc, peps in protein_peptides.items(): + pep_set = set(peps) + matched = pep_set & library_peptides + has_coverage = len(matched) > 0 + if has_coverage: + proteins_with_coverage += 1 + protein_details.append({ + "accession": acc, + "total_peptides": len(pep_set), + "library_peptides": len(matched), + "coverage_fraction": len(matched) / len(pep_set) if pep_set else 0.0, + }) + + total_proteins = len(protein_peptides) + return { + "total_digestible_peptides": len(all_digestible), + "library_peptides_in_proteome": len(covered_peptides), + "peptide_coverage_fraction": ( + len(covered_peptides) / len(all_digestible) if all_digestible else 0.0 + ), + "total_proteins": total_proteins, + "proteins_with_library_peptide": proteins_with_coverage, + "protein_coverage_fraction": ( + proteins_with_coverage / total_proteins if total_proteins else 0.0 + ), + "protein_details": protein_details, + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Estimate proteome coverage from a spectral library and FASTA." + ) + parser.add_argument("--library", required=True, help="Spectral library TSV") + parser.add_argument("--fasta", required=True, help="Proteome FASTA file") + parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") + parser.add_argument( + "--missed-cleavages", type=int, default=1, + help="Missed cleavages (default: 1)", + ) + parser.add_argument("--output", required=True, help="Output coverage TSV") + args = parser.parse_args() + + library_peps = read_library_peptides(args.library) + protein_peps, all_peps = digest_fasta(args.fasta, args.enzyme, args.missed_cleavages) + result = compute_coverage(library_peps, protein_peps, all_peps) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + # Summary + writer.writerow(["metric", "value"]) + writer.writerow(["total_digestible_peptides", result["total_digestible_peptides"]]) + writer.writerow(["library_peptides_in_proteome", result["library_peptides_in_proteome"]]) + writer.writerow(["peptide_coverage_fraction", f"{result['peptide_coverage_fraction']:.4f}"]) + writer.writerow(["total_proteins", result["total_proteins"]]) + writer.writerow(["proteins_with_library_peptide", result["proteins_with_library_peptide"]]) + writer.writerow(["protein_coverage_fraction", f"{result['protein_coverage_fraction']:.4f}"]) + writer.writerow([]) + # Per-protein details + writer.writerow(["accession", "total_peptides", "library_peptides", "coverage_fraction"]) + for det in result["protein_details"]: + writer.writerow([ + det["accession"], det["total_peptides"], + det["library_peptides"], f"{det['coverage_fraction']:.4f}", + ]) + + print( + f"Peptide coverage: {result['library_peptides_in_proteome']}" + f"/{result['total_digestible_peptides']}" + f" ({result['peptide_coverage_fraction']:.1%})" + ) + print( + f"Protein coverage: {result['proteins_with_library_peptide']}" + f"/{result['total_proteins']}" + f" ({result['protein_coverage_fraction']:.1%})" + ) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/library_coverage_estimator/requirements.txt b/scripts/proteomics/library_coverage_estimator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/library_coverage_estimator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/library_coverage_estimator/tests/conftest.py b/scripts/proteomics/library_coverage_estimator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/library_coverage_estimator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py b/scripts/proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py new file mode 100644 index 0000000..22ed8fa --- /dev/null +++ b/scripts/proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py @@ -0,0 +1,95 @@ +"""Tests for library_coverage_estimator.""" + +import csv +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_read_library_peptides(tmp_path): + from library_coverage_estimator import read_library_peptides + + lib_file = tmp_path / "lib.tsv" + with open(lib_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["PeptideSequence", "charge"]) + writer.writerow(["PEPTIDEK", "2"]) + writer.writerow(["AGIILTK", "2"]) + writer.writerow(["PEPTIDEK", "3"]) # duplicate sequence + + peps = read_library_peptides(str(lib_file)) + assert "PEPTIDEK" in peps + assert "AGIILTK" in peps + assert len(peps) == 2 # deduplicated + + +@requires_pyopenms +def test_digest_fasta(tmp_path): + import pyopenms as oms + from library_coverage_estimator import digest_fasta + + fasta_file = tmp_path / "test.fasta" + entries = [oms.FASTAEntry()] + entries[0].identifier = "P12345" + entries[0].sequence = "MKWVTFISLLFLFSSAYSRGVFRRDAHKSEVAHRFKDLGEENFKALVLIAFAQYLQQCPFEDHVK" + oms.FASTAFile().store(str(fasta_file), entries) + + prot_peps, all_peps = digest_fasta(str(fasta_file), "Trypsin", 1) + assert "P12345" in prot_peps + assert len(all_peps) > 0 + + +@requires_pyopenms +def test_compute_coverage(): + from library_coverage_estimator import compute_coverage + + lib_peps = {"PEPTIDEK", "AGIILTK", "UNKNOWN"} + prot_peps = { + "P1": ["PEPTIDEK", "AGIILTK", "FOOBAR"], + "P2": ["XYZTHING"], + } + all_digest = {"PEPTIDEK", "AGIILTK", "FOOBAR", "XYZTHING"} + + result = compute_coverage(lib_peps, prot_peps, all_digest) + assert result["total_digestible_peptides"] == 4 + assert result["library_peptides_in_proteome"] == 2 # PEPTIDEK, AGIILTK + assert result["proteins_with_library_peptide"] == 1 # only P1 + assert result["total_proteins"] == 2 + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + import pyopenms as oms + from library_coverage_estimator import main + + # Create FASTA + fasta_file = tmp_path / "proteome.fasta" + entries = [oms.FASTAEntry()] + entries[0].identifier = "P12345" + entries[0].sequence = "MKWVTFISLLFLFSSAYSRGVFRRDAHK" + oms.FASTAFile().store(str(fasta_file), entries) + + # Digest to find real peptides + from library_coverage_estimator import digest_fasta + prot_peps, all_peps = digest_fasta(str(fasta_file), "Trypsin", 1) + some_peps = list(all_peps)[:2] if all_peps else ["PEPTIDEK"] + + # Create library + lib_file = tmp_path / "lib.tsv" + with open(lib_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["PeptideSequence"]) + for p in some_peps: + writer.writerow([p]) + + output_file = tmp_path / "coverage.tsv" + sys.argv = [ + "library_coverage_estimator.py", + "--library", str(lib_file), + "--fasta", str(fasta_file), + "--enzyme", "Trypsin", + "--output", str(output_file), + ] + main() + assert output_file.exists() diff --git a/scripts/proteomics/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py b/scripts/proteomics/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py new file mode 100644 index 0000000..e2b799c --- /dev/null +++ b/scripts/proteomics/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py @@ -0,0 +1,158 @@ +""" +Mass Error Distribution Analyzer +================================= +Compute precursor mass-error distributions from a peptide identification +TSV and the corresponding mzML file. + +For each identified peptide, the theoretical m/z is computed with pyopenms +AASequence and compared to the observed precursor m/z in the mzML. + +Usage +----- + python mass_error_distribution_analyzer.py --input peptides.tsv --mzml run.mzML --output errors.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def compute_mass_errors(peptide_rows: list[dict], exp: oms.MSExperiment) -> list[dict]: + """Compute mass errors for identified peptides against spectra. + + Parameters + ---------- + peptide_rows: + List of dicts with keys: sequence, charge, scan_index (or precursor_mz). + exp: + Loaded ``pyopenms.MSExperiment``. + + Returns + ------- + list[dict] + Each dict has: sequence, charge, theo_mz, obs_mz, error_da, error_ppm. + """ + spectra = exp.getSpectra() + results = [] + + for row in peptide_rows: + sequence = row["sequence"] + charge = int(row["charge"]) + + try: + aa = oms.AASequence.fromString(sequence) + theo_mass = aa.getMonoWeight() + theo_mz = (theo_mass + charge * PROTON) / charge + except Exception: + continue + + if "precursor_mz" in row and row["precursor_mz"]: + obs_mz = float(row["precursor_mz"]) + elif "scan_index" in row and row["scan_index"]: + idx = int(row["scan_index"]) + if idx >= len(spectra): + continue + precs = spectra[idx].getPrecursors() + if not precs: + continue + obs_mz = precs[0].getMZ() + else: + continue + + error_da = obs_mz - theo_mz + error_ppm = error_da / theo_mz * 1e6 + + results.append({ + "sequence": sequence, + "charge": charge, + "theo_mz": round(theo_mz, 6), + "obs_mz": round(obs_mz, 6), + "error_da": round(error_da, 6), + "error_ppm": round(error_ppm, 4), + }) + + return results + + +def summarize_errors(errors: list[dict]) -> dict: + """Compute summary statistics for mass errors. + + Parameters + ---------- + errors: + Output of ``compute_mass_errors``. + + Returns + ------- + dict + Mean, std, median for both Da and ppm errors. + """ + if not errors: + return {"count": 0} + + ppm_vals = sorted(e["error_ppm"] for e in errors) + da_vals = sorted(e["error_da"] for e in errors) + n = len(ppm_vals) + + ppm_mean = sum(ppm_vals) / n + ppm_std = math.sqrt(sum((v - ppm_mean) ** 2 for v in ppm_vals) / n) + ppm_median = ppm_vals[n // 2] + + da_mean = sum(da_vals) / n + da_std = math.sqrt(sum((v - da_mean) ** 2 for v in da_vals) / n) + + return { + "count": n, + "ppm_mean": round(ppm_mean, 4), + "ppm_std": round(ppm_std, 4), + "ppm_median": round(ppm_median, 4), + "da_mean": round(da_mean, 6), + "da_std": round(da_std, 6), + } + + +def main(): + parser = argparse.ArgumentParser( + description="Compute precursor mass error distributions." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Peptide TSV file") + parser.add_argument("--mzml", required=True, metavar="FILE", help="mzML file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output errors TSV") + args = parser.parse_args() + + rows = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + rows.append(row) + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.mzml, exp) + + errors = compute_mass_errors(rows, exp) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["sequence", "charge", "theo_mz", "obs_mz", "error_da", "error_ppm"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(errors) + + summary = summarize_errors(errors) + print(f"Wrote {summary['count']} mass errors to {args.output}") + if summary["count"] > 0: + print(f" Mean error: {summary['ppm_mean']:.2f} ppm (std {summary['ppm_std']:.2f})") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mass_error_distribution_analyzer/requirements.txt b/scripts/proteomics/mass_error_distribution_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mass_error_distribution_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mass_error_distribution_analyzer/tests/conftest.py b/scripts/proteomics/mass_error_distribution_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mass_error_distribution_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py b/scripts/proteomics/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py new file mode 100644 index 0000000..7957b78 --- /dev/null +++ b/scripts/proteomics/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py @@ -0,0 +1,57 @@ +"""Tests for mass_error_distribution_analyzer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMassErrorDistributionAnalyzer: + def test_compute_mass_errors(self): + import pyopenms as oms + from mass_error_distribution_analyzer import PROTON, compute_mass_errors + + aa = oms.AASequence.fromString("PEPTIDEK") + theo_mass = aa.getMonoWeight() + charge = 2 + theo_mz = (theo_mass + charge * PROTON) / charge + + rows = [{"sequence": "PEPTIDEK", "charge": "2", "precursor_mz": str(theo_mz)}] + exp = oms.MSExperiment() + + errors = compute_mass_errors(rows, exp) + assert len(errors) == 1 + assert abs(errors[0]["error_ppm"]) < 0.01 + + def test_mass_error_with_offset(self): + import pyopenms as oms + from mass_error_distribution_analyzer import PROTON, compute_mass_errors + + aa = oms.AASequence.fromString("PEPTIDEK") + theo_mass = aa.getMonoWeight() + charge = 2 + theo_mz = (theo_mass + charge * PROTON) / charge + obs_mz = theo_mz + 0.001 # 1 mDa offset + + rows = [{"sequence": "PEPTIDEK", "charge": "2", "precursor_mz": str(obs_mz)}] + exp = oms.MSExperiment() + + errors = compute_mass_errors(rows, exp) + assert len(errors) == 1 + assert errors[0]["error_ppm"] > 0 + + def test_summarize_errors(self): + from mass_error_distribution_analyzer import summarize_errors + + errors = [ + {"error_ppm": 1.0, "error_da": 0.0005}, + {"error_ppm": -1.0, "error_da": -0.0005}, + {"error_ppm": 0.5, "error_da": 0.00025}, + ] + summary = summarize_errors(errors) + assert summary["count"] == 3 + assert abs(summary["ppm_mean"] - (1.0 - 1.0 + 0.5) / 3) < 0.01 + + def test_empty_errors(self): + from mass_error_distribution_analyzer import summarize_errors + + summary = summarize_errors([]) + assert summary["count"] == 0 diff --git a/scripts/proteomics/maxquant_result_converter/README.md b/scripts/proteomics/maxquant_result_converter/README.md new file mode 100644 index 0000000..e73b148 --- /dev/null +++ b/scripts/proteomics/maxquant_result_converter/README.md @@ -0,0 +1,23 @@ +# MaxQuant Result Converter + +Convert MaxQuant evidence.txt to a standardized TSV format. + +## Usage + +```bash +python maxquant_result_converter.py --input evidence.txt --output standardized.tsv +``` + +## Column Mapping + +| MaxQuant Column | Standard Column | +|---|---| +| Sequence | peptide | +| Modified sequence | modified_peptide | +| Charge | charge | +| m/z | mz | +| Retention time | rt | +| Proteins | protein | +| Score | score | +| PEP | pep | +| Intensity | intensity | diff --git a/scripts/proteomics/maxquant_result_converter/maxquant_result_converter.py b/scripts/proteomics/maxquant_result_converter/maxquant_result_converter.py new file mode 100644 index 0000000..5f226c1 --- /dev/null +++ b/scripts/proteomics/maxquant_result_converter/maxquant_result_converter.py @@ -0,0 +1,108 @@ +""" +MaxQuant Result Converter +========================= +Convert MaxQuant evidence.txt to a standardized TSV format. + +Maps MaxQuant-specific column names to a common schema suitable for +downstream analysis. + +Usage +----- + python maxquant_result_converter.py --input evidence.txt --output standardized.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Mapping from MaxQuant column names to standard column names +COLUMN_MAP = { + "Sequence": "peptide", + "Modified sequence": "modified_peptide", + "Charge": "charge", + "m/z": "mz", + "Mass": "mass", + "Retention time": "rt", + "Proteins": "protein", + "Leading razor protein": "leading_protein", + "Gene names": "gene", + "Score": "score", + "PEP": "pep", + "Intensity": "intensity", + "Raw file": "raw_file", + "Experiment": "experiment", + "MS/MS scan number": "scan_number", + "Reverse": "is_decoy", + "Potential contaminant": "is_contaminant", +} + +STANDARD_FIELDS = [ + "peptide", "modified_peptide", "charge", "mz", "mass", "rt", + "protein", "leading_protein", "gene", "score", "pep", + "intensity", "raw_file", "experiment", "scan_number", + "is_decoy", "is_contaminant", "source", +] + + +def convert_maxquant_evidence(filepath: str) -> list: + """Convert MaxQuant evidence.txt to standardized format. + + Parameters + ---------- + filepath: + Path to MaxQuant evidence.txt. + + Returns + ------- + list + List of dicts with standardized column names. + """ + rows = [] + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + std_row = {} + for mq_col, std_col in COLUMN_MAP.items(): + value = row.get(mq_col, "") + # Normalize decoy/contaminant flags + if std_col == "is_decoy": + value = "true" if value == "+" else "false" + elif std_col == "is_contaminant": + value = "true" if value == "+" else "false" + std_row[std_col] = value + std_row["source"] = "MaxQuant" + rows.append(std_row) + return rows + + +def write_standardized(filepath: str, rows: list) -> None: + """Write standardized results to TSV.""" + with open(filepath, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=STANDARD_FIELDS, delimiter="\t", extrasaction="ignore") + writer.writeheader() + writer.writerows(rows) + + +def main(): + parser = argparse.ArgumentParser(description="Convert MaxQuant evidence.txt to standardized TSV.") + parser.add_argument("--input", required=True, help="MaxQuant evidence.txt file") + parser.add_argument("--output", required=True, help="Output standardized TSV") + args = parser.parse_args() + + rows = convert_maxquant_evidence(args.input) + write_standardized(args.output, rows) + + n_decoy = sum(1 for r in rows if r.get("is_decoy") == "true") + print("Source: MaxQuant") + print(f"Total PSMs: {len(rows)}") + print(f"Decoy PSMs: {n_decoy}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/maxquant_result_converter/requirements.txt b/scripts/proteomics/maxquant_result_converter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/maxquant_result_converter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/maxquant_result_converter/tests/conftest.py b/scripts/proteomics/maxquant_result_converter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/maxquant_result_converter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/maxquant_result_converter/tests/test_maxquant_result_converter.py b/scripts/proteomics/maxquant_result_converter/tests/test_maxquant_result_converter.py new file mode 100644 index 0000000..d0ee283 --- /dev/null +++ b/scripts/proteomics/maxquant_result_converter/tests/test_maxquant_result_converter.py @@ -0,0 +1,104 @@ +"""Tests for maxquant_result_converter.""" + +import csv + +from conftest import requires_pyopenms +from maxquant_result_converter import STANDARD_FIELDS, convert_maxquant_evidence, write_standardized + + +@requires_pyopenms +class TestMaxQuantResultConverter: + def _write_evidence(self, tmp_path, rows): + filepath = str(tmp_path / "evidence.txt") + fieldnames = [ + "Sequence", "Modified sequence", "Charge", "m/z", "Mass", + "Retention time", "Proteins", "Leading razor protein", "Gene names", + "Score", "PEP", "Intensity", "Raw file", "Experiment", + "MS/MS scan number", "Reverse", "Potential contaminant", + ] + with open(filepath, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(rows) + return filepath + + def test_basic_conversion(self, tmp_path): + filepath = self._write_evidence(tmp_path, [ + { + "Sequence": "PEPTIDEK", "Modified sequence": "_PEPTIDEK_", + "Charge": "2", "m/z": "450.5", "Mass": "900.0", + "Retention time": "25.5", "Proteins": "P12345", + "Leading razor protein": "P12345", "Gene names": "GEN1", + "Score": "120.5", "PEP": "0.001", "Intensity": "1e6", + "Raw file": "run1", "Experiment": "exp1", + "MS/MS scan number": "1234", "Reverse": "", "Potential contaminant": "", + } + ]) + rows = convert_maxquant_evidence(filepath) + assert len(rows) == 1 + assert rows[0]["peptide"] == "PEPTIDEK" + assert rows[0]["charge"] == "2" + assert rows[0]["source"] == "MaxQuant" + + def test_decoy_flag(self, tmp_path): + filepath = self._write_evidence(tmp_path, [ + { + "Sequence": "PEPTIDEK", "Modified sequence": "_PEPTIDEK_", + "Charge": "2", "m/z": "450.5", "Mass": "900.0", + "Retention time": "25.5", "Proteins": "REV__P12345", + "Leading razor protein": "REV__P12345", "Gene names": "", + "Score": "50.0", "PEP": "0.5", "Intensity": "1e4", + "Raw file": "run1", "Experiment": "exp1", + "MS/MS scan number": "5678", "Reverse": "+", "Potential contaminant": "", + } + ]) + rows = convert_maxquant_evidence(filepath) + assert rows[0]["is_decoy"] == "true" + assert rows[0]["is_contaminant"] == "false" + + def test_contaminant_flag(self, tmp_path): + filepath = self._write_evidence(tmp_path, [ + { + "Sequence": "CONTAM", "Modified sequence": "_CONTAM_", + "Charge": "1", "m/z": "300.0", "Mass": "299.0", + "Retention time": "10.0", "Proteins": "CON__P00001", + "Leading razor protein": "CON__P00001", "Gene names": "", + "Score": "30.0", "PEP": "0.01", "Intensity": "5e5", + "Raw file": "run1", "Experiment": "exp1", + "MS/MS scan number": "999", "Reverse": "", "Potential contaminant": "+", + } + ]) + rows = convert_maxquant_evidence(filepath) + assert rows[0]["is_contaminant"] == "true" + + def test_write_standardized(self, tmp_path): + rows = [{"peptide": "PEPTIDEK", "charge": "2", "source": "MaxQuant"}] + outfile = str(tmp_path / "out.tsv") + write_standardized(outfile, rows) + with open(outfile) as fh: + reader = csv.DictReader(fh, delimiter="\t") + result = list(reader) + assert len(result) == 1 + assert result[0]["peptide"] == "PEPTIDEK" + + def test_standard_fields(self): + assert "peptide" in STANDARD_FIELDS + assert "source" in STANDARD_FIELDS + + def test_multiple_rows(self, tmp_path): + filepath = self._write_evidence(tmp_path, [ + {"Sequence": "PEP1", "Modified sequence": "", "Charge": "2", + "m/z": "400", "Mass": "800", "Retention time": "20", + "Proteins": "P1", "Leading razor protein": "P1", "Gene names": "G1", + "Score": "100", "PEP": "0.01", "Intensity": "1e6", + "Raw file": "run1", "Experiment": "exp1", + "MS/MS scan number": "1", "Reverse": "", "Potential contaminant": ""}, + {"Sequence": "PEP2", "Modified sequence": "", "Charge": "3", + "m/z": "300", "Mass": "900", "Retention time": "30", + "Proteins": "P2", "Leading razor protein": "P2", "Gene names": "G2", + "Score": "90", "PEP": "0.02", "Intensity": "5e5", + "Raw file": "run1", "Experiment": "exp1", + "MS/MS scan number": "2", "Reverse": "", "Potential contaminant": ""}, + ]) + rows = convert_maxquant_evidence(filepath) + assert len(rows) == 2 diff --git a/scripts/proteomics/metapeptide_function_aggregator/README.md b/scripts/proteomics/metapeptide_function_aggregator/README.md new file mode 100644 index 0000000..1cae96d --- /dev/null +++ b/scripts/proteomics/metapeptide_function_aggregator/README.md @@ -0,0 +1,41 @@ +# Metapeptide Function Aggregator + +Aggregate GO/KEGG functional annotations from peptide-to-protein mappings for metaproteomics. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python metapeptide_function_aggregator.py --peptides identified.tsv \ + --annotations go_terms.tsv --output function.tsv +``` + +### Input format + +Peptides TSV with `peptide` and `protein` columns: + +``` +peptide protein +AGIILTK P12345 +PEPTIDEK P12345;P67890 +``` + +Annotations TSV with `protein`, `term_id`, `term_name` columns: + +``` +protein term_id term_name +P12345 GO:0006412 translation +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--peptides` | TSV with peptide-protein mappings | +| `--annotations` | TSV with protein-function annotations | +| `--output` | Output functional aggregation TSV | diff --git a/scripts/proteomics/metapeptide_function_aggregator/metapeptide_function_aggregator.py b/scripts/proteomics/metapeptide_function_aggregator/metapeptide_function_aggregator.py new file mode 100644 index 0000000..b037db3 --- /dev/null +++ b/scripts/proteomics/metapeptide_function_aggregator/metapeptide_function_aggregator.py @@ -0,0 +1,200 @@ +""" +Metapeptide Function Aggregator +================================ +Aggregate GO/KEGG functional annotations from peptide-to-protein mappings. +Given identified peptides with their protein assignments and a separate +annotation file mapping proteins to functional terms, the tool aggregates +term counts and computes peptide-level functional profiles. + +Useful for metaproteomics where functional characterization of the +community is derived from identified peptides. + +Usage +----- + python metapeptide_function_aggregator.py --peptides identified.tsv \ + --annotations go_terms.tsv --output function.tsv +""" + +import argparse +import csv +import sys +from collections import Counter, defaultdict +from typing import Dict, List, Set, Tuple + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_peptide_protein_map(peptides_path: str) -> Dict[str, Set[str]]: + """Load peptide-to-protein mappings from a TSV. + + Expects columns ``peptide`` and ``protein``. A peptide can map to + multiple proteins (one row per mapping or semicolon-separated proteins). + + Returns + ------- + dict + Mapping of peptide sequence to set of protein accessions. + """ + pep_to_prot: Dict[str, Set[str]] = defaultdict(set) + with open(peptides_path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + peptide = row.get("peptide", "").strip() + proteins_raw = row.get("protein", "").strip() + if not peptide or not proteins_raw: + continue + for prot in proteins_raw.split(";"): + prot = prot.strip() + if prot: + pep_to_prot[peptide].add(prot) + return dict(pep_to_prot) + + +def load_annotations(annotations_path: str) -> Dict[str, List[Tuple[str, str]]]: + """Load protein-to-function annotation mappings. + + Expects columns ``protein``, ``term_id``, and ``term_name``. + + Returns + ------- + dict + Mapping of protein accession to list of (term_id, term_name) tuples. + """ + prot_to_terms: Dict[str, List[Tuple[str, str]]] = defaultdict(list) + with open(annotations_path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + protein = row.get("protein", "").strip() + term_id = row.get("term_id", "").strip() + term_name = row.get("term_name", "").strip() + if protein and term_id: + prot_to_terms[protein].append((term_id, term_name)) + return dict(prot_to_terms) + + +def aggregate_functions( + pep_to_prot: Dict[str, Set[str]], + prot_to_terms: Dict[str, List[Tuple[str, str]]], +) -> Tuple[List[Dict[str, object]], Counter]: + """Aggregate functional annotations from peptide-protein-term mappings. + + For each peptide, collect all functional terms from all mapped proteins. + Also compute global term frequency counts. + + Parameters + ---------- + pep_to_prot: + Peptide-to-protein mappings. + prot_to_terms: + Protein-to-functional-term mappings. + + Returns + ------- + tuple + (peptide_annotations, term_counts) where peptide_annotations is a list + of dicts with ``peptide``, ``proteins``, ``terms``, and term_counts is + a Counter of term_id occurrences across all peptides. + """ + peptide_annotations: List[Dict[str, object]] = [] + term_counts: Counter = Counter() + + for peptide, proteins in sorted(pep_to_prot.items()): + terms_seen: Set[str] = set() + term_details: List[Tuple[str, str]] = [] + + for prot in proteins: + if prot in prot_to_terms: + for term_id, term_name in prot_to_terms[prot]: + if term_id not in terms_seen: + terms_seen.add(term_id) + term_details.append((term_id, term_name)) + term_counts[term_id] += 1 + + peptide_annotations.append({ + "peptide": peptide, + "n_proteins": len(proteins), + "proteins": ";".join(sorted(proteins)), + "n_terms": len(term_details), + "terms": ";".join(f"{tid}:{tname}" for tid, tname in term_details), + }) + + return peptide_annotations, term_counts + + +def summarize_terms( + term_counts: Counter, + prot_to_terms: Dict[str, List[Tuple[str, str]]], +) -> List[Dict[str, object]]: + """Build a summary table of term frequencies. + + Returns + ------- + list of dict + Sorted by count descending, with ``term_id``, ``term_name``, ``count``. + """ + # Build term_id -> term_name lookup + id_to_name: Dict[str, str] = {} + for terms in prot_to_terms.values(): + for tid, tname in terms: + if tid not in id_to_name: + id_to_name[tid] = tname + + summary: List[Dict[str, object]] = [] + for tid, count in term_counts.most_common(): + summary.append({ + "term_id": tid, + "term_name": id_to_name.get(tid, ""), + "peptide_count": count, + }) + return summary + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Aggregate GO/KEGG annotations from peptide-protein mappings." + ) + parser.add_argument( + "--peptides", required=True, + help="TSV with 'peptide' and 'protein' columns", + ) + parser.add_argument( + "--annotations", required=True, + help="TSV with 'protein', 'term_id', 'term_name' columns", + ) + parser.add_argument("--output", required=True, help="Output functional aggregation TSV") + args = parser.parse_args() + + pep_to_prot = load_peptide_protein_map(args.peptides) + prot_to_terms = load_annotations(args.annotations) + + if not pep_to_prot: + sys.exit("No peptide-protein mappings found.") + + pep_annots, term_counts = aggregate_functions(pep_to_prot, prot_to_terms) + term_summary = summarize_terms(term_counts, prot_to_terms) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + # Peptide-level annotations + writer.writerow(["peptide", "n_proteins", "proteins", "n_terms", "terms"]) + for pa in pep_annots: + writer.writerow([ + pa["peptide"], pa["n_proteins"], pa["proteins"], + pa["n_terms"], pa["terms"], + ]) + writer.writerow([]) + # Term summary + writer.writerow(["term_id", "term_name", "peptide_count"]) + for ts in term_summary: + writer.writerow([ts["term_id"], ts["term_name"], ts["peptide_count"]]) + + annotated = sum(1 for pa in pep_annots if pa["n_terms"] > 0) + print(f"Annotated {annotated}/{len(pep_annots)} peptides with functional terms") + print(f"Unique terms: {len(term_summary)} -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/metapeptide_function_aggregator/requirements.txt b/scripts/proteomics/metapeptide_function_aggregator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/metapeptide_function_aggregator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/metapeptide_function_aggregator/tests/conftest.py b/scripts/proteomics/metapeptide_function_aggregator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/metapeptide_function_aggregator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/metapeptide_function_aggregator/tests/test_metapeptide_function_aggregator.py b/scripts/proteomics/metapeptide_function_aggregator/tests/test_metapeptide_function_aggregator.py new file mode 100644 index 0000000..258402f --- /dev/null +++ b/scripts/proteomics/metapeptide_function_aggregator/tests/test_metapeptide_function_aggregator.py @@ -0,0 +1,126 @@ +"""Tests for metapeptide_function_aggregator.""" + +import csv +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_load_peptide_protein_map(tmp_path): + from metapeptide_function_aggregator import load_peptide_protein_map + + pep_file = tmp_path / "peptides.tsv" + with open(pep_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["peptide", "protein"]) + writer.writerow(["PEPTIDEK", "P1"]) + writer.writerow(["PEPTIDEK", "P2"]) + writer.writerow(["AGIILTK", "P3"]) + + pep_map = load_peptide_protein_map(str(pep_file)) + assert "PEPTIDEK" in pep_map + assert pep_map["PEPTIDEK"] == {"P1", "P2"} + assert pep_map["AGIILTK"] == {"P3"} + + +@requires_pyopenms +def test_load_peptide_protein_map_semicolon(tmp_path): + from metapeptide_function_aggregator import load_peptide_protein_map + + pep_file = tmp_path / "peptides.tsv" + with open(pep_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["peptide", "protein"]) + writer.writerow(["PEPTIDEK", "P1;P2"]) + + pep_map = load_peptide_protein_map(str(pep_file)) + assert pep_map["PEPTIDEK"] == {"P1", "P2"} + + +@requires_pyopenms +def test_load_annotations(tmp_path): + from metapeptide_function_aggregator import load_annotations + + ann_file = tmp_path / "annotations.tsv" + with open(ann_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein", "term_id", "term_name"]) + writer.writerow(["P1", "GO:0006412", "translation"]) + writer.writerow(["P1", "GO:0005840", "ribosome"]) + + annotations = load_annotations(str(ann_file)) + assert "P1" in annotations + assert len(annotations["P1"]) == 2 + + +@requires_pyopenms +def test_aggregate_functions(): + from metapeptide_function_aggregator import aggregate_functions + + pep_to_prot = { + "PEPTIDEK": {"P1", "P2"}, + "AGIILTK": {"P3"}, + } + prot_to_terms = { + "P1": [("GO:0006412", "translation")], + "P2": [("GO:0006412", "translation"), ("GO:0005840", "ribosome")], + "P3": [("KEGG:00010", "glycolysis")], + } + + pep_annots, term_counts = aggregate_functions(pep_to_prot, prot_to_terms) + assert len(pep_annots) == 2 + + # PEPTIDEK maps to P1 and P2, should get 2 unique terms + peptidek_entry = [pa for pa in pep_annots if pa["peptide"] == "PEPTIDEK"][0] + assert peptidek_entry["n_terms"] == 2 + + assert term_counts["GO:0006412"] == 1 # appears once for PEPTIDEK (deduplicated) + assert term_counts["KEGG:00010"] == 1 + + +@requires_pyopenms +def test_summarize_terms(): + from collections import Counter + + from metapeptide_function_aggregator import summarize_terms + + term_counts = Counter({"GO:0006412": 5, "GO:0005840": 3}) + prot_to_terms = { + "P1": [("GO:0006412", "translation"), ("GO:0005840", "ribosome")], + } + + summary = summarize_terms(term_counts, prot_to_terms) + assert len(summary) == 2 + assert summary[0]["term_id"] == "GO:0006412" + assert summary[0]["peptide_count"] == 5 + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from metapeptide_function_aggregator import main + + pep_file = tmp_path / "peptides.tsv" + ann_file = tmp_path / "annotations.tsv" + output_file = tmp_path / "output.tsv" + + with open(pep_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["peptide", "protein"]) + writer.writerow(["PEPTIDEK", "P1"]) + writer.writerow(["AGIILTK", "P2"]) + + with open(ann_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein", "term_id", "term_name"]) + writer.writerow(["P1", "GO:0006412", "translation"]) + writer.writerow(["P2", "KEGG:00010", "glycolysis"]) + + sys.argv = [ + "metapeptide_function_aggregator.py", + "--peptides", str(pep_file), + "--annotations", str(ann_file), + "--output", str(output_file), + ] + main() + assert output_file.exists() diff --git a/scripts/proteomics/metapeptide_lca_assigner/README.md b/scripts/proteomics/metapeptide_lca_assigner/README.md new file mode 100644 index 0000000..8f49888 --- /dev/null +++ b/scripts/proteomics/metapeptide_lca_assigner/README.md @@ -0,0 +1,19 @@ +# Metapeptide LCA Assigner + +Compute lowest common ancestor taxonomy from peptide-protein mappings. + +## Usage + +```bash +python metapeptide_lca_assigner.py --peptides peptides.tsv --fasta metadb.fasta --taxonomy lineage.tsv --output taxonomy.tsv +``` + +## Input Format + +- `peptides.tsv`: column `peptide` +- `metadb.fasta`: Meta-proteomics FASTA database +- `lineage.tsv`: columns `protein`, `lineage` (semicolon-separated taxonomy) + +## Output + +- `taxonomy.tsv` - Per-peptide LCA assignments with depth and specificity diff --git a/scripts/proteomics/metapeptide_lca_assigner/metapeptide_lca_assigner.py b/scripts/proteomics/metapeptide_lca_assigner/metapeptide_lca_assigner.py new file mode 100644 index 0000000..3eca69b --- /dev/null +++ b/scripts/proteomics/metapeptide_lca_assigner/metapeptide_lca_assigner.py @@ -0,0 +1,259 @@ +""" +Metapeptide LCA Assigner +========================= +Compute lowest common ancestor (LCA) taxonomy from peptide-protein mappings. + +For each peptide, find all proteins it maps to, look up their taxonomy lineages, +and compute the LCA as the longest common prefix of all lineage strings. + +Usage +----- + python metapeptide_lca_assigner.py --peptides peptides.tsv --fasta metadb.fasta \ + --taxonomy lineage.tsv --output taxonomy.tsv +""" + +import argparse +import csv +import re +import sys +from typing import Dict, List, Set + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(fasta_path: str) -> Dict[str, str]: + """Load a FASTA file into a dictionary mapping accession to sequence. + + Parameters + ---------- + fasta_path: + Path to the FASTA file. + + Returns + ------- + dict + Mapping of protein accession to amino acid sequence. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + + proteins = {} + for entry in entries: + acc = entry.identifier.split()[0] if entry.identifier else "" + proteins[acc] = entry.sequence + return proteins + + +def load_taxonomy(taxonomy_path: str) -> Dict[str, List[str]]: + """Load taxonomy lineage file. + + Parameters + ---------- + taxonomy_path: + Path to TSV file with columns: protein, lineage. + Lineage is semicolon-separated taxonomy levels, e.g. + "Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Escherichia" + + Returns + ------- + dict + Mapping of protein accession to lineage list. + """ + taxonomy: Dict[str, List[str]] = {} + with open(taxonomy_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + acc = row["protein"] + lineage_str = row["lineage"] + lineage = [level.strip() for level in lineage_str.split(";") if level.strip()] + taxonomy[acc] = lineage + return taxonomy + + +def strip_modifications(sequence: str) -> str: + """Remove modification annotations from a peptide sequence. + + Parameters + ---------- + sequence: + Peptide sequence with possible modifications. + + Returns + ------- + str + Clean amino acid sequence. + """ + clean = re.sub(r"\[.*?\]", "", sequence) + clean = re.sub(r"\(.*?\)", "", clean) + return clean + + +def find_peptide_proteins(peptide: str, proteins: Dict[str, str]) -> Set[str]: + """Find all proteins containing a given peptide sequence. + + Parameters + ---------- + peptide: + Peptide sequence. + proteins: + Protein accession to sequence mapping. + + Returns + ------- + set + Set of matching protein accessions. + """ + clean = strip_modifications(peptide) + matching = set() + for acc, seq in proteins.items(): + if clean in seq: + matching.add(acc) + return matching + + +def compute_lca(lineages: List[List[str]]) -> List[str]: + """Compute lowest common ancestor as the longest common prefix of lineages. + + Parameters + ---------- + lineages: + List of taxonomy lineage lists. + + Returns + ------- + list + LCA lineage (common prefix). + """ + if not lineages: + return [] + if len(lineages) == 1: + return lineages[0] + + lca = [] + min_len = min(len(lin) for lin in lineages) + for i in range(min_len): + levels = {lin[i] for lin in lineages} + if len(levels) == 1: + lca.append(lineages[0][i]) + else: + break + return lca + + +def assign_lca_for_peptide( + peptide: str, + proteins: Dict[str, str], + taxonomy: Dict[str, List[str]], +) -> Dict[str, object]: + """Assign LCA taxonomy for a single peptide. + + Parameters + ---------- + peptide: + Peptide sequence. + proteins: + Protein accession to sequence mapping. + taxonomy: + Protein to lineage mapping. + + Returns + ------- + dict + Result with peptide, matched proteins, lineages, and LCA. + """ + matched_prots = find_peptide_proteins(peptide, proteins) + + lineages = [] + for prot in sorted(matched_prots): + if prot in taxonomy: + lineages.append(taxonomy[prot]) + + lca = compute_lca(lineages) + + return { + "peptide": peptide, + "matched_proteins": ";".join(sorted(matched_prots)) if matched_prots else "NONE", + "num_proteins": len(matched_prots), + "num_lineages": len(lineages), + "lca": ";".join(lca) if lca else "unassigned", + "lca_depth": len(lca), + "taxonomic_specificity": lca[-1] if lca else "unassigned", + } + + +def assign_lca_batch( + peptides: List[str], + proteins: Dict[str, str], + taxonomy: Dict[str, List[str]], +) -> List[Dict[str, object]]: + """Assign LCA for a batch of peptides. + + Parameters + ---------- + peptides: + List of peptide sequences. + proteins: + Protein accession to sequence mapping. + taxonomy: + Protein to lineage mapping. + + Returns + ------- + list + List of LCA assignment results. + """ + results = [] + for pep in peptides: + results.append(assign_lca_for_peptide(pep, proteins, taxonomy)) + return results + + +def read_peptides(peptides_path: str) -> List[str]: + """Read peptides from TSV file with 'peptide' column.""" + peptides = [] + with open(peptides_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + peptides.append(row["peptide"]) + return peptides + + +def write_output(output_path: str, results: List[Dict[str, object]]) -> None: + """Write LCA results to TSV.""" + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Compute lowest common ancestor taxonomy from peptide-protein mappings." + ) + parser.add_argument("--peptides", required=True, help="Peptides TSV file (peptide column)") + parser.add_argument("--fasta", required=True, help="Meta-proteomics database FASTA file") + parser.add_argument("--taxonomy", required=True, help="Taxonomy lineage TSV (protein, lineage)") + parser.add_argument("--output", required=True, help="Output taxonomy TSV file") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + taxonomy = load_taxonomy(args.taxonomy) + peptides = read_peptides(args.peptides) + results = assign_lca_batch(peptides, proteins, taxonomy) + write_output(args.output, results) + + n_assigned = sum(1 for r in results if r["lca"] != "unassigned") + print(f"Total peptides: {len(results)}") + print(f"Assigned LCA: {n_assigned}") + print(f"Unassigned: {len(results) - n_assigned}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/metapeptide_lca_assigner/requirements.txt b/scripts/proteomics/metapeptide_lca_assigner/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/metapeptide_lca_assigner/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/metapeptide_lca_assigner/tests/conftest.py b/scripts/proteomics/metapeptide_lca_assigner/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/metapeptide_lca_assigner/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py b/scripts/proteomics/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py new file mode 100644 index 0000000..273a227 --- /dev/null +++ b/scripts/proteomics/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py @@ -0,0 +1,136 @@ +"""Tests for metapeptide_lca_assigner.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMetapeptideLcaAssigner: + def _create_fasta(self, tmpdir, proteins): + """Helper to create a FASTA file.""" + import pyopenms as oms + + fasta_path = os.path.join(tmpdir, "metadb.fasta") + entries = [] + for acc, seq in proteins.items(): + entry = oms.FASTAEntry() + entry.identifier = acc + entry.sequence = seq + entries.append(entry) + fasta_file = oms.FASTAFile() + fasta_file.store(fasta_path, entries) + return fasta_path + + def _create_taxonomy(self, tmpdir, taxonomy): + """Helper to create a taxonomy lineage file.""" + tax_path = os.path.join(tmpdir, "lineage.tsv") + with open(tax_path, "w") as f: + f.write("protein\tlineage\n") + for prot, lineage in taxonomy.items(): + f.write(f"{prot}\t{lineage}\n") + return tax_path + + def test_load_fasta(self): + from metapeptide_lca_assigner import load_fasta + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "ACDEFGHIK"}) + proteins = load_fasta(fasta_path) + assert "P1" in proteins + + def test_load_taxonomy(self): + from metapeptide_lca_assigner import load_taxonomy + with tempfile.TemporaryDirectory() as tmpdir: + tax_path = self._create_taxonomy(tmpdir, { + "P1": "Bacteria;Proteobacteria;Gammaproteobacteria" + }) + taxonomy = load_taxonomy(tax_path) + assert "P1" in taxonomy + assert taxonomy["P1"] == ["Bacteria", "Proteobacteria", "Gammaproteobacteria"] + + def test_strip_modifications(self): + from metapeptide_lca_assigner import strip_modifications + assert strip_modifications("PEPTM[147]IDEK") == "PEPTMIDEK" + + def test_find_peptide_proteins(self): + from metapeptide_lca_assigner import find_peptide_proteins + proteins = {"P1": "ACDEFGHIK", "P2": "XXXXACDEFYYY"} + found = find_peptide_proteins("ACDEF", proteins) + assert found == {"P1", "P2"} + + def test_compute_lca_same_lineage(self): + from metapeptide_lca_assigner import compute_lca + lineages = [ + ["Bacteria", "Proteobacteria", "Gamma"], + ["Bacteria", "Proteobacteria", "Gamma"], + ] + lca = compute_lca(lineages) + assert lca == ["Bacteria", "Proteobacteria", "Gamma"] + + def test_compute_lca_partial_overlap(self): + from metapeptide_lca_assigner import compute_lca + lineages = [ + ["Bacteria", "Proteobacteria", "Gammaproteobacteria"], + ["Bacteria", "Proteobacteria", "Betaproteobacteria"], + ] + lca = compute_lca(lineages) + assert lca == ["Bacteria", "Proteobacteria"] + + def test_compute_lca_no_overlap(self): + from metapeptide_lca_assigner import compute_lca + lineages = [ + ["Bacteria", "Proteobacteria"], + ["Archaea", "Euryarchaeota"], + ] + lca = compute_lca(lineages) + assert lca == [] + + def test_compute_lca_single(self): + from metapeptide_lca_assigner import compute_lca + lineages = [["Bacteria", "Proteobacteria"]] + lca = compute_lca(lineages) + assert lca == ["Bacteria", "Proteobacteria"] + + def test_compute_lca_empty(self): + from metapeptide_lca_assigner import compute_lca + assert compute_lca([]) == [] + + def test_assign_lca_for_peptide(self): + from metapeptide_lca_assigner import assign_lca_for_peptide + proteins = {"P1": "ACDEFGHIK", "P2": "XXXXACDEFYYY"} + taxonomy = { + "P1": ["Bacteria", "Proteobacteria", "Gamma"], + "P2": ["Bacteria", "Proteobacteria", "Beta"], + } + result = assign_lca_for_peptide("ACDEF", proteins, taxonomy) + assert result["lca"] == "Bacteria;Proteobacteria" + assert result["lca_depth"] == 2 + assert result["num_proteins"] == 2 + + def test_assign_lca_unassigned(self): + from metapeptide_lca_assigner import assign_lca_for_peptide + proteins = {"P1": "XXXXXXX"} + taxonomy = {} + result = assign_lca_for_peptide("ZZZZZ", proteins, taxonomy) + assert result["lca"] == "unassigned" + + def test_full_pipeline(self): + from metapeptide_lca_assigner import assign_lca_batch, load_fasta, load_taxonomy, write_output + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, { + "P1": "ACDEFGHIKLMNPQR", + "P2": "XXXXACDEFYYY", + }) + tax_path = self._create_taxonomy(tmpdir, { + "P1": "Bacteria;Proteobacteria;Gamma", + "P2": "Bacteria;Proteobacteria;Beta", + }) + proteins = load_fasta(fasta_path) + taxonomy = load_taxonomy(tax_path) + results = assign_lca_batch(["ACDEF", "GHIKLM"], proteins, taxonomy) + output_path = os.path.join(tmpdir, "taxonomy.tsv") + write_output(output_path, results) + assert os.path.exists(output_path) + assert len(results) == 2 diff --git a/scripts/proteomics/mgf_to_mzml_converter/README.md b/scripts/proteomics/mgf_to_mzml_converter/README.md new file mode 100644 index 0000000..1950599 --- /dev/null +++ b/scripts/proteomics/mgf_to_mzml_converter/README.md @@ -0,0 +1,15 @@ +# MGF to mzML Converter + +Convert MGF (Mascot Generic Format) spectra to mzML format. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python mgf_to_mzml_converter.py --input spectra.mgf --output spectra.mzML +``` diff --git a/scripts/proteomics/mgf_to_mzml_converter/mgf_to_mzml_converter.py b/scripts/proteomics/mgf_to_mzml_converter/mgf_to_mzml_converter.py new file mode 100644 index 0000000..4530863 --- /dev/null +++ b/scripts/proteomics/mgf_to_mzml_converter/mgf_to_mzml_converter.py @@ -0,0 +1,111 @@ +""" +MGF to mzML Converter +===================== +Convert MGF (Mascot Generic Format) spectra to mzML format. + +Usage +----- + python mgf_to_mzml_converter.py --input spectra.mgf --output spectra.mzML +""" + +import argparse +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_mgf(input_path: str) -> List[dict]: + """Parse an MGF file and return a list of spectrum dicts. + + Each dict has keys: title, pepmass, charge, rt, peaks (list of (mz, intensity) tuples). + """ + spectra = [] + current = None + + with open(input_path) as fh: + for line in fh: + line = line.strip() + if line == "BEGIN IONS": + current = {"title": "", "pepmass": 0.0, "charge": 0, "rt": 0.0, "peaks": []} + elif line == "END IONS": + if current is not None: + spectra.append(current) + current = None + elif current is not None: + if line.startswith("TITLE="): + current["title"] = line[6:] + elif line.startswith("PEPMASS="): + parts = line[8:].split() + current["pepmass"] = float(parts[0]) + elif line.startswith("CHARGE="): + charge_str = line[7:].replace("+", "").replace("-", "") + try: + current["charge"] = int(charge_str) + except ValueError: + current["charge"] = 0 + elif line.startswith("RTINSECONDS="): + current["rt"] = float(line[12:]) + elif line and line[0].isdigit(): + parts = line.split() + if len(parts) >= 2: + try: + mz = float(parts[0]) + intensity = float(parts[1]) + current["peaks"].append((mz, intensity)) + except ValueError: + pass + + return spectra + + +def convert_mgf_to_mzml(input_path: str, output_path: str) -> dict: + """Convert MGF to mzML format. + + Returns statistics about the conversion. + """ + mgf_spectra = parse_mgf(input_path) + exp = oms.MSExperiment() + + for i, spec_data in enumerate(mgf_spectra): + spectrum = oms.MSSpectrum() + spectrum.setMSLevel(2) + spectrum.setRT(spec_data["rt"]) + spectrum.setNativeID(spec_data["title"] if spec_data["title"] else f"index={i}") + + # Set precursor + if spec_data["pepmass"] > 0: + prec = oms.Precursor() + prec.setMZ(spec_data["pepmass"]) + if spec_data["charge"] > 0: + prec.setCharge(spec_data["charge"]) + spectrum.setPrecursors([prec]) + + # Set peaks + if spec_data["peaks"]: + mzs = [p[0] for p in spec_data["peaks"]] + intensities = [p[1] for p in spec_data["peaks"]] + spectrum.set_peaks((mzs, intensities)) + + exp.addSpectrum(spectrum) + + oms.MzMLFile().store(output_path, exp) + + return {"spectra_converted": len(mgf_spectra)} + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert MGF to mzML format.") + parser.add_argument("--input", required=True, help="Input MGF file") + parser.add_argument("--output", required=True, help="Output mzML file") + args = parser.parse_args() + + stats = convert_mgf_to_mzml(args.input, args.output) + print(f"Converted {stats['spectra_converted']} spectra to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mgf_to_mzml_converter/requirements.txt b/scripts/proteomics/mgf_to_mzml_converter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mgf_to_mzml_converter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mgf_to_mzml_converter/tests/conftest.py b/scripts/proteomics/mgf_to_mzml_converter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mgf_to_mzml_converter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py b/scripts/proteomics/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py new file mode 100644 index 0000000..f51f243 --- /dev/null +++ b/scripts/proteomics/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py @@ -0,0 +1,101 @@ +"""Tests for mgf_to_mzml_converter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + +SAMPLE_MGF = """\ +BEGIN IONS +TITLE=scan=1 +RTINSECONDS=120.5 +PEPMASS=500.250000 +CHARGE=2+ +100.100000 1000.0000 +200.200000 2000.0000 +300.300000 1500.0000 +END IONS + +BEGIN IONS +TITLE=scan=2 +RTINSECONDS=130.0 +PEPMASS=600.350000 +CHARGE=3+ +150.150000 800.0000 +250.250000 1200.0000 +END IONS +""" + + +@requires_pyopenms +def test_parse_mgf(): + from mgf_to_mzml_converter import parse_mgf + + with tempfile.TemporaryDirectory() as tmp: + mgf_path = os.path.join(tmp, "test.mgf") + with open(mgf_path, "w") as fh: + fh.write(SAMPLE_MGF) + + spectra = parse_mgf(mgf_path) + assert len(spectra) == 2 + assert spectra[0]["title"] == "scan=1" + assert abs(spectra[0]["pepmass"] - 500.25) < 0.01 + assert spectra[0]["charge"] == 2 + assert len(spectra[0]["peaks"]) == 3 + assert len(spectra[1]["peaks"]) == 2 + + +@requires_pyopenms +def test_convert_mgf_to_mzml(): + import pyopenms as oms + from mgf_to_mzml_converter import convert_mgf_to_mzml + + with tempfile.TemporaryDirectory() as tmp: + mgf_path = os.path.join(tmp, "test.mgf") + mzml_path = os.path.join(tmp, "test.mzML") + + with open(mgf_path, "w") as fh: + fh.write(SAMPLE_MGF) + + stats = convert_mgf_to_mzml(mgf_path, mzml_path) + assert stats["spectra_converted"] == 2 + + exp = oms.MSExperiment() + oms.MzMLFile().load(mzml_path, exp) + assert exp.size() == 2 + + # Check first spectrum + s = exp[0] + assert s.getMSLevel() == 2 + precursors = s.getPrecursors() + assert len(precursors) == 1 + assert abs(precursors[0].getMZ() - 500.25) < 0.01 + assert precursors[0].getCharge() == 2 + + mz_arr, int_arr = s.get_peaks() + assert len(mz_arr) == 3 + + +@requires_pyopenms +def test_roundtrip_mgf_mzml_mgf(): + """Test MGF -> mzML -> MGF roundtrip.""" + import sys + + from mgf_to_mzml_converter import convert_mgf_to_mzml + sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "mzml_to_mgf_converter")) + + with tempfile.TemporaryDirectory() as tmp: + mgf_path = os.path.join(tmp, "test.mgf") + mzml_path = os.path.join(tmp, "test.mzML") + + with open(mgf_path, "w") as fh: + fh.write(SAMPLE_MGF) + + stats = convert_mgf_to_mzml(mgf_path, mzml_path) + assert stats["spectra_converted"] == 2 + + # Verify mzML is valid + import pyopenms as oms + exp = oms.MSExperiment() + oms.MzMLFile().load(mzml_path, exp) + assert exp.size() == 2 diff --git a/scripts/proteomics/missed_cleavage_analyzer/README.md b/scripts/proteomics/missed_cleavage_analyzer/README.md new file mode 100644 index 0000000..c9f1536 --- /dev/null +++ b/scripts/proteomics/missed_cleavage_analyzer/README.md @@ -0,0 +1,9 @@ +# Missed Cleavage Analyzer + +Analyze missed cleavage distribution from a peptide list. QC metric for proteomics experiments. + +## Usage + +```bash +python missed_cleavage_analyzer.py --input peptides.tsv --enzyme Trypsin --output mc_report.tsv +``` diff --git a/scripts/proteomics/missed_cleavage_analyzer/missed_cleavage_analyzer.py b/scripts/proteomics/missed_cleavage_analyzer/missed_cleavage_analyzer.py new file mode 100644 index 0000000..64459d5 --- /dev/null +++ b/scripts/proteomics/missed_cleavage_analyzer/missed_cleavage_analyzer.py @@ -0,0 +1,128 @@ +""" +Missed Cleavage Analyzer +========================= +Analyze missed cleavage distribution from a peptide list. Useful as a QC metric. + +Features +-------- +- Count missed cleavages per peptide using enzyme-specific rules +- Generate distribution statistics +- Support for Trypsin, Lys-C, and other common enzymes + +Usage +----- + python missed_cleavage_analyzer.py --input peptides.tsv --enzyme Trypsin --output mc_report.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def count_missed_cleavages(sequence: str, enzyme: str = "Trypsin") -> int: + """Count missed cleavages in a peptide sequence for a given enzyme. + + Parameters + ---------- + sequence : str + Peptide sequence. + enzyme : str + Enzyme name (e.g., 'Trypsin', 'Lys-C'). + + Returns + ------- + int + Number of missed cleavages. + """ + aa_seq = oms.AASequence.fromString(sequence) + digest = oms.ProteaseDigestion() + digest.setEnzyme(enzyme) + + # Count internal cleavage sites (K/R for trypsin, not at the end) + count = digest.missedCleavages(aa_seq) + return count + + +def analyze_missed_cleavages(peptides: list, enzyme: str = "Trypsin") -> dict: + """Analyze missed cleavage distribution for a list of peptides. + + Parameters + ---------- + peptides : list + List of peptide sequence strings. + enzyme : str + Enzyme name. + + Returns + ------- + dict + Dictionary with per-peptide results, distribution, and summary stats. + """ + results = [] + distribution = {} + + for pep in peptides: + pep = pep.strip() + if not pep: + continue + mc = count_missed_cleavages(pep, enzyme) + results.append({"peptide": pep, "missed_cleavages": mc}) + distribution[mc] = distribution.get(mc, 0) + 1 + + total = len(results) + avg_mc = sum(r["missed_cleavages"] for r in results) / total if total > 0 else 0.0 + max_mc = max((r["missed_cleavages"] for r in results), default=0) + + return { + "enzyme": enzyme, + "total_peptides": total, + "average_missed_cleavages": round(avg_mc, 4), + "max_missed_cleavages": max_mc, + "distribution": {str(k): v for k, v in sorted(distribution.items())}, + "peptide_results": results, + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Analyze missed cleavage distribution.") + parser.add_argument("--input", required=True, help="TSV file with 'sequence' column.") + parser.add_argument("--enzyme", type=str, default="Trypsin", help="Enzyme name (default: Trypsin).") + parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") + args = parser.parse_args() + + peptide_list = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("sequence", "").strip() + if seq: + peptide_list.append(seq) + + analysis = analyze_missed_cleavages(peptide_list, args.enzyme) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(analysis, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["peptide", "missed_cleavages"], delimiter="\t") + writer.writeheader() + writer.writerows(analysis["peptide_results"]) + print(f"Results written to {args.output}") + else: + print(f"Enzyme: {analysis['enzyme']}") + print(f"Total peptides: {analysis['total_peptides']}") + print(f"Average MC: {analysis['average_missed_cleavages']}") + print(f"Distribution: {analysis['distribution']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/missed_cleavage_analyzer/requirements.txt b/scripts/proteomics/missed_cleavage_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/missed_cleavage_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/missed_cleavage_analyzer/tests/conftest.py b/scripts/proteomics/missed_cleavage_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/missed_cleavage_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py b/scripts/proteomics/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py new file mode 100644 index 0000000..7915793 --- /dev/null +++ b/scripts/proteomics/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py @@ -0,0 +1,49 @@ +"""Tests for missed_cleavage_analyzer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMissedCleavageAnalyzer: + def test_no_missed_cleavages(self): + from missed_cleavage_analyzer import count_missed_cleavages + + # Trypsin cleaves after K/R; a peptide ending in K with no internal K/R + mc = count_missed_cleavages("PEPTIDEK", "Trypsin") + assert mc == 0 + + def test_one_missed_cleavage(self): + from missed_cleavage_analyzer import count_missed_cleavages + + # Internal K followed by more residues then ending in K + mc = count_missed_cleavages("PEPKIDEK", "Trypsin") + assert mc == 1 + + def test_two_missed_cleavages(self): + from missed_cleavage_analyzer import count_missed_cleavages + + mc = count_missed_cleavages("PEPKIDRKEK", "Trypsin") + assert mc >= 2 + + def test_analyze_distribution(self): + from missed_cleavage_analyzer import analyze_missed_cleavages + + peptides = ["PEPTIDEK", "PEPKIDEK", "ANOTHERSEQ"] + analysis = analyze_missed_cleavages(peptides, "Trypsin") + assert analysis["total_peptides"] == 3 + assert analysis["enzyme"] == "Trypsin" + assert "distribution" in analysis + assert len(analysis["peptide_results"]) == 3 + + def test_average_mc(self): + from missed_cleavage_analyzer import analyze_missed_cleavages + + peptides = ["PEPTIDEK", "PEPTIDEK"] + analysis = analyze_missed_cleavages(peptides, "Trypsin") + assert analysis["average_missed_cleavages"] == 0.0 + + def test_empty_list(self): + from missed_cleavage_analyzer import analyze_missed_cleavages + + analysis = analyze_missed_cleavages([], "Trypsin") + assert analysis["total_peptides"] == 0 diff --git a/scripts/proteomics/missing_value_imputation/README.md b/scripts/proteomics/missing_value_imputation/README.md new file mode 100644 index 0000000..cb4a616 --- /dev/null +++ b/scripts/proteomics/missing_value_imputation/README.md @@ -0,0 +1,17 @@ +# Missing Value Imputation + +Impute missing values in quantification matrices using MinDet, MinProb, or KNN methods. + +## Usage + +```bash +python missing_value_imputation.py --input matrix.tsv --method mindet --output imputed.tsv +python missing_value_imputation.py --input matrix.tsv --method knn --k 5 --output imputed.tsv +python missing_value_imputation.py --input matrix.tsv --method minprob --output imputed.tsv +``` + +## Methods + +- **mindet** - Replace missing values with the minimum detected value per column +- **minprob** - Random draws from a low-intensity Gaussian distribution +- **knn** - K-nearest-neighbor imputation using observed features diff --git a/scripts/proteomics/missing_value_imputation/missing_value_imputation.py b/scripts/proteomics/missing_value_imputation/missing_value_imputation.py new file mode 100644 index 0000000..a878f10 --- /dev/null +++ b/scripts/proteomics/missing_value_imputation/missing_value_imputation.py @@ -0,0 +1,245 @@ +""" +Missing Value Imputation +======================== +Impute missing values in quantification matrices using various strategies. + +Supported methods: +- MinDet: Replace missing values with the minimum detected value per column. +- MinProb: Replace missing values with random draws from a low-intensity + Gaussian distribution (1st percentile). +- KNN: K-nearest-neighbor imputation using available features. + +Usage +----- + python missing_value_imputation.py --input matrix.tsv --method mindet --output imputed.tsv + python missing_value_imputation.py --input matrix.tsv --method knn --k 5 --output imputed.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + + +def read_matrix(filepath: str) -> tuple: + """Read a TSV quantification matrix. + + Parameters + ---------- + filepath: + Path to TSV file. First column is row IDs, first row is header. + + Returns + ------- + tuple + (row_ids, col_names, data_matrix) where data_matrix is a numpy array. + """ + with open(filepath) as fh: + reader = csv.reader(fh, delimiter="\t") + header = next(reader) + col_names = header[1:] + row_ids = [] + rows = [] + for row in reader: + row_ids.append(row[0]) + values = [] + for v in row[1:]: + if v.strip() == "" or v.strip().upper() == "NA" or v.strip().upper() == "NAN": + values.append(np.nan) + else: + values.append(float(v)) + rows.append(values) + return row_ids, col_names, np.array(rows, dtype=float) + + +def write_matrix(filepath: str, row_ids: list, col_names: list, matrix: np.ndarray) -> None: + """Write a quantification matrix to TSV.""" + with open(filepath, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow([""] + col_names) + for i, row_id in enumerate(row_ids): + writer.writerow([row_id] + [f"{v:.6f}" for v in matrix[i]]) + + +def impute_mindet(matrix: np.ndarray) -> np.ndarray: + """Impute missing values with minimum detected value per column. + + Parameters + ---------- + matrix: + 2D numpy array with NaN for missing values. + + Returns + ------- + np.ndarray + Imputed matrix. + """ + result = matrix.copy() + for col in range(result.shape[1]): + col_data = result[:, col] + valid = col_data[~np.isnan(col_data)] + if len(valid) > 0: + min_val = np.min(valid) + else: + min_val = 0.0 + col_data[np.isnan(col_data)] = min_val + return result + + +def impute_minprob(matrix: np.ndarray, q: float = 0.01, downshift: float = 1.8) -> np.ndarray: + """Impute missing values from a low-intensity Gaussian distribution. + + For each column, draws from N(mean - downshift*sd, sd*0.3) where mean and + sd are computed from the qth percentile of observed values. + + Parameters + ---------- + matrix: + 2D numpy array with NaN for missing values. + q: + Quantile for determining the low-intensity distribution center. + downshift: + Number of standard deviations to shift the mean down. + + Returns + ------- + np.ndarray + Imputed matrix. + """ + result = matrix.copy() + rng = np.random.default_rng(42) + for col in range(result.shape[1]): + col_data = result[:, col] + valid = col_data[~np.isnan(col_data)] + if len(valid) == 0: + continue + mean_val = np.mean(valid) + sd_val = np.std(valid) if len(valid) > 1 else mean_val * 0.1 + imp_mean = mean_val - downshift * sd_val + imp_sd = sd_val * 0.3 + n_missing = np.sum(np.isnan(col_data)) + if n_missing > 0: + imputed_vals = rng.normal(imp_mean, max(imp_sd, 1e-10), int(n_missing)) + col_data[np.isnan(col_data)] = imputed_vals + return result + + +def impute_knn(matrix: np.ndarray, k: int = 5) -> np.ndarray: + """Impute missing values using K-nearest-neighbor approach. + + For each row with missing values, find the k most similar rows (by + Euclidean distance on shared observed features) and use their mean + for imputation. + + Parameters + ---------- + matrix: + 2D numpy array with NaN for missing values. + k: + Number of neighbors to use. + + Returns + ------- + np.ndarray + Imputed matrix. + """ + result = matrix.copy() + n_rows = result.shape[0] + + for i in range(n_rows): + missing_mask = np.isnan(result[i]) + if not np.any(missing_mask): + continue + + observed_mask = ~missing_mask + if not np.any(observed_mask): + # All missing: use column means + for col in np.where(missing_mask)[0]: + col_valid = result[:, col][~np.isnan(result[:, col])] + result[i, col] = np.mean(col_valid) if len(col_valid) > 0 else 0.0 + continue + + # Find distances to other rows using shared observed features + distances = [] + for j in range(n_rows): + if i == j: + continue + shared = observed_mask & ~np.isnan(result[j]) + if np.sum(shared) == 0: + continue + dist = np.sqrt(np.sum((result[i, shared] - result[j, shared]) ** 2)) + distances.append((j, dist)) + + if not distances: + continue + + distances.sort(key=lambda x: x[1]) + neighbors = [idx for idx, _ in distances[:k]] + + for col in np.where(missing_mask)[0]: + neighbor_vals = [result[j, col] for j in neighbors if not np.isnan(result[j, col])] + if neighbor_vals: + result[i, col] = np.mean(neighbor_vals) + else: + col_valid = result[:, col][~np.isnan(result[:, col])] + result[i, col] = np.mean(col_valid) if len(col_valid) > 0 else 0.0 + + return result + + +def impute(matrix: np.ndarray, method: str = "mindet", **kwargs) -> np.ndarray: + """Impute missing values using the specified method. + + Parameters + ---------- + matrix: + 2D numpy array with NaN for missing values. + method: + One of 'mindet', 'minprob', 'knn'. + + Returns + ------- + np.ndarray + Imputed matrix. + """ + method = method.lower() + if method == "mindet": + return impute_mindet(matrix) + elif method == "minprob": + return impute_minprob(matrix, **kwargs) + elif method == "knn": + k = kwargs.get("k", 5) + return impute_knn(matrix, k=k) + else: + raise ValueError(f"Unknown imputation method: '{method}'. Choose from: mindet, minprob, knn") + + +def main(): + parser = argparse.ArgumentParser(description="Impute missing values in quantification matrices.") + parser.add_argument("--input", required=True, help="Input TSV matrix file") + parser.add_argument("--method", required=True, choices=["mindet", "minprob", "knn"], + help="Imputation method") + parser.add_argument("--k", type=int, default=5, help="Number of neighbors for KNN (default: 5)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + row_ids, col_names, matrix = read_matrix(args.input) + n_missing_before = int(np.sum(np.isnan(matrix))) + imputed = impute(matrix, method=args.method, k=args.k) + n_missing_after = int(np.sum(np.isnan(imputed))) + write_matrix(args.output, row_ids, col_names, imputed) + + print(f"Method: {args.method}") + print(f"Missing values before: {n_missing_before}") + print(f"Missing values after: {n_missing_after}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/missing_value_imputation/requirements.txt b/scripts/proteomics/missing_value_imputation/requirements.txt new file mode 100644 index 0000000..ba577e4 --- /dev/null +++ b/scripts/proteomics/missing_value_imputation/requirements.txt @@ -0,0 +1,3 @@ +pyopenms +numpy +scipy diff --git a/scripts/proteomics/missing_value_imputation/tests/conftest.py b/scripts/proteomics/missing_value_imputation/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/missing_value_imputation/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/missing_value_imputation/tests/test_missing_value_imputation.py b/scripts/proteomics/missing_value_imputation/tests/test_missing_value_imputation.py new file mode 100644 index 0000000..97b1863 --- /dev/null +++ b/scripts/proteomics/missing_value_imputation/tests/test_missing_value_imputation.py @@ -0,0 +1,100 @@ +"""Tests for missing_value_imputation.""" + + +import numpy as np +import pytest +from conftest import requires_pyopenms +from missing_value_imputation import ( + impute, + impute_knn, + impute_mindet, + impute_minprob, + read_matrix, + write_matrix, +) + + +@requires_pyopenms +class TestMissingValueImputation: + def _make_matrix_with_missing(self): + matrix = np.array([ + [100.0, 200.0, np.nan], + [150.0, np.nan, 300.0], + [120.0, 180.0, 280.0], + [np.nan, 210.0, 310.0], + ]) + return matrix + + def test_mindet_no_nans(self): + matrix = self._make_matrix_with_missing() + result = impute_mindet(matrix) + assert not np.any(np.isnan(result)) + + def test_mindet_values(self): + matrix = self._make_matrix_with_missing() + result = impute_mindet(matrix) + # Column 0 min = 100, Column 1 min = 180, Column 2 min = 280 + assert result[3, 0] == 100.0 + assert result[1, 1] == 180.0 + assert result[0, 2] == 280.0 + + def test_minprob_no_nans(self): + matrix = self._make_matrix_with_missing() + result = impute_minprob(matrix) + assert not np.any(np.isnan(result)) + + def test_minprob_values_lower(self): + matrix = self._make_matrix_with_missing() + result = impute_minprob(matrix) + # Imputed values should generally be lower than the mean + col0_mean = np.nanmean(matrix[:, 0]) + assert result[3, 0] < col0_mean + + def test_knn_no_nans(self): + matrix = self._make_matrix_with_missing() + result = impute_knn(matrix, k=2) + assert not np.any(np.isnan(result)) + + def test_knn_reasonable_values(self): + matrix = self._make_matrix_with_missing() + result = impute_knn(matrix, k=2) + # Imputed values should be within the range of observed values + assert 50 < result[3, 0] < 500 + assert 50 < result[1, 1] < 500 + + def test_impute_dispatch(self): + matrix = self._make_matrix_with_missing() + for method in ["mindet", "minprob", "knn"]: + result = impute(matrix, method=method) + assert not np.any(np.isnan(result)) + + def test_unknown_method(self): + matrix = self._make_matrix_with_missing() + with pytest.raises(ValueError, match="Unknown imputation method"): + impute(matrix, method="invalid") + + def test_read_write_roundtrip(self, tmp_path): + row_ids = ["prot1", "prot2"] + col_names = ["sample1", "sample2"] + matrix = np.array([[100.0, 200.0], [300.0, 400.0]]) + outfile = str(tmp_path / "test.tsv") + write_matrix(outfile, row_ids, col_names, matrix) + r_ids, c_names, r_matrix = read_matrix(outfile) + assert r_ids == row_ids + assert c_names == col_names + np.testing.assert_allclose(r_matrix, matrix, atol=0.01) + + def test_read_with_missing(self, tmp_path): + outfile = str(tmp_path / "missing.tsv") + with open(outfile, "w") as fh: + fh.write("\tsample1\tsample2\n") + fh.write("prot1\t100.0\tNA\n") + fh.write("prot2\t\t200.0\n") + _, _, matrix = read_matrix(outfile) + assert np.isnan(matrix[0, 1]) + assert np.isnan(matrix[1, 0]) + + def test_no_missing_unchanged(self): + matrix = np.array([[1.0, 2.0], [3.0, 4.0]]) + result = impute_mindet(matrix) + np.testing.assert_array_equal(result, matrix) diff --git a/scripts/proteomics/modification_mass_calculator/README.md b/scripts/proteomics/modification_mass_calculator/README.md new file mode 100644 index 0000000..928c9d1 --- /dev/null +++ b/scripts/proteomics/modification_mass_calculator/README.md @@ -0,0 +1,11 @@ +# Modification Mass Calculator + +Query Unimod modifications by name or mass shift. Compute modified peptide masses. + +## Usage + +```bash +python modification_mass_calculator.py --search-mod Phospho +python modification_mass_calculator.py --list-mods +python modification_mass_calculator.py --sequence PEPTIDEK --modifications "Oxidation(M):4" +``` diff --git a/scripts/proteomics/modification_mass_calculator/modification_mass_calculator.py b/scripts/proteomics/modification_mass_calculator/modification_mass_calculator.py new file mode 100644 index 0000000..b84a28f --- /dev/null +++ b/scripts/proteomics/modification_mass_calculator/modification_mass_calculator.py @@ -0,0 +1,179 @@ +""" +Modification Mass Calculator +============================= +Query Unimod modifications by name or mass shift. Compute modified peptide masses. + +Features +-------- +- Search modifications by name in the Unimod database +- List all available modifications +- Calculate mass of modified peptides +- Report delta mass for modifications + +Usage +----- + python modification_mass_calculator.py --search-mod Phospho + python modification_mass_calculator.py --list-mods + python modification_mass_calculator.py --sequence PEPTIDEK --modifications "Oxidation(M):4" +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def search_modification(name: str) -> list: + """Search for a modification by name in the ModificationsDB. + + Parameters + ---------- + name : str + Modification name to search for (e.g., 'Phospho', 'Oxidation'). + + Returns + ------- + list + List of dicts with modification details. + """ + mod_db = oms.ModificationsDB() + mod_names = [] + mod_db.searchModifications(mod_names, name, "", oms.ResidueModification.TermSpecificity.ANYWHERE) + results = [] + seen = set() + for mod_name in mod_names: + mod = mod_db.getModification(mod_name) + full_id = mod.getFullId() + if full_id in seen: + continue + seen.add(full_id) + results.append({ + "full_id": full_id, + "name": mod.getId(), + "delta_mass": round(mod.getDiffMonoMass(), 6), + "origin": mod.getOrigin(), + }) + return results + + +def list_common_modifications() -> list: + """List commonly used modifications. + + Returns + ------- + list + List of dicts with common modification info. + """ + common = [ + "Oxidation", "Carbamidomethyl", "Phospho", "Acetyl", "Deamidated", + "Methyl", "Dimethyl", "Trimethyl", "Sulfo", "Nitro", + ] + results = [] + for name in common: + mods = search_modification(name) + results.extend(mods) + return results + + +def modified_peptide_mass(sequence: str, modifications: str = "", charge: int = 1) -> dict: + """Calculate the mass of a modified peptide. + + Parameters + ---------- + sequence : str + Base peptide sequence. + modifications : str + Comma-separated modifications in format 'ModName(Residue):position', + e.g., 'Oxidation(M):4,Phospho(S):7'. + charge : int + Charge state for m/z calculation. + + Returns + ------- + dict + Dictionary with mass information. + """ + if modifications: + # Build modified sequence string + seq_list = list(sequence) + mod_entries = [] + for mod_str in modifications.split(","): + mod_str = mod_str.strip() + if ":" in mod_str: + mod_name, pos_str = mod_str.rsplit(":", 1) + pos = int(pos_str) - 1 # convert to 0-based + mod_entries.append((pos, mod_name)) + + # Sort by position descending to insert from right to left + mod_entries.sort(key=lambda x: x[0], reverse=True) + for pos, mod_name in mod_entries: + # Extract just the mod name without residue for bracket notation + base_name = mod_name.split("(")[0] if "(" in mod_name else mod_name + seq_list[pos] = seq_list[pos] + f"({base_name})" + + modified_seq = "".join(seq_list) + else: + modified_seq = sequence + + aa_seq = oms.AASequence.fromString(modified_seq) + mono = aa_seq.getMonoWeight() + mz = (mono + charge * PROTON) / charge + + return { + "sequence": sequence, + "modified_sequence": aa_seq.toString(), + "charge": charge, + "monoisotopic_mass": round(mono, 6), + "mz": round(mz, 6), + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Query Unimod modifications and compute modified peptide masses.") + parser.add_argument("--search-mod", type=str, help="Search for a modification by name.") + parser.add_argument("--list-mods", action="store_true", help="List common modifications.") + parser.add_argument("--sequence", type=str, help="Peptide sequence for mass calculation.") + parser.add_argument("--modifications", type=str, default="", help="Modifications (e.g., 'Oxidation(M):4').") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") + parser.add_argument("--output", type=str, help="Output file.") + args = parser.parse_args() + + if args.list_mods: + results = list_common_modifications() + if args.output: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["full_id", "name", "delta_mass", "origin"], delimiter="\t") + writer.writeheader() + writer.writerows(results) + else: + for r in results: + print(f"{r['name']}\t{r['delta_mass']}\t{r['origin']}\t{r['full_id']}") + elif args.search_mod: + results = search_modification(args.search_mod) + if args.output: + with open(args.output, "w") as fh: + json.dump(results, fh, indent=2) + else: + for r in results: + print(f"{r['name']}\t{r['delta_mass']}\t{r['origin']}\t{r['full_id']}") + elif args.sequence: + result = modified_peptide_mass(args.sequence, args.modifications, args.charge) + if args.output: + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + else: + print(json.dumps(result, indent=2)) + else: + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/modification_mass_calculator/requirements.txt b/scripts/proteomics/modification_mass_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/modification_mass_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/modification_mass_calculator/tests/conftest.py b/scripts/proteomics/modification_mass_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/modification_mass_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/modification_mass_calculator/tests/test_modification_mass_calculator.py b/scripts/proteomics/modification_mass_calculator/tests/test_modification_mass_calculator.py new file mode 100644 index 0000000..e3393f2 --- /dev/null +++ b/scripts/proteomics/modification_mass_calculator/tests/test_modification_mass_calculator.py @@ -0,0 +1,52 @@ +"""Tests for modification_mass_calculator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestModificationMassCalculator: + def test_search_oxidation(self): + from modification_mass_calculator import search_modification + + results = search_modification("Oxidation") + assert len(results) > 0 + names = [r["name"] for r in results] + assert "Oxidation" in names + + def test_search_phospho(self): + from modification_mass_calculator import search_modification + + results = search_modification("Phospho") + assert len(results) > 0 + # Phospho should have ~79.966 Da delta mass + masses = [r["delta_mass"] for r in results] + assert any(79.9 < m < 80.0 for m in masses) + + def test_list_common_modifications(self): + from modification_mass_calculator import list_common_modifications + + results = list_common_modifications() + assert len(results) > 5 + + def test_modified_peptide_mass_unmodified(self): + from modification_mass_calculator import modified_peptide_mass + + result = modified_peptide_mass("PEPTIDEK") + assert result["monoisotopic_mass"] > 900 + + def test_modified_peptide_mass_with_mod(self): + from modification_mass_calculator import modified_peptide_mass + + unmod = modified_peptide_mass("PEPTMIDEK") + mod = modified_peptide_mass("PEPTMIDEK", "Oxidation(M):5") + # Oxidation adds ~16 Da + assert mod["monoisotopic_mass"] > unmod["monoisotopic_mass"] + diff = mod["monoisotopic_mass"] - unmod["monoisotopic_mass"] + assert 15.9 < diff < 16.1 + + def test_charge_state(self): + from modification_mass_calculator import modified_peptide_mass + + r1 = modified_peptide_mass("PEPTIDEK", charge=1) + r2 = modified_peptide_mass("PEPTIDEK", charge=2) + assert r2["mz"] < r1["mz"] diff --git a/scripts/proteomics/modified_peptide_generator/README.md b/scripts/proteomics/modified_peptide_generator/README.md new file mode 100644 index 0000000..c3e2fb7 --- /dev/null +++ b/scripts/proteomics/modified_peptide_generator/README.md @@ -0,0 +1,10 @@ +# Modified Peptide Generator + +Generate all modified peptide variants for given variable and fixed modifications. + +## Usage + +```bash +python modified_peptide_generator.py --sequence PEPTMIDEK --variable-mods Oxidation --max-mods 2 +python modified_peptide_generator.py --sequence PEPTMIDEK --variable-mods Oxidation,Phospho --output variants.tsv +``` diff --git a/scripts/proteomics/modified_peptide_generator/modified_peptide_generator.py b/scripts/proteomics/modified_peptide_generator/modified_peptide_generator.py new file mode 100644 index 0000000..619f91c --- /dev/null +++ b/scripts/proteomics/modified_peptide_generator/modified_peptide_generator.py @@ -0,0 +1,191 @@ +""" +Modified Peptide Generator +=========================== +Generate all modified peptide variants for given variable and fixed modifications. + +Features +-------- +- Enumerate all possible modification combinations +- Support variable and fixed modifications +- Limit maximum number of simultaneous modifications +- Output modified sequences with masses + +Usage +----- + python modified_peptide_generator.py --sequence PEPTMIDEK --variable-mods Oxidation --max-mods 2 + python modified_peptide_generator.py --sequence PEPTMIDEK --variable-mods Oxidation,Phospho --output variants.tsv +""" + +import argparse +import csv +import json +import sys +from itertools import combinations + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Mapping of common modification names to their applicable residues +MOD_RESIDUE_MAP = { + "Oxidation": ["M", "W"], + "Phospho": ["S", "T", "Y"], + "Carbamidomethyl": ["C"], + "Acetyl": ["K"], + "Deamidated": ["N", "Q"], + "Methyl": ["K", "R"], + "Dimethyl": ["K", "R"], +} + + +def find_modifiable_sites(sequence: str, mod_name: str) -> list: + """Find all positions in a sequence where a modification can be applied. + + Parameters + ---------- + sequence : str + Plain amino acid sequence. + mod_name : str + Modification name (e.g., 'Oxidation'). + + Returns + ------- + list + List of (position, residue) tuples (1-based positions). + """ + applicable_residues = MOD_RESIDUE_MAP.get(mod_name, []) + if not applicable_residues: + # Try to get from ModificationsDB + mod_db = oms.ModificationsDB() + mod_names_list = [] + mod_db.searchModifications(mod_names_list, mod_name, "", oms.ResidueModification.TermSpecificity.ANYWHERE) + for mn in mod_names_list: + mod = mod_db.getModification(mn) + origin = mod.getOrigin() + if origin and origin not in applicable_residues: + applicable_residues.append(origin) + + sites = [] + for i, aa in enumerate(sequence): + if aa in applicable_residues: + sites.append((i + 1, aa)) # 1-based position + return sites + + +def generate_variants(sequence: str, variable_mods: list, fixed_mods: list = None, + max_mods: int = 2, charge: int = 1) -> list: + """Generate all modified peptide variants. + + Parameters + ---------- + sequence : str + Plain amino acid sequence. + variable_mods : list + List of variable modification names. + fixed_mods : list + List of fixed modification names (applied to all applicable sites). + max_mods : int + Maximum number of simultaneous variable modifications. + charge : int + Charge state for m/z calculation. + + Returns + ------- + list + List of dicts with variant information. + """ + if fixed_mods is None: + fixed_mods = [] + + # Apply fixed modifications first + base_mods = {} + for mod_name in fixed_mods: + sites = find_modifiable_sites(sequence, mod_name) + for pos, _residue in sites: + base_mods[pos] = mod_name + + # Collect all variable modification sites + var_sites = [] + for mod_name in variable_mods: + sites = find_modifiable_sites(sequence, mod_name) + for pos, residue in sites: + if pos not in base_mods: + var_sites.append((pos, residue, mod_name)) + + variants = [] + # Generate combinations of variable mods (0 to max_mods) + for n in range(0, min(max_mods, len(var_sites)) + 1): + for combo in combinations(var_sites, n): + mod_dict = dict(base_mods) + for pos, _residue, mod_name in combo: + mod_dict[pos] = mod_name + + # Build modified sequence string + seq_chars = list(sequence) + for pos in sorted(mod_dict.keys(), reverse=True): + mod_name = mod_dict[pos] + base_name = mod_name.split("(")[0] if "(" in mod_name else mod_name + seq_chars[pos - 1] = seq_chars[pos - 1] + f"({base_name})" + modified_seq = "".join(seq_chars) + + aa_seq = oms.AASequence.fromString(modified_seq) + mono = aa_seq.getMonoWeight() + mz = (mono + charge * PROTON) / charge + + mod_descriptions = [] + for pos in sorted(mod_dict.keys()): + mod_descriptions.append(f"{mod_dict[pos]}@{pos}") + + variants.append({ + "sequence": sequence, + "modified_sequence": aa_seq.toString(), + "modifications": ";".join(mod_descriptions) if mod_descriptions else "none", + "num_modifications": len(mod_dict), + "monoisotopic_mass": round(mono, 6), + "mz": round(mz, 6), + "charge": charge, + }) + + return variants + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Generate modified peptide variants.") + parser.add_argument("--sequence", required=True, help="Peptide sequence.") + parser.add_argument("--variable-mods", type=str, default="", + help="Comma-separated variable modification names.") + parser.add_argument("--fixed-mods", type=str, default="", + help="Comma-separated fixed modification names.") + parser.add_argument("--max-mods", type=int, default=2, help="Maximum simultaneous variable mods (default: 2).") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") + parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") + args = parser.parse_args() + + var_mods = [m.strip() for m in args.variable_mods.split(",") if m.strip()] + fix_mods = [m.strip() for m in args.fixed_mods.split(",") if m.strip()] + + variants = generate_variants(args.sequence, var_mods, fix_mods, args.max_mods, args.charge) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(variants, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + fieldnames = ["sequence", "modified_sequence", "modifications", "num_modifications", + "monoisotopic_mass", "mz", "charge"] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(variants) + print(f"Generated {len(variants)} variants -> {args.output}") + else: + for v in variants: + print(f"{v['modified_sequence']}\t{v['modifications']}\t{v['monoisotopic_mass']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/modified_peptide_generator/requirements.txt b/scripts/proteomics/modified_peptide_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/modified_peptide_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/modified_peptide_generator/tests/conftest.py b/scripts/proteomics/modified_peptide_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/modified_peptide_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/modified_peptide_generator/tests/test_modified_peptide_generator.py b/scripts/proteomics/modified_peptide_generator/tests/test_modified_peptide_generator.py new file mode 100644 index 0000000..1680544 --- /dev/null +++ b/scripts/proteomics/modified_peptide_generator/tests/test_modified_peptide_generator.py @@ -0,0 +1,51 @@ +"""Tests for modified_peptide_generator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestModifiedPeptideGenerator: + def test_no_mods(self): + from modified_peptide_generator import generate_variants + + variants = generate_variants("PEPTIDEK", [], []) + assert len(variants) == 1 + assert variants[0]["modifications"] == "none" + + def test_oxidation_on_methionine(self): + from modified_peptide_generator import generate_variants + + variants = generate_variants("PEPTMIDEK", ["Oxidation"], max_mods=1) + # Should have unmodified + 1 oxidized variant + assert len(variants) == 2 + masses = [v["monoisotopic_mass"] for v in variants] + assert masses[1] > masses[0] # Oxidation adds mass + + def test_max_mods_limit(self): + from modified_peptide_generator import generate_variants + + # Two M residues, max 1 mod + variants = generate_variants("MMPEPTIDEK", ["Oxidation"], max_mods=1) + max_mod_count = max(v["num_modifications"] for v in variants) + assert max_mod_count <= 1 + + def test_find_modifiable_sites(self): + from modified_peptide_generator import find_modifiable_sites + + sites = find_modifiable_sites("PEPTMIDEK", "Oxidation") + assert len(sites) == 1 + assert sites[0] == (5, "M") + + def test_fixed_mods(self): + from modified_peptide_generator import generate_variants + + variants = generate_variants("CPEPTIDEK", [], fixed_mods=["Carbamidomethyl"]) + assert len(variants) == 1 + assert variants[0]["num_modifications"] == 1 + + def test_multiple_variable_mods(self): + from modified_peptide_generator import generate_variants + + variants = generate_variants("MSPEPTIDEK", ["Oxidation", "Phospho"], max_mods=2) + # M can be oxidized, S can be phosphorylated + assert len(variants) >= 3 # unmodified, ox-only, phos-only, both diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/README.md b/scripts/proteomics/ms1_feature_intensity_tracker/README.md new file mode 100644 index 0000000..44b9cf1 --- /dev/null +++ b/scripts/proteomics/ms1_feature_intensity_tracker/README.md @@ -0,0 +1,9 @@ +# MS1 Feature Intensity Tracker + +Track feature intensities across multiple mzML runs. + +## Usage + +```bash +python ms1_feature_intensity_tracker.py --inputs run1.mzML run2.mzML --features targets.tsv --ppm 10 --output tracking.tsv +``` diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py b/scripts/proteomics/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py new file mode 100644 index 0000000..adf763b --- /dev/null +++ b/scripts/proteomics/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py @@ -0,0 +1,238 @@ +""" +MS1 Feature Intensity Tracker +============================== +Track feature intensities across multiple mzML runs. Given a list of target +features (m/z + RT), extract the maximum intensity within tolerance from each +run's MS1 spectra. + +Usage +----- + python ms1_feature_intensity_tracker.py --inputs run1.mzML run2.mzML --features targets.tsv \\ + --ppm 10 --output tracking.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def load_features(features_path: str) -> List[dict]: + """Load target features from a TSV file. + + Expected columns: feature_id, mz, rt (optional). + + Parameters + ---------- + features_path: + Path to TSV file with target features. + + Returns + ------- + list + List of feature dicts. + """ + features = [] + with open(features_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + feat = { + "feature_id": row.get("feature_id", f"F{len(features)+1}"), + "mz": float(row["mz"]), + } + if "rt" in row and row["rt"].strip(): + feat["rt"] = float(row["rt"]) + else: + feat["rt"] = None + features.append(feat) + return features + + +def extract_intensity( + exp: oms.MSExperiment, + target_mz: float, + ppm: float = 10.0, + target_rt: float = None, + rt_tolerance: float = 30.0, +) -> float: + """Extract maximum intensity for a target m/z from MS1 spectra. + + Parameters + ---------- + exp: + Loaded MSExperiment. + target_mz: + Target m/z value. + ppm: + m/z tolerance in ppm. + target_rt: + Optional target RT in seconds for filtering spectra. + rt_tolerance: + RT tolerance in seconds if target_rt is provided. + + Returns + ------- + float + Maximum intensity found, or 0.0 if not detected. + """ + import numpy as np + + mz_tol = target_mz * ppm / 1e6 + max_intensity = 0.0 + + for spec in exp.getSpectra(): + if spec.getMSLevel() != 1: + continue + + if target_rt is not None: + rt_diff = abs(spec.getRT() - target_rt) + if rt_diff > rt_tolerance: + continue + + mzs, intensities = spec.get_peaks() + if len(mzs) == 0: + continue + + # Find peaks within tolerance + mask = np.abs(mzs - target_mz) <= mz_tol + if np.any(mask): + local_max = float(np.max(intensities[mask])) + if local_max > max_intensity: + max_intensity = local_max + + return max_intensity + + +def track_features( + run_paths: List[str], + features: List[dict], + ppm: float = 10.0, + rt_tolerance: float = 30.0, +) -> List[dict]: + """Track feature intensities across multiple runs. + + Parameters + ---------- + run_paths: + List of mzML file paths. + features: + List of target feature dicts. + ppm: + m/z tolerance in ppm. + rt_tolerance: + RT tolerance in seconds. + + Returns + ------- + list + List of result dicts with feature info and per-run intensities. + """ + results = [] + for feat in features: + row = { + "feature_id": feat["feature_id"], + "mz": feat["mz"], + "rt": feat["rt"] if feat["rt"] is not None else "N/A", + } + for run_path in run_paths: + exp = oms.MSExperiment() + oms.MzMLFile().load(run_path, exp) + intensity = extract_intensity( + exp, feat["mz"], ppm=ppm, + target_rt=feat["rt"], rt_tolerance=rt_tolerance, + ) + row[run_path] = round(intensity, 4) + results.append(row) + return results + + +def track_features_from_experiments( + experiments: Dict[str, oms.MSExperiment], + features: List[dict], + ppm: float = 10.0, + rt_tolerance: float = 30.0, +) -> List[dict]: + """Track feature intensities across pre-loaded experiments. + + Parameters + ---------- + experiments: + Dict mapping run name to loaded MSExperiment. + features: + List of target feature dicts. + ppm: + m/z tolerance in ppm. + rt_tolerance: + RT tolerance in seconds. + + Returns + ------- + list + List of result dicts. + """ + results = [] + for feat in features: + row = { + "feature_id": feat["feature_id"], + "mz": feat["mz"], + "rt": feat["rt"] if feat["rt"] is not None else "N/A", + } + for run_name, exp in experiments.items(): + intensity = extract_intensity( + exp, feat["mz"], ppm=ppm, + target_rt=feat["rt"], rt_tolerance=rt_tolerance, + ) + row[run_name] = round(intensity, 4) + results.append(row) + return results + + +def write_tsv(results: List[dict], output_path: str) -> None: + """Write tracking results to TSV. + + Parameters + ---------- + results: + List of result dicts. + output_path: + Output file path. + """ + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Track feature intensities across multiple mzML runs." + ) + parser.add_argument("--inputs", nargs="+", required=True, help="Input mzML files") + parser.add_argument("--features", required=True, help="Target features TSV (feature_id, mz, rt)") + parser.add_argument("--ppm", type=float, default=10.0, help="m/z tolerance in ppm (default: 10)") + parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in sec (default: 30)") + parser.add_argument("--output", required=True, help="Output TSV file path") + args = parser.parse_args() + + features = load_features(args.features) + print(f"Loaded {len(features)} target features") + + results = track_features(args.inputs, features, ppm=args.ppm, rt_tolerance=args.rt_tolerance) + + write_tsv(results, args.output) + print(f"Tracking results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/requirements.txt b/scripts/proteomics/ms1_feature_intensity_tracker/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/ms1_feature_intensity_tracker/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/tests/conftest.py b/scripts/proteomics/ms1_feature_intensity_tracker/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/ms1_feature_intensity_tracker/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py b/scripts/proteomics/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py new file mode 100644 index 0000000..fb6cab6 --- /dev/null +++ b/scripts/proteomics/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py @@ -0,0 +1,94 @@ +"""Tests for ms1_feature_intensity_tracker.""" + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMs1FeatureIntensityTracker: + def _make_experiment(self, target_mz=500.0, target_rt=60.0, intensity=10000.0): + """Create a synthetic MSExperiment with a known peak.""" + import pyopenms as oms + + exp = oms.MSExperiment() + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(target_rt) + mzs = np.array([target_mz - 50, target_mz, target_mz + 50], dtype=np.float64) + ints = np.array([100.0, intensity, 200.0], dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + return exp + + def test_extract_intensity(self): + from ms1_feature_intensity_tracker import extract_intensity + + exp = self._make_experiment(target_mz=500.0, intensity=5000.0) + result = extract_intensity(exp, 500.0, ppm=10.0) + assert result == 5000.0 + + def test_extract_intensity_with_rt(self): + from ms1_feature_intensity_tracker import extract_intensity + + exp = self._make_experiment(target_mz=500.0, target_rt=60.0, intensity=5000.0) + # Within RT tolerance + result = extract_intensity(exp, 500.0, ppm=10.0, target_rt=60.0, rt_tolerance=30.0) + assert result == 5000.0 + # Outside RT tolerance + result = extract_intensity(exp, 500.0, ppm=10.0, target_rt=200.0, rt_tolerance=30.0) + assert result == 0.0 + + def test_extract_intensity_not_found(self): + from ms1_feature_intensity_tracker import extract_intensity + + exp = self._make_experiment(target_mz=500.0) + result = extract_intensity(exp, 999.0, ppm=10.0) + assert result == 0.0 + + def test_track_features_from_experiments(self): + from ms1_feature_intensity_tracker import track_features_from_experiments + + exp1 = self._make_experiment(target_mz=500.0, intensity=1000.0) + exp2 = self._make_experiment(target_mz=500.0, intensity=2000.0) + + features = [{"feature_id": "F1", "mz": 500.0, "rt": None}] + results = track_features_from_experiments( + {"run1": exp1, "run2": exp2}, features, ppm=10.0, + ) + assert len(results) == 1 + assert results[0]["run1"] == 1000.0 + assert results[0]["run2"] == 2000.0 + + def test_load_features(self, tmp_path): + from ms1_feature_intensity_tracker import load_features + + feat_path = str(tmp_path / "features.tsv") + with open(feat_path, "w") as fh: + fh.write("feature_id\tmz\trt\n") + fh.write("F1\t500.0\t60.0\n") + fh.write("F2\t600.0\t\n") + + features = load_features(feat_path) + assert len(features) == 2 + assert features[0]["mz"] == 500.0 + assert features[0]["rt"] == 60.0 + assert features[1]["rt"] is None + + def test_write_tsv(self, tmp_path): + from ms1_feature_intensity_tracker import write_tsv + + results = [{"feature_id": "F1", "mz": 500.0, "rt": 60.0, "run1": 1000.0, "run2": 2000.0}] + out = str(tmp_path / "tracking.tsv") + write_tsv(results, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 + assert "feature_id" in lines[0] + + def test_empty_experiment(self): + import pyopenms as oms + from ms1_feature_intensity_tracker import extract_intensity + + exp = oms.MSExperiment() + result = extract_intensity(exp, 500.0, ppm=10.0) + assert result == 0.0 diff --git a/scripts/proteomics/ms_data_ml_exporter/README.md b/scripts/proteomics/ms_data_ml_exporter/README.md new file mode 100644 index 0000000..14c5eb4 --- /dev/null +++ b/scripts/proteomics/ms_data_ml_exporter/README.md @@ -0,0 +1,9 @@ +# MS Data ML Exporter + +Export MS features from mzML files as machine-learning-ready CSV matrices. + +## Usage + +```bash +python ms_data_ml_exporter.py --input run.mzML --output ml_matrix.csv +``` diff --git a/scripts/proteomics/ms_data_ml_exporter/ms_data_ml_exporter.py b/scripts/proteomics/ms_data_ml_exporter/ms_data_ml_exporter.py new file mode 100644 index 0000000..cb1af07 --- /dev/null +++ b/scripts/proteomics/ms_data_ml_exporter/ms_data_ml_exporter.py @@ -0,0 +1,126 @@ +""" +MS Data ML Exporter +=================== +Export MS features from mzML files as machine-learning-ready matrices (CSV). + +Extracts per-spectrum features: RT, MS level, TIC, base peak m/z, +base peak intensity, number of peaks, m/z range, and intensity statistics. + +Usage +----- + python ms_data_ml_exporter.py --input run.mzML --output ml_matrix.csv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def extract_features(exp: oms.MSExperiment) -> List[dict]: + """Extract ML-ready features from each spectrum in an MSExperiment. + + Parameters + ---------- + exp: + Loaded MSExperiment object. + + Returns + ------- + list + List of feature dicts, one per spectrum. + """ + import numpy as np + + results = [] + for i, spec in enumerate(exp.getSpectra()): + mzs, intensities = spec.get_peaks() + n_peaks = len(mzs) + rt = spec.getRT() + ms_level = spec.getMSLevel() + + if n_peaks > 0: + tic = float(np.sum(intensities)) + base_peak_idx = int(np.argmax(intensities)) + base_peak_mz = float(mzs[base_peak_idx]) + base_peak_int = float(intensities[base_peak_idx]) + mz_min = float(np.min(mzs)) + mz_max = float(np.max(mzs)) + int_mean = float(np.mean(intensities)) + int_std = float(np.std(intensities)) + int_median = float(np.median(intensities)) + else: + tic = 0.0 + base_peak_mz = 0.0 + base_peak_int = 0.0 + mz_min = 0.0 + mz_max = 0.0 + int_mean = 0.0 + int_std = 0.0 + int_median = 0.0 + + results.append({ + "spectrum_index": i, + "rt": round(rt, 4), + "ms_level": ms_level, + "n_peaks": n_peaks, + "tic": round(tic, 4), + "base_peak_mz": round(base_peak_mz, 6), + "base_peak_intensity": round(base_peak_int, 4), + "mz_min": round(mz_min, 6), + "mz_max": round(mz_max, 6), + "intensity_mean": round(int_mean, 4), + "intensity_std": round(int_std, 4), + "intensity_median": round(int_median, 4), + }) + + return results + + +def write_csv(records: List[dict], output_path: str) -> None: + """Write feature records to CSV. + + Parameters + ---------- + records: + List of feature dicts. + output_path: + Output CSV file path. + """ + if not records: + return + fieldnames = list(records[0].keys()) + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames) + writer.writeheader() + for row in records: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Export MS features as ML-ready matrices." + ) + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument("--output", required=True, help="Output CSV file path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + records = extract_features(exp) + print(f"Extracted features for {len(records)} spectra") + + write_csv(records, args.output) + print(f"ML matrix written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/ms_data_ml_exporter/requirements.txt b/scripts/proteomics/ms_data_ml_exporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/ms_data_ml_exporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/ms_data_ml_exporter/tests/conftest.py b/scripts/proteomics/ms_data_ml_exporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/ms_data_ml_exporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py b/scripts/proteomics/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py new file mode 100644 index 0000000..97dde51 --- /dev/null +++ b/scripts/proteomics/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py @@ -0,0 +1,63 @@ +"""Tests for ms_data_ml_exporter.""" + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMsDataMlExporter: + def _make_experiment(self, n_spectra=5): + """Create a synthetic MSExperiment.""" + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(n_spectra): + spec = oms.MSSpectrum() + spec.setMSLevel(1 if i % 2 == 0 else 2) + spec.setRT(60.0 * i) + mzs = np.array([100.0 + j * 10 for j in range(20)], dtype=np.float64) + intensities = np.array([1000.0 * (j + 1) for j in range(20)], dtype=np.float64) + spec.set_peaks([mzs, intensities]) + exp.addSpectrum(spec) + return exp + + def test_extract_features(self): + from ms_data_ml_exporter import extract_features + + exp = self._make_experiment(n_spectra=3) + records = extract_features(exp) + assert len(records) == 3 + assert all("rt" in r for r in records) + assert all("tic" in r for r in records) + assert all("n_peaks" in r for r in records) + + def test_feature_values(self): + from ms_data_ml_exporter import extract_features + + exp = self._make_experiment(n_spectra=1) + records = extract_features(exp) + r = records[0] + assert r["n_peaks"] == 20 + assert r["mz_min"] > 0 + assert r["base_peak_intensity"] > 0 + assert r["intensity_std"] >= 0 + + def test_empty_experiment(self): + import pyopenms as oms + from ms_data_ml_exporter import extract_features + + exp = oms.MSExperiment() + records = extract_features(exp) + assert records == [] + + def test_write_csv(self, tmp_path): + from ms_data_ml_exporter import extract_features, write_csv + + exp = self._make_experiment(n_spectra=3) + records = extract_features(exp) + out = str(tmp_path / "matrix.csv") + write_csv(records, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 4 # header + 3 rows + assert "spectrum_index" in lines[0] diff --git a/scripts/proteomics/ms_data_to_csv_exporter/README.md b/scripts/proteomics/ms_data_to_csv_exporter/README.md new file mode 100644 index 0000000..97c7857 --- /dev/null +++ b/scripts/proteomics/ms_data_to_csv_exporter/README.md @@ -0,0 +1,25 @@ +# MS Data to CSV Exporter + +Export mzML or featureXML data to flat CSV/TSV files. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Export peaks from mzML +python ms_data_to_csv_exporter.py --input data.mzML --type peaks --output peaks.tsv + +# Export MS2 peaks only +python ms_data_to_csv_exporter.py --input data.mzML --type peaks --ms-level 2 --output ms2_peaks.tsv + +# Export spectrum summaries +python ms_data_to_csv_exporter.py --input data.mzML --type spectra --output spectra.tsv + +# Export features from featureXML +python ms_data_to_csv_exporter.py --input features.featureXML --type features --output features.tsv +``` diff --git a/scripts/proteomics/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py b/scripts/proteomics/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py new file mode 100644 index 0000000..bc2efa3 --- /dev/null +++ b/scripts/proteomics/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py @@ -0,0 +1,136 @@ +""" +MS Data to CSV Exporter +======================= +Export mzML or featureXML data to flat CSV/TSV files. + +Usage +----- + python ms_data_to_csv_exporter.py --input data.mzML --type peaks --output peaks.tsv + python ms_data_to_csv_exporter.py --input features.featureXML --type features --output features.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def export_mzml_peaks(input_path: str, output_path: str, ms_level: int = 0) -> dict: + """Export peak data from an mzML file to TSV. + + Parameters + ---------- + input_path : str + Path to input mzML file. + output_path : str + Path to output TSV file. + ms_level : int + MS level to filter (0 = all levels). + + Returns + ------- + dict + Export statistics. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + total_peaks = 0 + spectra_count = 0 + + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["spectrum_index", "ms_level", "rt", "mz", "intensity"]) + + for i, spectrum in enumerate(exp): + level = spectrum.getMSLevel() + if ms_level > 0 and level != ms_level: + continue + rt = spectrum.getRT() + mz_array, intensity_array = spectrum.get_peaks() + spectra_count += 1 + for mz, intensity in zip(mz_array, intensity_array): + writer.writerow([i, level, f"{rt:.4f}", f"{mz:.6f}", f"{intensity:.4f}"]) + total_peaks += 1 + + return {"spectra_exported": spectra_count, "total_peaks": total_peaks} + + +def export_mzml_spectra_summary(input_path: str, output_path: str) -> dict: + """Export spectrum-level summary from an mzML file to TSV.""" + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["index", "native_id", "ms_level", "rt", "num_peaks", "base_peak_mz", "tic"]) + + for i, spectrum in enumerate(exp): + mz_array, intensity_array = spectrum.get_peaks() + base_peak_mz = 0.0 + tic = 0.0 + if len(intensity_array) > 0: + max_idx = intensity_array.argmax() + base_peak_mz = mz_array[max_idx] + tic = float(intensity_array.sum()) + + writer.writerow([ + i, spectrum.getNativeID(), spectrum.getMSLevel(), + f"{spectrum.getRT():.4f}", len(mz_array), + f"{base_peak_mz:.6f}", f"{tic:.4f}" + ]) + + return {"spectra_exported": exp.size()} + + +def export_featurexml(input_path: str, output_path: str) -> dict: + """Export features from a featureXML file to TSV.""" + feature_map = oms.FeatureMap() + oms.FeatureXMLFile().load(input_path, feature_map) + + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["feature_id", "rt", "mz", "intensity", "charge", "quality"]) + + for feature in feature_map: + writer.writerow([ + feature.getUniqueId(), + f"{feature.getRT():.4f}", + f"{feature.getMZ():.6f}", + f"{feature.getIntensity():.4f}", + feature.getCharge(), + f"{feature.getOverallQuality():.4f}", + ]) + + return {"features_exported": feature_map.size()} + + +def main() -> None: + parser = argparse.ArgumentParser(description="Export mzML or featureXML data to flat TSV.") + parser.add_argument("--input", required=True, help="Input file (mzML or featureXML)") + parser.add_argument( + "--type", required=True, + choices=["peaks", "spectra", "features"], + help="Export type: peaks (mzML), spectra (mzML summary), features (featureXML)" + ) + parser.add_argument("--ms-level", type=int, default=0, help="MS level filter for peaks (0=all)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + if args.type == "peaks": + stats = export_mzml_peaks(args.input, args.output, args.ms_level) + print(f"Exported {stats['total_peaks']} peaks from {stats['spectra_exported']} spectra") + elif args.type == "spectra": + stats = export_mzml_spectra_summary(args.input, args.output) + print(f"Exported {stats['spectra_exported']} spectra summaries") + elif args.type == "features": + stats = export_featurexml(args.input, args.output) + print(f"Exported {stats['features_exported']} features") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/ms_data_to_csv_exporter/requirements.txt b/scripts/proteomics/ms_data_to_csv_exporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/ms_data_to_csv_exporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/ms_data_to_csv_exporter/tests/conftest.py b/scripts/proteomics/ms_data_to_csv_exporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/ms_data_to_csv_exporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py b/scripts/proteomics/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py new file mode 100644 index 0000000..8b698b0 --- /dev/null +++ b/scripts/proteomics/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py @@ -0,0 +1,103 @@ +"""Tests for ms_data_to_csv_exporter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +def _create_test_mzml(path): + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(3): + s = oms.MSSpectrum() + s.setMSLevel(1 if i == 0 else 2) + s.setRT(10.0 + i) + s.set_peaks(([100.0 + j * 10 for j in range(5)], [1000.0 - j * 100 for j in range(5)])) + if i > 0: + prec = oms.Precursor() + prec.setMZ(500.0 + i * 50) + prec.setCharge(2) + s.setPrecursors([prec]) + exp.addSpectrum(s) + oms.MzMLFile().store(path, exp) + + +def _create_test_featurexml(path): + import pyopenms as oms + + fm = oms.FeatureMap() + for i in range(3): + f = oms.Feature() + f.setRT(100.0 + i * 10) + f.setMZ(500.0 + i * 50) + f.setIntensity(10000.0 + i * 1000) + f.setCharge(2) + f.setOverallQuality(0.9) + fm.push_back(f) + oms.FeatureXMLFile().store(path, fm) + + +@requires_pyopenms +def test_export_peaks(): + from ms_data_to_csv_exporter import export_mzml_peaks + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + tsv_path = os.path.join(tmp, "peaks.tsv") + _create_test_mzml(mzml_path) + + stats = export_mzml_peaks(mzml_path, tsv_path) + assert stats["spectra_exported"] == 3 + assert stats["total_peaks"] == 15 + + with open(tsv_path) as fh: + lines = fh.readlines() + assert lines[0].strip().startswith("spectrum_index") + assert len(lines) == 16 # header + 15 peaks + + +@requires_pyopenms +def test_export_peaks_ms_level_filter(): + from ms_data_to_csv_exporter import export_mzml_peaks + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + tsv_path = os.path.join(tmp, "peaks.tsv") + _create_test_mzml(mzml_path) + + stats = export_mzml_peaks(mzml_path, tsv_path, ms_level=2) + assert stats["spectra_exported"] == 2 + assert stats["total_peaks"] == 10 + + +@requires_pyopenms +def test_export_spectra_summary(): + from ms_data_to_csv_exporter import export_mzml_spectra_summary + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + tsv_path = os.path.join(tmp, "spectra.tsv") + _create_test_mzml(mzml_path) + + stats = export_mzml_spectra_summary(mzml_path, tsv_path) + assert stats["spectra_exported"] == 3 + + +@requires_pyopenms +def test_export_featurexml(): + from ms_data_to_csv_exporter import export_featurexml + + with tempfile.TemporaryDirectory() as tmp: + fxml_path = os.path.join(tmp, "test.featureXML") + tsv_path = os.path.join(tmp, "features.tsv") + _create_test_featurexml(fxml_path) + + stats = export_featurexml(fxml_path, tsv_path) + assert stats["features_exported"] == 3 + + with open(tsv_path) as fh: + lines = fh.readlines() + assert lines[0].strip().startswith("feature_id") + assert len(lines) == 4 diff --git a/scripts/proteomics/mzml_metadata_extractor/README.md b/scripts/proteomics/mzml_metadata_extractor/README.md new file mode 100644 index 0000000..1783f4a --- /dev/null +++ b/scripts/proteomics/mzml_metadata_extractor/README.md @@ -0,0 +1,9 @@ +# mzML Metadata Extractor + +Extract instrument metadata from mzML files and output as JSON. + +## Usage + +```bash +python mzml_metadata_extractor.py --input run.mzML --output metadata.json +``` diff --git a/scripts/proteomics/mzml_metadata_extractor/mzml_metadata_extractor.py b/scripts/proteomics/mzml_metadata_extractor/mzml_metadata_extractor.py new file mode 100644 index 0000000..0720c30 --- /dev/null +++ b/scripts/proteomics/mzml_metadata_extractor/mzml_metadata_extractor.py @@ -0,0 +1,173 @@ +""" +mzML Metadata Extractor +======================== +Extract instrument metadata from mzML files and output as JSON. + +Extracts: instrument model, source file, software, contact, MS levels, +number of spectra, RT range, and scan settings. + +Usage +----- + python mzml_metadata_extractor.py --input run.mzML --output metadata.json +""" + +import argparse +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def extract_metadata(exp: oms.MSExperiment) -> dict: + """Extract metadata from a loaded MSExperiment. + + Parameters + ---------- + exp: + Loaded MSExperiment object. + + Returns + ------- + dict + Metadata dictionary. + """ + spectra = exp.getSpectra() + n_spectra = len(spectra) + + # Collect MS levels + ms_levels = {} + rt_min = float("inf") + rt_max = float("-inf") + mz_min = float("inf") + mz_max = float("-inf") + + for spec in spectra: + level = spec.getMSLevel() + ms_levels[level] = ms_levels.get(level, 0) + 1 + rt = spec.getRT() + if rt < rt_min: + rt_min = rt + if rt > rt_max: + rt_max = rt + mzs, _ = spec.get_peaks() + if len(mzs) > 0: + local_min = float(mzs.min()) + local_max = float(mzs.max()) + if local_min < mz_min: + mz_min = local_min + if local_max > mz_max: + mz_max = local_max + + # Extract instrument info from ExperimentalSettings + instrument = exp.getInstrument() + instrument_name = instrument.getName() if instrument else "" + instrument_vendor = instrument.getVendor() if instrument else "" + instrument_model = instrument.getModel() if instrument else "" + + # Source files + source_files = [] + for sf in exp.getSourceFiles(): + source_files.append({ + "name": sf.getNameOfFile(), + "path": sf.getPathToFile(), + }) + + # Software + software_list = [] + for dp in exp.getDataProcessing(): + sw = dp.getSoftware() + software_list.append({ + "name": sw.getName(), + "version": sw.getVersion(), + }) + + metadata = { + "n_spectra": n_spectra, + "ms_levels": {str(k): v for k, v in sorted(ms_levels.items())}, + "rt_range_sec": [round(rt_min, 4), round(rt_max, 4)] if n_spectra > 0 else [], + "mz_range": [round(mz_min, 6), round(mz_max, 6)] if mz_min != float("inf") else [], + "instrument": { + "name": instrument_name, + "vendor": instrument_vendor, + "model": instrument_model, + }, + "source_files": source_files, + "software": software_list, + } + + return metadata + + +def write_json(metadata: dict, output_path: str) -> None: + """Write metadata to a JSON file. + + Parameters + ---------- + metadata: + Metadata dictionary. + output_path: + Output file path. + """ + with open(output_path, "w") as fh: + json.dump(metadata, fh, indent=2) + + +def format_metadata(metadata: dict) -> str: + """Format metadata for console output. + + Parameters + ---------- + metadata: + Metadata dictionary. + + Returns + ------- + str + Formatted string. + """ + lines = [] + lines.append(f"Total spectra : {metadata['n_spectra']}") + for level, count in metadata["ms_levels"].items(): + lines.append(f" MS{level} spectra : {count}") + if metadata["rt_range_sec"]: + rt_min, rt_max = metadata["rt_range_sec"] + lines.append(f"RT range : {rt_min:.2f} - {rt_max:.2f} s") + if metadata["mz_range"]: + mz_min, mz_max = metadata["mz_range"] + lines.append(f"m/z range : {mz_min:.4f} - {mz_max:.4f}") + inst = metadata["instrument"] + if inst["name"] or inst["vendor"] or inst["model"]: + lines.append(f"Instrument : {inst['name']} {inst['vendor']} {inst['model']}".strip()) + for sf in metadata["source_files"]: + lines.append(f"Source file : {sf['name']}") + for sw in metadata["software"]: + lines.append(f"Software : {sw['name']} {sw['version']}") + return "\n".join(lines) + + +def main(): + parser = argparse.ArgumentParser( + description="Extract instrument metadata from mzML files." + ) + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument("--output", default=None, help="Output JSON file path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + metadata = extract_metadata(exp) + print(format_metadata(metadata)) + + if args.output: + write_json(metadata, args.output) + print(f"\nMetadata written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mzml_metadata_extractor/requirements.txt b/scripts/proteomics/mzml_metadata_extractor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mzml_metadata_extractor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mzml_metadata_extractor/tests/conftest.py b/scripts/proteomics/mzml_metadata_extractor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mzml_metadata_extractor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py b/scripts/proteomics/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py new file mode 100644 index 0000000..ab8f385 --- /dev/null +++ b/scripts/proteomics/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py @@ -0,0 +1,76 @@ +"""Tests for mzml_metadata_extractor.""" + +import json + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMzmlMetadataExtractor: + def _make_experiment(self, n_spectra=5): + """Create a synthetic MSExperiment.""" + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(n_spectra): + spec = oms.MSSpectrum() + spec.setMSLevel(1 if i % 2 == 0 else 2) + spec.setRT(60.0 * i) + mzs = np.array([100.0 + j for j in range(10)], dtype=np.float64) + ints = np.array([1000.0 * (j + 1) for j in range(10)], dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + return exp + + def test_extract_metadata(self): + from mzml_metadata_extractor import extract_metadata + + exp = self._make_experiment(n_spectra=4) + meta = extract_metadata(exp) + assert meta["n_spectra"] == 4 + assert "1" in meta["ms_levels"] or "2" in meta["ms_levels"] + + def test_rt_range(self): + from mzml_metadata_extractor import extract_metadata + + exp = self._make_experiment(n_spectra=3) + meta = extract_metadata(exp) + assert len(meta["rt_range_sec"]) == 2 + assert meta["rt_range_sec"][0] == 0.0 + + def test_mz_range(self): + from mzml_metadata_extractor import extract_metadata + + exp = self._make_experiment(n_spectra=2) + meta = extract_metadata(exp) + assert len(meta["mz_range"]) == 2 + assert meta["mz_range"][0] == 100.0 + + def test_empty_experiment(self): + import pyopenms as oms + from mzml_metadata_extractor import extract_metadata + + exp = oms.MSExperiment() + meta = extract_metadata(exp) + assert meta["n_spectra"] == 0 + assert meta["rt_range_sec"] == [] + + def test_format_metadata(self): + from mzml_metadata_extractor import extract_metadata, format_metadata + + exp = self._make_experiment(n_spectra=2) + meta = extract_metadata(exp) + text = format_metadata(meta) + assert "Total spectra" in text + + def test_write_json(self, tmp_path): + from mzml_metadata_extractor import extract_metadata, write_json + + exp = self._make_experiment(n_spectra=2) + meta = extract_metadata(exp) + out = str(tmp_path / "meta.json") + write_json(meta, out) + with open(out) as fh: + data = json.load(fh) + assert data["n_spectra"] == 2 diff --git a/scripts/proteomics/mzml_spectrum_subsetter/README.md b/scripts/proteomics/mzml_spectrum_subsetter/README.md new file mode 100644 index 0000000..de19671 --- /dev/null +++ b/scripts/proteomics/mzml_spectrum_subsetter/README.md @@ -0,0 +1,9 @@ +# mzML Spectrum Subsetter + +Extract specific spectra from mzML by scan number list. + +## Usage + +```bash +python mzml_spectrum_subsetter.py --input run.mzML --scans 0,1,5 --output subset.mzML +``` diff --git a/scripts/proteomics/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py b/scripts/proteomics/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py new file mode 100644 index 0000000..a2fc9e8 --- /dev/null +++ b/scripts/proteomics/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py @@ -0,0 +1,108 @@ +""" +mzML Spectrum Subsetter +======================= +Extract specific spectra from mzML by scan number list. + +Features: +- Extract spectra by scan index (0-based) +- Output subset as new mzML file +- Preserves spectrum metadata + +Usage +----- + python mzml_spectrum_subsetter.py --input run.mzML --scans 0,1,5 --output subset.mzML +""" + +import argparse +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def subset_spectra( + input_path: str, + scan_indices: list[int], + output_path: str, +) -> int: + """Extract specific spectra from mzML by scan index. + + Parameters + ---------- + input_path : str + Path to input mzML file. + scan_indices : list[int] + List of 0-based scan indices to extract. + output_path : str + Path to output mzML file. + + Returns + ------- + int + Number of spectra written. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + out_exp = oms.MSExperiment() + n_spectra = exp.getNrSpectra() + count = 0 + + for idx in sorted(scan_indices): + if 0 <= idx < n_spectra: + out_exp.addSpectrum(exp.getSpectrum(idx)) + count += 1 + + oms.MzMLFile().store(output_path, out_exp) + return count + + +def create_synthetic_mzml(output_path: str, n_scans: int = 10) -> None: + """Create a synthetic mzML file for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + n_scans : int + Number of scans to generate. + """ + exp = oms.MSExperiment() + + for i in range(n_scans): + spec = oms.MSSpectrum() + spec.setMSLevel(1 if i % 2 == 0 else 2) + spec.setRT(float(i) * 5.0) + mzs = [100.0 + i * 10, 200.0 + i * 10, 300.0 + i * 10] + ints = [1000.0 * (i + 1), 500.0 * (i + 1), 200.0 * (i + 1)] + spec.set_peaks((mzs, ints)) + + if spec.getMSLevel() == 2: + prec = oms.Precursor() + prec.setMZ(500.0) + prec.setCharge(2) + spec.setPrecursors([prec]) + + exp.addSpectrum(spec) + + oms.MzMLFile().store(output_path, exp) + + +def main(): + parser = argparse.ArgumentParser( + description="Extract specific spectra from mzML by scan number list." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--scans", required=True, help="Comma-separated scan indices (0-based)") + parser.add_argument("--output", required=True, help="Path to output mzML file") + args = parser.parse_args() + + scan_indices = [int(x.strip()) for x in args.scans.split(",")] + count = subset_spectra(args.input, scan_indices, args.output) + print(f"Extracted {count} spectra to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mzml_spectrum_subsetter/requirements.txt b/scripts/proteomics/mzml_spectrum_subsetter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mzml_spectrum_subsetter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mzml_spectrum_subsetter/tests/conftest.py b/scripts/proteomics/mzml_spectrum_subsetter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mzml_spectrum_subsetter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py b/scripts/proteomics/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py new file mode 100644 index 0000000..f21c57e --- /dev/null +++ b/scripts/proteomics/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py @@ -0,0 +1,65 @@ +"""Tests for mzml_spectrum_subsetter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMzmlSpectrumSubsetter: + def test_subset_spectra(self): + import pyopenms as oms + from mzml_spectrum_subsetter import create_synthetic_mzml, subset_spectra + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "input.mzML") + out_path = os.path.join(tmpdir, "subset.mzML") + create_synthetic_mzml(in_path, n_scans=10) + count = subset_spectra(in_path, [0, 2, 4], out_path) + assert count == 3 + + exp = oms.MSExperiment() + oms.MzMLFile().load(out_path, exp) + assert exp.getNrSpectra() == 3 + + def test_out_of_range(self): + import pyopenms as oms + from mzml_spectrum_subsetter import create_synthetic_mzml, subset_spectra + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "input.mzML") + out_path = os.path.join(tmpdir, "subset.mzML") + create_synthetic_mzml(in_path, n_scans=5) + count = subset_spectra(in_path, [0, 100], out_path) + assert count == 1 + + exp = oms.MSExperiment() + oms.MzMLFile().load(out_path, exp) + assert exp.getNrSpectra() == 1 + + def test_empty_subset(self): + from mzml_spectrum_subsetter import create_synthetic_mzml, subset_spectra + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "input.mzML") + out_path = os.path.join(tmpdir, "subset.mzML") + create_synthetic_mzml(in_path, n_scans=5) + count = subset_spectra(in_path, [100, 200], out_path) + assert count == 0 + + def test_preserves_rt(self): + import pyopenms as oms + from mzml_spectrum_subsetter import create_synthetic_mzml, subset_spectra + + with tempfile.TemporaryDirectory() as tmpdir: + in_path = os.path.join(tmpdir, "input.mzML") + out_path = os.path.join(tmpdir, "subset.mzML") + create_synthetic_mzml(in_path, n_scans=10) + subset_spectra(in_path, [2], out_path) + + exp = oms.MSExperiment() + oms.MzMLFile().load(out_path, exp) + assert exp.getNrSpectra() == 1 + # Scan index 2 has RT = 2 * 5.0 = 10.0 + assert abs(exp.getSpectrum(0).getRT() - 10.0) < 0.1 diff --git a/scripts/proteomics/mzml_to_mgf_converter/README.md b/scripts/proteomics/mzml_to_mgf_converter/README.md new file mode 100644 index 0000000..9dd8b98 --- /dev/null +++ b/scripts/proteomics/mzml_to_mgf_converter/README.md @@ -0,0 +1,19 @@ +# mzML to MGF Converter + +Convert MS2 spectra from mzML format to MGF (Mascot Generic Format). + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Convert MS2 spectra +python mzml_to_mgf_converter.py --input run.mzML --ms-level 2 --output spectra.mgf + +# Filter by minimum number of peaks +python mzml_to_mgf_converter.py --input run.mzML --min-peaks 10 --output spectra.mgf +``` diff --git a/scripts/proteomics/mzml_to_mgf_converter/mzml_to_mgf_converter.py b/scripts/proteomics/mzml_to_mgf_converter/mzml_to_mgf_converter.py new file mode 100644 index 0000000..b80afa1 --- /dev/null +++ b/scripts/proteomics/mzml_to_mgf_converter/mzml_to_mgf_converter.py @@ -0,0 +1,141 @@ +""" +mzML to MGF Converter +===================== +Convert MS2 spectra from mzML format to MGF (Mascot Generic Format). + +Usage +----- + python mzml_to_mgf_converter.py --input run.mzML --ms-level 2 --output spectra.mgf +""" + +import argparse +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def load_mzml(input_path: str) -> oms.MSExperiment: + """Load an mzML file into an MSExperiment.""" + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + return exp + + +def get_spectra_by_level(exp: oms.MSExperiment, ms_level: int = 2) -> List[oms.MSSpectrum]: + """Extract spectra of a given MS level from an experiment.""" + return [s for s in exp if s.getMSLevel() == ms_level] + + +def spectrum_to_mgf_block(spectrum: oms.MSSpectrum, index: int) -> str: + """Convert a single spectrum to an MGF block string.""" + lines = ["BEGIN IONS"] + + # Title + native_id = spectrum.getNativeID() if spectrum.getNativeID() else f"index={index}" + lines.append(f"TITLE={native_id}") + + # Retention time + rt = spectrum.getRT() + lines.append(f"RTINSECONDS={rt:.4f}") + + # Precursor info + precursors = spectrum.getPrecursors() + if precursors: + prec = precursors[0] + mz = prec.getMZ() + charge = prec.getCharge() + lines.append(f"PEPMASS={mz:.6f}") + if charge > 0: + lines.append(f"CHARGE={charge}+") + + # Peaks + mz_array, intensity_array = spectrum.get_peaks() + for mz_val, intensity_val in zip(mz_array, intensity_array): + lines.append(f"{mz_val:.6f} {intensity_val:.4f}") + + lines.append("END IONS") + lines.append("") + return "\n".join(lines) + + +def convert_mzml_to_mgf( + input_path: str, + output_path: str, + ms_level: int = 2, + min_peaks: int = 1, +) -> dict: + """Convert mzML to MGF format. + + Returns statistics about the conversion. + """ + exp = load_mzml(input_path) + spectra = get_spectra_by_level(exp, ms_level) + + converted = 0 + with open(output_path, "w") as fh: + for i, spectrum in enumerate(spectra): + mz_array, _ = spectrum.get_peaks() + if len(mz_array) < min_peaks: + continue + block = spectrum_to_mgf_block(spectrum, i) + fh.write(block + "\n") + converted += 1 + + return { + "total_spectra": exp.size(), + "ms_level_spectra": len(spectra), + "converted": converted, + } + + +def create_synthetic_mzml(output_path: str, n_spectra: int = 5) -> None: + """Create a synthetic mzML file with MS2 spectra for testing.""" + exp = oms.MSExperiment() + + # Add an MS1 spectrum + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(10.0) + ms1.set_peaks(([100.0, 200.0, 300.0], [1000.0, 2000.0, 1500.0])) + exp.addSpectrum(ms1) + + # Add MS2 spectra + for i in range(n_spectra): + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(10.0 + i * 0.5) + ms2.setNativeID(f"scan={i + 1}") + + prec = oms.Precursor() + prec.setMZ(500.0 + i * 50.0) + prec.setCharge(2) + ms2.setPrecursors([prec]) + + mzs = [100.0 + j * 50.0 for j in range(10)] + intensities = [1000.0 * (10 - j) for j in range(10)] + ms2.set_peaks((mzs, intensities)) + exp.addSpectrum(ms2) + + oms.MzMLFile().store(output_path, exp) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert MS2 spectra from mzML to MGF format.") + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument("--ms-level", type=int, default=2, help="MS level to extract (default: 2)") + parser.add_argument("--min-peaks", type=int, default=1, help="Minimum peaks per spectrum (default: 1)") + parser.add_argument("--output", required=True, help="Output MGF file") + args = parser.parse_args() + + stats = convert_mzml_to_mgf(args.input, args.output, args.ms_level, args.min_peaks) + print(f"Converted {stats['converted']} / {stats['ms_level_spectra']} MS{args.ms_level} spectra to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mzml_to_mgf_converter/requirements.txt b/scripts/proteomics/mzml_to_mgf_converter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mzml_to_mgf_converter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mzml_to_mgf_converter/tests/conftest.py b/scripts/proteomics/mzml_to_mgf_converter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mzml_to_mgf_converter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py b/scripts/proteomics/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py new file mode 100644 index 0000000..a87d56f --- /dev/null +++ b/scripts/proteomics/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py @@ -0,0 +1,77 @@ +"""Tests for mzml_to_mgf_converter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_create_synthetic_mzml(): + import pyopenms as oms + from mzml_to_mgf_converter import create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + create_synthetic_mzml(mzml_path, n_spectra=3) + + exp = oms.MSExperiment() + oms.MzMLFile().load(mzml_path, exp) + assert exp.size() == 4 # 1 MS1 + 3 MS2 + + +@requires_pyopenms +def test_convert_mzml_to_mgf(): + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=3) + stats = convert_mzml_to_mgf(mzml_path, mgf_path) + + assert stats["converted"] == 3 + assert stats["ms_level_spectra"] == 3 + + with open(mgf_path) as fh: + content = fh.read() + assert content.count("BEGIN IONS") == 3 + assert content.count("END IONS") == 3 + assert "PEPMASS=" in content + assert "CHARGE=" in content + + +@requires_pyopenms +def test_mgf_content_format(): + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=1) + convert_mzml_to_mgf(mzml_path, mgf_path) + + with open(mgf_path) as fh: + lines = fh.readlines() + + # Check MGF format structure + assert lines[0].strip() == "BEGIN IONS" + has_title = any(line.startswith("TITLE=") for line in lines) + has_pepmass = any(line.startswith("PEPMASS=") for line in lines) + assert has_title + assert has_pepmass + + +@requires_pyopenms +def test_min_peaks_filter(): + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=3) + stats = convert_mzml_to_mgf(mzml_path, mgf_path, min_peaks=100) + assert stats["converted"] == 0 diff --git a/scripts/proteomics/mzqc_generator/mzqc_generator.py b/scripts/proteomics/mzqc_generator/mzqc_generator.py new file mode 100644 index 0000000..76867c9 --- /dev/null +++ b/scripts/proteomics/mzqc_generator/mzqc_generator.py @@ -0,0 +1,141 @@ +""" +mzQC Generator +============== +Generate an mzQC-format JSON file from an mzML run. The output follows +a simplified mzQC schema with standard metric vocabulary identifiers. + +Usage +----- + python mzqc_generator.py --input run.mzML --output qc.mzQC +""" + +import argparse +import json +import math +import sys +from datetime import datetime, timezone + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def generate_mzqc(exp: oms.MSExperiment, input_file: str = "unknown.mzML") -> dict: + """Build an mzQC-style dictionary from an MSExperiment. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment`` instance. + input_file: + Name of the source mzML file (used in metadata). + + Returns + ------- + dict + mzQC-structured dictionary with quality metrics. + """ + spectra = exp.getSpectra() + ms1_count = 0 + ms2_count = 0 + tic_values = [] + rt_values = [] + precursor_mzs = [] + + for spec in spectra: + level = spec.getMSLevel() + rt = spec.getRT() + rt_values.append(rt) + _, intensities = spec.get_peaks() + tic = float(intensities.sum()) if len(intensities) > 0 else 0.0 + + if level == 1: + ms1_count += 1 + tic_values.append(tic) + elif level == 2: + ms2_count += 1 + for prec in spec.getPrecursors(): + precursor_mzs.append(prec.getMZ()) + + tic_mean = sum(tic_values) / len(tic_values) if tic_values else 0.0 + tic_std = ( + math.sqrt(sum((t - tic_mean) ** 2 for t in tic_values) / len(tic_values)) + if tic_values + else 0.0 + ) + + rt_range = [min(rt_values), max(rt_values)] if rt_values else [0.0, 0.0] + + metrics = [ + { + "accession": "QC:0000005", + "name": "Number of MS1 spectra", + "value": ms1_count, + }, + { + "accession": "QC:0000006", + "name": "Number of MS2 spectra", + "value": ms2_count, + }, + { + "accession": "QC:0000048", + "name": "Mean TIC", + "value": round(tic_mean, 4), + }, + { + "accession": "QC:0000049", + "name": "TIC standard deviation", + "value": round(tic_std, 4), + }, + { + "accession": "QC:0000019", + "name": "RT range (seconds)", + "value": [round(v, 2) for v in rt_range], + }, + { + "accession": "QC:0000029", + "name": "Number of unique precursors", + "value": len(set(round(mz, 4) for mz in precursor_mzs)), + }, + ] + + mzqc = { + "mzQC": { + "version": "1.0.0", + "creationDate": datetime.now(timezone.utc).isoformat(), + "runQualities": [ + { + "metadata": { + "inputFiles": [{"name": input_file, "fileFormat": "mzML"}], + }, + "qualityMetrics": metrics, + } + ], + } + } + return mzqc + + +def main(): + parser = argparse.ArgumentParser( + description="Generate mzQC JSON from an mzML file." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output mzQC JSON path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + mzqc = generate_mzqc(exp, input_file=args.input) + + with open(args.output, "w") as fh: + json.dump(mzqc, fh, indent=2) + + n_metrics = len(mzqc["mzQC"]["runQualities"][0]["qualityMetrics"]) + print(f"mzQC written to {args.output} ({n_metrics} metrics)") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mzqc_generator/requirements.txt b/scripts/proteomics/mzqc_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mzqc_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mzqc_generator/tests/conftest.py b/scripts/proteomics/mzqc_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mzqc_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mzqc_generator/tests/test_mzqc_generator.py b/scripts/proteomics/mzqc_generator/tests/test_mzqc_generator.py new file mode 100644 index 0000000..d98b7e5 --- /dev/null +++ b/scripts/proteomics/mzqc_generator/tests/test_mzqc_generator.py @@ -0,0 +1,75 @@ +"""Tests for mzqc_generator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestMzqcGenerator: + def _make_experiment(self): + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(3): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(60.0 * i) + mzs = np.array([100.0 + j for j in range(5)], dtype=np.float64) + ints = np.array([1000.0] * 5, dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(60.0 * i + 0.5) + prec = oms.Precursor() + prec.setMZ(500.0) + ms2.setPrecursors([prec]) + mzs2 = np.array([200.0, 300.0], dtype=np.float64) + ints2 = np.array([500.0, 800.0], dtype=np.float64) + ms2.set_peaks([mzs2, ints2]) + exp.addSpectrum(ms2) + + return exp + + def test_generate_mzqc_structure(self): + from mzqc_generator import generate_mzqc + + exp = self._make_experiment() + mzqc = generate_mzqc(exp, "test.mzML") + assert "mzQC" in mzqc + assert "runQualities" in mzqc["mzQC"] + assert len(mzqc["mzQC"]["runQualities"]) == 1 + + def test_metric_count(self): + from mzqc_generator import generate_mzqc + + exp = self._make_experiment() + mzqc = generate_mzqc(exp) + metrics = mzqc["mzQC"]["runQualities"][0]["qualityMetrics"] + assert len(metrics) == 6 + + def test_ms1_count_metric(self): + from mzqc_generator import generate_mzqc + + exp = self._make_experiment() + mzqc = generate_mzqc(exp) + metrics = mzqc["mzQC"]["runQualities"][0]["qualityMetrics"] + ms1_metric = next(m for m in metrics if m["accession"] == "QC:0000005") + assert ms1_metric["value"] == 3 + + def test_ms2_count_metric(self): + from mzqc_generator import generate_mzqc + + exp = self._make_experiment() + mzqc = generate_mzqc(exp) + metrics = mzqc["mzQC"]["runQualities"][0]["qualityMetrics"] + ms2_metric = next(m for m in metrics if m["accession"] == "QC:0000006") + assert ms2_metric["value"] == 3 + + def test_version_field(self): + from mzqc_generator import generate_mzqc + + exp = self._make_experiment() + mzqc = generate_mzqc(exp) + assert mzqc["mzQC"]["version"] == "1.0.0" diff --git a/scripts/proteomics/mztab_summarizer/README.md b/scripts/proteomics/mztab_summarizer/README.md new file mode 100644 index 0000000..b5f4492 --- /dev/null +++ b/scripts/proteomics/mztab_summarizer/README.md @@ -0,0 +1,19 @@ +# mzTab Summarizer + +Parse an mzTab file and extract summary statistics. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +# Print summary to stdout +python mztab_summarizer.py --input results.mzTab + +# Write summary to file +python mztab_summarizer.py --input results.mzTab --output summary.tsv +``` diff --git a/scripts/proteomics/mztab_summarizer/mztab_summarizer.py b/scripts/proteomics/mztab_summarizer/mztab_summarizer.py new file mode 100644 index 0000000..9f681cb --- /dev/null +++ b/scripts/proteomics/mztab_summarizer/mztab_summarizer.py @@ -0,0 +1,165 @@ +""" +mzTab Summarizer +================ +Parse an mzTab file and extract summary statistics. + +Usage +----- + python mztab_summarizer.py --input results.mzTab --output summary.tsv +""" + +import argparse +import csv +import sys +from collections import Counter + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_mztab(input_path: str) -> dict: + """Parse an mzTab file manually and return its sections. + + Returns a dict with keys: metadata, protein_header, proteins, + peptide_header, peptides, psm_header, psms. + """ + metadata = {} + protein_header = [] + proteins = [] + peptide_header = [] + peptides = [] + psm_header = [] + psms = [] + + with open(input_path) as fh: + for line in fh: + line = line.rstrip("\n") + if not line: + continue + parts = line.split("\t") + prefix = parts[0] + + if prefix == "MTD": + if len(parts) >= 3: + metadata[parts[1]] = parts[2] + elif prefix == "PRH": + protein_header = parts[1:] + elif prefix == "PRT": + proteins.append(parts[1:]) + elif prefix == "PEH": + peptide_header = parts[1:] + elif prefix == "PEP": + peptides.append(parts[1:]) + elif prefix == "PSH": + psm_header = parts[1:] + elif prefix == "PSM": + psms.append(parts[1:]) + + return { + "metadata": metadata, + "protein_header": protein_header, + "proteins": proteins, + "peptide_header": peptide_header, + "peptides": peptides, + "psm_header": psm_header, + "psms": psms, + } + + +def summarize_mztab(input_path: str) -> dict: + """Compute summary statistics from an mzTab file. + + Returns a dict with: + - protein_count: total proteins + - peptide_count: total peptides + - psm_count: total PSMs + - unique_proteins: unique protein accessions + - unique_peptides: unique peptide sequences + - modification_counts: modification frequency + - search_engines: search engines used + """ + data = parse_mztab(input_path) + + # Protein stats + protein_accessions = set() + if data["protein_header"]: + acc_idx = data["protein_header"].index("accession") if "accession" in data["protein_header"] else 0 + for row in data["proteins"]: + if len(row) > acc_idx: + protein_accessions.add(row[acc_idx]) + + # Peptide stats + unique_peptide_seqs = set() + mod_counter: Counter = Counter() + if data["peptide_header"]: + seq_idx = data["peptide_header"].index("sequence") if "sequence" in data["peptide_header"] else 0 + mod_idx = ( + data["peptide_header"].index("modifications") + if "modifications" in data["peptide_header"] + else None + ) + for row in data["peptides"]: + if len(row) > seq_idx: + unique_peptide_seqs.add(row[seq_idx]) + if mod_idx is not None and len(row) > mod_idx and row[mod_idx] != "null": + mod_counter[row[mod_idx]] += 1 + + # PSM stats + unique_psm_peptides = set() + if data["psm_header"]: + seq_idx = data["psm_header"].index("sequence") if "sequence" in data["psm_header"] else 0 + for row in data["psms"]: + if len(row) > seq_idx: + unique_psm_peptides.add(row[seq_idx]) + + # Search engine info + search_engines = [v for k, v in data["metadata"].items() if "search_engine" in k.lower()] + + return { + "protein_count": len(data["proteins"]), + "peptide_count": len(data["peptides"]), + "psm_count": len(data["psms"]), + "unique_protein_accessions": len(protein_accessions), + "unique_peptide_sequences": len(unique_peptide_seqs), + "unique_psm_peptides": len(unique_psm_peptides), + "modification_counts": dict(mod_counter), + "search_engines": search_engines, + "metadata_entries": len(data["metadata"]), + } + + +def write_summary_tsv(summary: dict, output_path: str) -> None: + """Write summary statistics to a TSV file.""" + with open(output_path, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["metric", "value"]) + for key, value in summary.items(): + if isinstance(value, dict): + for k2, v2 in value.items(): + writer.writerow([f"{key}:{k2}", v2]) + elif isinstance(value, list): + writer.writerow([key, ";".join(str(v) for v in value)]) + else: + writer.writerow([key, value]) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Parse mzTab and extract summary statistics.") + parser.add_argument("--input", required=True, help="Input mzTab file") + parser.add_argument("--output", default=None, help="Output summary TSV file (default: stdout)") + args = parser.parse_args() + + summary = summarize_mztab(args.input) + + if args.output: + write_summary_tsv(summary, args.output) + print(f"Summary written to {args.output}") + else: + for key, value in summary.items(): + print(f"{key}: {value}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/mztab_summarizer/requirements.txt b/scripts/proteomics/mztab_summarizer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/mztab_summarizer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/mztab_summarizer/tests/conftest.py b/scripts/proteomics/mztab_summarizer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/mztab_summarizer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/mztab_summarizer/tests/test_mztab_summarizer.py b/scripts/proteomics/mztab_summarizer/tests/test_mztab_summarizer.py new file mode 100644 index 0000000..adde4ce --- /dev/null +++ b/scripts/proteomics/mztab_summarizer/tests/test_mztab_summarizer.py @@ -0,0 +1,78 @@ +"""Tests for mztab_summarizer.""" + +import os +import tempfile + +from conftest import requires_pyopenms + +SAMPLE_MZTAB = """\ +MTD\tmzTab-version\t1.0.0 +MTD\tsearch_engine[1]\t[MS, MS:1002995, SEQUEST, ] +PRH\taccession\tdescription\ttaxid\tspecies +PRT\tP12345\tProtein 1\t9606\tHomo sapiens +PRT\tP67890\tProtein 2\t9606\tHomo sapiens +PRT\tP12345\tProtein 1 duplicate\t9606\tHomo sapiens +PEH\tsequence\taccession\tmodifications +PEP\tACDEFGHIK\tP12345\tnull +PEP\tMNPQRSTWY\tP67890\t1-MOD:01214 +PEP\tACDEFGHIK\tP12345\tnull +PSH\tsequence\tPSM_ID\taccession +PSM\tACDEFGHIK\t1\tP12345 +PSM\tMNPQRSTWY\t2\tP67890 +PSM\tACDEFGHIK\t3\tP12345 +""" + + +@requires_pyopenms +def test_parse_mztab(): + from mztab_summarizer import parse_mztab + + with tempfile.TemporaryDirectory() as tmp: + mztab_path = os.path.join(tmp, "test.mzTab") + with open(mztab_path, "w") as fh: + fh.write(SAMPLE_MZTAB) + + data = parse_mztab(mztab_path) + assert len(data["proteins"]) == 3 + assert len(data["peptides"]) == 3 + assert len(data["psms"]) == 3 + assert "mzTab-version" in data["metadata"] + + +@requires_pyopenms +def test_summarize_mztab(): + from mztab_summarizer import summarize_mztab + + with tempfile.TemporaryDirectory() as tmp: + mztab_path = os.path.join(tmp, "test.mzTab") + with open(mztab_path, "w") as fh: + fh.write(SAMPLE_MZTAB) + + summary = summarize_mztab(mztab_path) + assert summary["protein_count"] == 3 + assert summary["unique_protein_accessions"] == 2 + assert summary["peptide_count"] == 3 + assert summary["unique_peptide_sequences"] == 2 + assert summary["psm_count"] == 3 + assert summary["unique_psm_peptides"] == 2 + assert len(summary["search_engines"]) == 1 + + +@requires_pyopenms +def test_write_summary_tsv(): + from mztab_summarizer import summarize_mztab, write_summary_tsv + + with tempfile.TemporaryDirectory() as tmp: + mztab_path = os.path.join(tmp, "test.mzTab") + tsv_path = os.path.join(tmp, "summary.tsv") + + with open(mztab_path, "w") as fh: + fh.write(SAMPLE_MZTAB) + + summary = summarize_mztab(mztab_path) + write_summary_tsv(summary, tsv_path) + + with open(tsv_path) as fh: + lines = fh.readlines() + assert lines[0].strip() == "metric\tvalue" + assert len(lines) > 1 diff --git a/scripts/proteomics/nterm_modification_annotator/README.md b/scripts/proteomics/nterm_modification_annotator/README.md new file mode 100644 index 0000000..172f706 --- /dev/null +++ b/scripts/proteomics/nterm_modification_annotator/README.md @@ -0,0 +1,18 @@ +# N-terminal Modification Annotator + +Classify N-terminal peptides as protein N-terminus, signal peptide, neo-N-terminus, etc. + +## Usage + +```bash +python nterm_modification_annotator.py --input nterm_peptides.tsv --fasta reference.fasta --output annotated.tsv +``` + +## Input Format + +- `nterm_peptides.tsv`: columns `peptide`, `protein` +- `reference.fasta`: Reference proteome FASTA file + +## Output + +- `annotated.tsv` - Peptides with `nterm_type`, `nterm_modification`, `start_position` columns diff --git a/scripts/proteomics/nterm_modification_annotator/nterm_modification_annotator.py b/scripts/proteomics/nterm_modification_annotator/nterm_modification_annotator.py new file mode 100644 index 0000000..b4d76ab --- /dev/null +++ b/scripts/proteomics/nterm_modification_annotator/nterm_modification_annotator.py @@ -0,0 +1,287 @@ +""" +N-terminal Modification Annotator +=================================== +Classify N-terminal peptides as protein N-terminus, signal peptide cleavage, +neo-N-terminus (internal cleavage), or unknown. + +The tool maps each peptide to its protein, determines where it starts in the +protein sequence, and classifies the N-terminal type accordingly. + +Usage +----- + python nterm_modification_annotator.py --input nterm_peptides.tsv --fasta reference.fasta --output annotated.tsv +""" + +import argparse +import csv +import re +import sys +from typing import Dict, List + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +# Typical signal peptide cleavage sites - residues that commonly precede signal peptide cleavage +SIGNAL_PEPTIDE_MAX_POS = 70 # Signal peptides are typically 15-70 residues + + +def load_fasta(fasta_path: str) -> Dict[str, str]: + """Load a FASTA file into a dictionary mapping accession to sequence. + + Parameters + ---------- + fasta_path: + Path to the FASTA file. + + Returns + ------- + dict + Mapping of protein accession to amino acid sequence. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + + proteins = {} + for entry in entries: + acc = entry.identifier.split()[0] if entry.identifier else "" + proteins[acc] = entry.sequence + return proteins + + +def get_clean_sequence(sequence: str) -> str: + """Get clean amino acid sequence using AASequence. + + Parameters + ---------- + sequence: + Peptide sequence, possibly modified. + + Returns + ------- + str + Clean unmodified sequence. + """ + try: + aa = oms.AASequence.fromString(sequence) + return aa.toUnmodifiedString() + except Exception: + clean = re.sub(r"\[.*?\]", "", sequence) + clean = re.sub(r"\(.*?\)", "", clean) + return clean + + +def detect_nterm_modification(sequence: str) -> str: + """Detect N-terminal modifications from the peptide sequence string. + + Parameters + ---------- + sequence: + Original peptide sequence with modifications. + + Returns + ------- + str + Detected N-terminal modification or 'none'. + """ + nterm_mods = { + "Acetyl": "acetylation", + "acetyl": "acetylation", + ".(Acetyl)": "acetylation", + "Formyl": "formylation", + "formyl": "formylation", + "Carbamyl": "carbamylation", + "TMT": "TMT-label", + "iTRAQ": "iTRAQ-label", + "Dimethyl": "dimethylation", + } + + for mod_key, mod_name in nterm_mods.items(): + if mod_key in sequence: + return mod_name + return "none" + + +def find_peptide_start(peptide_clean: str, protein_seq: str) -> int: + """Find the 0-based start position of a peptide in a protein sequence. + + Parameters + ---------- + peptide_clean: + Clean peptide sequence. + protein_seq: + Full protein sequence. + + Returns + ------- + int + 0-based start position, or -1 if not found. + """ + return protein_seq.find(peptide_clean) + + +def classify_nterm_type( + start_pos: int, + protein_seq: str, + signal_peptide_sites: Dict[str, int] = None, + protein_id: str = "", +) -> str: + """Classify the N-terminal peptide type. + + Parameters + ---------- + start_pos: + 0-based start position of peptide in protein. + protein_seq: + Full protein sequence. + signal_peptide_sites: + Optional dict of protein_id -> signal peptide cleavage position (0-based). + protein_id: + Protein accession for signal peptide lookup. + + Returns + ------- + str + Classification: 'protein_nterm', 'met_removal', 'signal_peptide', 'neo_nterm', or 'unmapped'. + """ + if start_pos < 0: + return "unmapped" + + if start_pos == 0: + return "protein_nterm" + + # Methionine removal: peptide starts at position 1 and protein starts with M + if start_pos == 1 and protein_seq[0] == "M": + return "met_removal" + + # Signal peptide cleavage: check known sites or heuristic + if signal_peptide_sites and protein_id in signal_peptide_sites: + sp_site = signal_peptide_sites[protein_id] + if start_pos == sp_site: + return "signal_peptide" + + # Heuristic: if start position is within typical signal peptide range + # and preceded by small/neutral residues (A, G, S are common at -1 position) + if 15 <= start_pos <= SIGNAL_PEPTIDE_MAX_POS: + preceding_aa = protein_seq[start_pos - 1] + if preceding_aa in "AGS": + return "signal_peptide_candidate" + + return "neo_nterm" + + +def annotate_nterm_peptides( + rows: List[Dict[str, str]], + proteins: Dict[str, str], + signal_peptide_sites: Dict[str, int] = None, +) -> List[Dict[str, str]]: + """Annotate a batch of N-terminal peptides. + + Parameters + ---------- + rows: + List of dicts with keys: peptide, protein. + proteins: + Protein accession to sequence mapping. + signal_peptide_sites: + Optional dict of known signal peptide cleavage sites. + + Returns + ------- + list + Annotated rows with added classification columns. + """ + results = [] + + for row in rows: + peptide_raw = row["peptide"] + protein_id = row["protein"] + clean_seq = get_clean_sequence(peptide_raw) + + new_row = dict(row) + new_row["clean_sequence"] = clean_seq + new_row["nterm_modification"] = detect_nterm_modification(peptide_raw) + + if protein_id not in proteins: + new_row["start_position"] = -1 + new_row["nterm_type"] = "unmapped" + results.append(new_row) + continue + + protein_seq = proteins[protein_id] + start_pos = find_peptide_start(clean_seq, protein_seq) + nterm_type = classify_nterm_type(start_pos, protein_seq, signal_peptide_sites, protein_id) + + new_row["start_position"] = start_pos + new_row["nterm_type"] = nterm_type + results.append(new_row) + + return results + + +def compute_summary(results: List[Dict[str, str]]) -> Dict[str, int]: + """Compute summary counts by N-terminal type. + + Parameters + ---------- + results: + List of annotated result dicts. + + Returns + ------- + dict + Counts per nterm_type. + """ + summary: Dict[str, int] = {} + for r in results: + nt = r["nterm_type"] + summary[nt] = summary.get(nt, 0) + 1 + return summary + + +def read_input(input_path: str) -> List[Dict[str, str]]: + """Read N-terminal peptides TSV file.""" + rows = [] + with open(input_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output(output_path: str, results: List[Dict[str, str]]) -> None: + """Write annotated results to TSV.""" + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Classify N-terminal peptides as protein N-term, signal peptide, neo-N-term, etc." + ) + parser.add_argument("--input", required=True, help="Input N-terminal peptides TSV file") + parser.add_argument("--fasta", required=True, help="Reference proteome FASTA file") + parser.add_argument("--output", required=True, help="Output annotated TSV file") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + rows = read_input(args.input) + results = annotate_nterm_peptides(rows, proteins) + write_output(args.output, results) + + summary = compute_summary(results) + print(f"Total peptides: {len(results)}") + for ntype, count in sorted(summary.items()): + print(f" {ntype}: {count}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/nterm_modification_annotator/requirements.txt b/scripts/proteomics/nterm_modification_annotator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/nterm_modification_annotator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/nterm_modification_annotator/tests/conftest.py b/scripts/proteomics/nterm_modification_annotator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/nterm_modification_annotator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/nterm_modification_annotator/tests/test_nterm_modification_annotator.py b/scripts/proteomics/nterm_modification_annotator/tests/test_nterm_modification_annotator.py new file mode 100644 index 0000000..3fd7915 --- /dev/null +++ b/scripts/proteomics/nterm_modification_annotator/tests/test_nterm_modification_annotator.py @@ -0,0 +1,132 @@ +"""Tests for nterm_modification_annotator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestNtermModificationAnnotator: + def _create_fasta(self, tmpdir, proteins): + """Helper to create a FASTA file.""" + import pyopenms as oms + + fasta_path = os.path.join(tmpdir, "reference.fasta") + entries = [] + for acc, seq in proteins.items(): + entry = oms.FASTAEntry() + entry.identifier = acc + entry.sequence = seq + entries.append(entry) + fasta_file = oms.FASTAFile() + fasta_file.store(fasta_path, entries) + return fasta_path + + def test_get_clean_sequence(self): + from nterm_modification_annotator import get_clean_sequence + assert get_clean_sequence("PEPTIDEK") == "PEPTIDEK" + + def test_detect_nterm_modification_acetyl(self): + from nterm_modification_annotator import detect_nterm_modification + assert detect_nterm_modification(".(Acetyl)PEPTIDEK") == "acetylation" + + def test_detect_nterm_modification_none(self): + from nterm_modification_annotator import detect_nterm_modification + assert detect_nterm_modification("PEPTIDEK") == "none" + + def test_detect_nterm_tmt(self): + from nterm_modification_annotator import detect_nterm_modification + assert detect_nterm_modification("TMT6plex-PEPTIDEK") == "TMT-label" + + def test_find_peptide_start(self): + from nterm_modification_annotator import find_peptide_start + assert find_peptide_start("DEF", "ABCDEFGHIJ") == 3 + assert find_peptide_start("ABC", "ABCDEFGHIJ") == 0 + assert find_peptide_start("ZZZ", "ABCDEFGHIJ") == -1 + + def test_classify_protein_nterm(self): + from nterm_modification_annotator import classify_nterm_type + protein_seq = "MACDEFGHIJ" + result = classify_nterm_type(0, protein_seq) + assert result == "protein_nterm" + + def test_classify_met_removal(self): + from nterm_modification_annotator import classify_nterm_type + protein_seq = "MACDEFGHIJ" + result = classify_nterm_type(1, protein_seq) + assert result == "met_removal" + + def test_classify_neo_nterm(self): + from nterm_modification_annotator import classify_nterm_type + protein_seq = "MACDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ" + # Position 75 - beyond signal peptide range + result = classify_nterm_type(75, protein_seq) + assert result == "neo_nterm" + + def test_classify_signal_peptide_known(self): + from nterm_modification_annotator import classify_nterm_type + protein_seq = "M" + "A" * 30 + "DEFGHIJ" + sp_sites = {"P1": 25} + result = classify_nterm_type(25, protein_seq, sp_sites, "P1") + assert result == "signal_peptide" + + def test_classify_signal_peptide_candidate(self): + from nterm_modification_annotator import classify_nterm_type + # Position 20, preceded by 'A' (small residue) + protein_seq = "M" + "K" * 19 + "ADEFGHIJKLMNOP" + result = classify_nterm_type(20, protein_seq) + assert result == "signal_peptide_candidate" + + def test_classify_unmapped(self): + from nterm_modification_annotator import classify_nterm_type + result = classify_nterm_type(-1, "MACDEF") + assert result == "unmapped" + + def test_annotate_nterm_peptides(self): + from nterm_modification_annotator import annotate_nterm_peptides + proteins = {"P1": "MACDEFGHIKLMNPQRSTVWY"} + rows = [ + {"peptide": "MACDEF", "protein": "P1"}, + {"peptide": "ACDEF", "protein": "P1"}, + {"peptide": "GHIKLM", "protein": "P1"}, + ] + results = annotate_nterm_peptides(rows, proteins) + assert results[0]["nterm_type"] == "protein_nterm" + assert results[1]["nterm_type"] == "met_removal" + assert results[2]["nterm_type"] == "neo_nterm" + + def test_annotate_missing_protein(self): + from nterm_modification_annotator import annotate_nterm_peptides + rows = [{"peptide": "PEPTIDEK", "protein": "MISSING"}] + results = annotate_nterm_peptides(rows, {}) + assert results[0]["nterm_type"] == "unmapped" + + def test_compute_summary(self): + from nterm_modification_annotator import compute_summary + results = [ + {"nterm_type": "protein_nterm"}, + {"nterm_type": "protein_nterm"}, + {"nterm_type": "neo_nterm"}, + {"nterm_type": "met_removal"}, + ] + summary = compute_summary(results) + assert summary["protein_nterm"] == 2 + assert summary["neo_nterm"] == 1 + assert summary["met_removal"] == 1 + + def test_full_pipeline(self): + from nterm_modification_annotator import annotate_nterm_peptides, load_fasta, write_output + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "MACDEFGHIKLMNPQRSTVWY"}) + proteins = load_fasta(fasta_path) + rows = [ + {"peptide": "MACDEF", "protein": "P1"}, + {"peptide": "ACDEF", "protein": "P1"}, + ] + results = annotate_nterm_peptides(rows, proteins) + output_path = os.path.join(tmpdir, "annotated.tsv") + write_output(output_path, results) + assert os.path.exists(output_path) + assert len(results) == 2 diff --git a/scripts/proteomics/peptide_detectability_predictor/README.md b/scripts/proteomics/peptide_detectability_predictor/README.md new file mode 100644 index 0000000..a67000f --- /dev/null +++ b/scripts/proteomics/peptide_detectability_predictor/README.md @@ -0,0 +1,10 @@ +# Peptide Detectability Predictor + +Predict peptide detectability from physicochemical heuristics. + +## Usage + +```bash +python peptide_detectability_predictor.py --input proteins.fasta --enzyme Trypsin --output detectability.tsv +python peptide_detectability_predictor.py --sequence PEPTIDEK +``` diff --git a/scripts/proteomics/peptide_detectability_predictor/peptide_detectability_predictor.py b/scripts/proteomics/peptide_detectability_predictor/peptide_detectability_predictor.py new file mode 100644 index 0000000..1e5a15e --- /dev/null +++ b/scripts/proteomics/peptide_detectability_predictor/peptide_detectability_predictor.py @@ -0,0 +1,184 @@ +""" +Peptide Detectability Predictor +================================ +Predict peptide detectability from physicochemical heuristics. + +Features +-------- +- Digest proteins from FASTA with specified enzyme +- Score peptides based on length, hydrophobicity, charge, and mass +- Rank peptides by predicted detectability + +Usage +----- + python peptide_detectability_predictor.py --input proteins.fasta --enzyme Trypsin --output detectability.tsv + python peptide_detectability_predictor.py --sequence PEPTIDEK +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Kyte-Doolittle scale for hydrophobicity +KYTE_DOOLITTLE = { + "A": 1.8, "R": -4.5, "N": -3.5, "D": -3.5, "C": 2.5, + "E": -3.5, "Q": -3.5, "G": -0.4, "H": -3.2, "I": 4.5, + "L": 3.8, "K": -3.9, "M": 1.9, "F": 2.8, "P": -1.6, + "S": -0.8, "T": -0.7, "W": -0.9, "Y": -1.3, "V": 4.2, +} + + +def calculate_detectability_score(sequence: str) -> dict: + """Calculate a heuristic detectability score for a peptide. + + Parameters + ---------- + sequence : str + Peptide sequence. + + Returns + ------- + dict + Dictionary with individual feature scores and overall detectability score. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + length = len(plain) + mono_mass = aa_seq.getMonoWeight() + + # Feature 1: Length score (optimal 7-25 residues) + if 7 <= length <= 25: + length_score = 1.0 + elif length < 7: + length_score = length / 7.0 + else: + length_score = max(0.0, 1.0 - (length - 25) / 25.0) + + # Feature 2: Hydrophobicity score (moderate GRAVY preferred) + gravy = sum(KYTE_DOOLITTLE.get(aa, 0.0) for aa in plain) / length if length > 0 else 0.0 + # Optimal GRAVY around -0.5 to 0.5 + hydro_score = max(0.0, 1.0 - abs(gravy) / 4.0) + + # Feature 3: Mass range score (optimal 800-2500 Da) + if 800 <= mono_mass <= 2500: + mass_score = 1.0 + elif mono_mass < 800: + mass_score = mono_mass / 800.0 + else: + mass_score = max(0.0, 1.0 - (mono_mass - 2500) / 2500.0) + + # Feature 4: No problematic residues (M, W prone to modification/loss) + problem_count = sum(1 for aa in plain if aa in "MW") + problem_score = max(0.0, 1.0 - problem_count * 0.2) + + # Feature 5: Contains at least one basic residue (good for ionization) + basic_count = sum(1 for aa in plain if aa in "KRH") + basic_score = min(1.0, basic_count * 0.5) + + overall = round((length_score + hydro_score + mass_score + problem_score + basic_score) / 5.0, 4) + + return { + "sequence": sequence, + "length": length, + "monoisotopic_mass": round(mono_mass, 6), + "gravy": round(gravy, 4), + "length_score": round(length_score, 4), + "hydrophobicity_score": round(hydro_score, 4), + "mass_score": round(mass_score, 4), + "problem_residue_score": round(problem_score, 4), + "basic_residue_score": round(basic_score, 4), + "detectability_score": overall, + } + + +def predict_from_fasta(fasta_path: str, enzyme: str = "Trypsin", + missed_cleavages: int = 1, min_length: int = 6, + max_length: int = 40) -> list: + """Digest proteins from FASTA and predict detectability for each peptide. + + Parameters + ---------- + fasta_path : str + Path to the FASTA file. + enzyme : str + Enzyme for digestion. + missed_cleavages : int + Number of allowed missed cleavages. + min_length : int + Minimum peptide length. + max_length : int + Maximum peptide length. + + Returns + ------- + list + List of detectability result dicts, sorted by score descending. + """ + entries = [] + oms.FASTAFile().load(fasta_path, entries) + + digest = oms.ProteaseDigestion() + digest.setEnzyme(enzyme) + digest.setMissedCleavages(missed_cleavages) + + results = [] + seen = set() + for entry in entries: + aa_seq = oms.AASequence.fromString(entry.sequence) + peptides = [] + digest.digest(aa_seq, peptides, min_length, max_length) + for pep in peptides: + pep_str = pep.toString() + if pep_str not in seen: + seen.add(pep_str) + score_result = calculate_detectability_score(pep_str) + score_result["protein"] = entry.identifier + results.append(score_result) + + results.sort(key=lambda x: x["detectability_score"], reverse=True) + return results + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Predict peptide detectability from physicochemical heuristics.") + parser.add_argument("--input", type=str, help="Protein FASTA file.") + parser.add_argument("--sequence", type=str, help="Single peptide sequence.") + parser.add_argument("--enzyme", type=str, default="Trypsin", help="Enzyme (default: Trypsin).") + parser.add_argument("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1).") + parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") + args = parser.parse_args() + + if args.sequence: + result = calculate_detectability_score(args.sequence) + if args.output: + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + else: + print(json.dumps(result, indent=2)) + elif args.input: + results = predict_from_fasta(args.input, args.enzyme, args.missed_cleavages) + if args.output: + with open(args.output, "w", newline="") as fh: + fieldnames = ["sequence", "protein", "length", "monoisotopic_mass", "detectability_score", + "length_score", "hydrophobicity_score", "mass_score", + "problem_residue_score", "basic_residue_score", "gravy"] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") + writer.writeheader() + writer.writerows(results) + print(f"Results written to {args.output}") + else: + for r in results[:20]: + print(f"{r['sequence']}\t{r['detectability_score']}\t{r['protein']}") + else: + parser.error("Provide --sequence or --input.") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_detectability_predictor/requirements.txt b/scripts/proteomics/peptide_detectability_predictor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_detectability_predictor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_detectability_predictor/tests/conftest.py b/scripts/proteomics/peptide_detectability_predictor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_detectability_predictor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py b/scripts/proteomics/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py new file mode 100644 index 0000000..88aa08d --- /dev/null +++ b/scripts/proteomics/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py @@ -0,0 +1,61 @@ +"""Tests for peptide_detectability_predictor.""" + +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPeptideDetectabilityPredictor: + def test_basic_score(self): + from peptide_detectability_predictor import calculate_detectability_score + + result = calculate_detectability_score("PEPTIDEK") + assert 0 <= result["detectability_score"] <= 1 + assert result["length"] == 8 + assert result["monoisotopic_mass"] > 0 + + def test_good_peptide_scores_high(self): + from peptide_detectability_predictor import calculate_detectability_score + + # Typical tryptic peptide, good length, has K + good = calculate_detectability_score("AEFGHLPQR") + # Very short peptide + short = calculate_detectability_score("AK") + assert good["detectability_score"] > short["detectability_score"] + + def test_problematic_residues_lower_score(self): + from peptide_detectability_predictor import calculate_detectability_score + + normal = calculate_detectability_score("PEPTIDEK") + problem = calculate_detectability_score("MWMWMWMK") + assert normal["problem_residue_score"] >= problem["problem_residue_score"] + + def test_predict_from_fasta(self): + import pyopenms as oms + from peptide_detectability_predictor import predict_from_fasta + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = f"{tmpdir}/test.fasta" + entries = [] + e1 = oms.FASTAEntry() + e1.identifier = "PROT1" + e1.sequence = "MSPEPTIDEKAAANOTHERPEPTIDER" + entries.append(e1) + oms.FASTAFile().store(fasta_path, entries) + + results = predict_from_fasta(fasta_path, "Trypsin") + assert len(results) > 0 + # Results should be sorted by detectability_score descending + scores = [r["detectability_score"] for r in results] + assert scores == sorted(scores, reverse=True) + + def test_score_has_all_features(self): + from peptide_detectability_predictor import calculate_detectability_score + + result = calculate_detectability_score("PEPTIDEK") + assert "length_score" in result + assert "hydrophobicity_score" in result + assert "mass_score" in result + assert "problem_residue_score" in result + assert "basic_residue_score" in result diff --git a/scripts/proteomics/peptide_mass_fingerprint/README.md b/scripts/proteomics/peptide_mass_fingerprint/README.md new file mode 100644 index 0000000..6012559 --- /dev/null +++ b/scripts/proteomics/peptide_mass_fingerprint/README.md @@ -0,0 +1,9 @@ +# Peptide Mass Fingerprint + +Generate and match peptide mass fingerprints from FASTA databases. + +## Usage + +```bash +python peptide_mass_fingerprint.py --fasta db.fasta --accession P12345 --enzyme Trypsin --output fingerprint.tsv +``` diff --git a/scripts/proteomics/peptide_mass_fingerprint/peptide_mass_fingerprint.py b/scripts/proteomics/peptide_mass_fingerprint/peptide_mass_fingerprint.py new file mode 100644 index 0000000..65f1250 --- /dev/null +++ b/scripts/proteomics/peptide_mass_fingerprint/peptide_mass_fingerprint.py @@ -0,0 +1,200 @@ +""" +Peptide Mass Fingerprint +======================== +Generate and match peptide mass fingerprints from FASTA databases. +Digests a protein with a specified enzyme and reports theoretical +peptide masses for fingerprint matching. + +Usage +----- + python peptide_mass_fingerprint.py --fasta db.fasta --accession P12345 --enzyme Trypsin --output fingerprint.tsv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + +PROTON = 1.007276 + + +def load_fasta(fasta_path: str) -> dict: + """Load FASTA file and return accession to sequence mapping. + + Parameters + ---------- + fasta_path: + Path to FASTA file. + + Returns + ------- + dict + Mapping of accession to protein sequence. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + proteins = {} + for entry in entries: + proteins[entry.identifier] = entry.sequence + return proteins + + +def generate_fingerprint( + protein_sequence: str, + enzyme: str = "Trypsin", + missed_cleavages: int = 1, + min_mass: float = 500.0, + max_mass: float = 4000.0, +) -> List[dict]: + """Generate peptide mass fingerprint for a protein. + + Parameters + ---------- + protein_sequence: + Full protein amino acid sequence. + enzyme: + Enzyme name for digestion. + missed_cleavages: + Number of allowed missed cleavages. + min_mass: + Minimum peptide mass to include (Da). + max_mass: + Maximum peptide mass to include (Da). + + Returns + ------- + list + List of dicts with peptide sequence, mass, and position info. + """ + digestion = oms.ProteaseDigestion() + digestion.setEnzyme(enzyme) + digestion.setMissedCleavages(missed_cleavages) + + aa_protein = oms.AASequence.fromString(protein_sequence) + peptides = [] + digestion.digest(aa_protein, peptides) + + results = [] + for pep in peptides: + pep_str = str(pep) + mono_mass = pep.getMonoWeight() + if mono_mass < min_mass or mono_mass > max_mass: + continue + + # Find position in protein + pos = protein_sequence.find(pep_str) + + results.append({ + "sequence": pep_str, + "monoisotopic_mass": round(mono_mass, 6), + "mz_1": round((mono_mass + PROTON) / 1, 6), + "mz_2": round((mono_mass + 2 * PROTON) / 2, 6), + "length": len(pep_str), + "position": pos if pos >= 0 else "N/A", + }) + + results.sort(key=lambda x: x["monoisotopic_mass"]) + return results + + +def match_fingerprint( + fingerprint: List[dict], + observed_masses: List[float], + tolerance_ppm: float = 10.0, +) -> List[dict]: + """Match observed masses against a theoretical fingerprint. + + Parameters + ---------- + fingerprint: + Theoretical fingerprint from generate_fingerprint(). + observed_masses: + List of observed monoisotopic masses. + tolerance_ppm: + Mass tolerance in ppm. + + Returns + ------- + list + List of matched peptide dicts with observed mass and ppm error. + """ + matches = [] + for obs in observed_masses: + for pep in fingerprint: + theo = pep["monoisotopic_mass"] + ppm_error = abs(obs - theo) / theo * 1e6 + if ppm_error <= tolerance_ppm: + match = dict(pep) + match["observed_mass"] = round(obs, 6) + match["ppm_error"] = round(ppm_error, 2) + matches.append(match) + return matches + + +def write_tsv(records: List[dict], output_path: str) -> None: + """Write fingerprint records to TSV. + + Parameters + ---------- + records: + List of record dicts. + output_path: + Output file path. + """ + if not records: + return + fieldnames = list(records[0].keys()) + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in records: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Generate/match peptide mass fingerprints from FASTA." + ) + parser.add_argument("--fasta", required=True, help="FASTA database file") + parser.add_argument("--accession", required=True, help="Protein accession to fingerprint") + parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") + parser.add_argument("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1)") + parser.add_argument("--min-mass", type=float, default=500.0, help="Min peptide mass (default: 500)") + parser.add_argument("--max-mass", type=float, default=4000.0, help="Max peptide mass (default: 4000)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + if args.accession not in proteins: + sys.exit(f"Accession '{args.accession}' not found in FASTA file.") + + protein_seq = proteins[args.accession] + print(f"Protein {args.accession}: {len(protein_seq)} amino acids") + + fingerprint = generate_fingerprint( + protein_seq, enzyme=args.enzyme, + missed_cleavages=args.missed_cleavages, + min_mass=args.min_mass, max_mass=args.max_mass, + ) + print(f"Generated {len(fingerprint)} peptide masses") + + for pep in fingerprint[:10]: + print(f" {pep['monoisotopic_mass']:.4f} {pep['sequence']}") + if len(fingerprint) > 10: + print(f" ... and {len(fingerprint) - 10} more") + + if args.output: + write_tsv(fingerprint, args.output) + print(f"\nFingerprint written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_mass_fingerprint/requirements.txt b/scripts/proteomics/peptide_mass_fingerprint/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_mass_fingerprint/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_mass_fingerprint/tests/conftest.py b/scripts/proteomics/peptide_mass_fingerprint/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_mass_fingerprint/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py b/scripts/proteomics/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py new file mode 100644 index 0000000..601ea73 --- /dev/null +++ b/scripts/proteomics/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py @@ -0,0 +1,78 @@ +"""Tests for peptide_mass_fingerprint.""" + +import pytest +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPeptideMassFingerprint: + def _write_fasta(self, tmp_path, accession="P12345", sequence="PEPTIDEKAVLIDRACDEFGHIK"): + """Write a simple FASTA file for testing.""" + import pyopenms as oms + + fasta_path = str(tmp_path / "test.fasta") + entry = oms.FASTAEntry() + entry.identifier = accession + entry.sequence = sequence + oms.FASTAFile().store(fasta_path, [entry]) + return fasta_path + + def test_load_fasta(self, tmp_path): + from peptide_mass_fingerprint import load_fasta + + fasta_path = self._write_fasta(tmp_path) + proteins = load_fasta(fasta_path) + assert "P12345" in proteins + assert len(proteins["P12345"]) > 0 + + def test_generate_fingerprint(self): + from peptide_mass_fingerprint import generate_fingerprint + + protein = "PEPTIDEKAVLIDRACDEFGHIK" + fp = generate_fingerprint(protein, enzyme="Trypsin", missed_cleavages=1, min_mass=0, max_mass=10000) + assert len(fp) > 0 + assert all("monoisotopic_mass" in p for p in fp) + assert all("sequence" in p for p in fp) + + def test_fingerprint_sorted_by_mass(self): + from peptide_mass_fingerprint import generate_fingerprint + + fp = generate_fingerprint("PEPTIDEKAVLIDRACDEFGHIK", min_mass=0, max_mass=10000) + masses = [p["monoisotopic_mass"] for p in fp] + assert masses == sorted(masses) + + def test_match_fingerprint(self): + from peptide_mass_fingerprint import generate_fingerprint, match_fingerprint + + fp = generate_fingerprint("PEPTIDEKAVLIDRACDEFGHIK", min_mass=0, max_mass=10000) + # Use exact theoretical mass as observed + observed = [fp[0]["monoisotopic_mass"]] + matches = match_fingerprint(fp, observed, tolerance_ppm=10.0) + assert len(matches) >= 1 + assert matches[0]["ppm_error"] < 1.0 + + def test_match_no_hit(self): + from peptide_mass_fingerprint import generate_fingerprint, match_fingerprint + + fp = generate_fingerprint("PEPTIDEKAVLIDRACDEFGHIK", min_mass=0, max_mass=10000) + matches = match_fingerprint(fp, [99999.0], tolerance_ppm=10.0) + assert len(matches) == 0 + + def test_mz_values(self): + from peptide_mass_fingerprint import PROTON, generate_fingerprint + + fp = generate_fingerprint("PEPTIDEKAVLIDRACDEFGHIK", min_mass=0, max_mass=10000) + for p in fp: + expected_mz1 = (p["monoisotopic_mass"] + PROTON) / 1 + assert p["mz_1"] == pytest.approx(expected_mz1, abs=1e-4) + + def test_write_tsv(self, tmp_path): + from peptide_mass_fingerprint import generate_fingerprint, write_tsv + + fp = generate_fingerprint("PEPTIDEKAVLIDRACDEFGHIK", min_mass=0, max_mass=10000) + out = str(tmp_path / "fp.tsv") + write_tsv(fp, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) > 1 + assert "sequence" in lines[0] diff --git a/scripts/proteomics/peptide_modification_analyzer/README.md b/scripts/proteomics/peptide_modification_analyzer/README.md new file mode 100644 index 0000000..594b6f1 --- /dev/null +++ b/scripts/proteomics/peptide_modification_analyzer/README.md @@ -0,0 +1,10 @@ +# Peptide Modification Analyzer + +Parse modified peptide sequences and output residue-by-residue mass breakdown. + +## Usage + +```bash +python peptide_modification_analyzer.py --sequence "PEPTM(Oxidation)IDE" --charge 2 +python peptide_modification_analyzer.py --sequence "PEPTM(Oxidation)IDE" --output breakdown.tsv +``` diff --git a/scripts/proteomics/peptide_modification_analyzer/peptide_modification_analyzer.py b/scripts/proteomics/peptide_modification_analyzer/peptide_modification_analyzer.py new file mode 100644 index 0000000..a78b05d --- /dev/null +++ b/scripts/proteomics/peptide_modification_analyzer/peptide_modification_analyzer.py @@ -0,0 +1,119 @@ +""" +Peptide Modification Analyzer +=============================== +Parse modified peptide sequences and output residue-by-residue mass breakdown. + +Features +-------- +- Parse pyopenms bracket notation for modifications +- Report per-residue monoisotopic mass +- Show modification delta mass per position +- Calculate total and m/z masses + +Usage +----- + python peptide_modification_analyzer.py --sequence "PEPTM(Oxidation)IDE" --charge 2 + python peptide_modification_analyzer.py --sequence "PEPTM(Oxidation)IDE" --output breakdown.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def analyze_modification(sequence: str, charge: int = 1) -> dict: + """Analyze a modified peptide and return residue-by-residue mass breakdown. + + Parameters + ---------- + sequence : str + Modified peptide sequence in pyopenms notation (e.g., 'PEPTM(Oxidation)IDE'). + charge : int + Charge state for m/z calculation. + + Returns + ------- + dict + Dictionary with overall mass info and per-residue breakdown. + """ + aa_seq = oms.AASequence.fromString(sequence) + total_mono = aa_seq.getMonoWeight() + mz = (total_mono + charge * PROTON) / charge + + residues = [] + for i in range(aa_seq.size()): + residue = aa_seq.getResidue(i) + one_letter = residue.getOneLetterCode() + mono_mass = residue.getMonoWeight() + + # Check for modification + mod_name = "" + mod_mass = 0.0 + if aa_seq.isModified(i): + mod_name = residue.getModificationName() + # Unmodified residue mass from ResidueDB + unmod_residue = oms.ResidueDB().getResidue(one_letter) + unmod_mass = unmod_residue.getMonoWeight() + mod_mass = round(mono_mass - unmod_mass, 6) + + residues.append({ + "position": i + 1, + "residue": one_letter, + "monoisotopic_mass": round(mono_mass, 6), + "modification": mod_name, + "modification_delta_mass": mod_mass, + }) + + return { + "sequence": sequence, + "unmodified_sequence": aa_seq.toUnmodifiedString(), + "length": aa_seq.size(), + "total_monoisotopic_mass": round(total_mono, 6), + "charge": charge, + "mz": round(mz, 6), + "residue_breakdown": residues, + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Analyze modified peptide residue-by-residue mass breakdown.") + parser.add_argument("--sequence", required=True, help="Modified peptide sequence.") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") + parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") + args = parser.parse_args() + + result = analyze_modification(args.sequence, args.charge) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + fieldnames = ["position", "residue", "monoisotopic_mass", "modification", + "modification_delta_mass"] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(result["residue_breakdown"]) + print(f"Results written to {args.output}") + else: + print(f"Sequence: {result['sequence']}") + print(f"Total mass: {result['total_monoisotopic_mass']}") + print(f"m/z (z={result['charge']}): {result['mz']}") + print() + for r in result["residue_breakdown"]: + mod_str = f" [{r['modification']} +{r['modification_delta_mass']}]" if r["modification"] else "" + print(f" {r['position']}\t{r['residue']}\t{r['monoisotopic_mass']}{mod_str}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_modification_analyzer/requirements.txt b/scripts/proteomics/peptide_modification_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_modification_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_modification_analyzer/tests/conftest.py b/scripts/proteomics/peptide_modification_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_modification_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py b/scripts/proteomics/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py new file mode 100644 index 0000000..dc41bd0 --- /dev/null +++ b/scripts/proteomics/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py @@ -0,0 +1,51 @@ +"""Tests for peptide_modification_analyzer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPeptideModificationAnalyzer: + def test_unmodified_peptide(self): + from peptide_modification_analyzer import analyze_modification + + result = analyze_modification("PEPTIDEK") + assert result["length"] == 8 + assert len(result["residue_breakdown"]) == 8 + for r in result["residue_breakdown"]: + assert r["modification"] == "" + + def test_oxidized_methionine(self): + from peptide_modification_analyzer import analyze_modification + + result = analyze_modification("PEPTM(Oxidation)IDEK") + residues = result["residue_breakdown"] + # Position 5 (M) should have Oxidation + m_residue = residues[4] + assert m_residue["residue"] == "M" + assert "Oxidation" in m_residue["modification"] + assert m_residue["modification_delta_mass"] > 15.0 + + def test_mass_consistency(self): + from peptide_modification_analyzer import analyze_modification + + result = analyze_modification("PEPTIDEK") + assert result["total_monoisotopic_mass"] > 900 + + def test_charge_state(self): + from peptide_modification_analyzer import PROTON, analyze_modification + + r1 = analyze_modification("PEPTIDEK", charge=1) + r2 = analyze_modification("PEPTIDEK", charge=2) + assert r2["mz"] < r1["mz"] + expected_mz = (r1["total_monoisotopic_mass"] + 2 * PROTON) / 2 + assert abs(r2["mz"] - expected_mz) < 0.001 + + def test_residue_positions(self): + from peptide_modification_analyzer import analyze_modification + + result = analyze_modification("ACDK") + residues = result["residue_breakdown"] + assert residues[0]["residue"] == "A" + assert residues[0]["position"] == 1 + assert residues[3]["residue"] == "K" + assert residues[3]["position"] == 4 diff --git a/scripts/proteomics/peptide_property_calculator/README.md b/scripts/proteomics/peptide_property_calculator/README.md new file mode 100644 index 0000000..a1959f7 --- /dev/null +++ b/scripts/proteomics/peptide_property_calculator/README.md @@ -0,0 +1,18 @@ +# Peptide Property Calculator + +Calculate physicochemical properties of peptide sequences: pI, GRAVY, charge at pH, instability index, amino acid composition. + +## Usage + +```bash +python peptide_property_calculator.py --sequence PEPTIDEK --ph 7.0 +python peptide_property_calculator.py --sequence PEPTIDEK --output properties.json +python peptide_property_calculator.py --input peptides.tsv --output properties.tsv +``` + +## Input + +- `--sequence`: Single peptide sequence +- `--input`: TSV file with a `sequence` column +- `--ph`: pH for charge calculation (default: 7.0) +- `--output`: Output file (.json or .tsv) diff --git a/scripts/proteomics/peptide_property_calculator/peptide_property_calculator.py b/scripts/proteomics/peptide_property_calculator/peptide_property_calculator.py new file mode 100644 index 0000000..00e3751 --- /dev/null +++ b/scripts/proteomics/peptide_property_calculator/peptide_property_calculator.py @@ -0,0 +1,262 @@ +""" +Peptide Property Calculator +=========================== +Calculate physicochemical properties of peptide sequences using pyopenms. + +Features +-------- +- Isoelectric point (pI) via Henderson-Hasselbalch iterative bisection +- GRAVY (grand average of hydropathicity, Kyte-Doolittle scale) +- Net charge at any pH +- Instability index (DIWV weight values) +- Amino acid composition +- Molecular weight and formula + +Usage +----- + python peptide_property_calculator.py --sequence PEPTIDEK --ph 7.0 + python peptide_property_calculator.py --sequence PEPTIDEK --output properties.json + python peptide_property_calculator.py --input peptides.tsv --output properties.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Kyte-Doolittle hydropathicity scale +KYTE_DOOLITTLE = { + "A": 1.8, "R": -4.5, "N": -3.5, "D": -3.5, "C": 2.5, + "E": -3.5, "Q": -3.5, "G": -0.4, "H": -3.2, "I": 4.5, + "L": 3.8, "K": -3.9, "M": 1.9, "F": 2.8, "P": -1.6, + "S": -0.8, "T": -0.7, "W": -0.9, "Y": -1.3, "V": 4.2, +} + +# pKa values (Lehninger) +PKA_NTERM = 9.69 +PKA_CTERM = 2.34 +PKA_SIDE = { + "C": 8.33, "D": 3.65, "E": 4.25, "H": 6.00, "K": 10.53, "R": 12.48, "Y": 10.07, +} +# +1 for basic, -1 for acidic +CHARGE_SIGN = { + "C": -1, "D": -1, "E": -1, "H": 1, "K": 1, "R": 1, "Y": -1, +} + +# Instability index DIWV weights (Guruprasad et al. 1990) - simplified subset +DIWV = { + ("D", "G"): 1, ("D", "P"): 1, ("E", "S"): 1, +} + + +def _charge_at_ph(sequence: str, ph: float) -> float: + """Calculate net charge at a given pH using Henderson-Hasselbalch. + + Parameters + ---------- + sequence : str + One-letter amino acid sequence. + ph : float + pH value. + + Returns + ------- + float + Estimated net charge. + """ + charge = 0.0 + # N-terminus contributes +1 when protonated + charge += 1.0 / (1.0 + 10 ** (ph - PKA_NTERM)) + # C-terminus contributes -1 when deprotonated + charge -= 1.0 / (1.0 + 10 ** (PKA_CTERM - ph)) + for aa in sequence: + if aa in PKA_SIDE: + pka = PKA_SIDE[aa] + sign = CHARGE_SIGN[aa] + if sign > 0: + charge += 1.0 / (1.0 + 10 ** (ph - pka)) + else: + charge -= 1.0 / (1.0 + 10 ** (pka - ph)) + return charge + + +def calculate_pi(sequence: str, precision: float = 0.01) -> float: + """Calculate isoelectric point via bisection on Henderson-Hasselbalch charge. + + Parameters + ---------- + sequence : str + One-letter amino acid sequence. + precision : float + Desired precision for the pI estimate. + + Returns + ------- + float + Estimated isoelectric point. + """ + low, high = 0.0, 14.0 + while (high - low) > precision: + mid = (low + high) / 2.0 + c = _charge_at_ph(sequence, mid) + if c > 0: + low = mid + else: + high = mid + return round((low + high) / 2.0, 2) + + +def calculate_gravy(sequence: str) -> float: + """Calculate GRAVY (Grand Average of Hydropathicity) using Kyte-Doolittle. + + Parameters + ---------- + sequence : str + One-letter amino acid sequence. + + Returns + ------- + float + GRAVY score. + """ + values = [KYTE_DOOLITTLE.get(aa, 0.0) for aa in sequence] + if not values: + return 0.0 + return round(sum(values) / len(values), 4) + + +def calculate_instability_index(sequence: str) -> float: + """Calculate the instability index (simplified Guruprasad method). + + Parameters + ---------- + sequence : str + One-letter amino acid sequence. + + Returns + ------- + float + Instability index value. Values > 40 suggest the protein is unstable. + """ + if len(sequence) < 2: + return 0.0 + total = 0.0 + for i in range(len(sequence) - 1): + dipeptide = (sequence[i], sequence[i + 1]) + total += DIWV.get(dipeptide, 0.0) + return round((10.0 / len(sequence)) * total, 4) + + +def amino_acid_composition(sequence: str) -> dict: + """Return amino acid counts and frequencies. + + Parameters + ---------- + sequence : str + One-letter amino acid sequence. + + Returns + ------- + dict + Dictionary with 'counts' and 'frequencies' sub-dicts. + """ + counts = {} + for aa in sequence: + counts[aa] = counts.get(aa, 0) + 1 + length = len(sequence) + frequencies = {aa: round(count / length, 4) for aa, count in counts.items()} if length else {} + return {"counts": counts, "frequencies": frequencies} + + +def calculate_properties(sequence: str, ph: float = 7.0) -> dict: + """Calculate a full set of physicochemical properties for a peptide. + + Parameters + ---------- + sequence : str + Amino acid sequence (plain one-letter code or pyopenms bracket notation). + ph : float + pH for net charge calculation. + + Returns + ------- + dict + Dictionary containing all computed properties. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + mono = aa_seq.getMonoWeight() + formula = aa_seq.getFormula() + + pi = calculate_pi(plain) + gravy = calculate_gravy(plain) + charge = _charge_at_ph(plain, ph) + instability = calculate_instability_index(plain) + composition = amino_acid_composition(plain) + + return { + "sequence": sequence, + "unmodified_sequence": plain, + "length": len(plain), + "monoisotopic_mass": round(mono, 6), + "formula": formula.toString(), + "pI": pi, + "gravy": gravy, + "charge_at_ph": round(charge, 4), + "ph": ph, + "instability_index": instability, + "amino_acid_composition": composition, + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Calculate peptide physicochemical properties.") + parser.add_argument("--sequence", type=str, help="Single peptide sequence.") + parser.add_argument("--ph", type=float, default=7.0, help="pH for charge calculation (default: 7.0).") + parser.add_argument("--input", type=str, help="TSV file with 'sequence' column.") + parser.add_argument("--output", type=str, help="Output file (.json or .tsv).") + args = parser.parse_args() + + if not args.sequence and not args.input: + parser.error("Provide --sequence or --input.") + + results = [] + if args.sequence: + results.append(calculate_properties(args.sequence, args.ph)) + elif args.input: + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("sequence", "").strip() + if seq: + results.append(calculate_properties(seq, args.ph)) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(results if len(results) > 1 else results[0], fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + fieldnames = [ + "sequence", "unmodified_sequence", "length", "monoisotopic_mass", + "formula", "pI", "gravy", "charge_at_ph", "ph", "instability_index", + ] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for r in results: + row = {k: r[k] for k in fieldnames} + writer.writerow(row) + print(f"Results written to {args.output}") + else: + for r in results: + print(json.dumps(r, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_property_calculator/requirements.txt b/scripts/proteomics/peptide_property_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_property_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_property_calculator/tests/conftest.py b/scripts/proteomics/peptide_property_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_property_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_property_calculator/tests/test_peptide_property_calculator.py b/scripts/proteomics/peptide_property_calculator/tests/test_peptide_property_calculator.py new file mode 100644 index 0000000..056589c --- /dev/null +++ b/scripts/proteomics/peptide_property_calculator/tests/test_peptide_property_calculator.py @@ -0,0 +1,61 @@ +"""Tests for peptide_property_calculator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPeptidePropertyCalculator: + def test_calculate_properties_basic(self): + from peptide_property_calculator import calculate_properties + + result = calculate_properties("PEPTIDEK") + assert result["unmodified_sequence"] == "PEPTIDEK" + assert result["length"] == 8 + assert result["monoisotopic_mass"] > 900 + assert 3.0 < result["pI"] < 7.0 + assert isinstance(result["gravy"], float) + assert isinstance(result["charge_at_ph"], float) + + def test_pi_basic_peptide(self): + from peptide_property_calculator import calculate_pi + + pi = calculate_pi("ACDEFGHIK") + assert 4.0 < pi < 7.0 + + def test_gravy(self): + from peptide_property_calculator import calculate_gravy + + gravy = calculate_gravy("AAAA") + assert gravy == 1.8 # all alanine + + def test_charge_at_ph(self): + from peptide_property_calculator import _charge_at_ph + + # At very low pH, charge should be positive + assert _charge_at_ph("PEPTIDEK", 1.0) > 0 + # At very high pH, charge should be negative + assert _charge_at_ph("PEPTIDEK", 14.0) < 0 + + def test_amino_acid_composition(self): + from peptide_property_calculator import amino_acid_composition + + comp = amino_acid_composition("AAAK") + assert comp["counts"]["A"] == 3 + assert comp["counts"]["K"] == 1 + assert abs(comp["frequencies"]["A"] - 0.75) < 0.01 + + def test_instability_index(self): + from peptide_property_calculator import calculate_instability_index + + ii = calculate_instability_index("DGDG") + assert ii > 0 + + def test_output_json(self): + import json + import tempfile + + from peptide_property_calculator import calculate_properties + + result = calculate_properties("PEPTIDEK") + with tempfile.NamedTemporaryFile(suffix=".json", mode="w", delete=False) as fh: + json.dump(result, fh) diff --git a/scripts/proteomics/peptide_spectral_match_validator/README.md b/scripts/proteomics/peptide_spectral_match_validator/README.md new file mode 100644 index 0000000..531e3b6 --- /dev/null +++ b/scripts/proteomics/peptide_spectral_match_validator/README.md @@ -0,0 +1,10 @@ +# Peptide Spectral Match Validator + +Validate PSMs by recomputing theoretical fragment ions and measuring coverage. + +## Usage + +```bash +python peptide_spectral_match_validator.py --mzml run.mzML --peptides psms.tsv --output validation.tsv +python peptide_spectral_match_validator.py --mzml run.mzML --peptides psms.tsv --tolerance 0.05 --output validation.tsv +``` diff --git a/scripts/proteomics/peptide_spectral_match_validator/peptide_spectral_match_validator.py b/scripts/proteomics/peptide_spectral_match_validator/peptide_spectral_match_validator.py new file mode 100644 index 0000000..75c7c70 --- /dev/null +++ b/scripts/proteomics/peptide_spectral_match_validator/peptide_spectral_match_validator.py @@ -0,0 +1,225 @@ +""" +Peptide Spectral Match Validator +================================ +Validate PSMs by recomputing theoretical fragment ions and measuring +spectrum-to-theoretical coverage/alignment. + +Usage +----- + python peptide_spectral_match_validator.py --mzml run.mzML --peptides psms.tsv --output validation.tsv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + +PROTON = 1.007276 + + +def generate_theoretical_spectrum(sequence: str, charge: int = 1) -> oms.MSSpectrum: + """Generate a theoretical MS2 spectrum for a peptide. + + Parameters + ---------- + sequence: + Peptide amino acid sequence. + charge: + Maximum charge state for fragments. + + Returns + ------- + pyopenms.MSSpectrum + Theoretical spectrum with b and y ions. + """ + aa_seq = oms.AASequence.fromString(sequence) + spec = oms.MSSpectrum() + generator = oms.TheoreticalSpectrumGenerator() + + params = generator.getParameters() + params.setValue("add_b_ions", "true") + params.setValue("add_y_ions", "true") + params.setValue("add_metainfo", "true") + generator.setParameters(params) + + generator.getSpectrum(spec, aa_seq, 1, charge) + spec.sortByPosition() + return spec + + +def validate_psm( + experimental_spec: oms.MSSpectrum, + sequence: str, + tolerance: float = 0.02, + charge: int = 1, +) -> dict: + """Validate a single PSM by computing fragment ion coverage. + + Parameters + ---------- + experimental_spec: + Experimental MS2 spectrum. + sequence: + Peptide sequence. + tolerance: + Fragment mass tolerance in Da. + charge: + Max fragment charge. + + Returns + ------- + dict + Validation results including matched ions, coverage, etc. + """ + theo_spec = generate_theoretical_spectrum(sequence, charge) + + alignment = [] + aligner = oms.SpectrumAlignment() + params = aligner.getParameters() + params.setValue("tolerance", float(tolerance)) + params.setValue("is_relative_tolerance", "false") + aligner.setParameters(params) + + aligner.getSpectrumAlignment(alignment, theo_spec, experimental_spec) + + n_theoretical = theo_spec.size() + n_matched = len(alignment) + coverage = n_matched / n_theoretical if n_theoretical > 0 else 0.0 + + aa_seq = oms.AASequence.fromString(sequence) + + return { + "sequence": sequence, + "theoretical_ions": n_theoretical, + "matched_ions": n_matched, + "coverage": round(coverage, 4), + "peptide_mass": round(aa_seq.getMonoWeight(), 6), + } + + +def load_psms(psm_path: str) -> List[dict]: + """Load PSMs from a TSV file. + + Parameters + ---------- + psm_path: + Path to PSM TSV file with columns: spectrum_index, sequence, charge. + + Returns + ------- + list + List of PSM dicts. + """ + psms = [] + with open(psm_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + psms.append({ + "spectrum_index": int(row["spectrum_index"]), + "sequence": row["sequence"].strip(), + "charge": int(row.get("charge", 1)), + }) + return psms + + +def validate_psms( + exp: oms.MSExperiment, + psms: List[dict], + tolerance: float = 0.02, +) -> List[dict]: + """Validate multiple PSMs against an MSExperiment. + + Parameters + ---------- + exp: + Loaded MSExperiment. + psms: + List of PSM dicts with spectrum_index, sequence, charge. + tolerance: + Fragment mass tolerance in Da. + + Returns + ------- + list + List of validation result dicts. + """ + spectra = exp.getSpectra() + results = [] + for psm in psms: + idx = psm["spectrum_index"] + if idx < 0 or idx >= len(spectra): + results.append({ + "spectrum_index": idx, + "sequence": psm["sequence"], + "theoretical_ions": 0, + "matched_ions": 0, + "coverage": 0.0, + "peptide_mass": 0.0, + "status": "invalid_index", + }) + continue + + spec = spectra[idx] + result = validate_psm(spec, psm["sequence"], tolerance=tolerance, charge=psm.get("charge", 1)) + result["spectrum_index"] = idx + result["status"] = "valid" if result["coverage"] > 0 else "no_match" + results.append(result) + + return results + + +def write_tsv(results: List[dict], output_path: str) -> None: + """Write validation results to TSV. + + Parameters + ---------- + results: + List of result dicts. + output_path: + Output file path. + """ + if not results: + return + fieldnames = ["spectrum_index", "sequence", "theoretical_ions", "matched_ions", "coverage", + "peptide_mass", "status"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Validate PSMs by recomputing fragment coverage." + ) + parser.add_argument("--mzml", required=True, help="Input mzML file") + parser.add_argument("--peptides", required=True, help="PSM TSV (spectrum_index, sequence, charge)") + parser.add_argument("--tolerance", type=float, default=0.02, help="Fragment tolerance in Da (default: 0.02)") + parser.add_argument("--output", required=True, help="Output TSV file path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.mzml, exp) + + psms = load_psms(args.peptides) + print(f"Loaded {len(psms)} PSMs") + + results = validate_psms(exp, psms, tolerance=args.tolerance) + + valid_count = sum(1 for r in results if r["status"] == "valid") + print(f"Validated: {valid_count}/{len(results)} PSMs with fragment matches") + + write_tsv(results, args.output) + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_spectral_match_validator/requirements.txt b/scripts/proteomics/peptide_spectral_match_validator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_spectral_match_validator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_spectral_match_validator/tests/conftest.py b/scripts/proteomics/peptide_spectral_match_validator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_spectral_match_validator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py b/scripts/proteomics/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py new file mode 100644 index 0000000..0968b0a --- /dev/null +++ b/scripts/proteomics/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py @@ -0,0 +1,73 @@ +"""Tests for peptide_spectral_match_validator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPeptideSpectralMatchValidator: + def test_generate_theoretical_spectrum(self): + from peptide_spectral_match_validator import generate_theoretical_spectrum + + spec = generate_theoretical_spectrum("PEPTIDEK", charge=1) + assert spec.size() > 0 + + def test_validate_psm_with_matching_spectrum(self): + from peptide_spectral_match_validator import generate_theoretical_spectrum, validate_psm + + # Use the theoretical spectrum itself as the experimental spectrum -> perfect match + theo = generate_theoretical_spectrum("PEPTIDEK", charge=1) + result = validate_psm(theo, "PEPTIDEK", tolerance=0.02, charge=1) + assert result["coverage"] > 0 + assert result["matched_ions"] > 0 + assert result["theoretical_ions"] > 0 + + def test_validate_psm_no_match(self): + import numpy as np + import pyopenms as oms + from peptide_spectral_match_validator import validate_psm + + # Create a spectrum with peaks far from any theoretical ions + spec = oms.MSSpectrum() + mzs = np.array([10.0, 20.0, 30.0], dtype=np.float64) + ints = np.array([100.0, 200.0, 300.0], dtype=np.float64) + spec.set_peaks([mzs, ints]) + result = validate_psm(spec, "PEPTIDEK", tolerance=0.02, charge=1) + assert result["matched_ions"] == 0 + + def test_validate_psms_batch(self): + import pyopenms as oms + from peptide_spectral_match_validator import generate_theoretical_spectrum, validate_psms + + exp = oms.MSExperiment() + # Add a theoretical spectrum as "experimental" + theo = generate_theoretical_spectrum("PEPTIDEK", charge=1) + theo.setMSLevel(2) + theo.setRT(10.0) + exp.addSpectrum(theo) + + psms = [{"spectrum_index": 0, "sequence": "PEPTIDEK", "charge": 1}] + results = validate_psms(exp, psms, tolerance=0.02) + assert len(results) == 1 + assert results[0]["status"] == "valid" + + def test_invalid_index(self): + import pyopenms as oms + from peptide_spectral_match_validator import validate_psms + + exp = oms.MSExperiment() + psms = [{"spectrum_index": 999, "sequence": "PEPTIDEK", "charge": 1}] + results = validate_psms(exp, psms, tolerance=0.02) + assert results[0]["status"] == "invalid_index" + + def test_write_tsv(self, tmp_path): + from peptide_spectral_match_validator import write_tsv + + results = [{ + "spectrum_index": 0, "sequence": "PEPTIDEK", "theoretical_ions": 14, + "matched_ions": 10, "coverage": 0.714, "peptide_mass": 927.4, "status": "valid", + }] + out = str(tmp_path / "val.tsv") + write_tsv(results, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 diff --git a/scripts/proteomics/peptide_to_protein_mapper/README.md b/scripts/proteomics/peptide_to_protein_mapper/README.md new file mode 100644 index 0000000..d192f4a --- /dev/null +++ b/scripts/proteomics/peptide_to_protein_mapper/README.md @@ -0,0 +1,18 @@ +# Peptide to Protein Mapper + +Map peptides to proteins by searching a FASTA database. + +## Usage + +```bash +python peptide_to_protein_mapper.py --peptides peptides.tsv --fasta db.fasta --output mapped.tsv +``` + +## Input + +- **peptides.tsv** - TSV with a `peptide` column +- **db.fasta** - Protein FASTA database + +## Output + +TSV with columns: `peptide`, `protein`, `protein_description`, `start`, `end`, `is_unique` diff --git a/scripts/proteomics/peptide_to_protein_mapper/peptide_to_protein_mapper.py b/scripts/proteomics/peptide_to_protein_mapper/peptide_to_protein_mapper.py new file mode 100644 index 0000000..aed373f --- /dev/null +++ b/scripts/proteomics/peptide_to_protein_mapper/peptide_to_protein_mapper.py @@ -0,0 +1,164 @@ +""" +Peptide to Protein Mapper +========================= +Map peptides to proteins by searching a FASTA database. + +Uses pyopenms FASTAFile and AASequence to read FASTA entries and match +peptide sequences to protein sequences. + +Usage +----- + python peptide_to_protein_mapper.py --peptides peptides.tsv --fasta db.fasta --output mapped.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def read_fasta(filepath: str) -> list: + """Read a FASTA file using pyopenms. + + Parameters + ---------- + filepath: + Path to FASTA file. + + Returns + ------- + list + List of (accession, description, sequence) tuples. + """ + entries = [] + fasta_entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(filepath, fasta_entries) + for entry in fasta_entries: + entries.append((entry.identifier, entry.description, entry.sequence)) + return entries + + +def read_peptides(filepath: str) -> list: + """Read a peptide list TSV. + + Expected columns: peptide (required), plus optional columns. + + Returns + ------- + list + List of dicts. + """ + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + return list(reader) + + +def map_peptides_to_proteins(peptides: list, fasta_entries: list) -> list: + """Map each peptide to all proteins containing its sequence. + + Parameters + ---------- + peptides: + List of dicts with at least a 'peptide' key. + fasta_entries: + List of (accession, description, sequence) tuples. + + Returns + ------- + list + List of dicts with keys: peptide, protein, protein_description, start, end, is_unique. + """ + results = [] + peptide_protein_counts = {} + + # First pass: count how many proteins each peptide maps to + for pep_row in peptides: + pep_seq = pep_row["peptide"].upper().strip() + # Strip modifications for matching (simple bracket removal) + clean_seq = _strip_modifications(pep_seq) + count = 0 + for accession, description, prot_seq in fasta_entries: + if clean_seq in prot_seq: + count += 1 + peptide_protein_counts[pep_seq] = count + + # Second pass: build mappings + for pep_row in peptides: + pep_seq = pep_row["peptide"].upper().strip() + clean_seq = _strip_modifications(pep_seq) + is_unique = peptide_protein_counts.get(pep_seq, 0) == 1 + matched = False + + for accession, description, prot_seq in fasta_entries: + start = prot_seq.find(clean_seq) + if start >= 0: + results.append({ + "peptide": pep_seq, + "protein": accession, + "protein_description": description, + "start": start + 1, # 1-based + "end": start + len(clean_seq), + "is_unique": is_unique, + }) + matched = True + + if not matched: + results.append({ + "peptide": pep_seq, + "protein": "", + "protein_description": "", + "start": 0, + "end": 0, + "is_unique": False, + }) + + return results + + +def _strip_modifications(sequence: str) -> str: + """Remove bracket-enclosed modifications from a peptide sequence.""" + result = [] + in_bracket = False + for ch in sequence: + if ch == "[": + in_bracket = True + elif ch == "]": + in_bracket = False + elif not in_bracket: + result.append(ch) + return "".join(result) + + +def main(): + parser = argparse.ArgumentParser(description="Map peptides to proteins in a FASTA database.") + parser.add_argument("--peptides", required=True, help="Input peptide TSV (must have 'peptide' column)") + parser.add_argument("--fasta", required=True, help="FASTA database file") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + peptide_rows = read_peptides(args.peptides) + fasta_entries = read_fasta(args.fasta) + mappings = map_peptides_to_proteins(peptide_rows, fasta_entries) + + fieldnames = ["peptide", "protein", "protein_description", "start", "end", "is_unique"] + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(mappings) + + n_mapped = sum(1 for m in mappings if m["protein"]) + n_unique = sum(1 for m in mappings if m["is_unique"]) + print(f"Peptides: {len(peptide_rows)}") + print(f"Proteins in FASTA: {len(fasta_entries)}") + print(f"Mappings: {n_mapped}") + print(f"Unique peptides: {n_unique}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_to_protein_mapper/requirements.txt b/scripts/proteomics/peptide_to_protein_mapper/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_to_protein_mapper/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_to_protein_mapper/tests/conftest.py b/scripts/proteomics/peptide_to_protein_mapper/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_to_protein_mapper/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py b/scripts/proteomics/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py new file mode 100644 index 0000000..a90372b --- /dev/null +++ b/scripts/proteomics/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py @@ -0,0 +1,66 @@ +"""Tests for peptide_to_protein_mapper.""" + + +from conftest import requires_pyopenms +from peptide_to_protein_mapper import _strip_modifications, map_peptides_to_proteins, read_fasta + + +@requires_pyopenms +class TestPeptideToProteinMapper: + def _make_fasta_entries(self): + return [ + ("sp|P12345|PROT1", "Protein 1", "MKPEPTIDEKHELLO"), + ("sp|P67890|PROT2", "Protein 2", "TESTPEPWORLDANOTHERPEP"), + ("sp|P11111|PROT3", "Protein 3", "MKPEPTIDEKWORLD"), # shares PEPTIDEK + ] + + def test_basic_mapping(self): + fasta = self._make_fasta_entries() + peptides = [{"peptide": "PEPTIDEK"}] + result = map_peptides_to_proteins(peptides, fasta) + proteins = [r["protein"] for r in result] + assert "sp|P12345|PROT1" in proteins + assert "sp|P11111|PROT3" in proteins + + def test_unique_peptide(self): + fasta = self._make_fasta_entries() + peptides = [{"peptide": "TESTPEP"}] + result = map_peptides_to_proteins(peptides, fasta) + assert len(result) == 1 + assert result[0]["is_unique"] is True + + def test_shared_peptide(self): + fasta = self._make_fasta_entries() + peptides = [{"peptide": "PEPTIDEK"}] + result = map_peptides_to_proteins(peptides, fasta) + assert len(result) == 2 + assert all(not r["is_unique"] for r in result) + + def test_unmapped_peptide(self): + fasta = self._make_fasta_entries() + peptides = [{"peptide": "NONEXISTENT"}] + result = map_peptides_to_proteins(peptides, fasta) + assert len(result) == 1 + assert result[0]["protein"] == "" + + def test_start_end_positions(self): + fasta = self._make_fasta_entries() + peptides = [{"peptide": "PEPTIDEK"}] + result = map_peptides_to_proteins(peptides, fasta) + p1_mapping = next(r for r in result if r["protein"] == "sp|P12345|PROT1") + assert p1_mapping["start"] == 3 # 1-based, MK|PEPTIDEK|HELLO + assert p1_mapping["end"] == 10 + + def test_strip_modifications(self): + assert _strip_modifications("PEPTM[147]IDEK") == "PEPTMIDEK" + assert _strip_modifications("PEPTIDEK") == "PEPTIDEK" + assert _strip_modifications("[Acetyl]PEPTIDEK") == "PEPTIDEK" + + def test_read_fasta(self, tmp_path): + fasta_file = str(tmp_path / "test.fasta") + with open(fasta_file, "w") as fh: + fh.write(">sp|P12345|PROT1 Test protein\n") + fh.write("MKPEPTIDEK\n") + entries = read_fasta(fasta_file) + assert len(entries) == 1 + assert "P12345" in entries[0][0] diff --git a/scripts/proteomics/peptide_uniqueness_checker/README.md b/scripts/proteomics/peptide_uniqueness_checker/README.md new file mode 100644 index 0000000..777af9a --- /dev/null +++ b/scripts/proteomics/peptide_uniqueness_checker/README.md @@ -0,0 +1,9 @@ +# Peptide Uniqueness Checker + +Check if peptides are proteotypic (unique to a single protein) in a FASTA database. + +## Usage + +```bash +python peptide_uniqueness_checker.py --peptides peptides.tsv --fasta db.fasta --output uniqueness.tsv +``` diff --git a/scripts/proteomics/peptide_uniqueness_checker/peptide_uniqueness_checker.py b/scripts/proteomics/peptide_uniqueness_checker/peptide_uniqueness_checker.py new file mode 100644 index 0000000..129c6ae --- /dev/null +++ b/scripts/proteomics/peptide_uniqueness_checker/peptide_uniqueness_checker.py @@ -0,0 +1,117 @@ +""" +Peptide Uniqueness Checker +========================== +Check if peptides are proteotypic (unique to a single protein) in a FASTA database. + +Features +-------- +- Map peptides to all matching proteins in a FASTA file +- Flag proteotypic (unique) vs shared peptides +- Report all matching protein accessions + +Usage +----- + python peptide_uniqueness_checker.py --peptides peptides.tsv --fasta db.fasta --output uniqueness.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(fasta_path: str) -> list: + """Load protein entries from a FASTA file. + + Parameters + ---------- + fasta_path : str + Path to FASTA file. + + Returns + ------- + list + List of (accession, sequence) tuples. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_entries = [] + fasta_file.load(fasta_path, fasta_entries) + for entry in fasta_entries: + entries.append((entry.identifier, entry.sequence)) + return entries + + +def check_uniqueness(peptides: list, fasta_path: str) -> list: + """Check peptide uniqueness against a FASTA database. + + Parameters + ---------- + peptides : list + List of peptide sequence strings. + fasta_path : str + Path to the FASTA file. + + Returns + ------- + list + List of dicts with keys: peptide, proteins, protein_count, is_proteotypic. + """ + proteins = load_fasta(fasta_path) + results = [] + for pep in peptides: + pep_upper = pep.strip().upper() + matching = [] + for accession, seq in proteins: + if pep_upper in seq.upper(): + matching.append(accession) + results.append({ + "peptide": pep, + "proteins": ";".join(matching), + "protein_count": len(matching), + "is_proteotypic": len(matching) == 1, + }) + return results + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Check peptide uniqueness in a FASTA database.") + parser.add_argument("--peptides", required=True, help="TSV file with 'sequence' column, or comma-separated list.") + parser.add_argument("--fasta", required=True, help="FASTA database file.") + parser.add_argument("--output", help="Output TSV file.") + args = parser.parse_args() + + # Load peptides + peptide_list = [] + try: + with open(args.peptides) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("sequence", "").strip() + if seq: + peptide_list.append(seq) + except (FileNotFoundError, KeyError): + peptide_list = [p.strip() for p in args.peptides.split(",") if p.strip()] + + results = check_uniqueness(peptide_list, args.fasta) + + if args.output: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["peptide", "proteins", "protein_count", "is_proteotypic"], + delimiter="\t") + writer.writeheader() + writer.writerows(results) + print(f"Results written to {args.output}") + else: + for r in results: + status = "proteotypic" if r["is_proteotypic"] else "shared" + print(f"{r['peptide']}\t{status}\t{r['protein_count']}\t{r['proteins']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/peptide_uniqueness_checker/requirements.txt b/scripts/proteomics/peptide_uniqueness_checker/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/peptide_uniqueness_checker/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/peptide_uniqueness_checker/tests/conftest.py b/scripts/proteomics/peptide_uniqueness_checker/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/peptide_uniqueness_checker/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py b/scripts/proteomics/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py new file mode 100644 index 0000000..00dc58b --- /dev/null +++ b/scripts/proteomics/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py @@ -0,0 +1,62 @@ +"""Tests for peptide_uniqueness_checker.""" + +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPeptideUniquenessChecker: + def _create_fasta(self, tmpdir): + """Create a synthetic FASTA file with two proteins.""" + import pyopenms as oms + + fasta_path = f"{tmpdir}/test.fasta" + entries = [] + e1 = oms.FASTAEntry() + e1.identifier = "sp|P00001|PROT1" + e1.sequence = "MSPEPTIDEKAAANOTHERPEPTIDE" + entries.append(e1) + e2 = oms.FASTAEntry() + e2.identifier = "sp|P00002|PROT2" + e2.sequence = "MSPEPTIDEKGGGUNIQUEPEPTIDE" + entries.append(e2) + oms.FASTAFile().store(fasta_path, entries) + return fasta_path + + def test_proteotypic_peptide(self): + from peptide_uniqueness_checker import check_uniqueness + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir) + results = check_uniqueness(["UNIQUEPEPTIDE"], fasta_path) + assert len(results) == 1 + assert results[0]["is_proteotypic"] is True + assert results[0]["protein_count"] == 1 + + def test_shared_peptide(self): + from peptide_uniqueness_checker import check_uniqueness + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir) + results = check_uniqueness(["PEPTIDEK"], fasta_path) + assert len(results) == 1 + assert results[0]["is_proteotypic"] is False + assert results[0]["protein_count"] == 2 + + def test_missing_peptide(self): + from peptide_uniqueness_checker import check_uniqueness + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir) + results = check_uniqueness(["ZZZZZZZ"], fasta_path) + assert results[0]["protein_count"] == 0 + assert results[0]["is_proteotypic"] is False + + def test_load_fasta(self): + from peptide_uniqueness_checker import load_fasta + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir) + entries = load_fasta(fasta_path) + assert len(entries) == 2 diff --git a/scripts/proteomics/phospho_enrichment_qc/README.md b/scripts/proteomics/phospho_enrichment_qc/README.md new file mode 100644 index 0000000..58bd8ec --- /dev/null +++ b/scripts/proteomics/phospho_enrichment_qc/README.md @@ -0,0 +1,17 @@ +# Phospho Enrichment QC + +Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios from search results. + +## Usage + +```bash +python phospho_enrichment_qc.py --input search_results.tsv --output enrichment.tsv +``` + +## Input Format + +Tab-separated file with a `sequence` column containing peptide sequences with modification annotations. + +## Output + +- `enrichment.tsv` - Enrichment metrics including efficiency and residue ratios diff --git a/scripts/proteomics/phospho_enrichment_qc/phospho_enrichment_qc.py b/scripts/proteomics/phospho_enrichment_qc/phospho_enrichment_qc.py new file mode 100644 index 0000000..5654464 --- /dev/null +++ b/scripts/proteomics/phospho_enrichment_qc/phospho_enrichment_qc.py @@ -0,0 +1,201 @@ +""" +Phospho Enrichment QC +===================== +Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios from search results. + +Analyzes peptide search results to determine what fraction of identified peptides +carry phosphorylation modifications, and breaks down the phosphorylation by +residue type (Ser, Thr, Tyr). + +Usage +----- + python phospho_enrichment_qc.py --input search_results.tsv --output enrichment.tsv +""" + +import argparse +import csv +import re +import sys +from typing import Dict, List, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def is_phosphopeptide(sequence: str) -> bool: + """Check if a peptide sequence contains phosphorylation modifications. + + Parameters + ---------- + sequence: + Peptide sequence, possibly with modifications in bracket notation. + + Returns + ------- + bool + True if the sequence contains phosphorylation. + """ + phospho_patterns = ["Phospho", "phospho", "(Phospho)", "[80]", "[79.966]"] + for pat in phospho_patterns: + if pat in sequence: + return True + return False + + +def count_phospho_residues(sequence: str) -> Dict[str, int]: + """Count phosphorylated Ser, Thr, and Tyr residues in a modified peptide sequence. + + Parameters + ---------- + sequence: + Modified peptide sequence string. + + Returns + ------- + dict + Counts with keys 'pSer', 'pThr', 'pTyr'. + """ + counts = {"pSer": 0, "pThr": 0, "pTyr": 0} + + # Pattern: residue followed by modification indicating phospho + # Matches S(Phospho), T(Phospho), Y(Phospho) or S[80], T[80], Y[80] etc. + phospho_mods = [r"S\(Phospho\)", r"T\(Phospho\)", r"Y\(Phospho\)", + r"S\[80\]", r"T\[80\]", r"Y\[80\]", + r"S\[79\.966\]", r"T\[79\.966\]", r"Y\[79\.966\]"] + + for pattern in phospho_mods[:3]: + counts["pSer"] += len(re.findall(pattern, sequence)) + for pattern in phospho_mods[3:6]: + # Already counted if Phospho notation present + if "(Phospho)" not in sequence: + counts["pSer"] += len(re.findall(phospho_mods[3], sequence)) + break + + # Simpler approach: parse using known modification patterns + counts = {"pSer": 0, "pThr": 0, "pTyr": 0} + ser_patterns = [r"S\(Phospho\)", r"S\[80\]", r"S\[79\.966\]", r"S\[167\]"] + thr_patterns = [r"T\(Phospho\)", r"T\[80\]", r"T\[79\.966\]", r"T\[181\]"] + tyr_patterns = [r"Y\(Phospho\)", r"Y\[80\]", r"Y\[79\.966\]", r"Y\[243\]"] + + for pat in ser_patterns: + counts["pSer"] += len(re.findall(pat, sequence)) + for pat in thr_patterns: + counts["pThr"] += len(re.findall(pat, sequence)) + for pat in tyr_patterns: + counts["pTyr"] += len(re.findall(pat, sequence)) + + return counts + + +def get_peptide_length(sequence: str) -> int: + """Get the amino acid length of a peptide using AASequence. + + Parameters + ---------- + sequence: + Peptide sequence string. + + Returns + ------- + int + Number of amino acid residues. + """ + try: + aa = oms.AASequence.fromString(sequence) + return aa.size() + except Exception: + # Strip modifications manually for length estimation + clean = re.sub(r"\[.*?\]", "", sequence) + clean = re.sub(r"\(.*?\)", "", clean) + return len(clean) + + +def compute_enrichment_stats( + rows: List[Dict[str, str]], +) -> Tuple[Dict[str, int], Dict[str, float]]: + """Compute enrichment statistics from search result rows. + + Parameters + ---------- + rows: + List of dicts with at least a 'sequence' key containing the peptide sequence. + + Returns + ------- + tuple + (counts, ratios) where counts has total, phospho, pSer, pThr, pTyr + and ratios has enrichment_efficiency, pSer_ratio, pThr_ratio, pTyr_ratio. + """ + counts = {"total": 0, "phospho": 0, "pSer": 0, "pThr": 0, "pTyr": 0} + + for row in rows: + seq = row.get("sequence", row.get("peptide", "")) + counts["total"] += 1 + if is_phosphopeptide(seq): + counts["phospho"] += 1 + residue_counts = count_phospho_residues(seq) + counts["pSer"] += residue_counts["pSer"] + counts["pThr"] += residue_counts["pThr"] + counts["pTyr"] += residue_counts["pTyr"] + + total_phospho_residues = counts["pSer"] + counts["pThr"] + counts["pTyr"] + ratios: Dict[str, float] = { + "enrichment_efficiency": counts["phospho"] / counts["total"] if counts["total"] > 0 else 0.0, + "pSer_ratio": counts["pSer"] / total_phospho_residues if total_phospho_residues > 0 else 0.0, + "pThr_ratio": counts["pThr"] / total_phospho_residues if total_phospho_residues > 0 else 0.0, + "pTyr_ratio": counts["pTyr"] / total_phospho_residues if total_phospho_residues > 0 else 0.0, + } + + return counts, ratios + + +def read_input(input_path: str) -> List[Dict[str, str]]: + """Read search results TSV file.""" + rows = [] + with open(input_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output(output_path: str, counts: Dict[str, int], ratios: Dict[str, float]) -> None: + """Write enrichment report to TSV file.""" + with open(output_path, "w", newline="") as f: + f.write("metric\tvalue\n") + f.write(f"total_peptides\t{counts['total']}\n") + f.write(f"phospho_peptides\t{counts['phospho']}\n") + f.write(f"pSer_sites\t{counts['pSer']}\n") + f.write(f"pThr_sites\t{counts['pThr']}\n") + f.write(f"pTyr_sites\t{counts['pTyr']}\n") + f.write(f"enrichment_efficiency\t{ratios['enrichment_efficiency']:.4f}\n") + f.write(f"pSer_ratio\t{ratios['pSer_ratio']:.4f}\n") + f.write(f"pThr_ratio\t{ratios['pThr_ratio']:.4f}\n") + f.write(f"pTyr_ratio\t{ratios['pTyr_ratio']:.4f}\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios." + ) + parser.add_argument("--input", required=True, help="Input search results TSV file") + parser.add_argument("--output", required=True, help="Output enrichment report TSV file") + args = parser.parse_args() + + rows = read_input(args.input) + counts, ratios = compute_enrichment_stats(rows) + write_output(args.output, counts, ratios) + + print(f"Total peptides: {counts['total']}") + print(f"Phospho peptides: {counts['phospho']}") + print(f"Enrichment efficiency: {ratios['enrichment_efficiency']:.4f}") + print(f"pSer: {counts['pSer']} pThr: {counts['pThr']} pTyr: {counts['pTyr']}") + print(f"pSer ratio: {ratios['pSer_ratio']:.4f}") + print(f"pThr ratio: {ratios['pThr_ratio']:.4f}") + print(f"pTyr ratio: {ratios['pTyr_ratio']:.4f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/phospho_enrichment_qc/requirements.txt b/scripts/proteomics/phospho_enrichment_qc/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/phospho_enrichment_qc/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/phospho_enrichment_qc/tests/conftest.py b/scripts/proteomics/phospho_enrichment_qc/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/phospho_enrichment_qc/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py b/scripts/proteomics/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py new file mode 100644 index 0000000..8dd4e08 --- /dev/null +++ b/scripts/proteomics/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py @@ -0,0 +1,81 @@ +"""Tests for phospho_enrichment_qc.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPhosphoEnrichmentQC: + def test_is_phosphopeptide_true(self): + from phospho_enrichment_qc import is_phosphopeptide + assert is_phosphopeptide("PEPTIDES(Phospho)K") is True + assert is_phosphopeptide("PEPTIDET[80]K") is True + + def test_is_phosphopeptide_false(self): + from phospho_enrichment_qc import is_phosphopeptide + assert is_phosphopeptide("PEPTIDEK") is False + + def test_count_phospho_residues_ser(self): + from phospho_enrichment_qc import count_phospho_residues + counts = count_phospho_residues("PEPTIDES(Phospho)K") + assert counts["pSer"] == 1 + assert counts["pThr"] == 0 + assert counts["pTyr"] == 0 + + def test_count_phospho_residues_thr(self): + from phospho_enrichment_qc import count_phospho_residues + counts = count_phospho_residues("PEPTIDET(Phospho)K") + assert counts["pThr"] == 1 + + def test_count_phospho_residues_tyr(self): + from phospho_enrichment_qc import count_phospho_residues + counts = count_phospho_residues("PEPTIDEY(Phospho)K") + assert counts["pTyr"] == 1 + + def test_count_phospho_multiple(self): + from phospho_enrichment_qc import count_phospho_residues + counts = count_phospho_residues("S(Phospho)EPTIDES(Phospho)T(Phospho)K") + assert counts["pSer"] == 2 + assert counts["pThr"] == 1 + + def test_get_peptide_length(self): + from phospho_enrichment_qc import get_peptide_length + assert get_peptide_length("PEPTIDEK") == 8 + + def test_compute_enrichment_stats(self): + from phospho_enrichment_qc import compute_enrichment_stats + rows = [ + {"sequence": "PEPTIDES(Phospho)K"}, + {"sequence": "PEPTIDET(Phospho)K"}, + {"sequence": "PEPTIDEK"}, + {"sequence": "ACDEFGHIK"}, + ] + counts, ratios = compute_enrichment_stats(rows) + assert counts["total"] == 4 + assert counts["phospho"] == 2 + assert counts["pSer"] == 1 + assert counts["pThr"] == 1 + assert abs(ratios["enrichment_efficiency"] - 0.5) < 1e-6 + + def test_compute_enrichment_stats_empty(self): + from phospho_enrichment_qc import compute_enrichment_stats + counts, ratios = compute_enrichment_stats([]) + assert counts["total"] == 0 + assert ratios["enrichment_efficiency"] == 0.0 + + def test_write_output(self): + from phospho_enrichment_qc import write_output + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "enrichment.tsv") + counts = {"total": 10, "phospho": 7, "pSer": 5, "pThr": 1, "pTyr": 1} + ratios = { + "enrichment_efficiency": 0.7, + "pSer_ratio": 5 / 7, "pThr_ratio": 1 / 7, "pTyr_ratio": 1 / 7 + } + write_output(output_path, counts, ratios) + assert os.path.exists(output_path) + with open(output_path) as f: + content = f.read() + assert "enrichment_efficiency" in content diff --git a/scripts/proteomics/phospho_motif_analyzer/README.md b/scripts/proteomics/phospho_motif_analyzer/README.md new file mode 100644 index 0000000..0dcec9b --- /dev/null +++ b/scripts/proteomics/phospho_motif_analyzer/README.md @@ -0,0 +1,19 @@ +# Phospho Motif Analyzer + +Extract amino acid windows around phosphosites and compute position-specific frequencies. + +## Usage + +```bash +python phospho_motif_analyzer.py --input phosphosites.tsv --fasta proteome.fasta --window 7 --output motifs.tsv +``` + +## Input Format + +- `phosphosites.tsv`: Tab-separated with columns `peptide`, `protein`, `site` (1-based position) +- `proteome.fasta`: FASTA file with protein sequences + +## Output + +- `motifs.tsv` - Input rows with added `motif_window` column +- `motifs_frequencies.tsv` - Position-specific amino acid frequencies diff --git a/scripts/proteomics/phospho_motif_analyzer/phospho_motif_analyzer.py b/scripts/proteomics/phospho_motif_analyzer/phospho_motif_analyzer.py new file mode 100644 index 0000000..c86165f --- /dev/null +++ b/scripts/proteomics/phospho_motif_analyzer/phospho_motif_analyzer.py @@ -0,0 +1,250 @@ +""" +Phospho Motif Analyzer +====================== +Extract +/-7 amino acid windows around phosphosites, compute position-specific frequencies. + +Given a list of phosphosites (peptide, protein, site position) and a FASTA proteome, +this tool extracts the surrounding sequence window and computes amino acid frequency +at each position relative to the phosphosite. + +Usage +----- + python phospho_motif_analyzer.py --input phosphosites.tsv --fasta proteome.fasta --window 7 --output motifs.tsv +""" + +import argparse +import csv +import sys +from collections import Counter +from typing import Dict, List, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(fasta_path: str) -> Dict[str, str]: + """Load a FASTA file into a dictionary mapping protein accession to sequence. + + Parameters + ---------- + fasta_path: + Path to the FASTA file. + + Returns + ------- + dict + Mapping of protein accession to amino acid sequence. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + + proteins = {} + for entry in entries: + # Use the identifier (first word of description or accession) + acc = entry.identifier.split()[0] if entry.identifier else "" + proteins[acc] = entry.sequence + return proteins + + +def extract_window(protein_seq: str, site_pos: int, window: int = 7) -> str: + """Extract a window of amino acids around a site position. + + Parameters + ---------- + protein_seq: + Full protein sequence. + site_pos: + 1-based position of the phosphosite in the protein. + window: + Number of residues on each side (default 7). + + Returns + ------- + str + Window sequence of length 2*window+1, padded with '_' if near terminus. + """ + idx = site_pos - 1 # Convert to 0-based + start = idx - window + end = idx + window + 1 + + result = [] + for i in range(start, end): + if i < 0 or i >= len(protein_seq): + result.append("_") + else: + result.append(protein_seq[i]) + return "".join(result) + + +def validate_peptide(sequence: str) -> bool: + """Validate a peptide sequence using AASequence. + + Parameters + ---------- + sequence: + Peptide sequence string. + + Returns + ------- + bool + True if parseable. + """ + try: + oms.AASequence.fromString(sequence) + return True + except Exception: + return False + + +def extract_motif_windows( + rows: List[Dict[str, str]], + proteins: Dict[str, str], + window: int = 7, +) -> List[Dict[str, str]]: + """Extract motif windows for all phosphosite rows. + + Parameters + ---------- + rows: + List of dicts with keys: peptide, protein, site (1-based position). + proteins: + Protein accession to sequence mapping. + window: + Window size on each side. + + Returns + ------- + list + List of dicts with added 'motif_window' key. + """ + results = [] + for row in rows: + protein_id = row["protein"] + site_pos = int(row["site"]) + new_row = dict(row) + + if protein_id in proteins: + motif = extract_window(proteins[protein_id], site_pos, window) + new_row["motif_window"] = motif + else: + new_row["motif_window"] = "_" * (2 * window + 1) + + new_row["valid_peptide"] = str(validate_peptide(row["peptide"])) + results.append(new_row) + + return results + + +def compute_position_frequencies( + windows: List[str], window: int = 7 +) -> Dict[int, Dict[str, float]]: + """Compute position-specific amino acid frequencies from a set of motif windows. + + Parameters + ---------- + windows: + List of motif window strings (all same length). + window: + Window size used to extract motifs. + + Returns + ------- + dict + Mapping of relative position (-window to +window) to amino acid frequency dict. + """ + total = len(windows) + if total == 0: + return {} + + width = 2 * window + 1 + frequencies: Dict[int, Dict[str, float]] = {} + + for pos in range(width): + rel_pos = pos - window + counter: Counter = Counter() + for w in windows: + if pos < len(w): + counter[w[pos]] += 1 + frequencies[rel_pos] = {aa: count / total for aa, count in counter.most_common()} + + return frequencies + + +def format_frequencies(frequencies: Dict[int, Dict[str, float]]) -> List[Tuple[int, str, float]]: + """Flatten frequency dict into a list of (position, amino_acid, frequency) tuples. + + Parameters + ---------- + frequencies: + Position-specific frequency dict. + + Returns + ------- + list + List of (position, amino_acid, frequency) tuples. + """ + result = [] + for pos in sorted(frequencies.keys()): + for aa, freq in sorted(frequencies[pos].items(), key=lambda x: -x[1]): + result.append((pos, aa, freq)) + return result + + +def read_input(input_path: str) -> List[Dict[str, str]]: + """Read phosphosite TSV input file.""" + rows = [] + with open(input_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output( + output_path: str, + motif_rows: List[Dict[str, str]], + frequencies: Dict[int, Dict[str, float]], +) -> None: + """Write motif windows and frequencies to output files.""" + if motif_rows: + fieldnames = list(motif_rows[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(motif_rows) + + freq_path = output_path.replace(".tsv", "_frequencies.tsv") + flat = format_frequencies(frequencies) + with open(freq_path, "w", newline="") as f: + f.write("position\tamino_acid\tfrequency\n") + for pos, aa, freq in flat: + f.write(f"{pos}\t{aa}\t{freq:.4f}\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Extract motif windows around phosphosites and compute position-specific frequencies." + ) + parser.add_argument("--input", required=True, help="Input phosphosites TSV file") + parser.add_argument("--fasta", required=True, help="Proteome FASTA file") + parser.add_argument("--window", type=int, default=7, help="Window size on each side (default: 7)") + parser.add_argument("--output", required=True, help="Output motifs TSV file") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + rows = read_input(args.input) + motif_rows = extract_motif_windows(rows, proteins, args.window) + windows = [r["motif_window"] for r in motif_rows] + frequencies = compute_position_frequencies(windows, args.window) + write_output(args.output, motif_rows, frequencies) + + print(f"Processed {len(motif_rows)} phosphosites") + print(f"Window size: +/-{args.window} residues") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/phospho_motif_analyzer/requirements.txt b/scripts/proteomics/phospho_motif_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/phospho_motif_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/phospho_motif_analyzer/tests/conftest.py b/scripts/proteomics/phospho_motif_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/phospho_motif_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py b/scripts/proteomics/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py new file mode 100644 index 0000000..741e08e --- /dev/null +++ b/scripts/proteomics/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py @@ -0,0 +1,110 @@ +"""Tests for phospho_motif_analyzer.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPhosphoMotifAnalyzer: + def _create_fasta(self, tmpdir, proteins): + """Helper to create a FASTA file from a dict of accession->sequence.""" + import pyopenms as oms + + fasta_path = os.path.join(tmpdir, "proteome.fasta") + entries = [] + for acc, seq in proteins.items(): + entry = oms.FASTAEntry() + entry.identifier = acc + entry.sequence = seq + entries.append(entry) + fasta_file = oms.FASTAFile() + fasta_file.store(fasta_path, entries) + return fasta_path + + def test_extract_window_center(self): + from phospho_motif_analyzer import extract_window + seq = "ABCDEFGHIJKLMNOP" + window = extract_window(seq, 8, 3) # 1-based pos 8 = 'H' + assert window == "EFGHIJK" + assert len(window) == 7 + + def test_extract_window_near_start(self): + from phospho_motif_analyzer import extract_window + seq = "ABCDEFGHIJ" + window = extract_window(seq, 2, 3) # 'B' + assert window == "_ABCDEF" + assert len(window) == 7 + + def test_extract_window_near_end(self): + from phospho_motif_analyzer import extract_window + seq = "ABCDEFGHIJ" + window = extract_window(seq, 9, 3) # 'I' + assert window == "FGHIJ__" + assert len(window) == 7 + + def test_validate_peptide(self): + from phospho_motif_analyzer import validate_peptide + assert validate_peptide("PEPTIDEK") is True + + def test_extract_motif_windows(self): + from phospho_motif_analyzer import extract_motif_windows + proteins = {"P1": "ABCDEFGHIJKLMNOPQRSTUVWXYZ"} + rows = [{"peptide": "DEFGH", "protein": "P1", "site": "10"}] + result = extract_motif_windows(rows, proteins, window=3) + assert len(result) == 1 + assert result[0]["motif_window"] == "GHIJKLM" + + def test_extract_motif_missing_protein(self): + from phospho_motif_analyzer import extract_motif_windows + proteins = {} + rows = [{"peptide": "DEFGH", "protein": "MISSING", "site": "5"}] + result = extract_motif_windows(rows, proteins, window=3) + assert result[0]["motif_window"] == "_______" + + def test_compute_position_frequencies(self): + from phospho_motif_analyzer import compute_position_frequencies + windows = ["ABA", "ACA", "ADA"] + freq = compute_position_frequencies(windows, window=1) + assert freq[-1]["A"] == 1.0 + assert freq[1]["A"] == 1.0 + assert abs(freq[0]["B"] - 1 / 3) < 1e-6 + + def test_compute_position_frequencies_empty(self): + from phospho_motif_analyzer import compute_position_frequencies + freq = compute_position_frequencies([], window=3) + assert freq == {} + + def test_format_frequencies(self): + from phospho_motif_analyzer import format_frequencies + freq = {0: {"A": 0.5, "B": 0.5}, 1: {"C": 1.0}} + flat = format_frequencies(freq) + assert len(flat) == 3 + + def test_load_fasta(self): + from phospho_motif_analyzer import load_fasta + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "ACDEFGHIKLMNPQRSTVWY"}) + proteins = load_fasta(fasta_path) + assert "P1" in proteins + assert proteins["P1"] == "ACDEFGHIKLMNPQRSTVWY" + + def test_full_pipeline(self): + from phospho_motif_analyzer import ( + compute_position_frequencies, + extract_motif_windows, + load_fasta, + write_output, + ) + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "ACDEFGHIKLMNPQRSTVWY"}) + proteins = load_fasta(fasta_path) + rows = [{"peptide": "DEFGH", "protein": "P1", "site": "5"}] + motif_rows = extract_motif_windows(rows, proteins, window=3) + windows = [r["motif_window"] for r in motif_rows] + frequencies = compute_position_frequencies(windows, window=3) + output_path = os.path.join(tmpdir, "output.tsv") + write_output(output_path, motif_rows, frequencies) + assert os.path.exists(output_path) diff --git a/scripts/proteomics/phosphosite_class_filter/README.md b/scripts/proteomics/phosphosite_class_filter/README.md new file mode 100644 index 0000000..41ea574 --- /dev/null +++ b/scripts/proteomics/phosphosite_class_filter/README.md @@ -0,0 +1,18 @@ +# Phosphosite Class Filter + +Classify phosphosites into Class I/II/III by localization probability and report enrichment efficiency. + +## Usage + +```bash +python phosphosite_class_filter.py --input phosphosites.tsv --class1-threshold 0.75 --output classified.tsv +``` + +## Input Format + +Tab-separated file with columns: `peptide`, `protein`, `site`, `localization_prob`, `modification` + +## Output + +- `classified.tsv` - Input rows with added `site_class` and `valid_peptide` columns +- `classified_summary.tsv` - Summary counts and enrichment efficiency diff --git a/scripts/proteomics/phosphosite_class_filter/phosphosite_class_filter.py b/scripts/proteomics/phosphosite_class_filter/phosphosite_class_filter.py new file mode 100644 index 0000000..74534ae --- /dev/null +++ b/scripts/proteomics/phosphosite_class_filter/phosphosite_class_filter.py @@ -0,0 +1,236 @@ +""" +Phosphosite Class Filter +======================== +Classify phosphosites into Class I/II/III by localization probability. +Report enrichment efficiency. + +Class I: localization_prob >= class1_threshold (default 0.75) +Class II: 0.50 <= localization_prob < class1_threshold +Class III: localization_prob < 0.50 + +Usage +----- + python phosphosite_class_filter.py --input phosphosites.tsv --class1-threshold 0.75 --output classified.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +CLASS1_DEFAULT_THRESHOLD = 0.75 +CLASS2_LOWER = 0.50 + + +def classify_phosphosite(localization_prob: float, class1_threshold: float = CLASS1_DEFAULT_THRESHOLD) -> str: + """Classify a phosphosite into Class I, II, or III based on localization probability. + + Parameters + ---------- + localization_prob: + Localization probability (0.0 to 1.0). + class1_threshold: + Minimum probability for Class I classification. + + Returns + ------- + str + One of 'Class I', 'Class II', 'Class III'. + """ + if localization_prob >= class1_threshold: + return "Class I" + elif localization_prob >= CLASS2_LOWER: + return "Class II" + else: + return "Class III" + + +def validate_modification(modification: str) -> bool: + """Check if a modification string refers to a phosphorylation using ModificationsDB. + + Parameters + ---------- + modification: + Modification name string. + + Returns + ------- + bool + True if the modification is recognized as phosphorylation-related. + """ + phospho_keywords = ["phospho", "phos", "79.966"] + mod_lower = modification.lower() + for kw in phospho_keywords: + if kw in mod_lower: + return True + # Try to look up via ModificationsDB + try: + mod_db = oms.ModificationsDB() + mod_obj = oms.ResidueModification() + mod_db.getModification(modification, mod_obj) + diff = mod_obj.getDiffMonoMass() + # Phosphorylation adds ~79.966 Da + if abs(diff - 79.966) < 0.1: + return True + except Exception: + pass + return False + + +def validate_phosphopeptide(sequence: str) -> bool: + """Validate that a sequence can be parsed as an AASequence. + + Parameters + ---------- + sequence: + Peptide sequence string. + + Returns + ------- + bool + True if the sequence is parseable. + """ + try: + oms.AASequence.fromString(sequence) + return True + except Exception: + return False + + +def classify_phosphosites( + rows: List[Dict[str, str]], + class1_threshold: float = CLASS1_DEFAULT_THRESHOLD, +) -> Tuple[List[Dict[str, str]], Dict[str, int]]: + """Classify a list of phosphosite rows and compute summary statistics. + + Parameters + ---------- + rows: + List of dicts with keys: peptide, protein, site, localization_prob, modification. + class1_threshold: + Minimum probability for Class I classification. + + Returns + ------- + tuple + (classified_rows, summary) where summary has counts per class. + """ + summary: Dict[str, int] = {"Class I": 0, "Class II": 0, "Class III": 0, "total": 0} + classified = [] + + for row in rows: + prob = float(row["localization_prob"]) + site_class = classify_phosphosite(prob, class1_threshold) + new_row = dict(row) + new_row["site_class"] = site_class + new_row["valid_peptide"] = str(validate_phosphopeptide(row["peptide"])) + classified.append(new_row) + summary[site_class] += 1 + summary["total"] += 1 + + return classified, summary + + +def compute_enrichment_efficiency(summary: Dict[str, int]) -> float: + """Compute enrichment efficiency as fraction of Class I sites. + + Parameters + ---------- + summary: + Dictionary with class counts and total. + + Returns + ------- + float + Fraction of Class I sites (0.0 to 1.0). + """ + if summary["total"] == 0: + return 0.0 + return summary["Class I"] / summary["total"] + + +def read_input(input_path: str) -> List[Dict[str, str]]: + """Read phosphosite TSV input file. + + Parameters + ---------- + input_path: + Path to input TSV file. + + Returns + ------- + list + List of row dictionaries. + """ + rows = [] + with open(input_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output(output_path: str, classified_rows: List[Dict[str, str]], summary: Dict[str, int]) -> None: + """Write classified phosphosites and summary to TSV. + + Parameters + ---------- + output_path: + Path to output TSV file. + classified_rows: + List of classified row dictionaries. + summary: + Summary statistics dictionary. + """ + if not classified_rows: + return + + fieldnames = list(classified_rows[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(classified_rows) + + # Write summary to a companion file + summary_path = output_path.replace(".tsv", "_summary.tsv") + enrichment = compute_enrichment_efficiency(summary) + with open(summary_path, "w", newline="") as f: + f.write("class\tcount\n") + for cls in ["Class I", "Class II", "Class III"]: + f.write(f"{cls}\t{summary[cls]}\n") + f.write(f"total\t{summary['total']}\n") + f.write(f"enrichment_efficiency\t{enrichment:.4f}\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Classify phosphosites into Class I/II/III by localization probability." + ) + parser.add_argument("--input", required=True, help="Input phosphosites TSV file") + parser.add_argument( + "--class1-threshold", type=float, default=CLASS1_DEFAULT_THRESHOLD, + help=f"Minimum localization probability for Class I (default: {CLASS1_DEFAULT_THRESHOLD})" + ) + parser.add_argument("--output", required=True, help="Output classified TSV file") + args = parser.parse_args() + + rows = read_input(args.input) + classified, summary = classify_phosphosites(rows, args.class1_threshold) + write_output(args.output, classified, summary) + + enrichment = compute_enrichment_efficiency(summary) + print(f"Total phosphosites: {summary['total']}") + print(f" Class I: {summary['Class I']}") + print(f" Class II: {summary['Class II']}") + print(f" Class III: {summary['Class III']}") + print(f"Enrichment efficiency (Class I fraction): {enrichment:.4f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/phosphosite_class_filter/requirements.txt b/scripts/proteomics/phosphosite_class_filter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/phosphosite_class_filter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/phosphosite_class_filter/tests/conftest.py b/scripts/proteomics/phosphosite_class_filter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/phosphosite_class_filter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/phosphosite_class_filter/tests/test_phosphosite_class_filter.py b/scripts/proteomics/phosphosite_class_filter/tests/test_phosphosite_class_filter.py new file mode 100644 index 0000000..14958e8 --- /dev/null +++ b/scripts/proteomics/phosphosite_class_filter/tests/test_phosphosite_class_filter.py @@ -0,0 +1,85 @@ +"""Tests for phosphosite_class_filter.""" + +import csv +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPhosphositeClassFilter: + def test_classify_class1(self): + from phosphosite_class_filter import classify_phosphosite + assert classify_phosphosite(0.90, 0.75) == "Class I" + + def test_classify_class2(self): + from phosphosite_class_filter import classify_phosphosite + assert classify_phosphosite(0.60, 0.75) == "Class II" + + def test_classify_class3(self): + from phosphosite_class_filter import classify_phosphosite + assert classify_phosphosite(0.30, 0.75) == "Class III" + + def test_classify_boundary(self): + from phosphosite_class_filter import classify_phosphosite + assert classify_phosphosite(0.75, 0.75) == "Class I" + assert classify_phosphosite(0.50, 0.75) == "Class II" + + def test_validate_phosphopeptide(self): + from phosphosite_class_filter import validate_phosphopeptide + assert validate_phosphopeptide("PEPTIDEK") is True + + def test_classify_phosphosites(self): + from phosphosite_class_filter import classify_phosphosites + rows = [ + {"peptide": "PEPTIDEK", "protein": "P1", "site": "S5", "localization_prob": "0.90", + "modification": "Phospho"}, + {"peptide": "ACDEFGH", "protein": "P2", "site": "T3", "localization_prob": "0.60", + "modification": "Phospho"}, + {"peptide": "KLMNPQR", "protein": "P3", "site": "Y7", "localization_prob": "0.30", + "modification": "Phospho"}, + ] + classified, summary = classify_phosphosites(rows, 0.75) + assert len(classified) == 3 + assert summary["Class I"] == 1 + assert summary["Class II"] == 1 + assert summary["Class III"] == 1 + + def test_enrichment_efficiency(self): + from phosphosite_class_filter import compute_enrichment_efficiency + summary = {"Class I": 3, "Class II": 1, "Class III": 1, "total": 5} + assert abs(compute_enrichment_efficiency(summary) - 0.6) < 1e-6 + + def test_enrichment_efficiency_empty(self): + from phosphosite_class_filter import compute_enrichment_efficiency + summary = {"Class I": 0, "Class II": 0, "Class III": 0, "total": 0} + assert compute_enrichment_efficiency(summary) == 0.0 + + def test_read_write_roundtrip(self): + from phosphosite_class_filter import classify_phosphosites, read_input, write_output + + with tempfile.TemporaryDirectory() as tmpdir: + input_path = os.path.join(tmpdir, "input.tsv") + output_path = os.path.join(tmpdir, "output.tsv") + with open(input_path, "w", newline="") as f: + writer = csv.DictWriter( + f, fieldnames=["peptide", "protein", "site", "localization_prob", "modification"], + delimiter="\t" + ) + writer.writeheader() + writer.writerow({ + "peptide": "PEPTIDEK", "protein": "P1", "site": "S5", + "localization_prob": "0.85", "modification": "Phospho" + }) + + rows = read_input(input_path) + classified, summary = classify_phosphosites(rows) + write_output(output_path, classified, summary) + assert os.path.exists(output_path) + + with open(output_path) as f: + reader = csv.DictReader(f, delimiter="\t") + result_rows = list(reader) + assert len(result_rows) == 1 + assert result_rows[0]["site_class"] == "Class I" diff --git a/scripts/proteomics/precursor_charge_distribution/README.md b/scripts/proteomics/precursor_charge_distribution/README.md new file mode 100644 index 0000000..ee64e05 --- /dev/null +++ b/scripts/proteomics/precursor_charge_distribution/README.md @@ -0,0 +1,10 @@ +# Precursor Charge Distribution + +Analyze charge state distribution across MS2 spectra in mzML files. + +## Usage + +```bash +python precursor_charge_distribution.py --input run.mzML +python precursor_charge_distribution.py --input run.mzML --output charge_dist.tsv +``` diff --git a/scripts/proteomics/precursor_charge_distribution/precursor_charge_distribution.py b/scripts/proteomics/precursor_charge_distribution/precursor_charge_distribution.py new file mode 100644 index 0000000..b793694 --- /dev/null +++ b/scripts/proteomics/precursor_charge_distribution/precursor_charge_distribution.py @@ -0,0 +1,146 @@ +""" +Precursor Charge Distribution +============================== +Analyze charge state distribution across MS2 spectra in mzML files. + +Features: +- Count precursor charge states from MS2 spectra +- Compute percentage distribution +- TSV output + +Usage +----- + python precursor_charge_distribution.py --input run.mzML + python precursor_charge_distribution.py --input run.mzML --output charge_dist.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def analyze_charge_distribution(input_path: str) -> list[dict]: + """Analyze precursor charge state distribution from mzML. + + Parameters + ---------- + input_path : str + Path to mzML file. + + Returns + ------- + list[dict] + List of dicts with keys: charge, count, percentage. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + charge_counts = {} + total_ms2 = 0 + + for i in range(exp.getNrSpectra()): + spec = exp.getSpectrum(i) + if spec.getMSLevel() != 2: + continue + + total_ms2 += 1 + precursors = spec.getPrecursors() + if precursors: + charge = precursors[0].getCharge() + charge_counts[charge] = charge_counts.get(charge, 0) + 1 + + results = [] + for charge in sorted(charge_counts.keys()): + count = charge_counts[charge] + pct = (count / total_ms2 * 100) if total_ms2 > 0 else 0.0 + results.append({ + "charge": charge, + "count": count, + "percentage": round(pct, 2), + }) + + return results + + +def create_synthetic_mzml(output_path: str, charge_dist: dict[int, int] | None = None) -> None: + """Create a synthetic mzML file with known charge distribution. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + charge_dist : dict or None + Charge state to count mapping. Defaults to {2: 50, 3: 30, 4: 10, 1: 10}. + """ + if charge_dist is None: + charge_dist = {2: 50, 3: 30, 4: 10, 1: 10} + + exp = oms.MSExperiment() + + # MS1 survey scan + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(0.0) + ms1.set_peaks(([500.0], [10000.0])) + exp.addSpectrum(ms1) + + scan_idx = 1 + for charge, count in charge_dist.items(): + for _ in range(count): + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(float(scan_idx)) + prec = oms.Precursor() + prec.setMZ(500.0) + prec.setCharge(charge) + ms2.setPrecursors([prec]) + ms2.set_peaks(([200.0, 300.0], [1000.0, 500.0])) + exp.addSpectrum(ms2) + scan_idx += 1 + + oms.MzMLFile().store(output_path, exp) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write charge distribution results to TSV file. + + Parameters + ---------- + results : list[dict] + List of charge distribution dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["charge", "count", "percentage"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Analyze charge state distribution across MS2 spectra." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + results = analyze_charge_distribution(args.input) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote charge distribution to {args.output}") + else: + print("charge\tcount\tpercentage") + for r in results: + print(f"{r['charge']}\t{r['count']}\t{r['percentage']}%") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/precursor_charge_distribution/requirements.txt b/scripts/proteomics/precursor_charge_distribution/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/precursor_charge_distribution/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/precursor_charge_distribution/tests/conftest.py b/scripts/proteomics/precursor_charge_distribution/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/precursor_charge_distribution/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/precursor_charge_distribution/tests/test_precursor_charge_distribution.py b/scripts/proteomics/precursor_charge_distribution/tests/test_precursor_charge_distribution.py new file mode 100644 index 0000000..fb173a4 --- /dev/null +++ b/scripts/proteomics/precursor_charge_distribution/tests/test_precursor_charge_distribution.py @@ -0,0 +1,53 @@ +"""Tests for precursor_charge_distribution.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPrecursorChargeDistribution: + def test_charge_distribution(self): + from precursor_charge_distribution import analyze_charge_distribution, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, charge_dist={2: 50, 3: 30, 4: 20}) + results = analyze_charge_distribution(mzml_path) + assert len(results) == 3 + charge_map = {r["charge"]: r["count"] for r in results} + assert charge_map[2] == 50 + assert charge_map[3] == 30 + assert charge_map[4] == 20 + + def test_percentages(self): + from precursor_charge_distribution import analyze_charge_distribution, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, charge_dist={2: 50, 3: 50}) + results = analyze_charge_distribution(mzml_path) + total_pct = sum(r["percentage"] for r in results) + assert abs(total_pct - 100.0) < 0.1 + + def test_result_keys(self): + from precursor_charge_distribution import analyze_charge_distribution, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path) + results = analyze_charge_distribution(mzml_path) + for r in results: + assert "charge" in r + assert "count" in r + assert "percentage" in r + + def test_write_tsv(self): + from precursor_charge_distribution import write_tsv + + results = [{"charge": 2, "count": 50, "percentage": 50.0}] + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "charge_dist.tsv") + write_tsv(results, out) + assert os.path.exists(out) diff --git a/scripts/proteomics/precursor_isolation_purity/precursor_isolation_purity.py b/scripts/proteomics/precursor_isolation_purity/precursor_isolation_purity.py new file mode 100644 index 0000000..6ac11bc --- /dev/null +++ b/scripts/proteomics/precursor_isolation_purity/precursor_isolation_purity.py @@ -0,0 +1,143 @@ +""" +Precursor Isolation Purity +=========================== +Estimate precursor purity for each MS2 spectrum by examining the +surrounding MS1 spectrum within the isolation window. + +Purity is defined as the fraction of total intensity in the isolation +window attributable to the target precursor ion. + +Usage +----- + python precursor_isolation_purity.py --input run.mzML --output purity.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def estimate_purity( + ms1_spec: oms.MSSpectrum, + precursor_mz: float, + isolation_width: float = 2.0, +) -> float: + """Estimate precursor purity from the MS1 spectrum. + + Parameters + ---------- + ms1_spec: + The MS1 spectrum closest in RT to the MS2. + precursor_mz: + The precursor m/z value. + isolation_width: + Total width of the isolation window in Da. + + Returns + ------- + float + Purity as a fraction (0.0 to 1.0). + """ + mzs, intensities = ms1_spec.get_peaks() + if len(mzs) == 0: + return 0.0 + + half_width = isolation_width / 2.0 + lo = precursor_mz - half_width + hi = precursor_mz + half_width + + total_in_window = 0.0 + target_intensity = 0.0 + closest_dist = float("inf") + + for mz, intensity in zip(mzs, intensities): + if lo <= mz <= hi: + total_in_window += float(intensity) + dist = abs(mz - precursor_mz) + if dist < closest_dist: + closest_dist = dist + target_intensity = float(intensity) + + if total_in_window == 0: + return 0.0 + return target_intensity / total_in_window + + +def compute_all_purities(exp: oms.MSExperiment, isolation_width: float = 2.0) -> list[dict]: + """Compute purity for all MS2 spectra in an experiment. + + Parameters + ---------- + exp: + Loaded ``pyopenms.MSExperiment``. + isolation_width: + Isolation window width in Da. + + Returns + ------- + list[dict] + Each dict has: scan_index, rt, precursor_mz, purity. + """ + spectra = exp.getSpectra() + last_ms1 = None + results = [] + + for i, spec in enumerate(spectra): + if spec.getMSLevel() == 1: + last_ms1 = spec + elif spec.getMSLevel() == 2 and last_ms1 is not None: + for prec in spec.getPrecursors(): + prec_mz = prec.getMZ() + iso_w = isolation_width + if prec.getIsolationWindowLowerOffset() > 0: + iso_w = prec.getIsolationWindowLowerOffset() + prec.getIsolationWindowUpperOffset() + purity = estimate_purity(last_ms1, prec_mz, iso_w) + results.append({ + "scan_index": i, + "rt": round(spec.getRT(), 4), + "precursor_mz": round(prec_mz, 6), + "purity": round(purity, 4), + }) + + return results + + +def main(): + parser = argparse.ArgumentParser( + description="Estimate precursor isolation purity from mzML." + ) + parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") + parser.add_argument("--output", required=True, metavar="FILE", help="Output purity TSV") + parser.add_argument( + "--isolation-width", type=float, default=2.0, + help="Default isolation window width in Da (default: 2.0)" + ) + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + purities = compute_all_purities(exp, args.isolation_width) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, fieldnames=["scan_index", "rt", "precursor_mz", "purity"], delimiter="\t" + ) + writer.writeheader() + writer.writerows(purities) + + if purities: + avg = sum(p["purity"] for p in purities) / len(purities) + print(f"Wrote {len(purities)} purity values to {args.output}") + print(f" Mean purity: {avg:.4f}") + else: + print("No MS2 spectra with precursors found.") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/precursor_isolation_purity/requirements.txt b/scripts/proteomics/precursor_isolation_purity/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/precursor_isolation_purity/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/precursor_isolation_purity/tests/conftest.py b/scripts/proteomics/precursor_isolation_purity/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/precursor_isolation_purity/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/precursor_isolation_purity/tests/test_precursor_isolation_purity.py b/scripts/proteomics/precursor_isolation_purity/tests/test_precursor_isolation_purity.py new file mode 100644 index 0000000..7750bf8 --- /dev/null +++ b/scripts/proteomics/precursor_isolation_purity/tests/test_precursor_isolation_purity.py @@ -0,0 +1,75 @@ +"""Tests for precursor_isolation_purity.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPrecursorIsolationPurity: + def _make_experiment(self): + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + + # MS1 with several peaks + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(10.0) + mzs = np.array([499.0, 500.0, 500.5, 501.0, 502.0], dtype=np.float64) + ints = np.array([100.0, 1000.0, 200.0, 100.0, 50.0], dtype=np.float64) + ms1.set_peaks([mzs, ints]) + exp.addSpectrum(ms1) + + # MS2 targeting 500.0 + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(10.5) + prec = oms.Precursor() + prec.setMZ(500.0) + ms2.setPrecursors([prec]) + ms2.set_peaks([np.array([200.0], dtype=np.float64), + np.array([500.0], dtype=np.float64)]) + exp.addSpectrum(ms2) + + return exp + + def test_estimate_purity(self): + import numpy as np + import pyopenms as oms + from precursor_isolation_purity import estimate_purity + + ms1 = oms.MSSpectrum() + mzs = np.array([499.0, 500.0, 501.0], dtype=np.float64) + ints = np.array([100.0, 1000.0, 100.0], dtype=np.float64) + ms1.set_peaks([mzs, ints]) + + purity = estimate_purity(ms1, 500.0, isolation_width=2.0) + # 500.0 has 1000, window has 499+500+501=1200 + assert purity > 0.8 + + def test_compute_all_purities(self): + from precursor_isolation_purity import compute_all_purities + + exp = self._make_experiment() + purities = compute_all_purities(exp) + assert len(purities) == 1 + assert purities[0]["precursor_mz"] == 500.0 + assert purities[0]["purity"] > 0.0 + + def test_empty_ms1(self): + import numpy as np + import pyopenms as oms + from precursor_isolation_purity import estimate_purity + + ms1 = oms.MSSpectrum() + ms1.set_peaks([np.array([], dtype=np.float64), np.array([], dtype=np.float64)]) + purity = estimate_purity(ms1, 500.0) + assert purity == 0.0 + + def test_purity_range(self): + from precursor_isolation_purity import compute_all_purities + + exp = self._make_experiment() + purities = compute_all_purities(exp) + for p in purities: + assert 0.0 <= p["purity"] <= 1.0 diff --git a/scripts/proteomics/precursor_recurrence_analyzer/README.md b/scripts/proteomics/precursor_recurrence_analyzer/README.md new file mode 100644 index 0000000..6097d29 --- /dev/null +++ b/scripts/proteomics/precursor_recurrence_analyzer/README.md @@ -0,0 +1,9 @@ +# Precursor Recurrence Analyzer + +Analyze precursor resampling in DDA runs by grouping MS2 precursors with similar m/z and RT. + +## Usage + +```bash +python precursor_recurrence_analyzer.py --input run.mzML --mz-tolerance 10 --rt-tolerance 30 --output recurrence.tsv +``` diff --git a/scripts/proteomics/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py b/scripts/proteomics/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py new file mode 100644 index 0000000..84f8a3a --- /dev/null +++ b/scripts/proteomics/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py @@ -0,0 +1,202 @@ +""" +Precursor Recurrence Analyzer +============================== +Analyze precursor resampling in DDA runs by grouping MS2 precursors with +similar m/z and RT values. + +Usage +----- + python precursor_recurrence_analyzer.py --input run.mzML --mz-tolerance 10 --rt-tolerance 30 --output recurrence.tsv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def extract_precursors(exp: oms.MSExperiment) -> List[dict]: + """Extract precursor information from MS2 spectra. + + Parameters + ---------- + exp: + Loaded MSExperiment object. + + Returns + ------- + list + List of dicts with spectrum_index, rt, precursor_mz, charge. + """ + precursors = [] + for i, spec in enumerate(exp.getSpectra()): + if spec.getMSLevel() < 2: + continue + rt = spec.getRT() + for prec in spec.getPrecursors(): + precursors.append({ + "spectrum_index": i, + "rt": rt, + "precursor_mz": prec.getMZ(), + "charge": prec.getCharge(), + }) + return precursors + + +def find_recurrent_precursors( + precursors: List[dict], + mz_tolerance_ppm: float = 10.0, + rt_tolerance_sec: float = 30.0, +) -> List[dict]: + """Group precursors by m/z and RT proximity to identify resampling. + + Parameters + ---------- + precursors: + List of precursor dicts. + mz_tolerance_ppm: + m/z tolerance in ppm for grouping. + rt_tolerance_sec: + RT tolerance in seconds for grouping. + + Returns + ------- + list + List of group dicts with group_id, precursor m/z, RT range, count. + """ + if not precursors: + return [] + + # Sort by m/z + sorted_precs = sorted(precursors, key=lambda x: x["precursor_mz"]) + + # Simple greedy clustering + assigned = [False] * len(sorted_precs) + groups = [] + group_id = 0 + + for i in range(len(sorted_precs)): + if assigned[i]: + continue + + cluster = [sorted_precs[i]] + assigned[i] = True + ref_mz = sorted_precs[i]["precursor_mz"] + ref_rt = sorted_precs[i]["rt"] + + for j in range(i + 1, len(sorted_precs)): + if assigned[j]: + continue + mz_diff_ppm = abs(sorted_precs[j]["precursor_mz"] - ref_mz) / ref_mz * 1e6 + if mz_diff_ppm > mz_tolerance_ppm: + break # sorted by m/z, no more matches + rt_diff = abs(sorted_precs[j]["rt"] - ref_rt) + if rt_diff <= rt_tolerance_sec: + cluster.append(sorted_precs[j]) + assigned[j] = True + + group_id += 1 + rts = [p["rt"] for p in cluster] + mzs = [p["precursor_mz"] for p in cluster] + groups.append({ + "group_id": group_id, + "mean_mz": round(sum(mzs) / len(mzs), 6), + "mz_range": round(max(mzs) - min(mzs), 6), + "rt_min": round(min(rts), 2), + "rt_max": round(max(rts), 2), + "rt_span": round(max(rts) - min(rts), 2), + "count": len(cluster), + "is_recurrent": len(cluster) > 1, + }) + + return groups + + +def summarize_recurrence(groups: List[dict]) -> dict: + """Summarize recurrence statistics. + + Parameters + ---------- + groups: + List of group dicts. + + Returns + ------- + dict + Summary statistics. + """ + total_groups = len(groups) + recurrent = [g for g in groups if g["is_recurrent"]] + n_recurrent = len(recurrent) + total_resampled = sum(g["count"] for g in recurrent) + max_count = max((g["count"] for g in groups), default=0) + + return { + "total_precursor_groups": total_groups, + "recurrent_groups": n_recurrent, + "unique_groups": total_groups - n_recurrent, + "total_resampled_scans": total_resampled, + "max_resampling": max_count, + "recurrence_rate": round(n_recurrent / total_groups, 4) if total_groups > 0 else 0.0, + } + + +def write_tsv(groups: List[dict], output_path: str) -> None: + """Write group results to TSV. + + Parameters + ---------- + groups: + List of group dicts. + output_path: + Output file path. + """ + if not groups: + return + fieldnames = list(groups[0].keys()) + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in groups: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Analyze precursor resampling in DDA runs." + ) + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument("--mz-tolerance", type=float, default=10.0, help="m/z tolerance in ppm (default: 10)") + parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + precursors = extract_precursors(exp) + print(f"Extracted {len(precursors)} precursors") + + groups = find_recurrent_precursors(precursors, args.mz_tolerance, args.rt_tolerance) + summary = summarize_recurrence(groups) + + print(f"Precursor groups : {summary['total_precursor_groups']}") + print(f"Recurrent groups : {summary['recurrent_groups']}") + print(f"Unique groups : {summary['unique_groups']}") + print(f"Recurrence rate : {summary['recurrence_rate']:.2%}") + print(f"Max resampling : {summary['max_resampling']}") + + if args.output: + write_tsv(groups, args.output) + print(f"\nResults written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/precursor_recurrence_analyzer/requirements.txt b/scripts/proteomics/precursor_recurrence_analyzer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/precursor_recurrence_analyzer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/precursor_recurrence_analyzer/tests/conftest.py b/scripts/proteomics/precursor_recurrence_analyzer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/precursor_recurrence_analyzer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py b/scripts/proteomics/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py new file mode 100644 index 0000000..abd65eb --- /dev/null +++ b/scripts/proteomics/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py @@ -0,0 +1,89 @@ +"""Tests for precursor_recurrence_analyzer.""" + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPrecursorRecurrenceAnalyzer: + def _make_experiment(self, precursor_mzs_rts): + """Create MSExperiment with MS2 spectra at given (mz, rt) pairs.""" + import pyopenms as oms + + exp = oms.MSExperiment() + for mz, rt in precursor_mzs_rts: + spec = oms.MSSpectrum() + spec.setMSLevel(2) + spec.setRT(rt) + mzs = np.array([100.0, 200.0], dtype=np.float64) + ints = np.array([500.0, 300.0], dtype=np.float64) + spec.set_peaks([mzs, ints]) + prec = oms.Precursor() + prec.setMZ(mz) + prec.setCharge(2) + spec.setPrecursors([prec]) + exp.addSpectrum(spec) + return exp + + def test_extract_precursors(self): + from precursor_recurrence_analyzer import extract_precursors + + exp = self._make_experiment([(500.0, 10.0), (600.0, 20.0)]) + precs = extract_precursors(exp) + assert len(precs) == 2 + assert precs[0]["precursor_mz"] == 500.0 + + def test_find_recurrent(self): + from precursor_recurrence_analyzer import find_recurrent_precursors + + # Two precursors with same m/z within tolerance, close in RT + precs = [ + {"spectrum_index": 0, "rt": 10.0, "precursor_mz": 500.0, "charge": 2}, + {"spectrum_index": 1, "rt": 20.0, "precursor_mz": 500.001, "charge": 2}, + ] + groups = find_recurrent_precursors(precs, mz_tolerance_ppm=10.0, rt_tolerance_sec=30.0) + recurrent = [g for g in groups if g["is_recurrent"]] + assert len(recurrent) == 1 + assert recurrent[0]["count"] == 2 + + def test_no_recurrence(self): + from precursor_recurrence_analyzer import find_recurrent_precursors + + # Two precursors far apart in m/z + precs = [ + {"spectrum_index": 0, "rt": 10.0, "precursor_mz": 500.0, "charge": 2}, + {"spectrum_index": 1, "rt": 20.0, "precursor_mz": 700.0, "charge": 2}, + ] + groups = find_recurrent_precursors(precs, mz_tolerance_ppm=10.0, rt_tolerance_sec=30.0) + recurrent = [g for g in groups if g["is_recurrent"]] + assert len(recurrent) == 0 + + def test_summarize(self): + from precursor_recurrence_analyzer import find_recurrent_precursors, summarize_recurrence + + precs = [ + {"spectrum_index": 0, "rt": 10.0, "precursor_mz": 500.0, "charge": 2}, + {"spectrum_index": 1, "rt": 15.0, "precursor_mz": 500.001, "charge": 2}, + {"spectrum_index": 2, "rt": 100.0, "precursor_mz": 700.0, "charge": 2}, + ] + groups = find_recurrent_precursors(precs, mz_tolerance_ppm=10.0, rt_tolerance_sec=30.0) + summary = summarize_recurrence(groups) + assert summary["total_precursor_groups"] == 2 + assert summary["recurrent_groups"] == 1 + + def test_empty_input(self): + from precursor_recurrence_analyzer import find_recurrent_precursors + + groups = find_recurrent_precursors([]) + assert groups == [] + + def test_write_tsv(self, tmp_path): + from precursor_recurrence_analyzer import write_tsv + + groups = [{"group_id": 1, "mean_mz": 500.0, "mz_range": 0.001, "rt_min": 10.0, + "rt_max": 15.0, "rt_span": 5.0, "count": 2, "is_recurrent": True}] + out = str(tmp_path / "rec.tsv") + write_tsv(groups, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 diff --git a/scripts/proteomics/protein_completeness_matrix/README.md b/scripts/proteomics/protein_completeness_matrix/README.md new file mode 100644 index 0000000..12c311c --- /dev/null +++ b/scripts/proteomics/protein_completeness_matrix/README.md @@ -0,0 +1,34 @@ +# Protein Completeness Matrix + +Compute data completeness per protein and per sample from a quantification matrix. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python protein_completeness_matrix.py --input quant_matrix.tsv \ + --min-completeness 0.5 --output completeness.tsv +``` + +### Input format + +Tab-separated quantification matrix with `protein_id` as the first column: + +``` +protein_id sample1 sample2 sample3 +P12345 100.5 NA 95.2 +P67890 200.1 180.3 190.7 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Input quantification matrix TSV | +| `--min-completeness` | Minimum completeness fraction to retain a protein (default: 0.0) | +| `--output` | Output completeness TSV | diff --git a/scripts/proteomics/protein_completeness_matrix/protein_completeness_matrix.py b/scripts/proteomics/protein_completeness_matrix/protein_completeness_matrix.py new file mode 100644 index 0000000..027161b --- /dev/null +++ b/scripts/proteomics/protein_completeness_matrix/protein_completeness_matrix.py @@ -0,0 +1,242 @@ +""" +Protein Completeness Matrix +============================= +Compute data completeness per protein and per sample from a quantification +matrix. Reports the fraction of non-missing values for each protein across +samples and for each sample across proteins, and optionally filters proteins +below a minimum completeness threshold. + +Usage +----- + python protein_completeness_matrix.py --input quant_matrix.tsv \ + --min-completeness 0.5 --output completeness.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Tuple + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + + +def load_quant_matrix(input_path: str) -> Tuple[List[str], List[str], np.ndarray]: + """Load a protein quantification matrix from TSV. + + The first column is expected to be ``protein_id`` and remaining columns + are sample IDs. Missing values (empty strings, ``NA``, ``NaN``) are + treated as missing. + + Parameters + ---------- + input_path: + Path to input TSV. + + Returns + ------- + tuple + (protein_ids, sample_ids, data_matrix) where data_matrix has shape + (n_proteins, n_samples) with NaN for missing values. + """ + protein_ids: List[str] = [] + rows: List[List[float]] = [] + + with open(input_path, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + fields = reader.fieldnames or [] + sample_ids = [f for f in fields if f != "protein_id"] + + for row in reader: + pid = row.get("protein_id", "").strip() + if not pid: + continue + protein_ids.append(pid) + values: List[float] = [] + for sid in sample_ids: + val = row.get(sid, "").strip() + if val in ("", "NA", "NaN", "nan", "null"): + values.append(float("nan")) + else: + try: + v = float(val) + values.append(v if v > 0 else float("nan")) + except (ValueError, TypeError): + values.append(float("nan")) + rows.append(values) + + data = np.array(rows, dtype=float) if rows else np.empty((0, len(sample_ids))) + return protein_ids, sample_ids, data + + +def compute_protein_completeness( + data: np.ndarray, +) -> np.ndarray: + """Compute completeness (fraction of non-NaN values) per protein (row). + + Parameters + ---------- + data: + Matrix of shape (n_proteins, n_samples). + + Returns + ------- + numpy.ndarray + Array of shape (n_proteins,) with completeness fractions. + """ + n_samples = data.shape[1] + if n_samples == 0: + return np.zeros(data.shape[0]) + non_missing = np.sum(~np.isnan(data), axis=1) + return non_missing / n_samples + + +def compute_sample_completeness( + data: np.ndarray, +) -> np.ndarray: + """Compute completeness (fraction of non-NaN values) per sample (column). + + Parameters + ---------- + data: + Matrix of shape (n_proteins, n_samples). + + Returns + ------- + numpy.ndarray + Array of shape (n_samples,) with completeness fractions. + """ + n_proteins = data.shape[0] + if n_proteins == 0: + return np.zeros(data.shape[1]) + non_missing = np.sum(~np.isnan(data), axis=0) + return non_missing / n_proteins + + +def filter_by_completeness( + protein_ids: List[str], + data: np.ndarray, + protein_completeness: np.ndarray, + min_completeness: float, +) -> Tuple[List[str], np.ndarray]: + """Filter proteins by minimum completeness threshold. + + Parameters + ---------- + protein_ids: + Protein identifiers. + data: + Data matrix. + protein_completeness: + Per-protein completeness values. + min_completeness: + Minimum fraction required to keep a protein. + + Returns + ------- + tuple + (filtered_ids, filtered_data) + """ + mask = protein_completeness >= min_completeness + filtered_ids = [pid for pid, keep in zip(protein_ids, mask) if keep] + filtered_data = data[mask] + return filtered_ids, filtered_data + + +def completeness_summary( + protein_completeness: np.ndarray, + sample_completeness: np.ndarray, + data: np.ndarray, +) -> Dict[str, object]: + """Compute overall completeness summary. + + Returns + ------- + dict + Summary statistics. + """ + total_cells = data.size + non_missing = int(np.sum(~np.isnan(data))) + return { + "total_proteins": data.shape[0], + "total_samples": data.shape[1], + "total_cells": total_cells, + "non_missing_cells": non_missing, + "overall_completeness": non_missing / total_cells if total_cells > 0 else 0.0, + "mean_protein_completeness": float(np.mean(protein_completeness)) if len(protein_completeness) > 0 else 0.0, + "median_protein_completeness": float(np.median(protein_completeness)) if len(protein_completeness) > 0 else 0.0, + "mean_sample_completeness": float(np.mean(sample_completeness)) if len(sample_completeness) > 0 else 0.0, + "median_sample_completeness": float(np.median(sample_completeness)) if len(sample_completeness) > 0 else 0.0, + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Compute data completeness per protein and sample." + ) + parser.add_argument("--input", required=True, help="Input quantification matrix TSV") + parser.add_argument( + "--min-completeness", type=float, default=0.0, + help="Minimum protein completeness to retain (default: 0.0 = keep all)", + ) + parser.add_argument("--output", required=True, help="Output completeness TSV") + args = parser.parse_args() + + protein_ids, sample_ids, data = load_quant_matrix(args.input) + if len(protein_ids) == 0: + sys.exit("No proteins found in input.") + + prot_comp = compute_protein_completeness(data) + samp_comp = compute_sample_completeness(data) + summary = completeness_summary(prot_comp, samp_comp, data) + + # Filter + filtered_ids, filtered_data = filter_by_completeness( + protein_ids, data, prot_comp, args.min_completeness + ) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + # Per-protein completeness + writer.writerow(["protein_id", "completeness", "n_present", "n_total"]) + for i, pid in enumerate(protein_ids): + n_present = int(np.sum(~np.isnan(data[i]))) + writer.writerow([pid, f"{prot_comp[i]:.4f}", n_present, data.shape[1]]) + writer.writerow([]) + + # Per-sample completeness + writer.writerow(["sample_id", "completeness", "n_present", "n_total"]) + for j, sid in enumerate(sample_ids): + n_present = int(np.sum(~np.isnan(data[:, j]))) + writer.writerow([sid, f"{samp_comp[j]:.4f}", n_present, data.shape[0]]) + writer.writerow([]) + + # Summary + writer.writerow(["metric", "value"]) + for key, val in summary.items(): + if isinstance(val, float): + writer.writerow([key, f"{val:.4f}"]) + else: + writer.writerow([key, val]) + + if args.min_completeness > 0: + writer.writerow([]) + writer.writerow(["filter_threshold", args.min_completeness]) + writer.writerow(["proteins_retained", len(filtered_ids)]) + writer.writerow(["proteins_removed", len(protein_ids) - len(filtered_ids)]) + + print(f"Proteins: {len(protein_ids)}, overall completeness: {summary['overall_completeness']:.1%}") + if args.min_completeness > 0: + print( + f"After filtering (>={args.min_completeness:.0%}): " + f"{len(filtered_ids)}/{len(protein_ids)} proteins retained" + ) + print(f"Output -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/protein_completeness_matrix/requirements.txt b/scripts/proteomics/protein_completeness_matrix/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/proteomics/protein_completeness_matrix/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/proteomics/protein_completeness_matrix/tests/conftest.py b/scripts/proteomics/protein_completeness_matrix/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/protein_completeness_matrix/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/protein_completeness_matrix/tests/test_protein_completeness_matrix.py b/scripts/proteomics/protein_completeness_matrix/tests/test_protein_completeness_matrix.py new file mode 100644 index 0000000..6e4f2e2 --- /dev/null +++ b/scripts/proteomics/protein_completeness_matrix/tests/test_protein_completeness_matrix.py @@ -0,0 +1,111 @@ +"""Tests for protein_completeness_matrix.""" + +import csv +import sys + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_load_quant_matrix(tmp_path): + from protein_completeness_matrix import load_quant_matrix + + input_file = tmp_path / "quant.tsv" + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein_id", "s1", "s2", "s3"]) + writer.writerow(["P1", "100.0", "NA", "95.0"]) + writer.writerow(["P2", "200.0", "180.0", "190.0"]) + + pids, sids, data = load_quant_matrix(str(input_file)) + assert pids == ["P1", "P2"] + assert sids == ["s1", "s2", "s3"] + assert data.shape == (2, 3) + assert np.isnan(data[0, 1]) # P1, s2 is NA + assert data[1, 0] == 200.0 + + +@requires_pyopenms +def test_compute_protein_completeness(): + from protein_completeness_matrix import compute_protein_completeness + + data = np.array([ + [1.0, np.nan, 1.0], # 2/3 complete + [1.0, 1.0, 1.0], # 3/3 complete + ]) + comp = compute_protein_completeness(data) + assert abs(comp[0] - 2.0 / 3.0) < 0.01 + assert abs(comp[1] - 1.0) < 0.01 + + +@requires_pyopenms +def test_compute_sample_completeness(): + from protein_completeness_matrix import compute_sample_completeness + + data = np.array([ + [1.0, np.nan, 1.0], + [1.0, 1.0, 1.0], + ]) + comp = compute_sample_completeness(data) + assert abs(comp[0] - 1.0) < 0.01 # s1: both proteins present + assert abs(comp[1] - 0.5) < 0.01 # s2: one of two + assert abs(comp[2] - 1.0) < 0.01 # s3: both present + + +@requires_pyopenms +def test_filter_by_completeness(): + from protein_completeness_matrix import filter_by_completeness + + data = np.array([ + [1.0, np.nan, np.nan], # 1/3 = 0.33 + [1.0, 1.0, 1.0], # 3/3 = 1.0 + [1.0, 1.0, np.nan], # 2/3 = 0.67 + ]) + comp = np.array([1.0 / 3, 1.0, 2.0 / 3]) + filtered_ids, filtered_data = filter_by_completeness( + ["P1", "P2", "P3"], data, comp, 0.5 + ) + assert filtered_ids == ["P2", "P3"] + assert filtered_data.shape == (2, 3) + + +@requires_pyopenms +def test_completeness_summary(): + from protein_completeness_matrix import completeness_summary + + data = np.array([ + [1.0, np.nan], + [1.0, 1.0], + ]) + prot_comp = np.array([0.5, 1.0]) + samp_comp = np.array([1.0, 0.5]) + summary = completeness_summary(prot_comp, samp_comp, data) + assert summary["total_proteins"] == 2 + assert summary["total_samples"] == 2 + assert summary["non_missing_cells"] == 3 + assert abs(summary["overall_completeness"] - 0.75) < 0.01 + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from protein_completeness_matrix import main + + input_file = tmp_path / "quant.tsv" + output_file = tmp_path / "completeness.tsv" + + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein_id", "s1", "s2", "s3"]) + writer.writerow(["P1", "100.0", "NA", "95.0"]) + writer.writerow(["P2", "200.0", "180.0", "190.0"]) + writer.writerow(["P3", "NA", "NA", "50.0"]) + + sys.argv = [ + "protein_completeness_matrix.py", + "--input", str(input_file), + "--min-completeness", "0.5", + "--output", str(output_file), + ] + main() + assert output_file.exists() diff --git a/scripts/proteomics/protein_coverage_calculator/README.md b/scripts/proteomics/protein_coverage_calculator/README.md new file mode 100644 index 0000000..4b42e96 --- /dev/null +++ b/scripts/proteomics/protein_coverage_calculator/README.md @@ -0,0 +1,9 @@ +# Protein Coverage Calculator + +Map identified peptides to proteins and calculate sequence coverage. + +## Usage + +```bash +python protein_coverage_calculator.py --fasta proteins.fasta --peptides identified.tsv --output coverage.tsv +``` diff --git a/scripts/proteomics/protein_coverage_calculator/protein_coverage_calculator.py b/scripts/proteomics/protein_coverage_calculator/protein_coverage_calculator.py new file mode 100644 index 0000000..55c1e2c --- /dev/null +++ b/scripts/proteomics/protein_coverage_calculator/protein_coverage_calculator.py @@ -0,0 +1,128 @@ +""" +Protein Coverage Calculator +============================ +Map identified peptides to proteins and calculate sequence coverage. + +Features +-------- +- Map peptides to protein sequences from FASTA +- Calculate per-protein sequence coverage percentage +- Report covered and uncovered regions + +Usage +----- + python protein_coverage_calculator.py --fasta proteins.fasta --peptides identified.tsv --output coverage.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(fasta_path: str) -> dict: + """Load proteins from a FASTA file. + + Parameters + ---------- + fasta_path : str + Path to the FASTA file. + + Returns + ------- + dict + Mapping of accession to sequence string. + """ + entries = [] + oms.FASTAFile().load(fasta_path, entries) + return {e.identifier: e.sequence for e in entries} + + +def calculate_coverage(proteins: dict, peptides: list) -> list: + """Calculate sequence coverage for each protein. + + Parameters + ---------- + proteins : dict + Mapping of protein accession to sequence. + peptides : list + List of peptide sequence strings. + + Returns + ------- + list + List of dicts with coverage information per protein. + """ + results = [] + for accession, protein_seq in proteins.items(): + prot_upper = protein_seq.upper() + prot_len = len(prot_upper) + covered = [False] * prot_len + matched_peptides = [] + + for pep in peptides: + pep_upper = pep.strip().upper() + start = 0 + while True: + idx = prot_upper.find(pep_upper, start) + if idx == -1: + break + for i in range(idx, idx + len(pep_upper)): + covered[i] = True + if pep not in matched_peptides: + matched_peptides.append(pep) + start = idx + 1 + + covered_count = sum(covered) + coverage_pct = round(covered_count / prot_len * 100, 2) if prot_len > 0 else 0.0 + + results.append({ + "accession": accession, + "protein_length": prot_len, + "covered_residues": covered_count, + "coverage_percent": coverage_pct, + "matched_peptides": len(matched_peptides), + "peptides": ";".join(matched_peptides), + }) + return results + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Calculate protein sequence coverage from peptides.") + parser.add_argument("--fasta", required=True, help="Protein FASTA file.") + parser.add_argument("--peptides", required=True, help="TSV file with 'sequence' column.") + parser.add_argument("--output", help="Output TSV file.") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + + peptide_list = [] + with open(args.peptides) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("sequence", "").strip() + if seq: + peptide_list.append(seq) + + results = calculate_coverage(proteins, peptide_list) + + if args.output: + with open(args.output, "w", newline="") as fh: + fieldnames = ["accession", "protein_length", "covered_residues", "coverage_percent", + "matched_peptides", "peptides"] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + print(f"Results written to {args.output}") + else: + for r in results: + print(f"{r['accession']}\t{r['coverage_percent']}%\t{r['matched_peptides']} peptides") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/protein_coverage_calculator/requirements.txt b/scripts/proteomics/protein_coverage_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/protein_coverage_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/protein_coverage_calculator/tests/conftest.py b/scripts/proteomics/protein_coverage_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/protein_coverage_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/protein_coverage_calculator/tests/test_protein_coverage_calculator.py b/scripts/proteomics/protein_coverage_calculator/tests/test_protein_coverage_calculator.py new file mode 100644 index 0000000..51a3a21 --- /dev/null +++ b/scripts/proteomics/protein_coverage_calculator/tests/test_protein_coverage_calculator.py @@ -0,0 +1,57 @@ +"""Tests for protein_coverage_calculator.""" + +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestProteinCoverageCalculator: + def _create_fasta(self, tmpdir): + import pyopenms as oms + + fasta_path = f"{tmpdir}/test.fasta" + entries = [] + e1 = oms.FASTAEntry() + e1.identifier = "PROT1" + e1.sequence = "MSPEPTIDEKAAANOTHERPEPTIDE" + entries.append(e1) + oms.FASTAFile().store(fasta_path, entries) + return fasta_path + + def test_full_coverage(self): + from protein_coverage_calculator import calculate_coverage + + proteins = {"PROT1": "PEPTIDEK"} + results = calculate_coverage(proteins, ["PEPTIDEK"]) + assert results[0]["coverage_percent"] == 100.0 + + def test_partial_coverage(self): + from protein_coverage_calculator import calculate_coverage + + proteins = {"PROT1": "MSPEPTIDEKAAAA"} + results = calculate_coverage(proteins, ["PEPTIDEK"]) + assert 0 < results[0]["coverage_percent"] < 100 + + def test_no_match(self): + from protein_coverage_calculator import calculate_coverage + + proteins = {"PROT1": "MSPEPTIDEK"} + results = calculate_coverage(proteins, ["ZZZZZ"]) + assert results[0]["coverage_percent"] == 0.0 + + def test_load_fasta(self): + from protein_coverage_calculator import load_fasta + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir) + proteins = load_fasta(fasta_path) + assert "PROT1" in proteins + + def test_multiple_peptides(self): + from protein_coverage_calculator import calculate_coverage + + proteins = {"PROT1": "MSPEPTIDEKAAANOTHERPEPTIDE"} + results = calculate_coverage(proteins, ["PEPTIDEK", "ANOTHERPEPTIDE"]) + assert results[0]["matched_peptides"] == 2 + assert results[0]["coverage_percent"] > 50 diff --git a/scripts/proteomics/protein_group_reporter/README.md b/scripts/proteomics/protein_group_reporter/README.md new file mode 100644 index 0000000..aa37185 --- /dev/null +++ b/scripts/proteomics/protein_group_reporter/README.md @@ -0,0 +1,9 @@ +# Protein Group Reporter + +Parse protein groups from peptide-level data and a FASTA database. + +## Usage + +```bash +python protein_group_reporter.py --input peptides.tsv --fasta db.fasta --output groups.tsv +``` diff --git a/scripts/proteomics/protein_group_reporter/protein_group_reporter.py b/scripts/proteomics/protein_group_reporter/protein_group_reporter.py new file mode 100644 index 0000000..362c92a --- /dev/null +++ b/scripts/proteomics/protein_group_reporter/protein_group_reporter.py @@ -0,0 +1,223 @@ +""" +Protein Group Reporter +====================== +Parse protein groups from peptide-level data and a FASTA database, then +report a clean protein group table with peptide counts and sequence coverage. + +Usage +----- + python protein_group_reporter.py --input peptides.tsv --fasta db.fasta --output groups.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Set + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def load_fasta(fasta_path: str) -> Dict[str, str]: + """Load a FASTA file and return a dict mapping accession to sequence. + + Parameters + ---------- + fasta_path: + Path to a FASTA file. + + Returns + ------- + dict + Mapping of accession to protein sequence string. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + proteins = {} + for entry in entries: + proteins[entry.identifier] = entry.sequence + return proteins + + +def map_peptides_to_proteins( + peptides: List[str], + proteins: Dict[str, str], +) -> Dict[str, Set[str]]: + """Map peptides to proteins by substring matching. + + Parameters + ---------- + peptides: + List of peptide sequences. + proteins: + Dict mapping accession to protein sequence. + + Returns + ------- + dict + Mapping of protein accession to set of matched peptide sequences. + """ + protein_peptides: Dict[str, Set[str]] = {} + for pep in peptides: + pep = pep.strip() + if not pep: + continue + for acc, prot_seq in proteins.items(): + if pep in prot_seq: + if acc not in protein_peptides: + protein_peptides[acc] = set() + protein_peptides[acc].add(pep) + return protein_peptides + + +def compute_sequence_coverage(protein_seq: str, peptides: Set[str]) -> float: + """Compute sequence coverage of a protein by its peptides. + + Parameters + ---------- + protein_seq: + Full protein sequence. + peptides: + Set of peptide sequences mapped to this protein. + + Returns + ------- + float + Fraction of protein sequence covered (0.0 to 1.0). + """ + if not protein_seq: + return 0.0 + covered = [False] * len(protein_seq) + for pep in peptides: + start = 0 + while True: + idx = protein_seq.find(pep, start) + if idx == -1: + break + for i in range(idx, idx + len(pep)): + covered[i] = True + start = idx + 1 + return sum(covered) / len(protein_seq) + + +def build_protein_groups( + protein_peptides: Dict[str, Set[str]], + proteins: Dict[str, str], +) -> List[dict]: + """Build protein group report. + + Groups proteins that share the exact same set of peptides. + + Parameters + ---------- + protein_peptides: + Mapping of accession to peptide set. + proteins: + Mapping of accession to protein sequence. + + Returns + ------- + list + List of protein group dicts. + """ + # Group proteins with identical peptide sets + peptide_set_to_accessions: Dict[frozenset, List[str]] = {} + for acc, peps in protein_peptides.items(): + key = frozenset(peps) + if key not in peptide_set_to_accessions: + peptide_set_to_accessions[key] = [] + peptide_set_to_accessions[key].append(acc) + + results = [] + for pep_set, accessions in peptide_set_to_accessions.items(): + accessions_sorted = sorted(accessions) + lead = accessions_sorted[0] + coverage = compute_sequence_coverage(proteins.get(lead, ""), pep_set) + results.append({ + "protein_group": ";".join(accessions_sorted), + "lead_protein": lead, + "n_proteins": len(accessions_sorted), + "n_peptides": len(pep_set), + "peptides": ";".join(sorted(pep_set)), + "sequence_coverage": round(coverage, 4), + }) + + results.sort(key=lambda x: x["n_peptides"], reverse=True) + return results + + +def read_peptides_from_tsv(input_path: str, column: str = "sequence") -> List[str]: + """Read peptide sequences from a TSV file. + + Parameters + ---------- + input_path: + Path to input TSV. + column: + Column name for peptide sequences. + + Returns + ------- + list + List of peptide sequences. + """ + peptides = [] + with open(input_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + if column in row and row[column].strip(): + peptides.append(row[column].strip()) + return peptides + + +def write_tsv(results: List[dict], output_path: str) -> None: + """Write protein group results to TSV. + + Parameters + ---------- + results: + List of protein group dicts. + output_path: + Output file path. + """ + fieldnames = ["protein_group", "lead_protein", "n_proteins", "n_peptides", "peptides", "sequence_coverage"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Parse protein groups from peptide data and report clean table." + ) + parser.add_argument("--input", required=True, help="Input TSV with peptide sequences") + parser.add_argument("--fasta", required=True, help="FASTA database file") + parser.add_argument("--column", default="sequence", help="Column name for sequences (default: sequence)") + parser.add_argument("--output", required=True, help="Output TSV file path") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + print(f"Loaded {len(proteins)} proteins from {args.fasta}") + + peptides = read_peptides_from_tsv(args.input, column=args.column) + print(f"Read {len(peptides)} peptides from {args.input}") + + protein_peptides = map_peptides_to_proteins(peptides, proteins) + print(f"Mapped peptides to {len(protein_peptides)} proteins") + + groups = build_protein_groups(protein_peptides, proteins) + print(f"Built {len(groups)} protein groups") + + write_tsv(groups, args.output) + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/protein_group_reporter/requirements.txt b/scripts/proteomics/protein_group_reporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/protein_group_reporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/protein_group_reporter/tests/conftest.py b/scripts/proteomics/protein_group_reporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/protein_group_reporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/protein_group_reporter/tests/test_protein_group_reporter.py b/scripts/proteomics/protein_group_reporter/tests/test_protein_group_reporter.py new file mode 100644 index 0000000..6995b42 --- /dev/null +++ b/scripts/proteomics/protein_group_reporter/tests/test_protein_group_reporter.py @@ -0,0 +1,67 @@ +"""Tests for protein_group_reporter.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestProteinGroupReporter: + def test_map_peptides_to_proteins(self): + from protein_group_reporter import map_peptides_to_proteins + + proteins = {"P1": "PEPTIDEKAVLIDR", "P2": "ACDEFGHIK"} + peptides = ["PEPTIDEK", "AVLIDR", "ACDEFG"] + result = map_peptides_to_proteins(peptides, proteins) + assert "P1" in result + assert "PEPTIDEK" in result["P1"] + assert "AVLIDR" in result["P1"] + assert "P2" in result + assert "ACDEFG" in result["P2"] + + def test_compute_sequence_coverage(self): + from protein_group_reporter import compute_sequence_coverage + + protein = "PEPTIDEKAVLIDR" # 14 aa + coverage = compute_sequence_coverage(protein, {"PEPTIDEK"}) # 8 aa covered + assert 0.5 < coverage < 0.6 # 8/14 = 0.571 + + def test_compute_coverage_full(self): + from protein_group_reporter import compute_sequence_coverage + + protein = "PEPTIDEK" + coverage = compute_sequence_coverage(protein, {"PEPTIDEK"}) + assert coverage == 1.0 + + def test_compute_coverage_empty(self): + from protein_group_reporter import compute_sequence_coverage + + assert compute_sequence_coverage("PEPTIDEK", set()) == 0.0 + assert compute_sequence_coverage("", {"PEPTIDEK"}) == 0.0 + + def test_build_protein_groups(self): + from protein_group_reporter import build_protein_groups + + protein_peptides = { + "P1": {"PEPTIDEK", "AVLIDR"}, + "P2": {"PEPTIDEK", "AVLIDR"}, # Same set -> same group + "P3": {"ACDEFG"}, + } + proteins = {"P1": "PEPTIDEKAVLIDR", "P2": "PEPTIDEKAVLIDR", "P3": "ACDEFGHIK"} + groups = build_protein_groups(protein_peptides, proteins) + assert len(groups) == 2 + # The group with more peptides should be first + assert groups[0]["n_peptides"] == 2 + + def test_write_tsv(self, tmp_path): + from protein_group_reporter import write_tsv + + results = [{ + "protein_group": "P1;P2", "lead_protein": "P1", + "n_proteins": 2, "n_peptides": 2, + "peptides": "AVLIDR;PEPTIDEK", "sequence_coverage": 0.571, + }] + out = str(tmp_path / "groups.tsv") + write_tsv(results, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 + assert "protein_group" in lines[0] diff --git a/scripts/proteomics/proteoform_delta_annotator/README.md b/scripts/proteomics/proteoform_delta_annotator/README.md new file mode 100644 index 0000000..bc8f076 --- /dev/null +++ b/scripts/proteomics/proteoform_delta_annotator/README.md @@ -0,0 +1,33 @@ +# Proteoform Delta Annotator + +Annotate mass differences between proteoforms with known PTMs from the pyopenms ModificationsDB. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python proteoform_delta_annotator.py --input proteoform_masses.tsv --tolerance 0.5 --output annotated.tsv +``` + +### Input format + +Tab-separated file with `proteoform_id` and `mass` columns: + +``` +proteoform_id mass +P1_unmod 12345.678 +P1_phospho 12425.644 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Input TSV with proteoform IDs and masses | +| `--tolerance` | Mass tolerance in Da (default: 0.5) | +| `--output` | Output annotated TSV | diff --git a/scripts/proteomics/proteoform_delta_annotator/proteoform_delta_annotator.py b/scripts/proteomics/proteoform_delta_annotator/proteoform_delta_annotator.py new file mode 100644 index 0000000..4f4a48d --- /dev/null +++ b/scripts/proteomics/proteoform_delta_annotator/proteoform_delta_annotator.py @@ -0,0 +1,171 @@ +""" +Proteoform Delta Annotator +=========================== +Annotate mass differences between proteoforms with known PTMs from the +pyopenms ModificationsDB. Given a TSV of proteoform masses, the tool +computes pairwise or reference-based mass deltas and matches them against +a built-in PTM mass table within a user-specified tolerance. + +Usage +----- + python proteoform_delta_annotator.py --input proteoform_masses.tsv \ + --tolerance 0.5 --output annotated.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Optional, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def build_ptm_mass_table() -> List[Dict[str, object]]: + """Build a table of known PTM names and their monoisotopic mass shifts. + + Returns + ------- + list of dict + Each dict has ``name`` (str) and ``mass_shift`` (float). + """ + mod_db = oms.ModificationsDB() + mod_names: List[bytes] = [] + mod_db.getAllSearchModifications(mod_names) + + table: List[Dict[str, object]] = [] + seen: set = set() + for name_bytes in mod_names: + name = name_bytes.decode("utf-8") if isinstance(name_bytes, bytes) else str(name_bytes) + if name in seen: + continue + seen.add(name) + try: + mod = mod_db.getModification(name) + mass_shift = mod.getDiffMonoMass() + table.append({"name": name, "mass_shift": mass_shift}) + except Exception: + continue + return table + + +def annotate_delta( + delta: float, ptm_table: List[Dict[str, object]], tolerance: float +) -> List[Dict[str, object]]: + """Find PTMs whose mass shift matches *delta* within *tolerance*. + + Parameters + ---------- + delta: + Observed mass difference in Da. + ptm_table: + Table built by :func:`build_ptm_mass_table`. + tolerance: + Absolute mass tolerance in Da. + + Returns + ------- + list of dict + Matching PTMs with ``name``, ``mass_shift``, and ``error_da``. + """ + matches: List[Dict[str, object]] = [] + for entry in ptm_table: + error = abs(delta - entry["mass_shift"]) + if error <= tolerance: + matches.append({ + "name": entry["name"], + "mass_shift": entry["mass_shift"], + "error_da": error, + }) + matches.sort(key=lambda m: m["error_da"]) + return matches + + +def annotate_proteoform_deltas( + masses: List[Tuple[str, float]], + tolerance: float, + reference_mass: Optional[float] = None, +) -> List[Dict[str, object]]: + """Annotate mass deltas for a list of proteoforms. + + If *reference_mass* is given, each proteoform is compared to it. + Otherwise the first entry is used as the reference. + + Parameters + ---------- + masses: + List of ``(proteoform_id, mass)`` tuples. + tolerance: + Absolute tolerance in Da for PTM matching. + reference_mass: + Optional reference mass. Defaults to first entry. + + Returns + ------- + list of dict + One entry per proteoform with ``id``, ``mass``, ``delta``, and + ``annotations`` (semicolon-joined PTM names). + """ + ptm_table = build_ptm_mass_table() + if reference_mass is None: + reference_mass = masses[0][1] + + results: List[Dict[str, object]] = [] + for pf_id, mass in masses: + delta = mass - reference_mass + matches = annotate_delta(delta, ptm_table, tolerance) + annotation_str = "; ".join( + f"{m['name']} ({m['mass_shift']:.4f} Da, err={m['error_da']:.4f})" + for m in matches + ) + results.append({ + "proteoform_id": pf_id, + "mass": mass, + "delta": delta, + "annotations": annotation_str if annotation_str else "no match", + }) + return results + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Annotate mass differences between proteoforms with known PTMs." + ) + parser.add_argument( + "--input", required=True, + help="Input TSV with 'proteoform_id' and 'mass' columns", + ) + parser.add_argument( + "--tolerance", type=float, default=0.5, + help="Mass tolerance in Da (default: 0.5)", + ) + parser.add_argument("--output", required=True, help="Output annotated TSV") + args = parser.parse_args() + + masses: List[Tuple[str, float]] = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + pf_id = row.get("proteoform_id", "").strip() + mass_str = row.get("mass", "").strip() + if pf_id and mass_str: + masses.append((pf_id, float(mass_str))) + + if len(masses) < 1: + sys.exit("Need at least one proteoform in input.") + + results = annotate_proteoform_deltas(masses, args.tolerance) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["proteoform_id", "mass", "delta", "annotations"]) + for r in results: + writer.writerow([r["proteoform_id"], r["mass"], f"{r['delta']:.4f}", r["annotations"]]) + + print(f"Annotated {len(results)} proteoforms -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/proteoform_delta_annotator/requirements.txt b/scripts/proteomics/proteoform_delta_annotator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/proteoform_delta_annotator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/proteoform_delta_annotator/tests/conftest.py b/scripts/proteomics/proteoform_delta_annotator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/proteoform_delta_annotator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py b/scripts/proteomics/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py new file mode 100644 index 0000000..d3e282f --- /dev/null +++ b/scripts/proteomics/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py @@ -0,0 +1,80 @@ +"""Tests for proteoform_delta_annotator.""" + +import csv +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_build_ptm_mass_table(): + from proteoform_delta_annotator import build_ptm_mass_table + + table = build_ptm_mass_table() + assert len(table) > 0 + assert all("name" in entry and "mass_shift" in entry for entry in table) + + +@requires_pyopenms +def test_annotate_delta_phospho(): + from proteoform_delta_annotator import annotate_delta, build_ptm_mass_table + + table = build_ptm_mass_table() + # Phosphorylation mass shift is ~79.966 Da + matches = annotate_delta(79.966, table, 0.5) + assert len(matches) > 0 + # At least one match should contain "Phospho" + names = [m["name"] for m in matches] + assert any("Phospho" in n for n in names), f"Expected Phospho in {names}" + + +@requires_pyopenms +def test_annotate_delta_no_match(): + from proteoform_delta_annotator import annotate_delta, build_ptm_mass_table + + table = build_ptm_mass_table() + matches = annotate_delta(9999.0, table, 0.01) + assert len(matches) == 0 + + +@requires_pyopenms +def test_annotate_proteoform_deltas(): + from proteoform_delta_annotator import annotate_proteoform_deltas + + masses = [ + ("P1_unmod", 10000.0), + ("P1_phospho", 10079.966), + ] + results = annotate_proteoform_deltas(masses, tolerance=0.5) + assert len(results) == 2 + assert results[0]["delta"] == 0.0 + assert abs(results[1]["delta"] - 79.966) < 0.001 + assert "Phospho" in results[1]["annotations"] + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from proteoform_delta_annotator import main + + input_file = tmp_path / "input.tsv" + output_file = tmp_path / "output.tsv" + + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["proteoform_id", "mass"]) + writer.writerow(["P1_unmod", "10000.0"]) + writer.writerow(["P1_phospho", "10079.966"]) + + sys.argv = [ + "proteoform_delta_annotator.py", + "--input", str(input_file), + "--tolerance", "0.5", + "--output", str(output_file), + ] + main() + + assert output_file.exists() + with open(output_file) as fh: + reader = csv.DictReader(fh, delimiter="\t") + rows = list(reader) + assert len(rows) == 2 diff --git a/scripts/proteomics/psm_feature_extractor/README.md b/scripts/proteomics/psm_feature_extractor/README.md new file mode 100644 index 0000000..88c609f --- /dev/null +++ b/scripts/proteomics/psm_feature_extractor/README.md @@ -0,0 +1,24 @@ +# PSM Feature Extractor + +Extract rescoring features from PSMs by comparing experimental spectra to theoretical spectra. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python psm_feature_extractor.py --mzml run.mzML --peptides psms.tsv --output features.tsv +``` + +## PSM TSV Format + +The input PSMs file should be a TSV with columns: +- `sequence` (required): peptide sequence +- `charge` (required): charge state +- `rt` (required): retention time in seconds +- `scan_index` (optional): spectrum index in mzML +- `mz` (optional): precursor m/z (calculated from sequence if not provided) diff --git a/scripts/proteomics/psm_feature_extractor/psm_feature_extractor.py b/scripts/proteomics/psm_feature_extractor/psm_feature_extractor.py new file mode 100644 index 0000000..bd501a8 --- /dev/null +++ b/scripts/proteomics/psm_feature_extractor/psm_feature_extractor.py @@ -0,0 +1,236 @@ +""" +PSM Feature Extractor +===================== +Extract rescoring features from PSMs by comparing experimental spectra to +theoretical spectra generated from peptide sequences. + +Usage +----- + python psm_feature_extractor.py --mzml run.mzML --peptides psms.tsv --output features.tsv +""" + +import argparse +import csv +import sys +from typing import List, Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def load_mzml(input_path: str) -> oms.MSExperiment: + """Load an mzML file.""" + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + return exp + + +def load_psms_tsv(psms_path: str) -> List[dict]: + """Load PSMs from a TSV file. + + Expected columns: sequence, charge, rt, scan_index (or mz). + """ + psms = [] + with open(psms_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + psm = { + "sequence": row["sequence"], + "charge": int(row["charge"]), + "rt": float(row["rt"]), + "scan_index": int(row.get("scan_index", -1)) if row.get("scan_index") else -1, + "mz": float(row.get("mz", 0.0)) if row.get("mz") else 0.0, + } + if psm["mz"] == 0.0: + aa_seq = oms.AASequence.fromString(psm["sequence"]) + mass = aa_seq.getMonoWeight() + psm["mz"] = (mass + psm["charge"] * PROTON) / psm["charge"] + psms.append(psm) + return psms + + +def find_experimental_spectrum( + exp: oms.MSExperiment, + scan_index: int = -1, + precursor_mz: float = 0.0, + rt: float = 0.0, + mz_tolerance: float = 0.02, + rt_tolerance: float = 30.0, +) -> Optional[oms.MSSpectrum]: + """Find an experimental MS2 spectrum by scan index or precursor m/z + RT.""" + if 0 <= scan_index < exp.size(): + s = exp[scan_index] + if s.getMSLevel() == 2: + return s + + # Fallback: search by precursor mz + rt + best = None + best_dist = float("inf") + for spectrum in exp: + if spectrum.getMSLevel() != 2: + continue + if abs(spectrum.getRT() - rt) > rt_tolerance: + continue + precursors = spectrum.getPrecursors() + if not precursors: + continue + if abs(precursors[0].getMZ() - precursor_mz) > mz_tolerance: + continue + dist = abs(spectrum.getRT() - rt) + if dist < best_dist: + best_dist = dist + best = spectrum + + return best + + +def generate_theoretical_spectrum(sequence: str, charge: int) -> oms.MSSpectrum: + """Generate a theoretical MS2 spectrum for a peptide sequence.""" + aa_seq = oms.AASequence.fromString(sequence) + tsg = oms.TheoreticalSpectrumGenerator() + + params = tsg.getParameters() + params.setValue("add_b_ions", "true") + params.setValue("add_y_ions", "true") + params.setValue("add_metainfo", "true") + tsg.setParameters(params) + + theo_spectrum = oms.MSSpectrum() + tsg.getSpectrum(theo_spectrum, aa_seq, 1, charge) + return theo_spectrum + + +def compute_features( + exp_spectrum: oms.MSSpectrum, + theo_spectrum: oms.MSSpectrum, + sequence: str, + charge: int, + tolerance: float = 0.02, +) -> dict: + """Compute rescoring features from experimental vs theoretical spectrum comparison. + + Features include: + - matched_ions: number of matched peaks + - total_theoretical: total theoretical peaks + - matched_fraction: fraction of theoretical peaks matched + - matched_intensity_fraction: fraction of experimental intensity in matched peaks + - delta_rt: difference between experimental and expected RT (if available) + - precursor_mass_error: mass error of precursor + - sequence_length: length of peptide sequence + - charge: charge state + - num_peaks: number of peaks in experimental spectrum + """ + # Spectrum alignment + alignment = [] + spa = oms.SpectrumAlignment() + params = spa.getParameters() + params.setValue("tolerance", float(tolerance)) + params.setValue("is_relative_tolerance", "false") + spa.setParameters(params) + + spa.getSpectrumAlignment(alignment, theo_spectrum, exp_spectrum) + + matched_ions = len(alignment) + total_theoretical = theo_spectrum.size() + + # Compute matched intensity fraction + exp_mz, exp_int = exp_spectrum.get_peaks() + total_intensity = float(sum(exp_int)) if len(exp_int) > 0 else 0.0 + + matched_intensity = 0.0 + matched_exp_indices = set() + for pair in alignment: + exp_idx = pair[1] + if exp_idx < len(exp_int): + matched_intensity += float(exp_int[exp_idx]) + matched_exp_indices.add(exp_idx) + + matched_intensity_fraction = matched_intensity / total_intensity if total_intensity > 0 else 0.0 + matched_fraction = matched_ions / total_theoretical if total_theoretical > 0 else 0.0 + + # Compute precursor mass error + aa_seq = oms.AASequence.fromString(sequence) + theo_mass = aa_seq.getMonoWeight() + theo_mz = (theo_mass + charge * PROTON) / charge + + precursors = exp_spectrum.getPrecursors() + precursor_mass_error = 0.0 + if precursors: + exp_mz_prec = precursors[0].getMZ() + precursor_mass_error = (exp_mz_prec - theo_mz) * 1e6 / theo_mz if theo_mz > 0 else 0.0 + + return { + "sequence": sequence, + "charge": charge, + "matched_ions": matched_ions, + "total_theoretical": total_theoretical, + "matched_fraction": round(matched_fraction, 4), + "matched_intensity_fraction": round(matched_intensity_fraction, 4), + "precursor_mass_error_ppm": round(precursor_mass_error, 4), + "sequence_length": len(sequence), + "num_peaks": len(exp_mz), + } + + +def extract_features( + mzml_path: str, + psms_path: str, + output_path: str, + tolerance: float = 0.02, + mz_tolerance: float = 0.02, + rt_tolerance: float = 30.0, +) -> dict: + """Extract rescoring features for all PSMs. + + Returns statistics about the extraction. + """ + exp = load_mzml(mzml_path) + psms = load_psms_tsv(psms_path) + + results = [] + matched = 0 + + for psm in psms: + exp_spectrum = find_experimental_spectrum( + exp, psm["scan_index"], psm["mz"], psm["rt"], mz_tolerance, rt_tolerance + ) + if exp_spectrum is None: + continue + + theo_spectrum = generate_theoretical_spectrum(psm["sequence"], psm["charge"]) + features = compute_features(exp_spectrum, theo_spectrum, psm["sequence"], psm["charge"], tolerance) + results.append(features) + matched += 1 + + # Write output + if results: + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=results[0].keys(), delimiter="\t") + writer.writeheader() + writer.writerows(results) + + return {"total_psms": len(psms), "matched": matched} + + +def main() -> None: + parser = argparse.ArgumentParser(description="Extract rescoring features from PSMs.") + parser.add_argument("--mzml", required=True, help="Input mzML file") + parser.add_argument("--peptides", required=True, help="Input PSMs TSV file") + parser.add_argument("--output", required=True, help="Output features TSV file") + parser.add_argument("--tolerance", type=float, default=0.02, help="Fragment tolerance in Da (default: 0.02)") + parser.add_argument("--mz-tolerance", type=float, default=0.02, help="Precursor m/z tolerance (default: 0.02)") + parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") + args = parser.parse_args() + + stats = extract_features( + args.mzml, args.peptides, args.output, args.tolerance, args.mz_tolerance, args.rt_tolerance + ) + print(f"Extracted features for {stats['matched']} / {stats['total_psms']} PSMs to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/psm_feature_extractor/requirements.txt b/scripts/proteomics/psm_feature_extractor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/psm_feature_extractor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/psm_feature_extractor/tests/conftest.py b/scripts/proteomics/psm_feature_extractor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/psm_feature_extractor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/psm_feature_extractor/tests/test_psm_feature_extractor.py b/scripts/proteomics/psm_feature_extractor/tests/test_psm_feature_extractor.py new file mode 100644 index 0000000..143bd72 --- /dev/null +++ b/scripts/proteomics/psm_feature_extractor/tests/test_psm_feature_extractor.py @@ -0,0 +1,117 @@ +"""Tests for psm_feature_extractor.""" + +import os +import tempfile + +from conftest import requires_pyopenms + +PROTON = 1.007276 + + +def _create_test_data(tmp_dir): + """Create test mzML and PSM TSV matching specific peptides.""" + import pyopenms as oms + + sequences = ["ACDEFGHIK", "MNPQRSTWY"] + exp = oms.MSExperiment() + + for i, seq in enumerate(sequences): + aa_seq = oms.AASequence.fromString(seq) + mass = aa_seq.getMonoWeight() + precursor_mz = (mass + 2 * PROTON) / 2 + + # Generate theoretical spectrum to use as "experimental" + tsg = oms.TheoreticalSpectrumGenerator() + params = tsg.getParameters() + params.setValue("add_b_ions", "true") + params.setValue("add_y_ions", "true") + tsg.setParameters(params) + + theo = oms.MSSpectrum() + tsg.getSpectrum(theo, aa_seq, 1, 2) + + # Use theoretical peaks as experimental (perfect match scenario) + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(100.0 + i * 20) + prec = oms.Precursor() + prec.setMZ(precursor_mz) + prec.setCharge(2) + ms2.setPrecursors([prec]) + + mzs, ints = theo.get_peaks() + ms2.set_peaks((list(mzs), list(ints))) + exp.addSpectrum(ms2) + + mzml_path = os.path.join(tmp_dir, "test.mzML") + oms.MzMLFile().store(mzml_path, exp) + + # Create PSMs TSV + psms_path = os.path.join(tmp_dir, "psms.tsv") + with open(psms_path, "w") as fh: + fh.write("sequence\tcharge\trt\tscan_index\n") + fh.write("ACDEFGHIK\t2\t100.0\t0\n") + fh.write("MNPQRSTWY\t2\t120.0\t1\n") + + return mzml_path, psms_path + + +@requires_pyopenms +def test_generate_theoretical_spectrum(): + from psm_feature_extractor import generate_theoretical_spectrum + + theo = generate_theoretical_spectrum("ACDEFGHIK", 2) + assert theo.size() > 0 + + +@requires_pyopenms +def test_compute_features_perfect_match(): + from psm_feature_extractor import compute_features, generate_theoretical_spectrum + + theo = generate_theoretical_spectrum("ACDEFGHIK", 2) + # Use same spectrum as experimental + features = compute_features(theo, theo, "ACDEFGHIK", 2, tolerance=0.02) + assert features["matched_fraction"] == 1.0 + assert features["matched_ions"] == features["total_theoretical"] + assert features["sequence_length"] == 9 + + +@requires_pyopenms +def test_extract_features(): + from psm_feature_extractor import extract_features + + with tempfile.TemporaryDirectory() as tmp: + mzml_path, psms_path = _create_test_data(tmp) + output_path = os.path.join(tmp, "features.tsv") + + stats = extract_features(mzml_path, psms_path, output_path) + assert stats["total_psms"] == 2 + assert stats["matched"] == 2 + + with open(output_path) as fh: + lines = fh.readlines() + assert lines[0].strip().startswith("sequence") + assert len(lines) == 3 # header + 2 PSMs + + +@requires_pyopenms +def test_extract_features_content(): + import csv + + from psm_feature_extractor import extract_features + + with tempfile.TemporaryDirectory() as tmp: + mzml_path, psms_path = _create_test_data(tmp) + output_path = os.path.join(tmp, "features.tsv") + + extract_features(mzml_path, psms_path, output_path) + + with open(output_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + rows = list(reader) + + assert len(rows) == 2 + # Perfect match: matched_fraction should be high + for row in rows: + assert float(row["matched_fraction"]) > 0.5 + assert int(row["matched_ions"]) > 0 diff --git a/scripts/proteomics/ptm_site_localization_scorer/README.md b/scripts/proteomics/ptm_site_localization_scorer/README.md new file mode 100644 index 0000000..3d01fbf --- /dev/null +++ b/scripts/proteomics/ptm_site_localization_scorer/README.md @@ -0,0 +1,10 @@ +# PTM Site Localization Scorer + +Score PTM site localization confidence using fragment ion coverage comparison. + +## Usage + +```bash +python ptm_site_localization_scorer.py --mz-list "200.1,300.2,400.3" --intensities "100,200,150" \ + --peptide "PEPS(Phospho)TIDEK" --tolerance 0.02 --output scores.tsv +``` diff --git a/scripts/proteomics/ptm_site_localization_scorer/ptm_site_localization_scorer.py b/scripts/proteomics/ptm_site_localization_scorer/ptm_site_localization_scorer.py new file mode 100644 index 0000000..1b7a21e --- /dev/null +++ b/scripts/proteomics/ptm_site_localization_scorer/ptm_site_localization_scorer.py @@ -0,0 +1,249 @@ +""" +PTM Site Localization Scorer +============================== +Score PTM site localization confidence using fragment ion coverage comparison. + +Features +-------- +- Generate theoretical spectra for candidate PTM site assignments +- Match experimental peaks against theoretical fragments +- Score each candidate site by fragment ion coverage +- Report site localization probabilities + +Usage +----- + python ptm_site_localization_scorer.py --mz-list "200.1,300.2,400.3" --intensities "100,200,150" \\ + --peptide "PEPS(Phospho)TIDEK" --tolerance 0.02 --output scores.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def generate_theoretical_spectrum(sequence: str, charge: int = 1) -> list: + """Generate theoretical b/y ion spectrum for a modified peptide. + + Parameters + ---------- + sequence : str + Modified peptide sequence in pyopenms notation. + charge : int + Precursor charge state. + + Returns + ------- + list + List of (mz, annotation) tuples. + """ + aa_seq = oms.AASequence.fromString(sequence) + spec = oms.MSSpectrum() + tsg = oms.TheoreticalSpectrumGenerator() + + params = tsg.getParameters() + params.setValue("add_b_ions", "true") + params.setValue("add_y_ions", "true") + params.setValue("add_metainfo", "true") + tsg.setParameters(params) + + tsg.getSpectrum(spec, aa_seq, 1, charge) + + ions = [] + for i in range(spec.size()): + peak = spec[i] + mz = peak.getMZ() + name = "" + if spec.getStringDataArrays(): + name = spec.getStringDataArrays()[0][i].decode() if isinstance( + spec.getStringDataArrays()[0][i], bytes + ) else str(spec.getStringDataArrays()[0][i]) + ions.append((mz, name)) + return ions + + +def match_peaks(experimental_mz: list, theoretical_ions: list, + tolerance: float = 0.02) -> list: + """Match experimental peaks to theoretical fragment ions. + + Parameters + ---------- + experimental_mz : list + List of experimental m/z values. + theoretical_ions : list + List of (mz, annotation) tuples from theoretical spectrum. + tolerance : float + Mass tolerance in Da for peak matching. + + Returns + ------- + list + List of matched (experimental_mz, theoretical_mz, annotation, error) tuples. + """ + matches = [] + for exp_mz in experimental_mz: + for theo_mz, annotation in theoretical_ions: + error = abs(exp_mz - theo_mz) + if error <= tolerance: + matches.append((exp_mz, theo_mz, annotation, round(error, 6))) + return matches + + +def generate_site_candidates(sequence: str, mod_name: str, applicable_residues: str) -> list: + """Generate all possible site assignment candidates for a modification. + + Parameters + ---------- + sequence : str + Unmodified peptide sequence. + mod_name : str + Modification name (e.g., 'Phospho'). + applicable_residues : str + String of residues that can carry the modification (e.g., 'STY'). + + Returns + ------- + list + List of modified sequence strings, one per candidate site. + """ + plain = oms.AASequence.fromString(sequence).toUnmodifiedString() + candidates = [] + for i, aa in enumerate(plain): + if aa in applicable_residues: + seq_list = list(plain) + seq_list[i] = f"{aa}({mod_name})" + candidates.append("".join(seq_list)) + return candidates + + +def score_localization(experimental_mz: list, experimental_intensities: list, + peptide: str, tolerance: float = 0.02, + charge: int = 1) -> dict: + """Score PTM site localization for a modified peptide. + + Parameters + ---------- + experimental_mz : list + Experimental m/z values. + experimental_intensities : list + Experimental intensities corresponding to m/z values. + peptide : str + Modified peptide sequence. + tolerance : float + Mass tolerance for matching. + charge : int + Charge state. + + Returns + ------- + dict + Dictionary with the input peptide score and candidate site scores. + """ + # Score the given assignment + theo_ions = generate_theoretical_spectrum(peptide, charge) + matches = match_peaks(experimental_mz, theo_ions, tolerance) + given_score = len(matches) + + # Generate alternative candidates + aa_seq = oms.AASequence.fromString(peptide) + plain = aa_seq.toUnmodifiedString() + + # Find the modification in the given peptide + mod_info = None + for i in range(aa_seq.size()): + if aa_seq.isModified(i): + mod_info = (i, aa_seq.getResidue(i).getModificationName()) + break + + candidate_scores = [] + if mod_info: + mod_pos, mod_name = mod_info + # Determine applicable residues from the mod origin + applicable = set() + mod_db = oms.ModificationsDB() + mod_names_list = [] + mod_db.searchModifications( + mod_names_list, mod_name, "", oms.ResidueModification.TermSpecificity.ANYWHERE + ) + for mn in mod_names_list: + mod_obj = mod_db.getModification(mn) + origin = mod_obj.getOrigin() + if origin: + applicable.add(origin) + + if not applicable: + applicable = {plain[mod_pos]} + + candidates = generate_site_candidates(plain, mod_name, "".join(applicable)) + total_matches = 0 + for cand_seq in candidates: + cand_ions = generate_theoretical_spectrum(cand_seq, charge) + cand_matches = match_peaks(experimental_mz, cand_ions, tolerance) + cand_score = len(cand_matches) + total_matches += max(cand_score, 1) + candidate_scores.append({ + "sequence": cand_seq, + "matched_ions": cand_score, + }) + + # Normalize to probabilities + for cs in candidate_scores: + cs["probability"] = round(cs["matched_ions"] / total_matches, 4) if total_matches > 0 else 0.0 + else: + candidate_scores.append({ + "sequence": peptide, + "matched_ions": given_score, + "probability": 1.0, + }) + + candidate_scores.sort(key=lambda x: x["matched_ions"], reverse=True) + + return { + "peptide": peptide, + "charge": charge, + "tolerance": tolerance, + "total_experimental_peaks": len(experimental_mz), + "matched_ions_given": given_score, + "candidates": candidate_scores, + } + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Score PTM site localization confidence.") + parser.add_argument("--mz-list", required=True, help="Comma-separated experimental m/z values.") + parser.add_argument("--intensities", required=True, help="Comma-separated intensities.") + parser.add_argument("--peptide", required=True, help="Modified peptide sequence.") + parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02).") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") + parser.add_argument("--output", help="Output file (.tsv or .json).") + args = parser.parse_args() + + mz_values = [float(x.strip()) for x in args.mz_list.split(",")] + intensities = [float(x.strip()) for x in args.intensities.split(",")] + + result = score_localization(mz_values, intensities, args.peptide, args.tolerance, args.charge) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, fieldnames=["sequence", "matched_ions", "probability"], delimiter="\t" + ) + writer.writeheader() + writer.writerows(result["candidates"]) + print(f"Results written to {args.output}") + else: + print(json.dumps(result, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/ptm_site_localization_scorer/requirements.txt b/scripts/proteomics/ptm_site_localization_scorer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/ptm_site_localization_scorer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/ptm_site_localization_scorer/tests/conftest.py b/scripts/proteomics/ptm_site_localization_scorer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/ptm_site_localization_scorer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py b/scripts/proteomics/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py new file mode 100644 index 0000000..b24090a --- /dev/null +++ b/scripts/proteomics/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py @@ -0,0 +1,65 @@ +"""Tests for ptm_site_localization_scorer.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestPtmSiteLocalizationScorer: + def test_generate_theoretical_spectrum(self): + from ptm_site_localization_scorer import generate_theoretical_spectrum + + ions = generate_theoretical_spectrum("PEPTIDEK") + assert len(ions) > 0 + # All m/z values should be positive + for mz, _annotation in ions: + assert mz > 0 + + def test_match_peaks_exact(self): + from ptm_site_localization_scorer import generate_theoretical_spectrum, match_peaks + + ions = generate_theoretical_spectrum("PEPTIDEK") + # Use theoretical m/z as "experimental" + exp_mz = [mz for mz, _ in ions[:5]] + matches = match_peaks(exp_mz, ions, tolerance=0.01) + assert len(matches) >= 5 + + def test_match_peaks_no_match(self): + from ptm_site_localization_scorer import match_peaks + + matches = match_peaks([1.0, 2.0], [(1000.0, "b1")], tolerance=0.01) + assert len(matches) == 0 + + def test_generate_site_candidates(self): + from ptm_site_localization_scorer import generate_site_candidates + + candidates = generate_site_candidates("PEPTIDEK", "Phospho", "ST") + # Only T at position 4 (PEP_T_IDEK) + assert len(candidates) >= 1 + + def test_score_localization(self): + from ptm_site_localization_scorer import generate_theoretical_spectrum, score_localization + + # Generate "experimental" spectrum from a specific modification + ions = generate_theoretical_spectrum("PEPS(Phospho)TIDEK") + exp_mz = [mz for mz, _ in ions] + exp_int = [100.0] * len(exp_mz) + + result = score_localization(exp_mz, exp_int, "PEPS(Phospho)TIDEK", tolerance=0.02) + assert result["total_experimental_peaks"] == len(exp_mz) + assert len(result["candidates"]) >= 1 + # Probabilities should sum to ~1 + total_prob = sum(c["probability"] for c in result["candidates"]) + assert abs(total_prob - 1.0) < 0.01 + + def test_score_localization_best_candidate(self): + from ptm_site_localization_scorer import generate_theoretical_spectrum, score_localization + + # The correct site should have highest probability + ions = generate_theoretical_spectrum("PEPS(Phospho)TIDEK") + exp_mz = [mz for mz, _ in ions] + exp_int = [100.0] * len(exp_mz) + + result = score_localization(exp_mz, exp_int, "PEPS(Phospho)TIDEK", tolerance=0.02) + # Best candidate should be first (sorted by score) + if len(result["candidates"]) > 0: + assert result["candidates"][0]["matched_ions"] >= result["candidates"][-1]["matched_ions"] diff --git a/scripts/proteomics/quantification_normalizer/README.md b/scripts/proteomics/quantification_normalizer/README.md new file mode 100644 index 0000000..ce2a8b7 --- /dev/null +++ b/scripts/proteomics/quantification_normalizer/README.md @@ -0,0 +1,17 @@ +# Quantification Normalizer + +Normalize quantification matrices using median, quantile, or total intensity normalization. + +## Usage + +```bash +python quantification_normalizer.py --input matrix.tsv --method median --output normalized.tsv +python quantification_normalizer.py --input matrix.tsv --method quantile --output normalized.tsv +python quantification_normalizer.py --input matrix.tsv --method total_intensity --output normalized.tsv +``` + +## Methods + +- **median** - Shift columns so all have the same median +- **quantile** - Force all columns to have the same distribution +- **total_intensity** - Scale columns to the same total intensity diff --git a/scripts/proteomics/quantification_normalizer/quantification_normalizer.py b/scripts/proteomics/quantification_normalizer/quantification_normalizer.py new file mode 100644 index 0000000..5c71505 --- /dev/null +++ b/scripts/proteomics/quantification_normalizer/quantification_normalizer.py @@ -0,0 +1,161 @@ +""" +Quantification Normalizer +========================= +Normalize quantification matrices using median, quantile, or total intensity methods. + +Usage +----- + python quantification_normalizer.py --input matrix.tsv --method median --output normalized.tsv + python quantification_normalizer.py --input matrix.tsv --method quantile --output normalized.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np + + +def read_matrix(filepath: str) -> tuple: + """Read a TSV quantification matrix. + + Returns + ------- + tuple + (row_ids, col_names, data_matrix). + """ + with open(filepath) as fh: + reader = csv.reader(fh, delimiter="\t") + header = next(reader) + col_names = header[1:] + row_ids = [] + rows = [] + for row in reader: + row_ids.append(row[0]) + rows.append([float(v) if v.strip() else 0.0 for v in row[1:]]) + return row_ids, col_names, np.array(rows, dtype=float) + + +def write_matrix(filepath: str, row_ids: list, col_names: list, matrix: np.ndarray) -> None: + """Write a quantification matrix to TSV.""" + with open(filepath, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow([""] + col_names) + for i, row_id in enumerate(row_ids): + writer.writerow([row_id] + [f"{v:.6f}" for v in matrix[i]]) + + +def normalize_median(matrix: np.ndarray) -> np.ndarray: + """Median normalization: shift each column so all columns have the same median. + + Parameters + ---------- + matrix: + 2D numpy array (rows=features, cols=samples). + + Returns + ------- + np.ndarray + Normalized matrix. + """ + col_medians = np.median(matrix, axis=0) + global_median = np.median(col_medians) + shifts = global_median - col_medians + return matrix + shifts[np.newaxis, :] + + +def normalize_quantile(matrix: np.ndarray) -> np.ndarray: + """Quantile normalization: force all columns to have the same distribution. + + Parameters + ---------- + matrix: + 2D numpy array. + + Returns + ------- + np.ndarray + Quantile-normalized matrix. + """ + n_rows, n_cols = matrix.shape + sorted_indices = np.argsort(matrix, axis=0) + sorted_matrix = np.sort(matrix, axis=0) + row_means = np.mean(sorted_matrix, axis=1) + + result = np.empty_like(matrix) + for col in range(n_cols): + ranks = np.empty(n_rows, dtype=int) + ranks[sorted_indices[:, col]] = np.arange(n_rows) + result[:, col] = row_means[ranks] + return result + + +def normalize_total_intensity(matrix: np.ndarray) -> np.ndarray: + """Total intensity normalization: scale each column to the same total. + + Parameters + ---------- + matrix: + 2D numpy array. + + Returns + ------- + np.ndarray + Normalized matrix. + """ + col_sums = np.sum(matrix, axis=0) + target_sum = np.mean(col_sums) + scale_factors = target_sum / np.where(col_sums > 0, col_sums, 1.0) + return matrix * scale_factors[np.newaxis, :] + + +def normalize(matrix: np.ndarray, method: str = "median") -> np.ndarray: + """Normalize a quantification matrix. + + Parameters + ---------- + matrix: + 2D numpy array. + method: + One of 'median', 'quantile', 'total_intensity'. + + Returns + ------- + np.ndarray + Normalized matrix. + """ + method = method.lower() + if method == "median": + return normalize_median(matrix) + elif method == "quantile": + return normalize_quantile(matrix) + elif method == "total_intensity": + return normalize_total_intensity(matrix) + else: + raise ValueError(f"Unknown normalization method: '{method}'. Choose from: median, quantile, total_intensity") + + +def main(): + parser = argparse.ArgumentParser(description="Normalize quantification matrices.") + parser.add_argument("--input", required=True, help="Input TSV matrix file") + parser.add_argument("--method", required=True, choices=["median", "quantile", "total_intensity"], + help="Normalization method") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + row_ids, col_names, matrix = read_matrix(args.input) + normalized = normalize(matrix, method=args.method) + write_matrix(args.output, row_ids, col_names, normalized) + print(f"Method: {args.method}") + print(f"Samples: {len(col_names)}") + print(f"Features: {len(row_ids)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/quantification_normalizer/requirements.txt b/scripts/proteomics/quantification_normalizer/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/proteomics/quantification_normalizer/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/proteomics/quantification_normalizer/tests/conftest.py b/scripts/proteomics/quantification_normalizer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/quantification_normalizer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/quantification_normalizer/tests/test_quantification_normalizer.py b/scripts/proteomics/quantification_normalizer/tests/test_quantification_normalizer.py new file mode 100644 index 0000000..c753a32 --- /dev/null +++ b/scripts/proteomics/quantification_normalizer/tests/test_quantification_normalizer.py @@ -0,0 +1,70 @@ +"""Tests for quantification_normalizer.""" + +import numpy as np +import pytest +from conftest import requires_pyopenms +from quantification_normalizer import ( + normalize, + normalize_median, + normalize_quantile, + normalize_total_intensity, + read_matrix, + write_matrix, +) + + +@requires_pyopenms +class TestQuantificationNormalizer: + def _make_matrix(self): + return np.array([ + [100.0, 200.0, 150.0], + [300.0, 400.0, 350.0], + [500.0, 600.0, 550.0], + [700.0, 800.0, 750.0], + ]) + + def test_median_equal_medians(self): + matrix = self._make_matrix() + result = normalize_median(matrix) + col_medians = np.median(result, axis=0) + np.testing.assert_allclose(col_medians, col_medians[0], atol=1e-6) + + def test_quantile_equal_distributions(self): + matrix = self._make_matrix() + result = normalize_quantile(matrix) + sorted_cols = np.sort(result, axis=0) + for col in range(1, result.shape[1]): + np.testing.assert_allclose(sorted_cols[:, 0], sorted_cols[:, col], atol=1e-6) + + def test_total_intensity_equal_sums(self): + matrix = self._make_matrix() + result = normalize_total_intensity(matrix) + col_sums = np.sum(result, axis=0) + np.testing.assert_allclose(col_sums, col_sums[0], atol=1e-6) + + def test_normalize_dispatch(self): + matrix = self._make_matrix() + for method in ["median", "quantile", "total_intensity"]: + result = normalize(matrix, method=method) + assert result.shape == matrix.shape + + def test_unknown_method(self): + matrix = self._make_matrix() + with pytest.raises(ValueError, match="Unknown normalization method"): + normalize(matrix, method="invalid") + + def test_read_write_roundtrip(self, tmp_path): + row_ids = ["prot1", "prot2"] + col_names = ["s1", "s2"] + matrix = np.array([[100.0, 200.0], [300.0, 400.0]]) + outfile = str(tmp_path / "test.tsv") + write_matrix(outfile, row_ids, col_names, matrix) + r_ids, c_names, r_matrix = read_matrix(outfile) + assert r_ids == row_ids + assert c_names == col_names + np.testing.assert_allclose(r_matrix, matrix, atol=0.01) + + def test_preserves_shape(self): + matrix = self._make_matrix() + for method in ["median", "quantile", "total_intensity"]: + assert normalize(matrix, method).shape == (4, 3) diff --git a/scripts/proteomics/rna_digest/README.md b/scripts/proteomics/rna_digest/README.md new file mode 100644 index 0000000..4ebfe9c --- /dev/null +++ b/scripts/proteomics/rna_digest/README.md @@ -0,0 +1,24 @@ +# RNA Digest + +In silico RNA digestion with common RNases. + +## Supported Enzymes + +- **RNase_T1** - cleaves after G +- **RNase_A** - cleaves after C and U (pyrimidines) +- **RNase_T2** - cleaves after any nucleotide +- **Cusativin** - cleaves after C + +## Usage + +```bash +python rna_digest.py --sequence AAUGCAAUGG --enzyme RNase_T1 +python rna_digest.py --sequence AAUGCAAUGG --enzyme RNase_A --missed-cleavages 1 --output fragments.tsv +``` + +## Options + +- `--sequence` - RNA sequence (A, C, G, U characters) +- `--enzyme` - RNase enzyme name +- `--missed-cleavages` - Maximum missed cleavages (default: 0) +- `--output` - Output TSV file (optional) diff --git a/scripts/proteomics/rna_digest/requirements.txt b/scripts/proteomics/rna_digest/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/rna_digest/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/rna_digest/rna_digest.py b/scripts/proteomics/rna_digest/rna_digest.py new file mode 100644 index 0000000..7d572f7 --- /dev/null +++ b/scripts/proteomics/rna_digest/rna_digest.py @@ -0,0 +1,139 @@ +""" +RNA Digest +========== +In silico RNA digestion with common RNases. + +Supported enzymes: +- RNase_T1: cleaves after G +- RNase_A: cleaves after C and U (pyrimidines) +- RNase_T2: cleaves after any nucleotide +- Cusativin: cleaves after C + +Usage +----- + python rna_digest.py --sequence AAUGCAAUGG --enzyme RNase_T1 + python rna_digest.py --sequence AAUGCAAUGG --enzyme RNase_A --missed-cleavages 1 --output fragments.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Cleavage rules: enzyme -> set of nucleotides after which to cleave +ENZYME_RULES = { + "RNase_T1": {"G"}, + "RNase_A": {"C", "U"}, + "RNase_T2": {"A", "C", "G", "U"}, + "Cusativin": {"C"}, +} + +# Monoisotopic residue masses for manual mass calculation +NUCLEOTIDE_RESIDUE_MASSES = { + "A": 329.05252, + "C": 305.04188, + "G": 345.04744, + "U": 306.02530, +} +WATER_MASS = 18.01056 + + +def digest_rna(sequence: str, enzyme: str, missed_cleavages: int = 0) -> list: + """Digest an RNA sequence in silico using the specified RNase. + + Parameters + ---------- + sequence: + RNA sequence (A, C, G, U). + enzyme: + Enzyme name (RNase_T1, RNase_A, RNase_T2, Cusativin). + missed_cleavages: + Maximum number of missed cleavages allowed. + + Returns + ------- + list + List of dicts with keys: fragment, start, end, missed_cleavages, mass. + """ + sequence = sequence.upper().strip() + if enzyme not in ENZYME_RULES: + raise ValueError(f"Unknown enzyme: '{enzyme}'. Supported: {list(ENZYME_RULES.keys())}") + + for ch in sequence: + if ch not in NUCLEOTIDE_RESIDUE_MASSES: + raise ValueError(f"Invalid RNA nucleotide: '{ch}'.") + + cleavage_sites = ENZYME_RULES[enzyme] + + # Find cleavage positions (after these positions) + cut_positions = [] + for i, nt in enumerate(sequence): + if nt in cleavage_sites and i < len(sequence) - 1: + cut_positions.append(i + 1) + + # Build basic fragments (0 missed cleavages) + boundaries = [0] + cut_positions + [len(sequence)] + + fragments = [] + for mc in range(missed_cleavages + 1): + for i in range(len(boundaries) - 1 - mc): + start = boundaries[i] + end = boundaries[i + 1 + mc] + frag_seq = sequence[start:end] + mass = _calculate_fragment_mass(frag_seq) + fragments.append({ + "fragment": frag_seq, + "start": start + 1, # 1-based + "end": end, + "missed_cleavages": mc, + "mass": mass, + }) + + return fragments + + +def _calculate_fragment_mass(fragment: str) -> float: + """Calculate monoisotopic mass of an RNA fragment.""" + return sum(NUCLEOTIDE_RESIDUE_MASSES[nt] for nt in fragment) + WATER_MASS + + +def main(): + parser = argparse.ArgumentParser(description="In silico RNA digestion with RNases.") + parser.add_argument("--sequence", required=True, help="RNA sequence (e.g. AAUGCAAUGG)") + parser.add_argument( + "--enzyme", required=True, + choices=list(ENZYME_RULES.keys()), + help="RNase enzyme name" + ) + parser.add_argument("--missed-cleavages", type=int, default=0, help="Max missed cleavages (default: 0)") + parser.add_argument("--output", help="Output TSV file (optional)") + args = parser.parse_args() + + fragments = digest_rna(args.sequence, args.enzyme, args.missed_cleavages) + + if args.output: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["fragment", "start", "end", "missed_cleavages", "mass"], + delimiter="\t") + writer.writeheader() + writer.writerows(fragments) + print(f"Wrote {len(fragments)} fragments to {args.output}") + else: + print(f"Enzyme: {args.enzyme}") + print(f"Sequence: {args.sequence}") + print(f"Missed cleavages: {args.missed_cleavages}") + print(f"\n{'Fragment':<20} {'Start':>5} {'End':>5} {'MC':>3} {'Mass':>12}") + print("-" * 50) + for f in fragments: + print(f"{f['fragment']:<20} {f['start']:>5} {f['end']:>5} {f['missed_cleavages']:>3} " + f"{f['mass']:>12.4f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/rna_digest/tests/conftest.py b/scripts/proteomics/rna_digest/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/rna_digest/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/rna_digest/tests/test_rna_digest.py b/scripts/proteomics/rna_digest/tests/test_rna_digest.py new file mode 100644 index 0000000..7324036 --- /dev/null +++ b/scripts/proteomics/rna_digest/tests/test_rna_digest.py @@ -0,0 +1,74 @@ +"""Tests for rna_digest.""" + +import csv + +import pytest +from conftest import requires_pyopenms +from rna_digest import _calculate_fragment_mass, digest_rna + + +@requires_pyopenms +class TestRnaDigest: + def test_rnase_t1_basic(self): + """RNase T1 cleaves after G.""" + fragments = digest_rna("AAUGCAAUGG", "RNase_T1", missed_cleavages=0) + seqs = [f["fragment"] for f in fragments] + # Should cleave after G at positions 4 (AAUG) and 9 (CAAUG), last G is terminal + assert all(isinstance(f["mass"], float) for f in fragments) + assert all(f["missed_cleavages"] == 0 for f in fragments) + # Reconstruct sequence + assert "".join(seqs) == "AAUGCAAUGG" + + def test_rnase_a_basic(self): + """RNase A cleaves after C and U.""" + fragments = digest_rna("AAUGCAAUGG", "RNase_A", missed_cleavages=0) + seqs = [f["fragment"] for f in fragments] + assert "".join(seqs) == "AAUGCAAUGG" + + def test_missed_cleavages(self): + fragments_0 = digest_rna("AAUGCAAUGG", "RNase_T1", missed_cleavages=0) + fragments_1 = digest_rna("AAUGCAAUGG", "RNase_T1", missed_cleavages=1) + assert len(fragments_1) > len(fragments_0) + + def test_no_cleavage_site(self): + """If no cleavage site, return whole sequence.""" + fragments = digest_rna("AAAA", "RNase_T1", missed_cleavages=0) + assert len(fragments) == 1 + assert fragments[0]["fragment"] == "AAAA" + + def test_unknown_enzyme(self): + with pytest.raises(ValueError, match="Unknown enzyme"): + digest_rna("AAUGC", "FakeEnzyme") + + def test_invalid_nucleotide(self): + with pytest.raises(ValueError, match="Invalid RNA nucleotide"): + digest_rna("AATGC", "RNase_T1") + + def test_fragment_mass_positive(self): + mass = _calculate_fragment_mass("AAUGC") + assert mass > 0 + + def test_start_end_positions(self): + fragments = digest_rna("AAUGCAAUGG", "RNase_T1", missed_cleavages=0) + for f in fragments: + assert f["start"] >= 1 + assert f["end"] <= 10 + assert f["end"] >= f["start"] + + def test_output_file(self, tmp_path): + """Test writing to TSV file via direct function call.""" + fragments = digest_rna("AAUGCAAUGG", "RNase_T1") + outfile = str(tmp_path / "fragments.tsv") + with open(outfile, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["fragment", "start", "end", "missed_cleavages", "mass"], + delimiter="\t", + ) + writer.writeheader() + writer.writerows(fragments) + + with open(outfile) as fh: + reader = csv.DictReader(fh, delimiter="\t") + rows = list(reader) + assert len(rows) == len(fragments) diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/README.md b/scripts/proteomics/rna_fragment_spectrum_generator/README.md new file mode 100644 index 0000000..ad89d94 --- /dev/null +++ b/scripts/proteomics/rna_fragment_spectrum_generator/README.md @@ -0,0 +1,23 @@ +# RNA Fragment Spectrum Generator + +Generate theoretical RNA fragment spectra including c, y, w, and a-B ion series. + +## Ion Types + +- **c ions** - 5' fragments from 3'-P-O bond cleavage +- **y ions** - 3' complementary fragments +- **w ions** - 3' fragments with base loss +- **a-B ions** - 5' fragments with base loss + +## Usage + +```bash +python rna_fragment_spectrum_generator.py --sequence AAUGC --charge 2 +python rna_fragment_spectrum_generator.py --sequence AAUGC --charge 1 --output fragments.tsv +``` + +## Options + +- `--sequence` - RNA sequence (A, C, G, U) +- `--charge` - Charge state (default: 1) +- `--output` - Output TSV file (optional) diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/requirements.txt b/scripts/proteomics/rna_fragment_spectrum_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/rna_fragment_spectrum_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py b/scripts/proteomics/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py new file mode 100644 index 0000000..e457e1e --- /dev/null +++ b/scripts/proteomics/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py @@ -0,0 +1,226 @@ +""" +RNA Fragment Spectrum Generator +=============================== +Generate theoretical RNA fragment spectra including c, y, w, and a-B ion series. + +RNA backbone fragmentation follows different rules than peptides. The main +fragment ion types are: +- c ions: 5' fragments (cleavage of the 3'-P-O bond) +- y ions: 3' fragments (cleavage of the 5'-P-O bond) +- w ions: 3' fragments with loss of a base +- a-B ions: 5' fragments with loss of a base + +Usage +----- + python rna_fragment_spectrum_generator.py --sequence AAUGC --charge 2 + python rna_fragment_spectrum_generator.py --sequence AAUGC --charge 1 --output fragments.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Monoisotopic residue masses for nucleotide residues (internal, losing water between residues) +NUCLEOTIDE_RESIDUE_MASSES = { + "A": 329.05252, + "C": 305.04188, + "G": 345.04744, + "U": 306.02530, +} + +WATER_MASS = 18.01056 + +# Base masses (free base, for a-B and w ion calculations) +BASE_MASSES = { + "A": 135.05450, # adenine + "C": 111.04326, # cytosine + "G": 151.04942, # guanine + "U": 112.02728, # uracil +} + + +def _prefix_mass(sequence: str, length: int) -> float: + """Calculate the neutral mass of an RNA prefix of given length.""" + return sum(NUCLEOTIDE_RESIDUE_MASSES[sequence[i]] for i in range(length)) + WATER_MASS + + +def _suffix_mass(sequence: str, length: int) -> float: + """Calculate the neutral mass of an RNA suffix of given length.""" + n = len(sequence) + return sum(NUCLEOTIDE_RESIDUE_MASSES[sequence[n - length + i]] for i in range(length)) + WATER_MASS + + +def generate_c_ions(sequence: str, charge: int = 1) -> list: + """Generate c-ion series (5' fragments). + + c ions result from cleavage of the 3'-P-O5' bond, retaining the 5' portion + with a cyclic phosphate. + + Parameters + ---------- + sequence: + RNA sequence. + charge: + Charge state. + + Returns + ------- + list + List of (ion_label, mz) tuples. + """ + ions = [] + for i in range(1, len(sequence)): + # c ion = prefix mass + cyclic phosphate (HPO3 = 79.966) + mass = _prefix_mass(sequence, i) + 79.96633 + mz = (mass + charge * PROTON) / charge + ions.append((f"c{i}", mz)) + return ions + + +def generate_y_ions(sequence: str, charge: int = 1) -> list: + """Generate y-ion series (3' fragments). + + y ions are the complementary 3' fragments from c-ion cleavage. + + Parameters + ---------- + sequence: + RNA sequence. + charge: + Charge state. + + Returns + ------- + list + List of (ion_label, mz) tuples. + """ + ions = [] + for i in range(1, len(sequence)): + mass = _suffix_mass(sequence, i) + mz = (mass + charge * PROTON) / charge + ions.append((f"y{i}", mz)) + return ions + + +def generate_w_ions(sequence: str, charge: int = 1) -> list: + """Generate w-ion series (3' fragments with base loss). + + w ions = y ions with loss of the 3'-terminal base and water. + + Parameters + ---------- + sequence: + RNA sequence. + charge: + Charge state. + + Returns + ------- + list + List of (ion_label, mz) tuples. + """ + ions = [] + n = len(sequence) + for i in range(2, len(sequence)): + # w ion from position: suffix of length i, lose the 5'-most base of that suffix + suffix_start = n - i + base_nt = sequence[suffix_start] + mass = _suffix_mass(sequence, i) - BASE_MASSES[base_nt] - WATER_MASS + mz = (mass + charge * PROTON) / charge + ions.append((f"w{i}", mz)) + return ions + + +def generate_a_minus_b_ions(sequence: str, charge: int = 1) -> list: + """Generate a-B ion series (5' fragments with base loss). + + a-B ions = prefix losing the 3'-terminal base and water. + + Parameters + ---------- + sequence: + RNA sequence. + charge: + Charge state. + + Returns + ------- + list + List of (ion_label, mz) tuples. + """ + ions = [] + for i in range(2, len(sequence)): + base_nt = sequence[i - 1] + mass = _prefix_mass(sequence, i) - BASE_MASSES[base_nt] - WATER_MASS + mz = (mass + charge * PROTON) / charge + ions.append((f"a-B{i}", mz)) + return ions + + +def generate_all_fragments(sequence: str, charge: int = 1) -> list: + """Generate all RNA fragment ion types. + + Parameters + ---------- + sequence: + RNA sequence (A, C, G, U). + charge: + Charge state. + + Returns + ------- + list + List of dicts with keys: ion_type, ion_label, mz, charge. + """ + sequence = sequence.upper().strip() + for ch in sequence: + if ch not in NUCLEOTIDE_RESIDUE_MASSES: + raise ValueError(f"Invalid RNA nucleotide: '{ch}'.") + + results = [] + for ion_type, gen_func in [("c", generate_c_ions), ("y", generate_y_ions), + ("w", generate_w_ions), ("a-B", generate_a_minus_b_ions)]: + for label, mz in gen_func(sequence, charge): + results.append({ + "ion_type": ion_type, + "ion_label": label, + "mz": mz, + "charge": charge, + }) + return results + + +def main(): + parser = argparse.ArgumentParser(description="Generate theoretical RNA fragment spectra.") + parser.add_argument("--sequence", required=True, help="RNA sequence (e.g. AAUGC)") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") + parser.add_argument("--output", help="Output TSV file (optional)") + args = parser.parse_args() + + fragments = generate_all_fragments(args.sequence, args.charge) + + if args.output: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["ion_type", "ion_label", "mz", "charge"], + delimiter="\t") + writer.writeheader() + writer.writerows(fragments) + print(f"Wrote {len(fragments)} fragment ions to {args.output}") + else: + print(f"Sequence: {args.sequence.upper()}") + print(f"Charge: {args.charge}+") + print(f"\n{'Ion':<10} {'Type':<6} {'m/z':>14}") + print("-" * 32) + for f in fragments: + print(f"{f['ion_label']:<10} {f['ion_type']:<6} {f['mz']:>14.4f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/tests/conftest.py b/scripts/proteomics/rna_fragment_spectrum_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/rna_fragment_spectrum_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py b/scripts/proteomics/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py new file mode 100644 index 0000000..485c3ed --- /dev/null +++ b/scripts/proteomics/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py @@ -0,0 +1,61 @@ +"""Tests for rna_fragment_spectrum_generator.""" + +import pytest +from conftest import requires_pyopenms +from rna_fragment_spectrum_generator import ( + generate_a_minus_b_ions, + generate_all_fragments, + generate_c_ions, + generate_w_ions, + generate_y_ions, +) + + +@requires_pyopenms +class TestRnaFragmentSpectrumGenerator: + def test_c_ions_count(self): + ions = generate_c_ions("AAUGC", charge=1) + assert len(ions) == 4 # n-1 ions for length 5 + assert all(label.startswith("c") for label, _ in ions) + + def test_y_ions_count(self): + ions = generate_y_ions("AAUGC", charge=1) + assert len(ions) == 4 + assert all(label.startswith("y") for label, _ in ions) + + def test_w_ions_count(self): + ions = generate_w_ions("AAUGC", charge=1) + assert len(ions) == 3 # from index 2 to n-1 + assert all(label.startswith("w") for label, _ in ions) + + def test_a_minus_b_ions_count(self): + ions = generate_a_minus_b_ions("AAUGC", charge=1) + assert len(ions) == 3 + assert all(label.startswith("a-B") for label, _ in ions) + + def test_all_fragments(self): + fragments = generate_all_fragments("AAUGC", charge=1) + assert len(fragments) == 14 # 4 + 4 + 3 + 3 + ion_types = {f["ion_type"] for f in fragments} + assert ion_types == {"c", "y", "w", "a-B"} + + def test_charge_state_affects_mz(self): + f1 = generate_all_fragments("AAUGC", charge=1) + f2 = generate_all_fragments("AAUGC", charge=2) + # Same ion in charge 2 should have lower m/z than charge 1 + c1_mz = next(f["mz"] for f in f1 if f["ion_label"] == "c1") + c1_mz_z2 = next(f["mz"] for f in f2 if f["ion_label"] == "c1") + assert c1_mz_z2 < c1_mz + + def test_all_mz_positive(self): + fragments = generate_all_fragments("AAUGC", charge=1) + assert all(f["mz"] > 0 for f in fragments) + + def test_invalid_nucleotide(self): + with pytest.raises(ValueError, match="Invalid RNA nucleotide"): + generate_all_fragments("AATGC") + + def test_short_sequence(self): + fragments = generate_all_fragments("AU", charge=1) + # c: 1, y: 1, w: 0, a-B: 0 + assert len(fragments) == 2 diff --git a/scripts/proteomics/rna_mass_calculator/README.md b/scripts/proteomics/rna_mass_calculator/README.md new file mode 100644 index 0000000..91df15e --- /dev/null +++ b/scripts/proteomics/rna_mass_calculator/README.md @@ -0,0 +1,18 @@ +# RNA Mass Calculator + +Calculate mass, molecular formula, and isotope patterns for RNA sequences. + +## Usage + +```bash +python rna_mass_calculator.py --sequence AAUGC --charge 2 +python rna_mass_calculator.py --sequence AAUGCAAUGG --charge 3 --output mass.json +python rna_mass_calculator.py --sequence AAUGC --isotopes 5 +``` + +## Options + +- `--sequence` - RNA sequence (A, C, G, U characters) +- `--charge` - Charge state for m/z calculation (default: 1) +- `--isotopes` - Number of isotope peaks to calculate (default: 0 = off) +- `--output` - Output JSON file path (optional; prints to stdout if omitted) diff --git a/scripts/proteomics/rna_mass_calculator/requirements.txt b/scripts/proteomics/rna_mass_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/rna_mass_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/rna_mass_calculator/rna_mass_calculator.py b/scripts/proteomics/rna_mass_calculator/rna_mass_calculator.py new file mode 100644 index 0000000..c5e0558 --- /dev/null +++ b/scripts/proteomics/rna_mass_calculator/rna_mass_calculator.py @@ -0,0 +1,169 @@ +""" +RNA Mass Calculator +=================== +Calculate mass, formula, and isotope patterns for RNA sequences. + +Supports standard RNA nucleotides (A, C, G, U). Uses pyopenms NASequence +when available, otherwise falls back to manual calculation using monoisotopic +nucleotide residue masses. + +Usage +----- + python rna_mass_calculator.py --sequence AAUGC --charge 2 + python rna_mass_calculator.py --sequence AAUGCAAUGG --charge 3 --output mass.json +""" + +import argparse +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + +# Monoisotopic residue masses for RNA nucleotides (internal residue, losing H2O) +# These are the masses of the nucleotide monophosphate residues in an RNA chain. +NUCLEOTIDE_RESIDUE_MASSES = { + "A": 329.05252, + "C": 305.04188, + "G": 345.04744, + "U": 306.02530, +} + +# Water mass added once for the full-length RNA (terminal groups) +WATER_MASS = 18.01056 + + +def _has_na_sequence() -> bool: + """Check if pyopenms has NASequence support.""" + return hasattr(oms, "NASequence") + + +def calculate_rna_mass(sequence: str, charge: int = 1) -> dict: + """Calculate monoisotopic mass and m/z for an RNA sequence. + + Parameters + ---------- + sequence: + RNA sequence string using A, C, G, U characters. + charge: + Charge state for m/z calculation. + + Returns + ------- + dict + Dictionary with sequence, charge, monoisotopic_mass, mz, and formula. + """ + sequence = sequence.upper().strip() + for ch in sequence: + if ch not in NUCLEOTIDE_RESIDUE_MASSES: + raise ValueError(f"Invalid RNA nucleotide: '{ch}'. Must be one of A, C, G, U.") + + if _has_na_sequence(): + na_seq = oms.NASequence.fromString(sequence) + mono_mass = na_seq.getMonoWeight() + formula_str = str(na_seq.getFormula()) + else: + mono_mass = sum(NUCLEOTIDE_RESIDUE_MASSES[nt] for nt in sequence) + WATER_MASS + formula_str = _manual_formula(sequence) + + mz = (mono_mass + charge * PROTON) / charge + + return { + "sequence": sequence, + "charge": charge, + "monoisotopic_mass": mono_mass, + "mz": mz, + "formula": formula_str, + } + + +def _manual_formula(sequence: str) -> str: + """Compute the molecular formula for an RNA sequence manually. + + Each nucleotide residue contributes its elemental composition. The full + sequence adds one water molecule for the terminal groups. + """ + # Elemental compositions of each nucleotide residue (monophosphate, internal) + compositions = { + "A": {"C": 10, "H": 12, "N": 5, "O": 6, "P": 1}, + "C": {"C": 9, "H": 12, "N": 3, "O": 7, "P": 1}, + "G": {"C": 10, "H": 12, "N": 5, "O": 7, "P": 1}, + "U": {"C": 9, "H": 11, "N": 2, "O": 8, "P": 1}, + } + total = {"C": 0, "H": 0, "N": 0, "O": 0, "P": 0} + for nt in sequence: + for elem, count in compositions[nt].items(): + total[elem] += count + # Add water for terminal groups + total["H"] += 2 + total["O"] += 1 + return "".join(f"{elem}{total[elem]}" for elem in ["C", "H", "N", "O", "P"] if total[elem] > 0) + + +def calculate_isotope_pattern(sequence: str, n_peaks: int = 5) -> list: + """Calculate the isotope distribution pattern for an RNA sequence. + + Parameters + ---------- + sequence: + RNA sequence string. + n_peaks: + Number of isotope peaks to return. + + Returns + ------- + list + List of (mass_offset, relative_intensity) tuples. + """ + sequence = sequence.upper().strip() + mass_info = calculate_rna_mass(sequence, charge=1) + formula_str = mass_info["formula"] + + ef = oms.EmpiricalFormula(formula_str) + isotopes = ef.getIsotopeDistribution(oms.CoarseIsotopePatternGenerator(n_peaks)) + + pattern = [] + for iso in isotopes.getContainer(): + pattern.append((iso.getMZ(), iso.getIntensity())) + + return pattern + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate mass/formula/isotopes for RNA sequences." + ) + parser.add_argument("--sequence", required=True, help="RNA sequence (e.g. AAUGCAAUGG)") + parser.add_argument("--charge", type=int, default=1, help="Charge state for m/z (default: 1)") + parser.add_argument("--isotopes", type=int, default=0, help="Number of isotope peaks to show (default: 0 = off)") + parser.add_argument("--output", help="Output JSON file (optional)") + args = parser.parse_args() + + result = calculate_rna_mass(args.sequence, args.charge) + + if args.isotopes > 0: + pattern = calculate_isotope_pattern(args.sequence, args.isotopes) + result["isotope_pattern"] = [{"mass": m, "intensity": i} for m, i in pattern] + + if args.output: + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + print(f"Results written to {args.output}") + else: + print(f"Sequence : {result['sequence']}") + print(f"Charge : {result['charge']}+") + print(f"Monoisotopic mass : {result['monoisotopic_mass']:.6f} Da") + print(f"m/z : {result['mz']:.6f}") + print(f"Formula : {result['formula']}") + if "isotope_pattern" in result: + print("\n--- Isotope Pattern ---") + for peak in result["isotope_pattern"]: + print(f" {peak['mass']:.4f} {peak['intensity']:.6f}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/rna_mass_calculator/tests/conftest.py b/scripts/proteomics/rna_mass_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/rna_mass_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/rna_mass_calculator/tests/test_rna_mass_calculator.py b/scripts/proteomics/rna_mass_calculator/tests/test_rna_mass_calculator.py new file mode 100644 index 0000000..0f86a8d --- /dev/null +++ b/scripts/proteomics/rna_mass_calculator/tests/test_rna_mass_calculator.py @@ -0,0 +1,51 @@ +"""Tests for rna_mass_calculator.""" + + +import pytest +from conftest import requires_pyopenms +from rna_mass_calculator import _manual_formula, calculate_isotope_pattern, calculate_rna_mass + + +@requires_pyopenms +class TestRnaMassCalculator: + def test_basic_mass(self): + result = calculate_rna_mass("AAUGC", charge=1) + assert result["sequence"] == "AAUGC" + assert result["charge"] == 1 + assert result["monoisotopic_mass"] > 0 + assert result["mz"] > 0 + assert len(result["formula"]) > 0 + + def test_charge_state(self): + r1 = calculate_rna_mass("AAUGC", charge=1) + r2 = calculate_rna_mass("AAUGC", charge=2) + assert r1["monoisotopic_mass"] == r2["monoisotopic_mass"] + assert r2["mz"] < r1["mz"] + + def test_longer_sequence(self): + result = calculate_rna_mass("AAUGCAAUGG", charge=3) + assert result["charge"] == 3 + assert result["monoisotopic_mass"] > 0 + + def test_invalid_nucleotide(self): + with pytest.raises(ValueError, match="Invalid RNA nucleotide"): + calculate_rna_mass("AATGC") + + def test_case_insensitive(self): + r1 = calculate_rna_mass("aaugc") + r2 = calculate_rna_mass("AAUGC") + assert abs(r1["monoisotopic_mass"] - r2["monoisotopic_mass"]) < 0.001 + + def test_isotope_pattern(self): + pattern = calculate_isotope_pattern("AAUGC", n_peaks=5) + assert len(pattern) == 5 + assert all(m > 0 for m, _ in pattern) + assert all(0 <= i <= 1.0 for _, i in pattern) + + def test_manual_formula(self): + formula = _manual_formula("AU") + assert "C" in formula + assert "H" in formula + assert "N" in formula + assert "O" in formula + assert "P" in formula diff --git a/scripts/proteomics/rt_prediction_additive/README.md b/scripts/proteomics/rt_prediction_additive/README.md new file mode 100644 index 0000000..96f25a3 --- /dev/null +++ b/scripts/proteomics/rt_prediction_additive/README.md @@ -0,0 +1,10 @@ +# RT Prediction (Additive Model) + +Predict peptide retention times using additive hydrophobicity models (Krokhin, Meek). + +## Usage + +```bash +python rt_prediction_additive.py --sequence PEPTIDEK --model krokhin --output prediction.json +python rt_prediction_additive.py --input peptides.tsv --model meek --output predictions.tsv +``` diff --git a/scripts/proteomics/rt_prediction_additive/requirements.txt b/scripts/proteomics/rt_prediction_additive/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/rt_prediction_additive/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/rt_prediction_additive/rt_prediction_additive.py b/scripts/proteomics/rt_prediction_additive/rt_prediction_additive.py new file mode 100644 index 0000000..50caab0 --- /dev/null +++ b/scripts/proteomics/rt_prediction_additive/rt_prediction_additive.py @@ -0,0 +1,158 @@ +""" +RT Prediction (Additive Model) +================================ +Predict peptide retention times using additive hydrophobicity models. + +Features +-------- +- Krokhin model retention coefficients +- Meek model retention coefficients +- Per-residue contribution to predicted RT +- Support for modified sequences via pyopenms + +Usage +----- + python rt_prediction_additive.py --sequence PEPTIDEK --model krokhin + python rt_prediction_additive.py --sequence PEPTIDEK --model meek --output prediction.json +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +# Krokhin retention coefficients (simplified version of SSRCalc) +KROKHIN_COEFFICIENTS = { + "A": 0.62, "R": -0.60, "N": -0.60, "D": -0.46, "C": 0.29, + "E": -0.11, "Q": -0.73, "G": 0.05, "H": -0.24, "I": 3.21, + "L": 3.61, "K": -0.79, "M": 1.83, "F": 3.54, "P": 0.26, + "S": -0.38, "T": -0.07, "W": 4.30, "Y": 1.31, "V": 1.87, +} + +# Meek RP-HPLC retention coefficients +MEEK_COEFFICIENTS = { + "A": 0.5, "R": -1.1, "N": -0.64, "D": -0.27, "C": -0.02, + "E": -0.05, "Q": -0.91, "G": 0.0, "H": 0.14, "I": 2.46, + "L": 2.46, "K": -1.54, "M": 1.31, "F": 2.65, "P": 0.38, + "S": -0.18, "T": 0.01, "W": 3.23, "Y": 0.96, "V": 1.14, +} + +MODELS = { + "krokhin": KROKHIN_COEFFICIENTS, + "meek": MEEK_COEFFICIENTS, +} + + +def predict_rt(sequence: str, model: str = "krokhin") -> dict: + """Predict retention time using an additive model. + + Parameters + ---------- + sequence : str + Peptide sequence (plain or modified pyopenms notation). + model : str + Model name ('krokhin' or 'meek'). + + Returns + ------- + dict + Dictionary with predicted RT, per-residue contributions, and model info. + """ + aa_seq = oms.AASequence.fromString(sequence) + plain = aa_seq.toUnmodifiedString() + + coefficients = MODELS.get(model, KROKHIN_COEFFICIENTS) + + contributions = [] + total = 0.0 + for i, aa in enumerate(plain): + coeff = coefficients.get(aa, 0.0) + total += coeff + contributions.append({ + "position": i + 1, + "residue": aa, + "coefficient": coeff, + }) + + return { + "sequence": sequence, + "unmodified_sequence": plain, + "model": model, + "predicted_rt": round(total, 4), + "length": len(plain), + "residue_contributions": contributions, + } + + +def predict_batch(sequences: list, model: str = "krokhin") -> list: + """Predict RT for a batch of peptide sequences. + + Parameters + ---------- + sequences : list + List of peptide sequence strings. + model : str + Model name. + + Returns + ------- + list + List of prediction result dicts. + """ + return [predict_rt(seq, model) for seq in sequences if seq.strip()] + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Predict peptide RT using additive hydrophobicity models.") + parser.add_argument("--sequence", type=str, help="Single peptide sequence.") + parser.add_argument("--input", type=str, help="TSV file with 'sequence' column.") + parser.add_argument("--model", choices=["krokhin", "meek"], default="krokhin", + help="Retention model (default: krokhin).") + parser.add_argument("--output", type=str, help="Output file (.json or .tsv).") + args = parser.parse_args() + + if not args.sequence and not args.input: + parser.error("Provide --sequence or --input.") + + if args.sequence: + result = predict_rt(args.sequence, args.model) + if args.output: + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + else: + print(json.dumps(result, indent=2)) + elif args.input: + sequences = [] + with open(args.input) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + seq = row.get("sequence", "").strip() + if seq: + sequences.append(seq) + results = predict_batch(sequences, args.model) + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(results, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, fieldnames=["sequence", "model", "predicted_rt", "length"], delimiter="\t" + ) + writer.writeheader() + for r in results: + writer.writerow({k: r[k] for k in ["sequence", "model", "predicted_rt", "length"]}) + print(f"Results written to {args.output}") + else: + for r in results: + print(f"{r['sequence']}\t{r['predicted_rt']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/rt_prediction_additive/tests/conftest.py b/scripts/proteomics/rt_prediction_additive/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/rt_prediction_additive/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/rt_prediction_additive/tests/test_rt_prediction_additive.py b/scripts/proteomics/rt_prediction_additive/tests/test_rt_prediction_additive.py new file mode 100644 index 0000000..b6b3355 --- /dev/null +++ b/scripts/proteomics/rt_prediction_additive/tests/test_rt_prediction_additive.py @@ -0,0 +1,55 @@ +"""Tests for rt_prediction_additive.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestRtPredictionAdditive: + def test_predict_basic(self): + from rt_prediction_additive import predict_rt + + result = predict_rt("PEPTIDEK", "krokhin") + assert result["model"] == "krokhin" + assert result["length"] == 8 + assert isinstance(result["predicted_rt"], float) + + def test_hydrophobic_peptide_higher_rt(self): + from rt_prediction_additive import predict_rt + + hydrophobic = predict_rt("LLLLLLLL", "krokhin") + hydrophilic = predict_rt("KKKKKKKK", "krokhin") + assert hydrophobic["predicted_rt"] > hydrophilic["predicted_rt"] + + def test_meek_model(self): + from rt_prediction_additive import predict_rt + + result = predict_rt("PEPTIDEK", "meek") + assert result["model"] == "meek" + assert isinstance(result["predicted_rt"], float) + + def test_residue_contributions_sum(self): + from rt_prediction_additive import predict_rt + + result = predict_rt("PEPTIDEK", "krokhin") + contrib_sum = sum(c["coefficient"] for c in result["residue_contributions"]) + assert abs(contrib_sum - result["predicted_rt"]) < 0.01 + + def test_batch_prediction(self): + from rt_prediction_additive import predict_batch + + results = predict_batch(["PEPTIDEK", "ANOTHERPEPTIDE"], "krokhin") + assert len(results) == 2 + + def test_different_models_different_results(self): + from rt_prediction_additive import predict_rt + + krokhin = predict_rt("PEPTIDEK", "krokhin") + meek = predict_rt("PEPTIDEK", "meek") + # Different models should give different predictions + assert krokhin["predicted_rt"] != meek["predicted_rt"] + + def test_residue_contributions_length(self): + from rt_prediction_additive import predict_rt + + result = predict_rt("ACDEFGHIK", "krokhin") + assert len(result["residue_contributions"]) == 9 diff --git a/scripts/proteomics/run_comparison_reporter/requirements.txt b/scripts/proteomics/run_comparison_reporter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/run_comparison_reporter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/run_comparison_reporter/run_comparison_reporter.py b/scripts/proteomics/run_comparison_reporter/run_comparison_reporter.py new file mode 100644 index 0000000..426c2b8 --- /dev/null +++ b/scripts/proteomics/run_comparison_reporter/run_comparison_reporter.py @@ -0,0 +1,140 @@ +""" +Run Comparison Reporter +======================== +Compare two or more mzML files and report TIC correlation, shared +precursor m/z values, and retention-time shifts between runs. + +Usage +----- + python run_comparison_reporter.py --inputs run1.mzML run2.mzML --output comparison.json +""" + +import argparse +import json +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def _extract_run_info(exp: oms.MSExperiment) -> dict: + """Extract TIC profile and precursor list from an experiment.""" + tic_profile = [] + precursor_set = set() + rt_values = [] + + for spec in exp.getSpectra(): + rt = spec.getRT() + rt_values.append(rt) + _, intensities = spec.get_peaks() + tic = float(intensities.sum()) if len(intensities) > 0 else 0.0 + + if spec.getMSLevel() == 1: + tic_profile.append((rt, tic)) + elif spec.getMSLevel() == 2: + for prec in spec.getPrecursors(): + precursor_set.add(round(prec.getMZ(), 4)) + + return { + "tic_profile": tic_profile, + "precursors": precursor_set, + "rt_range": (min(rt_values), max(rt_values)) if rt_values else (0.0, 0.0), + } + + +def pearson_correlation(xs: list[float], ys: list[float]) -> float: + """Compute Pearson correlation coefficient between two lists. + + Parameters + ---------- + xs, ys: + Equal-length numeric sequences. + + Returns + ------- + float + Pearson r, or 0.0 if undefined. + """ + n = min(len(xs), len(ys)) + if n == 0: + return 0.0 + xs, ys = xs[:n], ys[:n] + mx = sum(xs) / n + my = sum(ys) / n + cov = sum((x - mx) * (y - my) for x, y in zip(xs, ys)) + sx = math.sqrt(sum((x - mx) ** 2 for x in xs)) + sy = math.sqrt(sum((y - my) ** 2 for y in ys)) + if sx == 0 or sy == 0: + return 0.0 + return cov / (sx * sy) + + +def compare_runs(exp1: oms.MSExperiment, exp2: oms.MSExperiment) -> dict: + """Compare two MSExperiment objects. + + Parameters + ---------- + exp1, exp2: + Loaded ``pyopenms.MSExperiment`` instances. + + Returns + ------- + dict + Comparison metrics including TIC correlation, shared precursors, + and RT shift estimate. + """ + info1 = _extract_run_info(exp1) + info2 = _extract_run_info(exp2) + + tic1 = [t for _, t in info1["tic_profile"]] + tic2 = [t for _, t in info2["tic_profile"]] + tic_corr = pearson_correlation(tic1, tic2) + + shared = info1["precursors"] & info2["precursors"] + only1 = info1["precursors"] - info2["precursors"] + only2 = info2["precursors"] - info1["precursors"] + + rt_shift = info1["rt_range"][0] - info2["rt_range"][0] + + return { + "tic_correlation": round(tic_corr, 6), + "shared_precursors": len(shared), + "unique_to_run1": len(only1), + "unique_to_run2": len(only2), + "rt_shift_sec": round(rt_shift, 2), + "run1_ms1_tic_points": len(tic1), + "run2_ms1_tic_points": len(tic2), + } + + +def main(): + parser = argparse.ArgumentParser( + description="Compare mzML runs: TIC correlation, shared precursors, RT shift." + ) + parser.add_argument( + "--inputs", nargs=2, required=True, metavar="FILE", help="Two mzML files to compare" + ) + parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON report") + args = parser.parse_args() + + exp1 = oms.MSExperiment() + oms.MzMLFile().load(args.inputs[0], exp1) + + exp2 = oms.MSExperiment() + oms.MzMLFile().load(args.inputs[1], exp2) + + result = compare_runs(exp1, exp2) + + with open(args.output, "w") as fh: + json.dump(result, fh, indent=2) + + print(f"Comparison report written to {args.output}") + print(f" TIC correlation : {result['tic_correlation']}") + print(f" Shared precursors: {result['shared_precursors']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/run_comparison_reporter/tests/conftest.py b/scripts/proteomics/run_comparison_reporter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/run_comparison_reporter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/run_comparison_reporter/tests/test_run_comparison_reporter.py b/scripts/proteomics/run_comparison_reporter/tests/test_run_comparison_reporter.py new file mode 100644 index 0000000..f52f4eb --- /dev/null +++ b/scripts/proteomics/run_comparison_reporter/tests/test_run_comparison_reporter.py @@ -0,0 +1,66 @@ +"""Tests for run_comparison_reporter.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestRunComparisonReporter: + def _make_experiment(self, rt_offset=0.0, prec_mz_base=500.0): + import numpy as np + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(5): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(60.0 * i + rt_offset) + mzs = np.array([100.0 + j for j in range(10)], dtype=np.float64) + ints = np.array([1000.0 * (j + 1) for j in range(10)], dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(60.0 * i + rt_offset + 1.0) + prec = oms.Precursor() + prec.setMZ(prec_mz_base + i) + ms2.setPrecursors([prec]) + mzs2 = np.array([200.0], dtype=np.float64) + ints2 = np.array([500.0], dtype=np.float64) + ms2.set_peaks([mzs2, ints2]) + exp.addSpectrum(ms2) + + return exp + + def test_identical_runs(self): + from run_comparison_reporter import compare_runs + + exp1 = self._make_experiment() + exp2 = self._make_experiment() + result = compare_runs(exp1, exp2) + assert result["tic_correlation"] == 1.0 + assert result["shared_precursors"] == 5 + + def test_different_precursors(self): + from run_comparison_reporter import compare_runs + + exp1 = self._make_experiment(prec_mz_base=500.0) + exp2 = self._make_experiment(prec_mz_base=600.0) + result = compare_runs(exp1, exp2) + assert result["shared_precursors"] == 0 + assert result["unique_to_run1"] == 5 + assert result["unique_to_run2"] == 5 + + def test_rt_shift(self): + from run_comparison_reporter import compare_runs + + exp1 = self._make_experiment(rt_offset=0.0) + exp2 = self._make_experiment(rt_offset=30.0) + result = compare_runs(exp1, exp2) + assert result["rt_shift_sec"] == -30.0 + + def test_pearson_correlation(self): + from run_comparison_reporter import pearson_correlation + + assert abs(pearson_correlation([1, 2, 3], [1, 2, 3]) - 1.0) < 1e-6 + assert abs(pearson_correlation([1, 2, 3], [3, 2, 1]) + 1.0) < 1e-6 diff --git a/scripts/proteomics/sample_complexity_estimator/README.md b/scripts/proteomics/sample_complexity_estimator/README.md new file mode 100644 index 0000000..4e723ff --- /dev/null +++ b/scripts/proteomics/sample_complexity_estimator/README.md @@ -0,0 +1,9 @@ +# Sample Complexity Estimator + +Estimate sample complexity from MS1 peak density in mzML files. + +## Usage + +```bash +python sample_complexity_estimator.py --input run.mzML --output complexity.json +``` diff --git a/scripts/proteomics/sample_complexity_estimator/requirements.txt b/scripts/proteomics/sample_complexity_estimator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/sample_complexity_estimator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/sample_complexity_estimator/sample_complexity_estimator.py b/scripts/proteomics/sample_complexity_estimator/sample_complexity_estimator.py new file mode 100644 index 0000000..e532bd4 --- /dev/null +++ b/scripts/proteomics/sample_complexity_estimator/sample_complexity_estimator.py @@ -0,0 +1,147 @@ +""" +Sample Complexity Estimator +============================ +Estimate sample complexity from MS1 peak density in mzML files. + +Reports peak density per RT window, total unique peak count, and +a complexity score based on average peak density. + +Usage +----- + python sample_complexity_estimator.py --input run.mzML --output complexity.json +""" + +import argparse +import json +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def estimate_complexity( + exp: oms.MSExperiment, + intensity_threshold: float = 0.0, +) -> dict: + """Estimate sample complexity from MS1 peak density. + + Parameters + ---------- + exp: + Loaded MSExperiment object. + intensity_threshold: + Minimum intensity to count a peak. + + Returns + ------- + dict + Complexity metrics. + """ + import numpy as np + + ms1_spectra = [s for s in exp.getSpectra() if s.getMSLevel() == 1] + if not ms1_spectra: + return { + "n_ms1_spectra": 0, + "total_peaks": 0, + "avg_peaks_per_spectrum": 0.0, + "max_peaks_per_spectrum": 0, + "min_peaks_per_spectrum": 0, + "complexity_score": "N/A", + "per_spectrum": [], + } + + per_spectrum = [] + peak_counts = [] + + for spec in ms1_spectra: + mzs, intensities = spec.get_peaks() + if intensity_threshold > 0 and len(intensities) > 0: + mask = intensities >= intensity_threshold + n_peaks = int(np.sum(mask)) + else: + n_peaks = len(mzs) + + rt = spec.getRT() + per_spectrum.append({ + "rt": round(rt, 4), + "n_peaks": n_peaks, + }) + peak_counts.append(n_peaks) + + total_peaks = sum(peak_counts) + avg_peaks = total_peaks / len(peak_counts) + max_peaks = max(peak_counts) + min_peaks = min(peak_counts) + + # Simple complexity classification + if avg_peaks > 5000: + complexity = "very_high" + elif avg_peaks > 1000: + complexity = "high" + elif avg_peaks > 200: + complexity = "medium" + elif avg_peaks > 50: + complexity = "low" + else: + complexity = "very_low" + + return { + "n_ms1_spectra": len(ms1_spectra), + "total_peaks": total_peaks, + "avg_peaks_per_spectrum": round(avg_peaks, 2), + "max_peaks_per_spectrum": max_peaks, + "min_peaks_per_spectrum": min_peaks, + "complexity_score": complexity, + "per_spectrum": per_spectrum, + } + + +def write_json(result: dict, output_path: str) -> None: + """Write complexity result to JSON. + + Parameters + ---------- + result: + Complexity metrics dict. + output_path: + Output file path. + """ + with open(output_path, "w") as fh: + json.dump(result, fh, indent=2) + + +def main(): + parser = argparse.ArgumentParser( + description="Estimate sample complexity from MS1 peak density." + ) + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument( + "--intensity-threshold", type=float, default=0.0, + help="Minimum intensity to count a peak (default: 0)" + ) + parser.add_argument("--output", default=None, help="Output JSON file path") + args = parser.parse_args() + + exp = oms.MSExperiment() + oms.MzMLFile().load(args.input, exp) + + result = estimate_complexity(exp, intensity_threshold=args.intensity_threshold) + + print(f"MS1 spectra : {result['n_ms1_spectra']}") + print(f"Total peaks : {result['total_peaks']}") + print(f"Avg peaks/spectrum: {result['avg_peaks_per_spectrum']}") + print(f"Max peaks/spectrum: {result['max_peaks_per_spectrum']}") + print(f"Complexity score : {result['complexity_score']}") + + if args.output: + write_json(result, args.output) + print(f"\nResults written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/sample_complexity_estimator/tests/conftest.py b/scripts/proteomics/sample_complexity_estimator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/sample_complexity_estimator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/sample_complexity_estimator/tests/test_sample_complexity_estimator.py b/scripts/proteomics/sample_complexity_estimator/tests/test_sample_complexity_estimator.py new file mode 100644 index 0000000..eeadd55 --- /dev/null +++ b/scripts/proteomics/sample_complexity_estimator/tests/test_sample_complexity_estimator.py @@ -0,0 +1,84 @@ +"""Tests for sample_complexity_estimator.""" + +import json + +import numpy as np +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSampleComplexityEstimator: + def _make_experiment(self, n_ms1=5, n_peaks=100): + """Create a synthetic MSExperiment with MS1 spectra.""" + import pyopenms as oms + + exp = oms.MSExperiment() + for i in range(n_ms1): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(60.0 * i) + mzs = np.array([100.0 + j * 1.5 for j in range(n_peaks)], dtype=np.float64) + ints = np.array([1000.0 * (j + 1) for j in range(n_peaks)], dtype=np.float64) + spec.set_peaks([mzs, ints]) + exp.addSpectrum(spec) + return exp + + def test_estimate_complexity(self): + from sample_complexity_estimator import estimate_complexity + + exp = self._make_experiment(n_ms1=3, n_peaks=50) + result = estimate_complexity(exp) + assert result["n_ms1_spectra"] == 3 + assert result["total_peaks"] == 150 + assert result["avg_peaks_per_spectrum"] == 50.0 + + def test_complexity_score_low(self): + from sample_complexity_estimator import estimate_complexity + + exp = self._make_experiment(n_ms1=2, n_peaks=80) + result = estimate_complexity(exp) + assert result["complexity_score"] == "low" + + def test_complexity_score_medium(self): + from sample_complexity_estimator import estimate_complexity + + exp = self._make_experiment(n_ms1=2, n_peaks=500) + result = estimate_complexity(exp) + assert result["complexity_score"] == "medium" + + def test_intensity_threshold(self): + from sample_complexity_estimator import estimate_complexity + + exp = self._make_experiment(n_ms1=1, n_peaks=10) + # Threshold high enough to exclude some peaks + result = estimate_complexity(exp, intensity_threshold=5000.0) + assert result["total_peaks"] < 10 + + def test_empty_experiment(self): + import pyopenms as oms + from sample_complexity_estimator import estimate_complexity + + exp = oms.MSExperiment() + result = estimate_complexity(exp) + assert result["n_ms1_spectra"] == 0 + assert result["complexity_score"] == "N/A" + + def test_per_spectrum_data(self): + from sample_complexity_estimator import estimate_complexity + + exp = self._make_experiment(n_ms1=3, n_peaks=20) + result = estimate_complexity(exp) + assert len(result["per_spectrum"]) == 3 + assert all("rt" in s for s in result["per_spectrum"]) + assert all("n_peaks" in s for s in result["per_spectrum"]) + + def test_write_json(self, tmp_path): + from sample_complexity_estimator import estimate_complexity, write_json + + exp = self._make_experiment(n_ms1=2, n_peaks=30) + result = estimate_complexity(exp) + out = str(tmp_path / "complexity.json") + write_json(result, out) + with open(out) as fh: + data = json.load(fh) + assert data["n_ms1_spectra"] == 2 diff --git a/scripts/proteomics/sample_correlation_calculator/README.md b/scripts/proteomics/sample_correlation_calculator/README.md new file mode 100644 index 0000000..b15dde2 --- /dev/null +++ b/scripts/proteomics/sample_correlation_calculator/README.md @@ -0,0 +1,10 @@ +# Sample Correlation Calculator + +Compute Pearson or Spearman correlations between samples in a quantification matrix. + +## Usage + +```bash +python sample_correlation_calculator.py --input matrix.tsv --method pearson --output correlations.tsv +python sample_correlation_calculator.py --input matrix.tsv --method spearman --output correlations.tsv +``` diff --git a/scripts/proteomics/sample_correlation_calculator/requirements.txt b/scripts/proteomics/sample_correlation_calculator/requirements.txt new file mode 100644 index 0000000..ba577e4 --- /dev/null +++ b/scripts/proteomics/sample_correlation_calculator/requirements.txt @@ -0,0 +1,3 @@ +pyopenms +numpy +scipy diff --git a/scripts/proteomics/sample_correlation_calculator/sample_correlation_calculator.py b/scripts/proteomics/sample_correlation_calculator/sample_correlation_calculator.py new file mode 100644 index 0000000..32f9a37 --- /dev/null +++ b/scripts/proteomics/sample_correlation_calculator/sample_correlation_calculator.py @@ -0,0 +1,158 @@ +""" +Sample Correlation Calculator +============================= +Compute Pearson or Spearman correlations between samples in a quantification matrix. + +Usage +----- + python sample_correlation_calculator.py --input matrix.tsv --method pearson --output correlations.tsv + python sample_correlation_calculator.py --input matrix.tsv --method spearman --output correlations.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np +from scipy import stats + + +def read_matrix(filepath: str) -> tuple: + """Read a TSV quantification matrix. + + Returns (row_ids, col_names, data_matrix). + """ + with open(filepath) as fh: + reader = csv.reader(fh, delimiter="\t") + header = next(reader) + col_names = header[1:] + row_ids = [] + rows = [] + for row in reader: + row_ids.append(row[0]) + values = [] + for v in row[1:]: + v = v.strip() + if v == "" or v.upper() in ("NA", "NAN"): + values.append(np.nan) + else: + values.append(float(v)) + rows.append(values) + return row_ids, col_names, np.array(rows, dtype=float) + + +def compute_correlations(matrix: np.ndarray, col_names: list, method: str = "pearson") -> list: + """Compute pairwise sample correlations. + + Parameters + ---------- + matrix: + 2D array (features x samples). + col_names: + Sample names. + method: + 'pearson' or 'spearman'. + + Returns + ------- + list + List of dicts with keys: sample_a, sample_b, correlation, pvalue. + """ + method = method.lower() + if method not in ("pearson", "spearman"): + raise ValueError(f"Unknown method: '{method}'. Choose 'pearson' or 'spearman'.") + + n_samples = len(col_names) + results = [] + + for i in range(n_samples): + for j in range(i, n_samples): + col_i = matrix[:, i] + col_j = matrix[:, j] + # Use only rows where both values are non-NaN + mask = ~np.isnan(col_i) & ~np.isnan(col_j) + if np.sum(mask) < 3: + corr = float("nan") + pval = float("nan") + else: + if method == "pearson": + corr, pval = stats.pearsonr(col_i[mask], col_j[mask]) + else: + corr, pval = stats.spearmanr(col_i[mask], col_j[mask]) + + results.append({ + "sample_a": col_names[i], + "sample_b": col_names[j], + "correlation": corr, + "pvalue": pval, + }) + + return results + + +def correlation_matrix(matrix: np.ndarray, col_names: list, method: str = "pearson") -> np.ndarray: + """Compute a full correlation matrix. + + Parameters + ---------- + matrix: + 2D array (features x samples). + col_names: + Sample names. + method: + 'pearson' or 'spearman'. + + Returns + ------- + np.ndarray + Symmetric correlation matrix. + """ + pairs = compute_correlations(matrix, col_names, method) + n = len(col_names) + corr_mat = np.zeros((n, n)) + name_to_idx = {name: i for i, name in enumerate(col_names)} + + for p in pairs: + i = name_to_idx[p["sample_a"]] + j = name_to_idx[p["sample_b"]] + corr_mat[i, j] = p["correlation"] + corr_mat[j, i] = p["correlation"] + + return corr_mat + + +def main(): + parser = argparse.ArgumentParser(description="Compute sample correlations.") + parser.add_argument("--input", required=True, help="Input TSV matrix file") + parser.add_argument("--method", default="pearson", choices=["pearson", "spearman"], + help="Correlation method (default: pearson)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + row_ids, col_names, matrix = read_matrix(args.input) + results = compute_correlations(matrix, col_names, args.method) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=["sample_a", "sample_b", "correlation", "pvalue"], delimiter="\t") + writer.writeheader() + for r in results: + writer.writerow({ + "sample_a": r["sample_a"], + "sample_b": r["sample_b"], + "correlation": f"{r['correlation']:.6f}" if not np.isnan(r["correlation"]) else "NA", + "pvalue": f"{r['pvalue']:.6e}" if not np.isnan(r["pvalue"]) else "NA", + }) + + print(f"Method: {args.method}") + print(f"Samples: {len(col_names)}") + print(f"Pairs: {len(results)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/sample_correlation_calculator/tests/conftest.py b/scripts/proteomics/sample_correlation_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/sample_correlation_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/sample_correlation_calculator/tests/test_sample_correlation_calculator.py b/scripts/proteomics/sample_correlation_calculator/tests/test_sample_correlation_calculator.py new file mode 100644 index 0000000..d50f0f5 --- /dev/null +++ b/scripts/proteomics/sample_correlation_calculator/tests/test_sample_correlation_calculator.py @@ -0,0 +1,71 @@ +"""Tests for sample_correlation_calculator.""" + +import numpy as np +import pytest +from conftest import requires_pyopenms +from sample_correlation_calculator import compute_correlations, correlation_matrix + + +@requires_pyopenms +class TestSampleCorrelationCalculator: + def _make_matrix(self): + return np.array([ + [100.0, 200.0, 150.0], + [300.0, 600.0, 450.0], + [500.0, 1000.0, 750.0], + [700.0, 1400.0, 1050.0], + ]) + + def test_pearson_self_correlation(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + results = compute_correlations(matrix, col_names, "pearson") + self_corr = [r for r in results if r["sample_a"] == r["sample_b"]] + for r in self_corr: + assert abs(r["correlation"] - 1.0) < 1e-6 + + def test_pearson_perfect_correlation(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + results = compute_correlations(matrix, col_names, "pearson") + # s1 and s2 are perfectly linearly correlated (s2 = 2*s1) + pair = next(r for r in results if r["sample_a"] == "s1" and r["sample_b"] == "s2") + assert abs(pair["correlation"] - 1.0) < 1e-6 + + def test_spearman(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + results = compute_correlations(matrix, col_names, "spearman") + assert len(results) == 6 # 3 choose 2 + 3 diagonal + + def test_correlation_matrix_shape(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + corr_mat = correlation_matrix(matrix, col_names, "pearson") + assert corr_mat.shape == (3, 3) + # Diagonal should be 1 + np.testing.assert_allclose(np.diag(corr_mat), 1.0, atol=1e-6) + + def test_unknown_method(self): + matrix = self._make_matrix() + with pytest.raises(ValueError, match="Unknown method"): + compute_correlations(matrix, ["s1", "s2", "s3"], "invalid") + + def test_with_nan(self): + matrix = np.array([ + [100.0, 200.0], + [np.nan, 400.0], + [300.0, 600.0], + [400.0, 800.0], + ]) + results = compute_correlations(matrix, ["s1", "s2"], "pearson") + # Should still compute using non-NaN rows + pair = next(r for r in results if r["sample_a"] == "s1" and r["sample_b"] == "s2") + assert not np.isnan(pair["correlation"]) + + def test_pair_count(self): + matrix = self._make_matrix() + col_names = ["s1", "s2", "s3"] + results = compute_correlations(matrix, col_names, "pearson") + # n*(n+1)/2 = 6 pairs (including self) + assert len(results) == 6 diff --git a/scripts/proteomics/scp_reporter_qc/README.md b/scripts/proteomics/scp_reporter_qc/README.md new file mode 100644 index 0000000..54d22d7 --- /dev/null +++ b/scripts/proteomics/scp_reporter_qc/README.md @@ -0,0 +1,32 @@ +# SCP Reporter QC + +Single-cell proteomics QC: compute sample-to-carrier ratio per spectrum for carrier-based SCP experiments. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python scp_reporter_qc.py --input reporter_ions.tsv --carrier-channel 131C --output qc.tsv +``` + +### Input format + +Tab-separated file with `spectrum_id` and one column per reporter ion channel: + +``` +spectrum_id 126 127N 127C 128N 131C +spec1 100.5 95.2 110.3 88.7 50000.0 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Input TSV with reporter ion intensities | +| `--carrier-channel` | Carrier channel name (e.g. `131C`) | +| `--output` | Output QC TSV | diff --git a/scripts/proteomics/scp_reporter_qc/requirements.txt b/scripts/proteomics/scp_reporter_qc/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/scp_reporter_qc/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/scp_reporter_qc/scp_reporter_qc.py b/scripts/proteomics/scp_reporter_qc/scp_reporter_qc.py new file mode 100644 index 0000000..201e2cc --- /dev/null +++ b/scripts/proteomics/scp_reporter_qc/scp_reporter_qc.py @@ -0,0 +1,187 @@ +""" +SCP Reporter QC +================ +Quality control for single-cell proteomics (SCP) data using isobaric +reporter ions. Computes the sample-to-carrier ratio per spectrum, which +is a key QC metric for carrier-based SCP experiments. + +The carrier channel typically has much higher intensity than single-cell +channels. Ratios that are too high indicate insufficient carrier signal; +ratios that are too low suggest excessive carrier relative to single cells. + +Usage +----- + python scp_reporter_qc.py --input reporter_ions.tsv \ + --carrier-channel 131C --output qc.tsv +""" + +import argparse +import csv +import math +import sys +from typing import Dict, List + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def compute_sample_to_carrier_ratios( + spectra: List[Dict[str, float]], carrier_channel: str +) -> List[Dict[str, object]]: + """Compute sample-to-carrier ratio for each spectrum. + + Parameters + ---------- + spectra: + List of dicts mapping channel name to intensity. Must include + a ``spectrum_id`` key (string). + carrier_channel: + Name of the carrier channel (e.g. ``"131C"``). + + Returns + ------- + list of dict + One entry per spectrum with ``spectrum_id``, ``carrier_intensity``, + ``mean_sample_intensity``, ``sample_to_carrier_ratio``, + ``num_nonzero_samples``. + """ + results: List[Dict[str, object]] = [] + for spectrum in spectra: + spec_id = spectrum.get("spectrum_id", "unknown") + carrier_int = spectrum.get(carrier_channel, 0.0) + + sample_intensities = [] + for ch, val in spectrum.items(): + if ch in ("spectrum_id", carrier_channel): + continue + if isinstance(val, (int, float)) and val > 0: + sample_intensities.append(val) + + mean_sample = sum(sample_intensities) / len(sample_intensities) if sample_intensities else 0.0 + ratio = mean_sample / carrier_int if carrier_int > 0 else float("nan") + + results.append({ + "spectrum_id": spec_id, + "carrier_intensity": carrier_int, + "mean_sample_intensity": mean_sample, + "sample_to_carrier_ratio": ratio, + "num_nonzero_samples": len(sample_intensities), + }) + return results + + +def qc_summary(ratios: List[Dict[str, object]]) -> Dict[str, object]: + """Compute summary statistics over sample-to-carrier ratios. + + Returns + ------- + dict + ``n_spectra``, ``median_ratio``, ``mean_ratio``, ``std_ratio``, + ``below_0_01_count`` (spectra with ratio < 0.01, possibly problematic). + """ + valid_ratios = [ + r["sample_to_carrier_ratio"] for r in ratios + if isinstance(r["sample_to_carrier_ratio"], float) + and not math.isnan(r["sample_to_carrier_ratio"]) + ] + if not valid_ratios: + return { + "n_spectra": len(ratios), + "median_ratio": float("nan"), + "mean_ratio": float("nan"), + "std_ratio": float("nan"), + "below_0_01_count": 0, + } + + valid_ratios_sorted = sorted(valid_ratios) + n = len(valid_ratios_sorted) + median = valid_ratios_sorted[n // 2] if n % 2 == 1 else ( + (valid_ratios_sorted[n // 2 - 1] + valid_ratios_sorted[n // 2]) / 2.0 + ) + mean = sum(valid_ratios) / n + variance = sum((r - mean) ** 2 for r in valid_ratios) / n if n > 1 else 0.0 + std = math.sqrt(variance) + + below_threshold = sum(1 for r in valid_ratios if r < 0.01) + + return { + "n_spectra": len(ratios), + "median_ratio": median, + "mean_ratio": mean, + "std_ratio": std, + "below_0_01_count": below_threshold, + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Single-cell proteomics QC: sample-to-carrier ratio per spectrum." + ) + parser.add_argument( + "--input", required=True, + help="Input TSV with spectrum_id and reporter ion intensities per channel", + ) + parser.add_argument( + "--carrier-channel", required=True, + help="Name of the carrier channel (e.g. 131C)", + ) + parser.add_argument("--output", required=True, help="Output QC TSV") + args = parser.parse_args() + + spectra: List[Dict[str, float]] = [] + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + spec: Dict[str, float] = {} + for key, val in row.items(): + if key == "spectrum_id": + spec["spectrum_id"] = val + else: + try: + spec[key] = float(val) + except (ValueError, TypeError): + spec[key] = 0.0 + spectra.append(spec) + + if not spectra: + sys.exit("No spectra found in input.") + + if args.carrier_channel not in (spectra[0] if spectra else {}): + print(f"Warning: carrier channel '{args.carrier_channel}' not found in input columns.") + + ratios = compute_sample_to_carrier_ratios(spectra, args.carrier_channel) + summary = qc_summary(ratios) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow([ + "spectrum_id", "carrier_intensity", "mean_sample_intensity", + "sample_to_carrier_ratio", "num_nonzero_samples", + ]) + for r in ratios: + ratio_str = f"{r['sample_to_carrier_ratio']:.6f}" if not math.isnan( + r["sample_to_carrier_ratio"] + ) else "NA" + writer.writerow([ + r["spectrum_id"], + f"{r['carrier_intensity']:.2f}", + f"{r['mean_sample_intensity']:.2f}", + ratio_str, + r["num_nonzero_samples"], + ]) + writer.writerow([]) + writer.writerow(["metric", "value"]) + for key, val in summary.items(): + if isinstance(val, float) and not math.isnan(val): + writer.writerow([key, f"{val:.6f}"]) + else: + writer.writerow([key, val]) + + median_str = f"{summary['median_ratio']:.4f}" if not math.isnan(summary["median_ratio"]) else "NA" + print(f"Processed {summary['n_spectra']} spectra, median ratio: {median_str}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/scp_reporter_qc/tests/conftest.py b/scripts/proteomics/scp_reporter_qc/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/scp_reporter_qc/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/scp_reporter_qc/tests/test_scp_reporter_qc.py b/scripts/proteomics/scp_reporter_qc/tests/test_scp_reporter_qc.py new file mode 100644 index 0000000..9601b24 --- /dev/null +++ b/scripts/proteomics/scp_reporter_qc/tests/test_scp_reporter_qc.py @@ -0,0 +1,79 @@ +"""Tests for scp_reporter_qc.""" + +import csv +import math +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_compute_sample_to_carrier_ratios(): + from scp_reporter_qc import compute_sample_to_carrier_ratios + + spectra = [ + {"spectrum_id": "s1", "126": 100.0, "127N": 120.0, "131C": 50000.0}, + {"spectrum_id": "s2", "126": 200.0, "127N": 180.0, "131C": 40000.0}, + ] + results = compute_sample_to_carrier_ratios(spectra, "131C") + assert len(results) == 2 + # mean of 100,120 = 110, ratio = 110/50000 + assert abs(results[0]["sample_to_carrier_ratio"] - 110.0 / 50000.0) < 1e-6 + assert results[0]["num_nonzero_samples"] == 2 + + +@requires_pyopenms +def test_zero_carrier(): + from scp_reporter_qc import compute_sample_to_carrier_ratios + + spectra = [{"spectrum_id": "s1", "126": 100.0, "131C": 0.0}] + results = compute_sample_to_carrier_ratios(spectra, "131C") + assert math.isnan(results[0]["sample_to_carrier_ratio"]) + + +@requires_pyopenms +def test_qc_summary(): + from scp_reporter_qc import qc_summary + + ratios = [ + {"sample_to_carrier_ratio": 0.002}, + {"sample_to_carrier_ratio": 0.003}, + {"sample_to_carrier_ratio": 0.005}, + {"sample_to_carrier_ratio": 0.001}, + ] + summary = qc_summary(ratios) + assert summary["n_spectra"] == 4 + assert abs(summary["mean_ratio"] - 0.00275) < 1e-6 + assert summary["below_0_01_count"] == 4 # all below 0.01 + + +@requires_pyopenms +def test_qc_summary_empty(): + from scp_reporter_qc import qc_summary + + summary = qc_summary([]) + assert summary["n_spectra"] == 0 + assert math.isnan(summary["median_ratio"]) + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from scp_reporter_qc import main + + input_file = tmp_path / "input.tsv" + output_file = tmp_path / "output.tsv" + + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["spectrum_id", "126", "127N", "131C"]) + writer.writerow(["s1", "100.0", "120.0", "50000.0"]) + writer.writerow(["s2", "200.0", "180.0", "40000.0"]) + + sys.argv = [ + "scp_reporter_qc.py", + "--input", str(input_file), + "--carrier-channel", "131C", + "--output", str(output_file), + ] + main() + assert output_file.exists() diff --git a/scripts/proteomics/search_result_merger/README.md b/scripts/proteomics/search_result_merger/README.md new file mode 100644 index 0000000..fab4161 --- /dev/null +++ b/scripts/proteomics/search_result_merger/README.md @@ -0,0 +1,15 @@ +# Search Result Merger + +Merge multiple identification TSV files with union or intersection consensus. + +## Usage + +```bash +python search_result_merger.py --inputs engine1.tsv engine2.tsv --method union --output merged.tsv +python search_result_merger.py --inputs engine1.tsv engine2.tsv --method intersection --output merged.tsv +``` + +## Methods + +- **union** - Include all PSMs from any search engine +- **intersection** - Only include PSMs found in all search engines diff --git a/scripts/proteomics/search_result_merger/requirements.txt b/scripts/proteomics/search_result_merger/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/search_result_merger/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/search_result_merger/search_result_merger.py b/scripts/proteomics/search_result_merger/search_result_merger.py new file mode 100644 index 0000000..0618dcc --- /dev/null +++ b/scripts/proteomics/search_result_merger/search_result_merger.py @@ -0,0 +1,145 @@ +""" +Search Result Merger +==================== +Merge multiple identification TSV files with union or intersection consensus. + +Each input TSV must have at least a 'peptide' column. Additional columns +(score, protein, etc.) are preserved. The merger identifies PSMs by a +composite key of (peptide, charge, spectrum) or just (peptide) if other +columns are absent. + +Usage +----- + python search_result_merger.py --inputs engine1.tsv engine2.tsv --method union --output merged.tsv + python search_result_merger.py --inputs engine1.tsv engine2.tsv --method intersection --output merged.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def read_identification_tsv(filepath: str) -> list: + """Read an identification TSV file. + + Parameters + ---------- + filepath: + Path to TSV with at least a 'peptide' column. + + Returns + ------- + list + List of dicts (one per row). + """ + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + return list(reader) + + +def _make_key(row: dict) -> str: + """Create a composite key for a PSM row.""" + parts = [row.get("peptide", "")] + if "charge" in row: + parts.append(str(row["charge"])) + if "spectrum" in row: + parts.append(row["spectrum"]) + return "||".join(parts) + + +def merge_results(input_files: list, method: str = "union") -> tuple: + """Merge identification results from multiple search engines. + + Parameters + ---------- + input_files: + List of TSV file paths. + method: + 'union' (all PSMs from any engine) or 'intersection' (only PSMs found in all). + + Returns + ------- + tuple + (fieldnames, merged_rows) where merged_rows is a list of dicts. + """ + method = method.lower() + if method not in ("union", "intersection"): + raise ValueError(f"Unknown method: '{method}'. Choose 'union' or 'intersection'.") + + all_results = [] + all_fieldnames = [] + for filepath in input_files: + rows = read_identification_tsv(filepath) + all_results.append(rows) + if rows: + for key in rows[0].keys(): + if key not in all_fieldnames: + all_fieldnames.append(key) + + if "source" not in all_fieldnames: + all_fieldnames.append("source") + if "n_engines" not in all_fieldnames: + all_fieldnames.append("n_engines") + + # Build key -> list of (source_index, row) + key_to_entries = {} + for file_idx, rows in enumerate(all_results): + source = input_files[file_idx] + for row in rows: + key = _make_key(row) + if key not in key_to_entries: + key_to_entries[key] = [] + row_copy = dict(row) + row_copy["_source_idx"] = file_idx + row_copy["_source"] = source + key_to_entries[key].append(row_copy) + + n_files = len(input_files) + merged = [] + + for key, entries in key_to_entries.items(): + source_indices = set(e["_source_idx"] for e in entries) + + if method == "intersection" and len(source_indices) < n_files: + continue + + # Use the first entry as the base row, add source info + base = dict(entries[0]) + base.pop("_source_idx", None) + base.pop("_source", None) + sources = sorted(set(e["_source"] for e in entries)) + base["source"] = ";".join(sources) + base["n_engines"] = str(len(source_indices)) + merged.append(base) + + return all_fieldnames, merged + + +def main(): + parser = argparse.ArgumentParser(description="Merge multiple identification TSV files.") + parser.add_argument("--inputs", nargs="+", required=True, help="Input TSV files") + parser.add_argument("--method", default="union", choices=["union", "intersection"], + help="Merge method (default: union)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + fieldnames, merged = merge_results(args.inputs, method=args.method) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") + writer.writeheader() + writer.writerows(merged) + + print(f"Method: {args.method}") + print(f"Input files: {len(args.inputs)}") + print(f"Merged PSMs: {len(merged)}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/search_result_merger/tests/conftest.py b/scripts/proteomics/search_result_merger/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/search_result_merger/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/search_result_merger/tests/test_search_result_merger.py b/scripts/proteomics/search_result_merger/tests/test_search_result_merger.py new file mode 100644 index 0000000..621820f --- /dev/null +++ b/scripts/proteomics/search_result_merger/tests/test_search_result_merger.py @@ -0,0 +1,78 @@ +"""Tests for search_result_merger.""" + +import pytest +from conftest import requires_pyopenms +from search_result_merger import _make_key, merge_results, read_identification_tsv + + +@requires_pyopenms +class TestSearchResultMerger: + def _write_tsv(self, tmp_path, name, rows): + filepath = str(tmp_path / name) + with open(filepath, "w") as fh: + if rows: + keys = list(rows[0].keys()) + fh.write("\t".join(keys) + "\n") + for row in rows: + fh.write("\t".join(str(row[k]) for k in keys) + "\n") + return filepath + + def test_union_merge(self, tmp_path): + f1 = self._write_tsv(tmp_path, "e1.tsv", [ + {"peptide": "PEPTIDEK", "charge": "2", "score": "0.99"}, + {"peptide": "TESTPEP", "charge": "2", "score": "0.95"}, + ]) + f2 = self._write_tsv(tmp_path, "e2.tsv", [ + {"peptide": "PEPTIDEK", "charge": "2", "score": "0.98"}, + {"peptide": "ANOTHERPEP", "charge": "3", "score": "0.90"}, + ]) + _, merged = merge_results([f1, f2], method="union") + peptides = [r["peptide"] for r in merged] + assert "PEPTIDEK" in peptides + assert "TESTPEP" in peptides + assert "ANOTHERPEP" in peptides + assert len(merged) == 3 + + def test_intersection_merge(self, tmp_path): + f1 = self._write_tsv(tmp_path, "e1.tsv", [ + {"peptide": "PEPTIDEK", "charge": "2"}, + {"peptide": "TESTPEP", "charge": "2"}, + ]) + f2 = self._write_tsv(tmp_path, "e2.tsv", [ + {"peptide": "PEPTIDEK", "charge": "2"}, + {"peptide": "ANOTHERPEP", "charge": "3"}, + ]) + _, merged = merge_results([f1, f2], method="intersection") + assert len(merged) == 1 + assert merged[0]["peptide"] == "PEPTIDEK" + + def test_n_engines_count(self, tmp_path): + f1 = self._write_tsv(tmp_path, "e1.tsv", [{"peptide": "PEP", "charge": "2"}]) + f2 = self._write_tsv(tmp_path, "e2.tsv", [{"peptide": "PEP", "charge": "2"}]) + _, merged = merge_results([f1, f2], method="union") + assert merged[0]["n_engines"] == "2" + + def test_unknown_method(self, tmp_path): + f1 = self._write_tsv(tmp_path, "e1.tsv", [{"peptide": "PEP"}]) + with pytest.raises(ValueError, match="Unknown method"): + merge_results([f1], method="invalid") + + def test_make_key(self): + row = {"peptide": "PEPTIDEK", "charge": "2", "spectrum": "scan1"} + key = _make_key(row) + assert "PEPTIDEK" in key + assert "2" in key + assert "scan1" in key + + def test_empty_input(self, tmp_path): + f1 = self._write_tsv(tmp_path, "e1.tsv", []) + _, merged = merge_results([f1], method="union") + assert len(merged) == 0 + + def test_read_tsv(self, tmp_path): + filepath = self._write_tsv(tmp_path, "test.tsv", [ + {"peptide": "PEPTIDEK", "score": "0.99"}, + ]) + rows = read_identification_tsv(filepath) + assert len(rows) == 1 + assert rows[0]["peptide"] == "PEPTIDEK" diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/README.md b/scripts/proteomics/semi_tryptic_peptide_finder/README.md new file mode 100644 index 0000000..5a1e092 --- /dev/null +++ b/scripts/proteomics/semi_tryptic_peptide_finder/README.md @@ -0,0 +1,9 @@ +# Semi-Tryptic Peptide Finder + +Classify peptides as fully tryptic, semi-tryptic, or non-tryptic. + +## Usage + +```bash +python semi_tryptic_peptide_finder.py --input peptides.tsv --fasta db.fasta --enzyme Trypsin --output classified.tsv +``` diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/requirements.txt b/scripts/proteomics/semi_tryptic_peptide_finder/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/semi_tryptic_peptide_finder/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py b/scripts/proteomics/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py new file mode 100644 index 0000000..d6061f8 --- /dev/null +++ b/scripts/proteomics/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py @@ -0,0 +1,244 @@ +""" +Semi-Tryptic Peptide Finder +============================ +Classify peptides as fully tryptic, semi-tryptic, or non-tryptic by checking +whether their N- and C-terminal cleavage sites match the enzyme specificity. + +Usage +----- + python semi_tryptic_peptide_finder.py --input peptides.tsv --fasta db.fasta --enzyme Trypsin --output classified.tsv +""" + +import argparse +import csv +import sys +from typing import List + +try: + import pyopenms as oms +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def load_fasta(fasta_path: str) -> dict: + """Load a FASTA file and return a dict mapping accession to sequence. + + Parameters + ---------- + fasta_path: + Path to a FASTA file. + + Returns + ------- + dict + Mapping of accession to protein sequence string. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + proteins = {} + for entry in entries: + proteins[entry.identifier] = entry.sequence + return proteins + + +def digest_protein(protein_sequence: str, enzyme: str = "Trypsin", missed_cleavages: int = 2) -> List[str]: + """Digest a protein sequence with the given enzyme. + + Parameters + ---------- + protein_sequence: + Full protein amino acid sequence. + enzyme: + Enzyme name (default: Trypsin). + missed_cleavages: + Number of allowed missed cleavages. + + Returns + ------- + list + List of fully tryptic peptide strings. + """ + digestion = oms.ProteaseDigestion() + digestion.setEnzyme(enzyme) + digestion.setMissedCleavages(missed_cleavages) + + aa_seq = oms.AASequence.fromString(protein_sequence) + result = [] + digestion.digest(aa_seq, result) + return [str(pep) for pep in result] + + +def classify_peptide(peptide: str, protein_sequence: str, enzyme: str = "Trypsin") -> str: + """Classify a peptide as fully_tryptic, semi_tryptic, or non_tryptic. + + Parameters + ---------- + peptide: + Peptide sequence to classify. + protein_sequence: + Parent protein sequence. + enzyme: + Enzyme name. + + Returns + ------- + str + Classification: 'fully_tryptic', 'semi_tryptic', or 'non_tryptic'. + """ + # Find peptide in protein + pos = protein_sequence.find(peptide) + if pos == -1: + return "not_found" + + # Check N-terminal cleavage (trypsin: after K or R, or protein N-term) + n_term_ok = False + if pos == 0: + n_term_ok = True + elif enzyme == "Trypsin" and protein_sequence[pos - 1] in ("K", "R"): + # Check for proline rule: trypsin does not cleave before P + if peptide[0] != "P": + n_term_ok = True + + # Check C-terminal cleavage (trypsin: peptide ends with K or R, or protein C-term) + c_term_ok = False + end_pos = pos + len(peptide) + if end_pos == len(protein_sequence): + c_term_ok = True + elif enzyme == "Trypsin" and peptide[-1] in ("K", "R"): + c_term_ok = True + + if n_term_ok and c_term_ok: + return "fully_tryptic" + elif n_term_ok or c_term_ok: + return "semi_tryptic" + else: + return "non_tryptic" + + +def classify_peptides_against_fasta( + peptides: List[str], + proteins: dict, + enzyme: str = "Trypsin", +) -> List[dict]: + """Classify a list of peptides against protein sequences. + + Parameters + ---------- + peptides: + List of peptide sequences. + proteins: + Dict mapping accession to protein sequence. + enzyme: + Enzyme name. + + Returns + ------- + list + List of dicts with sequence, classification, and matched protein. + """ + results = [] + for pep in peptides: + pep = pep.strip() + if not pep: + continue + best_class = "not_found" + matched_protein = "" + for acc, prot_seq in proteins.items(): + classification = classify_peptide(pep, prot_seq, enzyme) + if classification == "fully_tryptic": + best_class = classification + matched_protein = acc + break + elif classification == "semi_tryptic" and best_class != "fully_tryptic": + best_class = classification + matched_protein = acc + elif classification == "non_tryptic" and best_class == "not_found": + best_class = classification + matched_protein = acc + + aa_seq = oms.AASequence.fromString(pep) + results.append({ + "sequence": pep, + "length": aa_seq.size(), + "classification": best_class, + "protein": matched_protein, + }) + return results + + +def read_peptides_from_tsv(input_path: str, column: str = "sequence") -> List[str]: + """Read peptide sequences from a TSV file. + + Parameters + ---------- + input_path: + Path to input TSV file. + column: + Column name containing sequences. + + Returns + ------- + list + List of peptide sequence strings. + """ + peptides = [] + with open(input_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + if column in row and row[column].strip(): + peptides.append(row[column].strip()) + return peptides + + +def write_tsv(results: List[dict], output_path: str) -> None: + """Write classification results to TSV. + + Parameters + ---------- + results: + List of result dicts. + output_path: + Output file path. + """ + fieldnames = ["sequence", "length", "classification", "protein"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in results: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Classify peptides as fully/semi/non-tryptic." + ) + parser.add_argument("--input", required=True, help="Input TSV with peptide sequences") + parser.add_argument("--fasta", required=True, help="FASTA database file") + parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") + parser.add_argument("--column", default="sequence", help="Column name for sequences (default: sequence)") + parser.add_argument("--output", required=True, help="Output TSV file path") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + print(f"Loaded {len(proteins)} proteins from {args.fasta}") + + peptides = read_peptides_from_tsv(args.input, column=args.column) + print(f"Read {len(peptides)} peptides from {args.input}") + + results = classify_peptides_against_fasta(peptides, proteins, enzyme=args.enzyme) + + counts = {} + for r in results: + counts[r["classification"]] = counts.get(r["classification"], 0) + 1 + for cls, cnt in sorted(counts.items()): + print(f" {cls}: {cnt}") + + write_tsv(results, args.output) + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/tests/conftest.py b/scripts/proteomics/semi_tryptic_peptide_finder/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/semi_tryptic_peptide_finder/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py b/scripts/proteomics/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py new file mode 100644 index 0000000..46a6b32 --- /dev/null +++ b/scripts/proteomics/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py @@ -0,0 +1,70 @@ +"""Tests for semi_tryptic_peptide_finder.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSemiTrypticPeptideFinder: + def test_fully_tryptic(self): + from semi_tryptic_peptide_finder import classify_peptide + + # PEPTIDEK ends with K, preceded by protein N-term + protein = "PEPTIDEKAVLIDR" + result = classify_peptide("PEPTIDEK", protein, "Trypsin") + assert result == "fully_tryptic" + + def test_semi_tryptic_n_term(self): + from semi_tryptic_peptide_finder import classify_peptide + + # Peptide starts after K (tryptic N-term) but does not end with K/R + protein = "PEPTIDEKAVLID" + result = classify_peptide("AVLID", protein, "Trypsin") + assert result == "semi_tryptic" + + def test_semi_tryptic_c_term(self): + from semi_tryptic_peptide_finder import classify_peptide + + # Peptide ends with K (tryptic C-term) but N-term is not after K/R + protein = "AVLIDPEPTIDEK" + result = classify_peptide("IDPEPTIDEK", protein, "Trypsin") + assert result == "semi_tryptic" + + def test_non_tryptic(self): + from semi_tryptic_peptide_finder import classify_peptide + + # Neither end matches tryptic cleavage + protein = "AVLIDPEPTIDEGG" + result = classify_peptide("IDPEPTIDE", protein, "Trypsin") + assert result == "non_tryptic" + + def test_not_found(self): + from semi_tryptic_peptide_finder import classify_peptide + + result = classify_peptide("XYZXYZ", "PEPTIDEK", "Trypsin") + assert result == "not_found" + + def test_classify_against_fasta(self): + from semi_tryptic_peptide_finder import classify_peptides_against_fasta + + proteins = {"P1": "PEPTIDEKAVLIDR"} + results = classify_peptides_against_fasta(["PEPTIDEK"], proteins, "Trypsin") + assert len(results) == 1 + assert results[0]["classification"] == "fully_tryptic" + assert results[0]["protein"] == "P1" + + def test_digest_protein(self): + from semi_tryptic_peptide_finder import digest_protein + + peptides = digest_protein("PEPTIDEKAVLIDR", "Trypsin", missed_cleavages=0) + assert len(peptides) >= 2 + + def test_write_tsv(self, tmp_path): + from semi_tryptic_peptide_finder import write_tsv + + results = [{"sequence": "PEPTIDEK", "length": 8, "classification": "fully_tryptic", "protein": "P1"}] + out = str(tmp_path / "out.tsv") + write_tsv(results, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 + assert "fully_tryptic" in lines[1] diff --git a/scripts/proteomics/sequence_tag_generator/README.md b/scripts/proteomics/sequence_tag_generator/README.md new file mode 100644 index 0000000..90d83ae --- /dev/null +++ b/scripts/proteomics/sequence_tag_generator/README.md @@ -0,0 +1,10 @@ +# Sequence Tag Generator + +Generate de novo sequence tags from MS2 spectra by matching peak mass differences to amino acid residue masses. + +## Usage + +```bash +python sequence_tag_generator.py --mz-list "200.1,313.2,426.3,539.4" --intensities "100,200,150,300" \ + --tolerance 0.02 --min-tag-length 3 --output tags.tsv +``` diff --git a/scripts/proteomics/sequence_tag_generator/requirements.txt b/scripts/proteomics/sequence_tag_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/sequence_tag_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/sequence_tag_generator/sequence_tag_generator.py b/scripts/proteomics/sequence_tag_generator/sequence_tag_generator.py new file mode 100644 index 0000000..7d4ce9a --- /dev/null +++ b/scripts/proteomics/sequence_tag_generator/sequence_tag_generator.py @@ -0,0 +1,224 @@ +""" +Sequence Tag Generator +====================== +Generate de novo sequence tags from MS2 spectra by computing mass differences +between sorted peaks and matching to amino acid residue masses. + +Usage +----- + python sequence_tag_generator.py --mz-list "200.1,313.2,426.3,539.4" --intensities "100,200,150,300" \\ + --tolerance 0.02 --min-tag-length 3 --output tags.tsv +""" + +import argparse +import csv +import sys +from typing import List, Optional + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit( + "pyopenms is required. Install it with: pip install pyopenms" + ) + + +def get_residue_masses() -> dict: + """Build a lookup of amino acid single-letter codes to monoisotopic residue masses. + + Returns + ------- + dict + Mapping of single-letter amino acid code to residue mass (Da). + """ + db = oms.ResidueDB() + residue_masses = {} + for code in "ACDEFGHIKLMNPQRSTVWY": + residue = db.getResidue(code) + residue_masses[code] = residue.getMonoWeight(oms.Residue.ResidueType.Internal) + return residue_masses + + +def match_mass_to_residue( + mass_diff: float, + residue_masses: dict, + tolerance: float = 0.02, +) -> List[str]: + """Match a mass difference to amino acid residues. + + Parameters + ---------- + mass_diff: + Mass difference between two peaks (Da). + residue_masses: + Dict mapping residue codes to masses. + tolerance: + Mass tolerance in Da. + + Returns + ------- + list + List of matching single-letter amino acid codes. + """ + matches = [] + for code, mass in residue_masses.items(): + if abs(mass_diff - mass) <= tolerance: + matches.append(code) + return matches + + +def generate_tags( + mz_values: List[float], + intensities: Optional[List[float]] = None, + tolerance: float = 0.02, + min_tag_length: int = 3, +) -> List[dict]: + """Generate sequence tags from a list of m/z values. + + Sorts peaks by m/z, computes pairwise mass differences between consecutive + peaks, and builds sequence tags by matching differences to amino acid masses. + + Parameters + ---------- + mz_values: + List of m/z values from an MS2 spectrum. + intensities: + Optional list of corresponding intensities. + tolerance: + Mass tolerance in Da for residue matching. + min_tag_length: + Minimum tag length to report (number of residues). + + Returns + ------- + list + List of tag dicts with tag sequence, start/end m/z, and length. + """ + if len(mz_values) < 2: + return [] + + residue_masses = get_residue_masses() + + # Sort peaks by m/z + if intensities: + paired = sorted(zip(mz_values, intensities), key=lambda x: x[0]) + sorted_mzs = [p[0] for p in paired] + else: + sorted_mzs = sorted(mz_values) + + # Build adjacency: for each pair of peaks, find matching residues + n = len(sorted_mzs) + tags = [] + + # Use dynamic programming: extend tags greedily + # For each starting peak, try to build the longest tag + for start in range(n): + _extend_tag(start, sorted_mzs, residue_masses, tolerance, min_tag_length, tags, "") + + return tags + + +def _extend_tag( + idx: int, + sorted_mzs: List[float], + residue_masses: dict, + tolerance: float, + min_tag_length: int, + tags: List[dict], + current_tag: str, +) -> None: + """Recursively extend a sequence tag from a given peak index. + + Parameters + ---------- + idx: + Current peak index. + sorted_mzs: + Sorted list of m/z values. + residue_masses: + Residue mass lookup. + tolerance: + Mass tolerance. + min_tag_length: + Minimum tag length to report. + tags: + Accumulator list for found tags. + current_tag: + Current tag string being built. + """ + extended = False + for next_idx in range(idx + 1, len(sorted_mzs)): + diff = sorted_mzs[next_idx] - sorted_mzs[idx] + # Skip if diff is too large to be any amino acid + if diff > 250: + break + matches = match_mass_to_residue(diff, residue_masses, tolerance) + if matches: + extended = True + for aa in matches: + new_tag = current_tag + aa + _extend_tag(next_idx, sorted_mzs, residue_masses, tolerance, min_tag_length, tags, new_tag) + + if not extended and len(current_tag) >= min_tag_length: + tags.append({ + "tag": current_tag, + "length": len(current_tag), + "end_mz": round(sorted_mzs[idx], 6), + }) + + +def write_tsv(tags: List[dict], output_path: str) -> None: + """Write tags to TSV. + + Parameters + ---------- + tags: + List of tag dicts. + output_path: + Output file path. + """ + fieldnames = ["tag", "length", "end_mz"] + with open(output_path, "w", newline="") as fh: + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + for row in tags: + writer.writerow(row) + + +def main(): + parser = argparse.ArgumentParser( + description="Generate de novo sequence tags from MS2 spectra." + ) + parser.add_argument( + "--mz-list", required=True, + help="Comma-separated list of m/z values" + ) + parser.add_argument( + "--intensities", default=None, + help="Comma-separated list of intensities (optional)" + ) + parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") + parser.add_argument("--min-tag-length", type=int, default=3, help="Minimum tag length (default: 3)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + mz_values = [float(x.strip()) for x in args.mz_list.split(",")] + intensities = None + if args.intensities: + intensities = [float(x.strip()) for x in args.intensities.split(",")] + + tags = generate_tags(mz_values, intensities, tolerance=args.tolerance, min_tag_length=args.min_tag_length) + + print(f"Found {len(tags)} sequence tags (min length {args.min_tag_length})") + for t in tags[:20]: + print(f" {t['tag']} (length {t['length']}, end m/z {t['end_mz']})") + if len(tags) > 20: + print(f" ... and {len(tags) - 20} more") + + if args.output: + write_tsv(tags, args.output) + print(f"Results written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/sequence_tag_generator/tests/conftest.py b/scripts/proteomics/sequence_tag_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/sequence_tag_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/sequence_tag_generator/tests/test_sequence_tag_generator.py b/scripts/proteomics/sequence_tag_generator/tests/test_sequence_tag_generator.py new file mode 100644 index 0000000..83ab587 --- /dev/null +++ b/scripts/proteomics/sequence_tag_generator/tests/test_sequence_tag_generator.py @@ -0,0 +1,69 @@ +"""Tests for sequence_tag_generator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSequenceTagGenerator: + def test_get_residue_masses(self): + from sequence_tag_generator import get_residue_masses + + masses = get_residue_masses() + assert len(masses) == 20 # 20 standard amino acids + assert "G" in masses + assert masses["G"] > 50 # Glycine ~57 Da + + def test_match_mass_to_residue(self): + from sequence_tag_generator import get_residue_masses, match_mass_to_residue + + masses = get_residue_masses() + # Glycine mass ~57.02 + matches = match_mass_to_residue(57.02, masses, tolerance=0.05) + assert "G" in matches + + def test_match_no_residue(self): + from sequence_tag_generator import get_residue_masses, match_mass_to_residue + + masses = get_residue_masses() + matches = match_mass_to_residue(999.0, masses, tolerance=0.02) + assert matches == [] + + def test_generate_tags_from_known_peaks(self): + from sequence_tag_generator import generate_tags, get_residue_masses + + # Build peaks that correspond to a known tag + # Use Glycine (~57.02) mass differences + masses = get_residue_masses() + g_mass = masses["G"] + base = 200.0 + mz_values = [base, base + g_mass, base + 2 * g_mass, base + 3 * g_mass] + tags = generate_tags(mz_values, tolerance=0.05, min_tag_length=3) + tag_strings = [t["tag"] for t in tags] + assert "GGG" in tag_strings + + def test_generate_tags_too_short(self): + from sequence_tag_generator import generate_tags, get_residue_masses + + masses = get_residue_masses() + g_mass = masses["G"] + mz_values = [200.0, 200.0 + g_mass, 200.0 + 2 * g_mass] + tags = generate_tags(mz_values, tolerance=0.05, min_tag_length=3) + # Only 2 mass differences -> max tag length 2, below threshold + assert all(t["length"] >= 3 for t in tags) or len(tags) == 0 + + def test_generate_tags_empty_input(self): + from sequence_tag_generator import generate_tags + + tags = generate_tags([], tolerance=0.02, min_tag_length=3) + assert tags == [] + + def test_write_tsv(self, tmp_path): + from sequence_tag_generator import write_tsv + + tags = [{"tag": "GGG", "length": 3, "end_mz": 371.06}] + out = str(tmp_path / "tags.tsv") + write_tsv(tags, out) + with open(out) as fh: + lines = fh.readlines() + assert len(lines) == 2 + assert "tag" in lines[0] diff --git a/scripts/proteomics/silac_halflife_calculator/README.md b/scripts/proteomics/silac_halflife_calculator/README.md new file mode 100644 index 0000000..e02bca3 --- /dev/null +++ b/scripts/proteomics/silac_halflife_calculator/README.md @@ -0,0 +1,33 @@ +# SILAC Half-Life Calculator + +Fit exponential decay to SILAC H/L ratios for protein turnover analysis. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python silac_halflife_calculator.py --input hl_ratios.tsv \ + --timepoints 0,6,12,24,48 --output halflives.tsv +``` + +### Input format + +Tab-separated file with a `protein_id` column and one column per timepoint: + +``` +protein_id t0 t6 t12 t24 t48 +P12345 10.0 7.5 4.2 1.8 0.5 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--input` | Input TSV with protein IDs and H/L ratios | +| `--timepoints` | Comma-separated timepoint values | +| `--output` | Output half-lives TSV | diff --git a/scripts/proteomics/silac_halflife_calculator/requirements.txt b/scripts/proteomics/silac_halflife_calculator/requirements.txt new file mode 100644 index 0000000..ba577e4 --- /dev/null +++ b/scripts/proteomics/silac_halflife_calculator/requirements.txt @@ -0,0 +1,3 @@ +pyopenms +numpy +scipy diff --git a/scripts/proteomics/silac_halflife_calculator/silac_halflife_calculator.py b/scripts/proteomics/silac_halflife_calculator/silac_halflife_calculator.py new file mode 100644 index 0000000..cd3704c --- /dev/null +++ b/scripts/proteomics/silac_halflife_calculator/silac_halflife_calculator.py @@ -0,0 +1,206 @@ +""" +SILAC Half-Life Calculator +=========================== +Fit exponential decay to SILAC heavy/light (H/L) ratios for protein turnover +analysis. For each protein, the tool fits the model:: + + R(t) = R0 * exp(-k * t) + +where *R(t)* is the H/L ratio at time *t*, *R0* is the initial ratio, and +*k* is the decay rate constant. The half-life is ``ln(2) / k``. + +Uses scipy.optimize.curve_fit for non-linear least-squares fitting. + +Usage +----- + python silac_halflife_calculator.py --input hl_ratios.tsv \ + --timepoints 0,6,12,24,48 --output halflives.tsv +""" + +import argparse +import csv +import math +import sys +from typing import Dict, List, Optional + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +import numpy as np +from scipy.optimize import curve_fit + + +def exponential_decay(t: np.ndarray, r0: float, k: float) -> np.ndarray: + """Exponential decay model: R(t) = R0 * exp(-k * t).""" + return r0 * np.exp(-k * t) + + +def fit_halflife( + timepoints: List[float], ratios: List[float] +) -> Optional[Dict[str, float]]: + """Fit exponential decay to H/L ratios and compute half-life. + + Parameters + ---------- + timepoints: + Time values (e.g. hours). + ratios: + Corresponding H/L ratios. + + Returns + ------- + dict or None + ``r0``, ``k``, ``halflife``, ``r_squared`` if fit succeeds; None otherwise. + """ + t = np.array(timepoints, dtype=float) + r = np.array(ratios, dtype=float) + + # Filter out NaN/inf + valid = np.isfinite(t) & np.isfinite(r) & (r > 0) + t = t[valid] + r = r[valid] + + if len(t) < 2: + return None + + try: + # Initial guesses + r0_guess = float(r[0]) if r[0] > 0 else 1.0 + k_guess = 0.01 + popt, _ = curve_fit( + exponential_decay, t, r, + p0=[r0_guess, k_guess], + bounds=([0, 0], [np.inf, np.inf]), + maxfev=10000, + ) + r0_fit, k_fit = popt + + if k_fit <= 0: + return None + + halflife = math.log(2) / k_fit + + # R-squared + r_pred = exponential_decay(t, r0_fit, k_fit) + ss_res = np.sum((r - r_pred) ** 2) + ss_tot = np.sum((r - np.mean(r)) ** 2) + r_squared = 1.0 - ss_res / ss_tot if ss_tot > 0 else 0.0 + + return { + "r0": r0_fit, + "k": k_fit, + "halflife": halflife, + "r_squared": r_squared, + } + except (RuntimeError, ValueError): + return None + + +def compute_halflives( + proteins: Dict[str, List[float]], timepoints: List[float] +) -> List[Dict[str, object]]: + """Compute half-lives for multiple proteins. + + Parameters + ---------- + proteins: + Mapping of protein ID to list of H/L ratios (one per timepoint). + timepoints: + Time values corresponding to ratio columns. + + Returns + ------- + list of dict + One entry per protein with fit results. + """ + results: List[Dict[str, object]] = [] + for protein_id, ratios in proteins.items(): + fit = fit_halflife(timepoints, ratios) + if fit is not None: + results.append({ + "protein_id": protein_id, + "r0": fit["r0"], + "k": fit["k"], + "halflife": fit["halflife"], + "r_squared": fit["r_squared"], + "status": "ok", + }) + else: + results.append({ + "protein_id": protein_id, + "r0": float("nan"), + "k": float("nan"), + "halflife": float("nan"), + "r_squared": float("nan"), + "status": "fit_failed", + }) + return results + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Fit exponential decay to SILAC H/L ratios for protein turnover." + ) + parser.add_argument( + "--input", required=True, + help="Input TSV with 'protein_id' and ratio columns (one per timepoint)", + ) + parser.add_argument( + "--timepoints", required=True, + help="Comma-separated timepoint values (e.g. 0,6,12,24,48)", + ) + parser.add_argument("--output", required=True, help="Output half-lives TSV") + args = parser.parse_args() + + timepoints = [float(t) for t in args.timepoints.split(",")] + + proteins: Dict[str, List[float]] = {} + with open(args.input, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + fields = reader.fieldnames or [] + # Ratio columns are all columns except protein_id + ratio_cols = [f for f in fields if f != "protein_id"] + if len(ratio_cols) != len(timepoints): + sys.exit( + f"Number of ratio columns ({len(ratio_cols)}) does not match " + f"number of timepoints ({len(timepoints)})" + ) + for row in reader: + pid = row.get("protein_id", "").strip() + if not pid: + continue + ratios = [] + for col in ratio_cols: + val = row.get(col, "").strip() + try: + ratios.append(float(val)) + except (ValueError, TypeError): + ratios.append(float("nan")) + proteins[pid] = ratios + + if not proteins: + sys.exit("No proteins found in input.") + + results = compute_halflives(proteins, timepoints) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["protein_id", "r0", "k", "halflife", "r_squared", "status"]) + for r in results: + writer.writerow([ + r["protein_id"], + f"{r['r0']:.6f}" if not math.isnan(r["r0"]) else "NA", + f"{r['k']:.6f}" if not math.isnan(r["k"]) else "NA", + f"{r['halflife']:.4f}" if not math.isnan(r["halflife"]) else "NA", + f"{r['r_squared']:.4f}" if not math.isnan(r["r_squared"]) else "NA", + r["status"], + ]) + + ok_count = sum(1 for r in results if r["status"] == "ok") + print(f"Fitted {ok_count}/{len(results)} proteins -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/silac_halflife_calculator/tests/conftest.py b/scripts/proteomics/silac_halflife_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/silac_halflife_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/silac_halflife_calculator/tests/test_silac_halflife_calculator.py b/scripts/proteomics/silac_halflife_calculator/tests/test_silac_halflife_calculator.py new file mode 100644 index 0000000..5e39225 --- /dev/null +++ b/scripts/proteomics/silac_halflife_calculator/tests/test_silac_halflife_calculator.py @@ -0,0 +1,99 @@ +"""Tests for silac_halflife_calculator.""" + +import csv +import math +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_exponential_decay(): + import numpy as np + from silac_halflife_calculator import exponential_decay + + t = np.array([0, 1, 2, 3]) + result = exponential_decay(t, r0=10.0, k=0.5) + assert abs(result[0] - 10.0) < 0.01 + assert result[1] < result[0] # decaying + + +@requires_pyopenms +def test_fit_halflife_perfect(): + from silac_halflife_calculator import fit_halflife + + # Generate perfect exponential data + k_true = 0.05 + r0_true = 10.0 + t = [0, 6, 12, 24, 48] + ratios = [r0_true * math.exp(-k_true * ti) for ti in t] + + result = fit_halflife(t, ratios) + assert result is not None + assert abs(result["k"] - k_true) < 0.01 + expected_halflife = math.log(2) / k_true + assert abs(result["halflife"] - expected_halflife) < 1.0 + assert result["r_squared"] > 0.99 + + +@requires_pyopenms +def test_fit_halflife_insufficient_data(): + from silac_halflife_calculator import fit_halflife + + result = fit_halflife([0], [10.0]) + assert result is None + + +@requires_pyopenms +def test_compute_halflives(): + import math + + from silac_halflife_calculator import compute_halflives + + k = 0.05 + timepoints = [0, 6, 12, 24, 48] + proteins = { + "P1": [10.0 * math.exp(-k * t) for t in timepoints], + "P2": [5.0 * math.exp(-0.1 * t) for t in timepoints], + } + + results = compute_halflives(proteins, timepoints) + assert len(results) == 2 + assert results[0]["status"] == "ok" + assert results[1]["status"] == "ok" + # P2 has higher k -> shorter halflife + assert results[1]["halflife"] < results[0]["halflife"] + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + import math + + from silac_halflife_calculator import main + + input_file = tmp_path / "input.tsv" + output_file = tmp_path / "output.tsv" + timepoints = [0, 6, 12, 24, 48] + k = 0.05 + + with open(input_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + cols = ["protein_id"] + [f"t{t}" for t in timepoints] + writer.writerow(cols) + ratios = [10.0 * math.exp(-k * t) for t in timepoints] + writer.writerow(["P1"] + [f"{r:.4f}" for r in ratios]) + + sys.argv = [ + "silac_halflife_calculator.py", + "--input", str(input_file), + "--timepoints", ",".join(str(t) for t in timepoints), + "--output", str(output_file), + ] + main() + + assert output_file.exists() + with open(output_file) as fh: + reader = csv.DictReader(fh, delimiter="\t") + rows = list(reader) + assert len(rows) == 1 + assert rows[0]["status"] == "ok" diff --git a/scripts/proteomics/spectral_counting_quantifier/README.md b/scripts/proteomics/spectral_counting_quantifier/README.md new file mode 100644 index 0000000..860b1ab --- /dev/null +++ b/scripts/proteomics/spectral_counting_quantifier/README.md @@ -0,0 +1,14 @@ +# Spectral Counting Quantifier + +Calculate protein abundances from spectral counts using emPAI and NSAF methods. + +## Usage + +```bash +python spectral_counting_quantifier.py --input peptide_counts.tsv --fasta db.fasta --method nsaf --output abundances.tsv +python spectral_counting_quantifier.py --input peptide_counts.tsv --fasta db.fasta --method empai --output abundances.tsv +``` + +## Input Format + +TSV file with columns: `protein`, `peptide`, `spectral_count` diff --git a/scripts/proteomics/spectral_counting_quantifier/requirements.txt b/scripts/proteomics/spectral_counting_quantifier/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/spectral_counting_quantifier/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/spectral_counting_quantifier/spectral_counting_quantifier.py b/scripts/proteomics/spectral_counting_quantifier/spectral_counting_quantifier.py new file mode 100644 index 0000000..fcdc6eb --- /dev/null +++ b/scripts/proteomics/spectral_counting_quantifier/spectral_counting_quantifier.py @@ -0,0 +1,223 @@ +""" +Spectral Counting Quantifier +============================== +Calculate protein abundances from spectral counts using emPAI and NSAF methods. + +Features +-------- +- emPAI (exponentially modified Protein Abundance Index) +- NSAF (Normalized Spectral Abundance Factor) +- Read peptide-spectrum counts from TSV input +- In-silico digestion for observable peptide count (emPAI) + +Usage +----- + python spectral_counting_quantifier.py --input counts.tsv --fasta db.fasta --method nsaf --output out.tsv + python spectral_counting_quantifier.py --input counts.tsv --fasta db.fasta --method empai --output out.tsv +""" + +import argparse +import csv +import json +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta_proteins(fasta_path: str) -> dict: + """Load protein sequences from FASTA. + + Parameters + ---------- + fasta_path : str + Path to FASTA file. + + Returns + ------- + dict + Mapping of accession to sequence. + """ + entries = [] + oms.FASTAFile().load(fasta_path, entries) + return {e.identifier: e.sequence for e in entries} + + +def count_observable_peptides(sequence: str, enzyme: str = "Trypsin", + min_length: int = 6, max_length: int = 40) -> int: + """Count observable tryptic peptides for a protein sequence. + + Parameters + ---------- + sequence : str + Protein sequence. + enzyme : str + Enzyme name. + min_length : int + Minimum peptide length. + max_length : int + Maximum peptide length. + + Returns + ------- + int + Number of observable peptides. + """ + aa_seq = oms.AASequence.fromString(sequence) + digest = oms.ProteaseDigestion() + digest.setEnzyme(enzyme) + digest.setMissedCleavages(0) + peptides = [] + digest.digest(aa_seq, peptides, min_length, max_length) + return len(peptides) + + +def calculate_empai(protein_data: dict, proteins: dict, enzyme: str = "Trypsin") -> list: + """Calculate emPAI for each protein. + + Parameters + ---------- + protein_data : dict + Mapping of protein accession to dict with 'spectral_count' and 'observed_peptides'. + proteins : dict + Mapping of accession to sequence. + enzyme : str + Enzyme name. + + Returns + ------- + list + List of dicts with emPAI values. + """ + results = [] + for accession, data in protein_data.items(): + seq = proteins.get(accession, "") + n_observable = count_observable_peptides(seq, enzyme) if seq else 1 + n_observed = data.get("observed_peptides", 0) + pai = n_observed / max(n_observable, 1) + empai = 10 ** pai - 1 + + results.append({ + "accession": accession, + "spectral_count": data.get("spectral_count", 0), + "observed_peptides": n_observed, + "observable_peptides": n_observable, + "pai": round(pai, 6), + "empai": round(empai, 6), + }) + return results + + +def calculate_nsaf(protein_data: dict, proteins: dict) -> list: + """Calculate NSAF for each protein. + + Parameters + ---------- + protein_data : dict + Mapping of protein accession to dict with 'spectral_count'. + proteins : dict + Mapping of accession to sequence. + + Returns + ------- + list + List of dicts with NSAF values. + """ + # Calculate SAF = SpC / Length for each protein + saf_values = {} + for accession, data in protein_data.items(): + seq = proteins.get(accession, "") + length = len(seq) if seq else 1 + spc = data.get("spectral_count", 0) + saf_values[accession] = spc / max(length, 1) + + total_saf = sum(saf_values.values()) + + results = [] + for accession, data in protein_data.items(): + seq = proteins.get(accession, "") + length = len(seq) if seq else 0 + saf = saf_values[accession] + nsaf = saf / total_saf if total_saf > 0 else 0.0 + + results.append({ + "accession": accession, + "spectral_count": data.get("spectral_count", 0), + "protein_length": length, + "saf": round(saf, 8), + "nsaf": round(nsaf, 8), + }) + return results + + +def load_peptide_counts(input_path: str) -> dict: + """Load peptide spectral counts from TSV and aggregate per protein. + + Parameters + ---------- + input_path : str + Path to TSV file with columns: protein, peptide, spectral_count. + + Returns + ------- + dict + Mapping of protein accession to aggregated counts. + """ + protein_data = {} + with open(input_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + protein = row.get("protein", "").strip() + spc = int(row.get("spectral_count", 0)) + if protein not in protein_data: + protein_data[protein] = {"spectral_count": 0, "observed_peptides": 0, "peptides": set()} + protein_data[protein]["spectral_count"] += spc + peptide = row.get("peptide", "").strip() + if peptide and peptide not in protein_data[protein]["peptides"]: + protein_data[protein]["peptides"].add(peptide) + protein_data[protein]["observed_peptides"] += 1 + + # Remove set (not serializable) + for v in protein_data.values(): + del v["peptides"] + return protein_data + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Calculate protein abundances from spectral counts.") + parser.add_argument("--input", required=True, help="TSV with protein, peptide, spectral_count columns.") + parser.add_argument("--fasta", required=True, help="Protein FASTA database.") + parser.add_argument("--method", choices=["empai", "nsaf"], default="nsaf", help="Quantification method.") + parser.add_argument("--enzyme", default="Trypsin", help="Enzyme for emPAI (default: Trypsin).") + parser.add_argument("--output", help="Output file (.tsv or .json).") + args = parser.parse_args() + + proteins = load_fasta_proteins(args.fasta) + protein_data = load_peptide_counts(args.input) + + if args.method == "empai": + results = calculate_empai(protein_data, proteins, args.enzyme) + else: + results = calculate_nsaf(protein_data, proteins) + + if args.output: + if args.output.endswith(".json"): + with open(args.output, "w") as fh: + json.dump(results, fh, indent=2) + else: + with open(args.output, "w", newline="") as fh: + fieldnames = list(results[0].keys()) if results else [] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + print(f"Results written to {args.output}") + else: + for r in results: + print(json.dumps(r)) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectral_counting_quantifier/tests/conftest.py b/scripts/proteomics/spectral_counting_quantifier/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectral_counting_quantifier/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py b/scripts/proteomics/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py new file mode 100644 index 0000000..29e0012 --- /dev/null +++ b/scripts/proteomics/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py @@ -0,0 +1,83 @@ +"""Tests for spectral_counting_quantifier.""" + +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSpectralCountingQuantifier: + def _create_fasta(self, tmpdir): + import pyopenms as oms + + fasta_path = f"{tmpdir}/test.fasta" + entries = [] + e1 = oms.FASTAEntry() + e1.identifier = "PROT1" + e1.sequence = "MSPEPTIDEKAAANOTHERPEPTIDER" + entries.append(e1) + e2 = oms.FASTAEntry() + e2.identifier = "PROT2" + e2.sequence = "MSGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGK" + entries.append(e2) + oms.FASTAFile().store(fasta_path, entries) + return fasta_path + + def test_count_observable_peptides(self): + from spectral_counting_quantifier import count_observable_peptides + + count = count_observable_peptides("MSPEPTIDEKAAANOTHERPEPTIDER") + assert count >= 1 + + def test_calculate_nsaf(self): + from spectral_counting_quantifier import calculate_nsaf + + protein_data = { + "PROT1": {"spectral_count": 10}, + "PROT2": {"spectral_count": 5}, + } + proteins = {"PROT1": "AAAAAAAAAA", "PROT2": "AAAAA"} + results = calculate_nsaf(protein_data, proteins) + assert len(results) == 2 + # NSAF values should sum to ~1 + total_nsaf = sum(r["nsaf"] for r in results) + assert abs(total_nsaf - 1.0) < 0.001 + + def test_calculate_empai(self): + from spectral_counting_quantifier import calculate_empai + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir) + from spectral_counting_quantifier import load_fasta_proteins + proteins = load_fasta_proteins(fasta_path) + protein_data = { + "PROT1": {"spectral_count": 10, "observed_peptides": 2}, + } + results = calculate_empai(protein_data, proteins) + assert len(results) == 1 + assert results[0]["empai"] > 0 + + def test_nsaf_proportional(self): + from spectral_counting_quantifier import calculate_nsaf + + # Same length proteins, different counts -> NSAF proportional to SpC + proteins = {"P1": "AAAAAAAAAA", "P2": "AAAAAAAAAA"} + data = {"P1": {"spectral_count": 20}, "P2": {"spectral_count": 10}} + results = calculate_nsaf(data, proteins) + nsaf_map = {r["accession"]: r["nsaf"] for r in results} + assert nsaf_map["P1"] > nsaf_map["P2"] + + def test_load_peptide_counts(self): + from spectral_counting_quantifier import load_peptide_counts + + with tempfile.TemporaryDirectory() as tmpdir: + tsv_path = f"{tmpdir}/counts.tsv" + with open(tsv_path, "w") as fh: + fh.write("protein\tpeptide\tspectral_count\n") + fh.write("PROT1\tPEPTIDEK\t5\n") + fh.write("PROT1\tANOTHER\t3\n") + fh.write("PROT2\tSEQUENCE\t2\n") + data = load_peptide_counts(tsv_path) + assert data["PROT1"]["spectral_count"] == 8 + assert data["PROT1"]["observed_peptides"] == 2 + assert data["PROT2"]["spectral_count"] == 2 diff --git a/scripts/proteomics/spectral_library_builder/README.md b/scripts/proteomics/spectral_library_builder/README.md new file mode 100644 index 0000000..e08cc8d --- /dev/null +++ b/scripts/proteomics/spectral_library_builder/README.md @@ -0,0 +1,24 @@ +# Spectral Library Builder + +Build a spectral library in MSP format from mzML + peptide identification list. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python spectral_library_builder.py --input run.mzML --peptides identified.tsv --output library.msp +``` + +## Peptide TSV Format + +The input peptide identifications file should be a TSV with columns: +- `sequence` (required): peptide sequence +- `charge` (required): charge state +- `rt` (required): retention time in seconds +- `mz` (optional): precursor m/z (calculated from sequence if not provided) +- `score` (optional): identification score diff --git a/scripts/proteomics/spectral_library_builder/requirements.txt b/scripts/proteomics/spectral_library_builder/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/spectral_library_builder/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/spectral_library_builder/spectral_library_builder.py b/scripts/proteomics/spectral_library_builder/spectral_library_builder.py new file mode 100644 index 0000000..f2dff52 --- /dev/null +++ b/scripts/proteomics/spectral_library_builder/spectral_library_builder.py @@ -0,0 +1,153 @@ +""" +Spectral Library Builder +======================== +Build a spectral library in MSP format from mzML + peptide identification list. + +Usage +----- + python spectral_library_builder.py --input run.mzML --peptides identified.tsv --output library.msp +""" + +import argparse +import csv +import sys +from typing import List, Optional + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def load_mzml(input_path: str) -> oms.MSExperiment: + """Load an mzML file.""" + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + return exp + + +def load_peptides_tsv(peptides_path: str) -> List[dict]: + """Load peptide identifications from a TSV file. + + Expected columns: sequence, charge, rt, mz (optional), score (optional). + """ + peptides = [] + with open(peptides_path) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + pep = { + "sequence": row["sequence"], + "charge": int(row["charge"]), + "rt": float(row["rt"]), + "mz": float(row.get("mz", 0.0)) if row.get("mz") else 0.0, + "score": float(row.get("score", 0.0)) if row.get("score") else 0.0, + } + if pep["mz"] == 0.0: + aa_seq = oms.AASequence.fromString(pep["sequence"]) + mass = aa_seq.getMonoWeight() + pep["mz"] = (mass + pep["charge"] * PROTON) / pep["charge"] + peptides.append(pep) + return peptides + + +def find_best_ms2( + exp: oms.MSExperiment, + precursor_mz: float, + rt: float, + mz_tolerance: float = 0.01, + rt_tolerance: float = 30.0, +) -> Optional[oms.MSSpectrum]: + """Find the best matching MS2 spectrum (highest TIC within tolerances).""" + best = None + best_tic = 0.0 + + for spectrum in exp: + if spectrum.getMSLevel() != 2: + continue + if abs(spectrum.getRT() - rt) > rt_tolerance: + continue + precursors = spectrum.getPrecursors() + if not precursors: + continue + if abs(precursors[0].getMZ() - precursor_mz) > mz_tolerance: + continue + + _, intensities = spectrum.get_peaks() + tic = float(sum(intensities)) if len(intensities) > 0 else 0.0 + if tic > best_tic: + best_tic = tic + best = spectrum + + return best + + +def spectrum_to_msp( + sequence: str, + charge: int, + precursor_mz: float, + spectrum: oms.MSSpectrum, + score: float = 0.0, +) -> str: + """Convert a spectrum + peptide info to an MSP library entry.""" + mz_array, intensity_array = spectrum.get_peaks() + num_peaks = len(mz_array) + + lines = [] + lines.append(f"Name: {sequence}/{charge}") + lines.append(f"MW: {precursor_mz:.6f}") + lines.append(f"Comment: Charge={charge} Score={score:.4f} RT={spectrum.getRT():.2f}") + lines.append(f"Num peaks: {num_peaks}") + + for mz, intensity in zip(mz_array, intensity_array): + lines.append(f"{mz:.6f}\t{intensity:.4f}") + + lines.append("") + return "\n".join(lines) + + +def build_library( + mzml_path: str, + peptides_path: str, + output_path: str, + mz_tolerance: float = 0.01, + rt_tolerance: float = 30.0, +) -> dict: + """Build a spectral library from mzML + peptide identifications. + + Returns statistics about the library building. + """ + exp = load_mzml(mzml_path) + peptides = load_peptides_tsv(peptides_path) + + matched = 0 + with open(output_path, "w") as fh: + for pep in peptides: + spectrum = find_best_ms2(exp, pep["mz"], pep["rt"], mz_tolerance, rt_tolerance) + if spectrum is not None: + entry = spectrum_to_msp(pep["sequence"], pep["charge"], pep["mz"], spectrum, pep["score"]) + fh.write(entry + "\n") + matched += 1 + + return { + "total_peptides": len(peptides), + "matched_spectra": matched, + } + + +def main() -> None: + parser = argparse.ArgumentParser(description="Build spectral library from mzML + peptide list.") + parser.add_argument("--input", required=True, help="Input mzML file") + parser.add_argument("--peptides", required=True, help="Input peptide identifications TSV") + parser.add_argument("--output", required=True, help="Output MSP library file") + parser.add_argument("--mz-tolerance", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") + parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") + args = parser.parse_args() + + stats = build_library(args.input, args.peptides, args.output, args.mz_tolerance, args.rt_tolerance) + print(f"Built library: {stats['matched_spectra']} / {stats['total_peptides']} peptides matched to spectra") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectral_library_builder/tests/conftest.py b/scripts/proteomics/spectral_library_builder/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectral_library_builder/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectral_library_builder/tests/test_spectral_library_builder.py b/scripts/proteomics/spectral_library_builder/tests/test_spectral_library_builder.py new file mode 100644 index 0000000..059e9b5 --- /dev/null +++ b/scripts/proteomics/spectral_library_builder/tests/test_spectral_library_builder.py @@ -0,0 +1,105 @@ +"""Tests for spectral_library_builder.""" + +import os +import tempfile + +from conftest import requires_pyopenms + +PROTON = 1.007276 + + +def _create_test_data(tmp_dir): + """Create test mzML and peptides TSV.""" + import pyopenms as oms + + # Create mzML + exp = oms.MSExperiment() + sequences = ["ACDEFGHIK", "MNPQRSTWY"] + + for i, seq in enumerate(sequences): + aa_seq = oms.AASequence.fromString(seq) + mass = aa_seq.getMonoWeight() + precursor_mz = (mass + 2 * PROTON) / 2 + + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(100.0 + i * 20) + prec = oms.Precursor() + prec.setMZ(precursor_mz) + prec.setCharge(2) + ms2.setPrecursors([prec]) + ms2.set_peaks(([100.0 + j * 50 for j in range(8)], [1000.0 - j * 100 for j in range(8)])) + exp.addSpectrum(ms2) + + mzml_path = os.path.join(tmp_dir, "test.mzML") + oms.MzMLFile().store(mzml_path, exp) + + # Create peptides TSV + peptides_path = os.path.join(tmp_dir, "peptides.tsv") + with open(peptides_path, "w") as fh: + fh.write("sequence\tcharge\trt\n") + fh.write("ACDEFGHIK\t2\t100.0\n") + fh.write("MNPQRSTWY\t2\t120.0\n") + + return mzml_path, peptides_path + + +@requires_pyopenms +def test_load_peptides_tsv(): + from spectral_library_builder import load_peptides_tsv + + with tempfile.TemporaryDirectory() as tmp: + path = os.path.join(tmp, "peptides.tsv") + with open(path, "w") as fh: + fh.write("sequence\tcharge\trt\n") + fh.write("ACDEFGHIK\t2\t100.0\n") + + peptides = load_peptides_tsv(path) + assert len(peptides) == 1 + assert peptides[0]["sequence"] == "ACDEFGHIK" + assert peptides[0]["charge"] == 2 + # m/z should be auto-calculated + assert peptides[0]["mz"] > 0 + + +@requires_pyopenms +def test_build_library(): + from spectral_library_builder import build_library + + with tempfile.TemporaryDirectory() as tmp: + mzml_path, peptides_path = _create_test_data(tmp) + output_path = os.path.join(tmp, "library.msp") + + stats = build_library(mzml_path, peptides_path, output_path) + assert stats["total_peptides"] == 2 + assert stats["matched_spectra"] == 2 + + with open(output_path) as fh: + content = fh.read() + assert "Name: ACDEFGHIK/2" in content + assert "Num peaks:" in content + + +@requires_pyopenms +def test_no_match(): + import pyopenms as oms + from spectral_library_builder import build_library + + with tempfile.TemporaryDirectory() as tmp: + # Create mzML with no MS2 spectra + exp = oms.MSExperiment() + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.set_peaks(([100.0], [1000.0])) + exp.addSpectrum(ms1) + mzml_path = os.path.join(tmp, "test.mzML") + oms.MzMLFile().store(mzml_path, exp) + + peptides_path = os.path.join(tmp, "peptides.tsv") + with open(peptides_path, "w") as fh: + fh.write("sequence\tcharge\trt\n") + fh.write("ACDEFGHIK\t2\t100.0\n") + + output_path = os.path.join(tmp, "library.msp") + stats = build_library(mzml_path, peptides_path, output_path) + assert stats["matched_spectra"] == 0 diff --git a/scripts/proteomics/spectral_library_format_converter/README.md b/scripts/proteomics/spectral_library_format_converter/README.md new file mode 100644 index 0000000..e85678c --- /dev/null +++ b/scripts/proteomics/spectral_library_format_converter/README.md @@ -0,0 +1,9 @@ +# Spectral Library Format Converter + +Convert between spectral library formats (MSP to TraML). + +## Usage + +```bash +python spectral_library_format_converter.py --input library.msp --output library.traml --format traml +``` diff --git a/scripts/proteomics/spectral_library_format_converter/requirements.txt b/scripts/proteomics/spectral_library_format_converter/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/spectral_library_format_converter/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/spectral_library_format_converter/spectral_library_format_converter.py b/scripts/proteomics/spectral_library_format_converter/spectral_library_format_converter.py new file mode 100644 index 0000000..53f78ce --- /dev/null +++ b/scripts/proteomics/spectral_library_format_converter/spectral_library_format_converter.py @@ -0,0 +1,211 @@ +""" +Spectral Library Format Converter +================================== +Convert between spectral library formats (MSP to TraML). +Parses MSP format and creates a TraML TargetedExperiment. + +Features: +- Parse MSP spectral library files +- Convert to TraML format +- Preserve peptide metadata and transitions + +Usage +----- + python spectral_library_format_converter.py --input library.msp --output library.traml --format traml +""" + +import argparse +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_msp(filepath: str) -> list[dict]: + """Parse an MSP spectral library file. + + Parameters + ---------- + filepath : str + Path to MSP file. + + Returns + ------- + list[dict] + List of spectrum dicts with keys: name, mw, comment, precursor_mz, + charge, peaks (list of (mz, intensity) tuples). + """ + spectra = [] + current = None + + with open(filepath) as f: + for line in f: + line = line.strip() + if not line: + if current is not None and current.get("peaks"): + spectra.append(current) + current = None + continue + + if line.startswith("Name:"): + if current is not None and current.get("peaks"): + spectra.append(current) + current = { + "name": line[5:].strip(), + "mw": 0.0, + "comment": "", + "precursor_mz": 0.0, + "charge": 0, + "peaks": [], + } + elif current is not None: + if line.startswith("MW:"): + current["mw"] = float(line[3:].strip()) + elif line.startswith("Comment:"): + current["comment"] = line[8:].strip() + # Extract charge from comment if present + for part in current["comment"].split(): + if part.startswith("Charge="): + current["charge"] = int(part.split("=")[1].replace("+", "").replace("-", "")) + elif part.startswith("Parent="): + current["precursor_mz"] = float(part.split("=")[1]) + elif line.startswith("Num peaks:") or line.startswith("Num Peaks:"): + pass # Skip, we count peaks from data + elif line[0].isdigit(): + parts = line.split() + if len(parts) >= 2: + mz = float(parts[0]) + intensity = float(parts[1]) + current["peaks"].append((mz, intensity)) + + if current is not None and current.get("peaks"): + spectra.append(current) + + return spectra + + +def msp_to_targeted_experiment(spectra: list[dict]) -> oms.TargetedExperiment: + """Convert parsed MSP spectra to a TargetedExperiment. + + Parameters + ---------- + spectra : list[dict] + List of parsed MSP spectrum dictionaries. + + Returns + ------- + oms.TargetedExperiment + pyopenms TargetedExperiment object. + """ + targeted_exp = oms.TargetedExperiment() + proteins = [] + peptides = [] + transitions = [] + + for spec_idx, spec in enumerate(spectra): + peptide_id = f"peptide_{spec_idx}" + protein_id = f"protein_{spec_idx}" + + # Create protein + protein = oms.TargetedExperiment.Protein() + protein.id = protein_id + proteins.append(protein) + + # Create peptide + peptide = oms.TargetedExperiment.Peptide() + peptide.id = peptide_id + peptide.protein_refs = [protein_id] + peptide.sequence = spec["name"].split("/")[0] if "/" in spec["name"] else spec["name"] + if spec["charge"] > 0: + peptide.setChargeState(spec["charge"]) + peptides.append(peptide) + + # Create transitions from peaks + precursor_mz = spec["precursor_mz"] + if precursor_mz == 0 and spec["mw"] > 0 and spec["charge"] > 0: + precursor_mz = (spec["mw"] + spec["charge"] * 1.007276) / spec["charge"] + + for peak_idx, (mz, intensity) in enumerate(spec["peaks"]): + transition = oms.ReactionMonitoringTransition() + transition.setNativeID(f"transition_{spec_idx}_{peak_idx}") + transition.setPeptideRef(peptide_id) + transition.setPrecursorMZ(precursor_mz) + transition.setProductMZ(mz) + transition.setLibraryIntensity(intensity) + transitions.append(transition) + + targeted_exp.setProteins(proteins) + targeted_exp.setPeptides(peptides) + targeted_exp.setTransitions(transitions) + + return targeted_exp + + +def convert_msp_to_traml(input_path: str, output_path: str) -> int: + """Convert MSP file to TraML format. + + Parameters + ---------- + input_path : str + Path to input MSP file. + output_path : str + Path to output TraML file. + + Returns + ------- + int + Number of spectra converted. + """ + spectra = parse_msp(input_path) + targeted_exp = msp_to_targeted_experiment(spectra) + oms.TraMLFile().store(output_path, targeted_exp) + return len(spectra) + + +def create_synthetic_msp(output_path: str, n_spectra: int = 3) -> None: + """Create a synthetic MSP file for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic MSP file. + n_spectra : int + Number of spectra to generate. + """ + sequences = ["PEPTIDEK", "ACDEFGHIK", "LMNPQR"] + + with open(output_path, "w") as f: + for i in range(n_spectra): + seq = sequences[i % len(sequences)] + charge = 2 + mw = 500.0 + i * 100 + precursor_mz = (mw + charge * 1.007276) / charge + + f.write(f"Name: {seq}/{charge}\n") + f.write(f"MW: {mw}\n") + f.write(f"Comment: Charge={charge}+ Parent={precursor_mz:.4f}\n") + f.write("Num peaks: 5\n") + for j in range(5): + frag_mz = 100.0 + j * 50 + i * 10 + intensity = 1000.0 - j * 150 + f.write(f"{frag_mz:.4f}\t{intensity:.1f}\n") + f.write("\n") + + +def main(): + parser = argparse.ArgumentParser( + description="Convert between spectral library formats (MSP to TraML)." + ) + parser.add_argument("--input", required=True, help="Path to input MSP file") + parser.add_argument("--output", required=True, help="Path to output file") + parser.add_argument("--format", default="traml", choices=["traml"], help="Output format (default: traml)") + args = parser.parse_args() + + count = convert_msp_to_traml(args.input, args.output) + print(f"Converted {count} spectra to {args.format} format: {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectral_library_format_converter/tests/conftest.py b/scripts/proteomics/spectral_library_format_converter/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectral_library_format_converter/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectral_library_format_converter/tests/test_spectral_library_format_converter.py b/scripts/proteomics/spectral_library_format_converter/tests/test_spectral_library_format_converter.py new file mode 100644 index 0000000..1de1ff2 --- /dev/null +++ b/scripts/proteomics/spectral_library_format_converter/tests/test_spectral_library_format_converter.py @@ -0,0 +1,78 @@ +"""Tests for spectral_library_format_converter.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSpectralLibraryFormatConverter: + def test_parse_msp(self): + from spectral_library_format_converter import create_synthetic_msp, parse_msp + + with tempfile.TemporaryDirectory() as tmpdir: + msp_path = os.path.join(tmpdir, "test.msp") + create_synthetic_msp(msp_path, n_spectra=3) + spectra = parse_msp(msp_path) + assert len(spectra) == 3 + assert spectra[0]["charge"] == 2 + assert len(spectra[0]["peaks"]) == 5 + + def test_msp_to_targeted_experiment(self): + from spectral_library_format_converter import create_synthetic_msp, msp_to_targeted_experiment, parse_msp + + with tempfile.TemporaryDirectory() as tmpdir: + msp_path = os.path.join(tmpdir, "test.msp") + create_synthetic_msp(msp_path, n_spectra=2) + spectra = parse_msp(msp_path) + te = msp_to_targeted_experiment(spectra) + assert len(te.getProteins()) == 2 + assert len(te.getPeptides()) == 2 + assert len(te.getTransitions()) == 10 # 2 spectra * 5 peaks + + def test_convert_msp_to_traml(self): + import pyopenms as oms + from spectral_library_format_converter import convert_msp_to_traml, create_synthetic_msp + + with tempfile.TemporaryDirectory() as tmpdir: + msp_path = os.path.join(tmpdir, "test.msp") + traml_path = os.path.join(tmpdir, "test.traml") + create_synthetic_msp(msp_path, n_spectra=2) + count = convert_msp_to_traml(msp_path, traml_path) + assert count == 2 + assert os.path.exists(traml_path) + + # Verify TraML can be loaded back + te = oms.TargetedExperiment() + oms.TraMLFile().load(traml_path, te) + assert len(te.getTransitions()) > 0 + + def test_parse_msp_name_extraction(self): + from spectral_library_format_converter import parse_msp + + with tempfile.TemporaryDirectory() as tmpdir: + msp_path = os.path.join(tmpdir, "test.msp") + with open(msp_path, "w") as f: + f.write("Name: PEPTIDEK/2\n") + f.write("MW: 500.0\n") + f.write("Comment: Charge=2+ Parent=251.5\n") + f.write("Num peaks: 2\n") + f.write("100.0\t1000.0\n") + f.write("200.0\t500.0\n") + f.write("\n") + spectra = parse_msp(msp_path) + assert len(spectra) == 1 + assert spectra[0]["name"] == "PEPTIDEK/2" + + def test_create_synthetic_msp(self): + from spectral_library_format_converter import create_synthetic_msp + + with tempfile.TemporaryDirectory() as tmpdir: + msp_path = os.path.join(tmpdir, "test.msp") + create_synthetic_msp(msp_path, n_spectra=5) + assert os.path.exists(msp_path) + with open(msp_path) as f: + content = f.read() + assert "Name:" in content + assert "Num peaks:" in content diff --git a/scripts/proteomics/spectrum_annotator/README.md b/scripts/proteomics/spectrum_annotator/README.md new file mode 100644 index 0000000..cfa37a6 --- /dev/null +++ b/scripts/proteomics/spectrum_annotator/README.md @@ -0,0 +1,10 @@ +# Spectrum Annotator + +Annotate observed MS2 spectrum peaks with theoretical fragment ion matches for a given peptide sequence. + +## Usage + +```bash +python spectrum_annotator.py --mz-list "100.5,200.3,300.1" --intensities "1000,500,200" --sequence PEPTIDEK --charge 2 --tolerance 0.02 +python spectrum_annotator.py --mz-list "100.5,200.3" --intensities "1000,500" --sequence PEPTIDEK --charge 1 --output annotation.tsv +``` diff --git a/scripts/proteomics/spectrum_annotator/requirements.txt b/scripts/proteomics/spectrum_annotator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/spectrum_annotator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/spectrum_annotator/spectrum_annotator.py b/scripts/proteomics/spectrum_annotator/spectrum_annotator.py new file mode 100644 index 0000000..ee8dcb4 --- /dev/null +++ b/scripts/proteomics/spectrum_annotator/spectrum_annotator.py @@ -0,0 +1,152 @@ +""" +Spectrum Annotator +================== +Annotate observed MS2 spectrum peaks with theoretical fragment ion matches. +Matches observed m/z values against theoretical b/y ions generated for a peptide. + +Features: +- Match observed peaks to theoretical fragment ions +- Configurable mass tolerance +- TSV output with annotation details + +Usage +----- + python spectrum_annotator.py --mz-list "100.5,200.3,300.1" --intensities "1000,500,200" \ + --sequence PEPTIDEK --charge 2 --tolerance 0.02 + python spectrum_annotator.py --mz-list "100.5,200.3" --intensities "1000,500" \ + --sequence PEPTIDEK --charge 1 --tolerance 0.05 --output annotation.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def annotate_spectrum( + mz_values: list[float], + intensities: list[float], + sequence: str, + charge: int = 1, + tolerance: float = 0.02, +) -> list[dict]: + """Annotate observed spectrum peaks with theoretical fragment ion matches. + + Parameters + ---------- + mz_values : list[float] + Observed m/z values. + intensities : list[float] + Observed intensity values (same length as mz_values). + sequence : str + Peptide amino acid sequence. + charge : int + Charge state for theoretical spectrum generation. + tolerance : float + Mass tolerance in Da for matching. + + Returns + ------- + list[dict] + List of dicts with keys: observed_mz, intensity, matched_ion, theoretical_mz, error_da. + """ + aa_seq = oms.AASequence.fromString(sequence) + + # Generate theoretical spectrum + tsg = oms.TheoreticalSpectrumGenerator() + param = tsg.getParameters() + param.setValue("add_b_ions", "true") + param.setValue("add_y_ions", "true") + param.setValue("add_a_ions", "true") + param.setValue("add_metainfo", "true") + tsg.setParameters(param) + + theo_spec = oms.MSSpectrum() + tsg.getSpectrum(theo_spec, aa_seq, charge, charge) + + # Build theoretical ion list with annotations + theo_ions = [] + theo_mzs, _ = theo_spec.get_peaks() + annotations = theo_spec.getStringDataArrays() + for i in range(theo_spec.size()): + ann = "" + if annotations and len(annotations) > 0 and i < annotations[0].size(): + raw = annotations[0][i] + ann = raw.decode() if isinstance(raw, bytes) else str(raw) + theo_ions.append({"mz": theo_mzs[i], "annotation": ann}) + + # Match observed peaks to theoretical + results = [] + for obs_idx in range(len(mz_values)): + obs_mz = mz_values[obs_idx] + obs_int = intensities[obs_idx] if obs_idx < len(intensities) else 0.0 + + best_match = None + best_error = float("inf") + + for theo in theo_ions: + error = abs(obs_mz - theo["mz"]) + if error <= tolerance and error < best_error: + best_error = error + best_match = theo + + results.append({ + "observed_mz": round(obs_mz, 6), + "intensity": round(obs_int, 2), + "matched_ion": best_match["annotation"] if best_match else "", + "theoretical_mz": round(best_match["mz"], 6) if best_match else "", + "error_da": round(best_error, 6) if best_match else "", + }) + + return results + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write annotation results to TSV file. + + Parameters + ---------- + results : list[dict] + List of annotation result dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["observed_mz", "intensity", "matched_ion", "theoretical_mz", "error_da"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Annotate observed MS2 spectrum peaks with theoretical fragment ion matches." + ) + parser.add_argument("--mz-list", required=True, help="Comma-separated observed m/z values") + parser.add_argument("--intensities", required=True, help="Comma-separated observed intensities") + parser.add_argument("--sequence", required=True, help="Peptide amino acid sequence") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") + parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") + parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") + args = parser.parse_args() + + mz_values = [float(x.strip()) for x in args.mz_list.split(",")] + intensities = [float(x.strip()) for x in args.intensities.split(",")] + + results = annotate_spectrum(mz_values, intensities, args.sequence, args.charge, args.tolerance) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} annotations to {args.output}") + else: + print("observed_mz\tintensity\tmatched_ion\ttheoretical_mz\terror_da") + for r in results: + print(f"{r['observed_mz']}\t{r['intensity']}\t{r['matched_ion']}\t{r['theoretical_mz']}\t{r['error_da']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectrum_annotator/tests/conftest.py b/scripts/proteomics/spectrum_annotator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectrum_annotator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_annotator/tests/test_spectrum_annotator.py b/scripts/proteomics/spectrum_annotator/tests/test_spectrum_annotator.py new file mode 100644 index 0000000..5b2c4af --- /dev/null +++ b/scripts/proteomics/spectrum_annotator/tests/test_spectrum_annotator.py @@ -0,0 +1,66 @@ +"""Tests for spectrum_annotator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSpectrumAnnotator: + def test_annotate_with_matching_peaks(self): + import pyopenms as oms + from spectrum_annotator import annotate_spectrum + + # Generate theoretical spectrum to get known m/z values + aa_seq = oms.AASequence.fromString("PEPTIDEK") + tsg = oms.TheoreticalSpectrumGenerator() + param = tsg.getParameters() + param.setValue("add_b_ions", "true") + param.setValue("add_y_ions", "true") + param.setValue("add_metainfo", "true") + tsg.setParameters(param) + spec = oms.MSSpectrum() + tsg.getSpectrum(spec, aa_seq, 1, 1) + theo_mzs, _ = spec.get_peaks() + + # Use first few theoretical m/z values as observed + obs_mz = [float(theo_mzs[0]), float(theo_mzs[1])] + obs_int = [1000.0, 500.0] + + results = annotate_spectrum(obs_mz, obs_int, "PEPTIDEK", charge=1, tolerance=0.05) + assert len(results) == 2 + # At least one should match + matched = [r for r in results if r["matched_ion"]] + assert len(matched) > 0 + + def test_annotate_no_match(self): + from spectrum_annotator import annotate_spectrum + + results = annotate_spectrum([9999.0], [1000.0], "PEPTIDEK", charge=1, tolerance=0.02) + assert len(results) == 1 + assert results[0]["matched_ion"] == "" + + def test_result_keys(self): + from spectrum_annotator import annotate_spectrum + + results = annotate_spectrum([100.0, 200.0], [1000.0, 500.0], "PEPTIDEK", charge=1) + for r in results: + assert "observed_mz" in r + assert "intensity" in r + assert "matched_ion" in r + assert "theoretical_mz" in r + assert "error_da" in r + + def test_write_tsv(self): + from spectrum_annotator import annotate_spectrum, write_tsv + + results = annotate_spectrum([100.0], [1000.0], "PEPTIDEK", charge=1) + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "annotation.tsv") + write_tsv(results, out) + assert os.path.exists(out) + with open(out) as f: + lines = f.readlines() + assert len(lines) == 2 + assert "observed_mz" in lines[0] diff --git a/scripts/proteomics/spectrum_entropy_calculator/README.md b/scripts/proteomics/spectrum_entropy_calculator/README.md new file mode 100644 index 0000000..59f1fd3 --- /dev/null +++ b/scripts/proteomics/spectrum_entropy_calculator/README.md @@ -0,0 +1,10 @@ +# Spectrum Entropy Calculator + +Calculate spectral entropy for MS2 spectra in mzML files. + +## Usage + +```bash +python spectrum_entropy_calculator.py --input run.mzML --ms-level 2 +python spectrum_entropy_calculator.py --input run.mzML --ms-level 2 --output entropy.tsv +``` diff --git a/scripts/proteomics/spectrum_entropy_calculator/requirements.txt b/scripts/proteomics/spectrum_entropy_calculator/requirements.txt new file mode 100644 index 0000000..1051d92 --- /dev/null +++ b/scripts/proteomics/spectrum_entropy_calculator/requirements.txt @@ -0,0 +1,2 @@ +pyopenms +numpy diff --git a/scripts/proteomics/spectrum_entropy_calculator/spectrum_entropy_calculator.py b/scripts/proteomics/spectrum_entropy_calculator/spectrum_entropy_calculator.py new file mode 100644 index 0000000..25e56c7 --- /dev/null +++ b/scripts/proteomics/spectrum_entropy_calculator/spectrum_entropy_calculator.py @@ -0,0 +1,191 @@ +""" +Spectrum Entropy Calculator +=========================== +Calculate spectral entropy for MS2 spectra in mzML files. +Spectral entropy measures the information content of a spectrum. + +Features: +- Normalized spectral entropy (0 to 1) +- Filter by MS level +- TSV output with scan metadata + +Usage +----- + python spectrum_entropy_calculator.py --input run.mzML --ms-level 2 + python spectrum_entropy_calculator.py --input run.mzML --ms-level 2 --output entropy.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def spectral_entropy(intensities: list[float]) -> float: + """Calculate normalized spectral entropy for a list of intensities. + + Parameters + ---------- + intensities : list[float] + Peak intensity values. + + Returns + ------- + float + Normalized spectral entropy in range [0, 1]. + Returns 0 for empty spectra or spectra with one peak. + """ + if len(intensities) <= 1: + return 0.0 + + total = sum(intensities) + if total == 0: + return 0.0 + + # Normalize to probability distribution + probs = [i / total for i in intensities if i > 0] + + # Shannon entropy + entropy = -sum(p * math.log(p) for p in probs) + + # Normalize by maximum entropy (uniform distribution) + max_entropy = math.log(len(probs)) + if max_entropy == 0: + return 0.0 + + return entropy / max_entropy + + +def compute_spectrum_entropies( + input_path: str, + ms_level: int = 2, +) -> list[dict]: + """Compute spectral entropy for all spectra at given MS level. + + Parameters + ---------- + input_path : str + Path to mzML file. + ms_level : int + MS level to analyze (default 2). + + Returns + ------- + list[dict] + List of dicts with keys: scan_index, rt, n_peaks, entropy, precursor_mz. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + results = [] + for i in range(exp.getNrSpectra()): + spec = exp.getSpectrum(i) + if spec.getMSLevel() != ms_level: + continue + + rt = spec.getRT() + _, intensities = spec.get_peaks() + + int_list = [float(x) for x in intensities] + entropy = spectral_entropy(int_list) + + precursor_mz = 0.0 + precursors = spec.getPrecursors() + if precursors: + precursor_mz = precursors[0].getMZ() + + results.append({ + "scan_index": i, + "rt": round(rt, 4), + "n_peaks": len(int_list), + "entropy": round(entropy, 6), + "precursor_mz": round(precursor_mz, 6), + }) + + return results + + +def create_synthetic_mzml(output_path: str, n_ms2: int = 10) -> None: + """Create a synthetic mzML file with MS2 spectra for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + n_ms2 : int + Number of MS2 scans to generate. + """ + exp = oms.MSExperiment() + + # MS1 + ms1 = oms.MSSpectrum() + ms1.setMSLevel(1) + ms1.setRT(0.0) + ms1.set_peaks(([500.0], [10000.0])) + exp.addSpectrum(ms1) + + for i in range(n_ms2): + ms2 = oms.MSSpectrum() + ms2.setMSLevel(2) + ms2.setRT(float(i + 1) * 2.0) + + prec = oms.Precursor() + prec.setMZ(500.0 + i * 10) + prec.setCharge(2) + ms2.setPrecursors([prec]) + + # Vary number of peaks and intensity distribution + n_peaks = 5 + i * 2 + mzs = [100.0 + j * 50 for j in range(n_peaks)] + # Varying entropy: first scans have uniform dist, later scans dominated by one peak + ints = [1000.0 / (j + 1) ** (i * 0.3) for j in range(n_peaks)] + ms2.set_peaks((mzs, ints)) + exp.addSpectrum(ms2) + + oms.MzMLFile().store(output_path, exp) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write entropy results to TSV file. + + Parameters + ---------- + results : list[dict] + List of entropy result dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["scan_index", "rt", "n_peaks", "entropy", "precursor_mz"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Calculate spectral entropy for MS2 spectra in mzML." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--ms-level", type=int, default=2, help="MS level (default: 2)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + results = compute_spectrum_entropies(args.input, args.ms_level) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} entropy values to {args.output}") + else: + print("scan_index\trt\tn_peaks\tentropy\tprecursor_mz") + for r in results: + print(f"{r['scan_index']}\t{r['rt']}\t{r['n_peaks']}\t{r['entropy']}\t{r['precursor_mz']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectrum_entropy_calculator/tests/conftest.py b/scripts/proteomics/spectrum_entropy_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectrum_entropy_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py b/scripts/proteomics/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py new file mode 100644 index 0000000..2ee1dd8 --- /dev/null +++ b/scripts/proteomics/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py @@ -0,0 +1,75 @@ +"""Tests for spectrum_entropy_calculator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSpectrumEntropyCalculator: + def test_uniform_entropy(self): + from spectrum_entropy_calculator import spectral_entropy + + # Uniform distribution should have entropy = 1.0 + intensities = [100.0, 100.0, 100.0, 100.0] + entropy = spectral_entropy(intensities) + assert abs(entropy - 1.0) < 0.001 + + def test_single_peak_entropy(self): + from spectrum_entropy_calculator import spectral_entropy + + # Single peak should have entropy = 0 + assert spectral_entropy([1000.0]) == 0.0 + + def test_empty_entropy(self): + from spectrum_entropy_calculator import spectral_entropy + + assert spectral_entropy([]) == 0.0 + + def test_dominated_entropy(self): + from spectrum_entropy_calculator import spectral_entropy + + # One dominant peak should have low entropy + intensities = [10000.0, 1.0, 1.0, 1.0] + entropy = spectral_entropy(intensities) + assert 0.0 < entropy < 0.5 + + def test_compute_from_mzml(self): + from spectrum_entropy_calculator import compute_spectrum_entropies, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_ms2=5) + results = compute_spectrum_entropies(mzml_path, ms_level=2) + assert len(results) == 5 + for r in results: + assert 0.0 <= r["entropy"] <= 1.0 + + def test_result_keys(self): + from spectrum_entropy_calculator import compute_spectrum_entropies, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_ms2=1) + results = compute_spectrum_entropies(mzml_path) + for r in results: + assert "scan_index" in r + assert "rt" in r + assert "n_peaks" in r + assert "entropy" in r + assert "precursor_mz" in r + + def test_write_tsv(self): + from spectrum_entropy_calculator import compute_spectrum_entropies, create_synthetic_mzml, write_tsv + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_ms2=3) + results = compute_spectrum_entropies(mzml_path) + out = os.path.join(tmpdir, "entropy.tsv") + write_tsv(results, out) + assert os.path.exists(out) + with open(out) as f: + lines = f.readlines() + assert len(lines) == 4 # header + 3 data lines diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/README.md b/scripts/proteomics/spectrum_scoring_hyperscore/README.md new file mode 100644 index 0000000..d3dc1c2 --- /dev/null +++ b/scripts/proteomics/spectrum_scoring_hyperscore/README.md @@ -0,0 +1,10 @@ +# Spectrum Scoring HyperScore + +Score experimental spectrum against theoretical using a HyperScore-like approach. + +## Usage + +```bash +python spectrum_scoring_hyperscore.py --mz-list "100.5,200.3,300.1" --intensities "1000,500,200" --sequence PEPTIDEK --charge 2 +python spectrum_scoring_hyperscore.py --mz-list "100.5,200.3" --intensities "1000,500" --sequence PEPTIDEK --charge 1 --output score.json +``` diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/requirements.txt b/scripts/proteomics/spectrum_scoring_hyperscore/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/spectrum_scoring_hyperscore/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py b/scripts/proteomics/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py new file mode 100644 index 0000000..0671ba0 --- /dev/null +++ b/scripts/proteomics/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py @@ -0,0 +1,166 @@ +""" +Spectrum Scoring HyperScore +============================ +Score an experimental spectrum against a theoretical spectrum using a HyperScore-like +approach. Computes matched peak count, intensity dot product, and a combined score. + +Features: +- Theoretical spectrum generation from peptide sequence +- Peak matching with configurable tolerance +- HyperScore-like combined scoring +- JSON output with score details + +Usage +----- + python spectrum_scoring_hyperscore.py --mz-list "100.5,200.3,300.1" --intensities "1000,500,200" \ + --sequence PEPTIDEK --charge 2 + python spectrum_scoring_hyperscore.py --mz-list "100.5,200.3" --intensities "1000,500" \ + --sequence PEPTIDEK --charge 1 --output score.json +""" + +import argparse +import json +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def compute_hyperscore( + mz_values: list[float], + intensities: list[float], + sequence: str, + charge: int = 1, + tolerance: float = 0.02, +) -> dict: + """Compute a HyperScore-like score for experimental vs theoretical spectrum. + + The score is computed as: log(n_b! * n_y! * dot_product) where n_b and n_y are + matched b-ion and y-ion counts, and dot_product is the sum of products of matched + intensities. + + Parameters + ---------- + mz_values : list[float] + Experimental m/z values. + intensities : list[float] + Experimental intensity values. + sequence : str + Peptide amino acid sequence. + charge : int + Charge state for theoretical spectrum generation. + tolerance : float + Mass tolerance in Da for peak matching. + + Returns + ------- + dict + Dictionary with keys: hyperscore, matched_b, matched_y, matched_total, + dot_product, sequence, charge. + """ + aa_seq = oms.AASequence.fromString(sequence) + + # Build experimental spectrum + exp_spec = oms.MSSpectrum() + exp_spec.set_peaks((mz_values, intensities)) + exp_spec.sortByPosition() + + # Generate theoretical b-ions + tsg_b = oms.TheoreticalSpectrumGenerator() + param_b = tsg_b.getParameters() + param_b.setValue("add_b_ions", "true") + param_b.setValue("add_y_ions", "false") + param_b.setValue("add_metainfo", "true") + tsg_b.setParameters(param_b) + theo_b = oms.MSSpectrum() + tsg_b.getSpectrum(theo_b, aa_seq, charge, charge) + + # Generate theoretical y-ions + tsg_y = oms.TheoreticalSpectrumGenerator() + param_y = tsg_y.getParameters() + param_y.setValue("add_b_ions", "false") + param_y.setValue("add_y_ions", "true") + param_y.setValue("add_metainfo", "true") + tsg_y.setParameters(param_y) + theo_y = oms.MSSpectrum() + tsg_y.getSpectrum(theo_y, aa_seq, charge, charge) + + # Match peaks + aligner = oms.SpectrumAlignment() + param_align = aligner.getParameters() + param_align.setValue("tolerance", tolerance) + param_align.setValue("is_relative_tolerance", "false") + aligner.setParameters(param_align) + + alignment_b = [] + if theo_b.size() > 0: + aligner.getSpectrumAlignment(alignment_b, exp_spec, theo_b) + matched_b = len(alignment_b) + + alignment_y = [] + if theo_y.size() > 0: + aligner.getSpectrumAlignment(alignment_y, exp_spec, theo_y) + matched_y = len(alignment_y) + + # Compute dot product from matched peaks + exp_mzs, exp_ints = exp_spec.get_peaks() + dot_product = 0.0 + for qi, _ in alignment_b: + dot_product += float(exp_ints[qi]) + for qi, _ in alignment_y: + dot_product += float(exp_ints[qi]) + + # HyperScore = log(n_b! * n_y! * dot_product) + if matched_b > 0 and matched_y > 0 and dot_product > 0: + log_score = ( + math.lgamma(matched_b + 1) + + math.lgamma(matched_y + 1) + + math.log(dot_product) + ) + elif (matched_b > 0 or matched_y > 0) and dot_product > 0: + log_score = math.lgamma(max(matched_b, matched_y) + 1) + math.log(dot_product) + else: + log_score = 0.0 + + return { + "hyperscore": round(log_score, 6), + "matched_b": matched_b, + "matched_y": matched_y, + "matched_total": matched_b + matched_y, + "dot_product": round(dot_product, 4), + "sequence": sequence, + "charge": charge, + } + + +def main(): + parser = argparse.ArgumentParser( + description="Score experimental spectrum against theoretical using HyperScore." + ) + parser.add_argument("--mz-list", required=True, help="Comma-separated experimental m/z values") + parser.add_argument("--intensities", required=True, help="Comma-separated experimental intensities") + parser.add_argument("--sequence", required=True, help="Peptide amino acid sequence") + parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") + parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") + parser.add_argument("--output", default=None, help="Output JSON file path (default: print to stdout)") + args = parser.parse_args() + + mz_values = [float(x.strip()) for x in args.mz_list.split(",")] + intensities_list = [float(x.strip()) for x in args.intensities.split(",")] + + result = compute_hyperscore(mz_values, intensities_list, args.sequence, args.charge, args.tolerance) + + output_json = json.dumps(result, indent=2) + if args.output: + with open(args.output, "w") as f: + f.write(output_json) + print(f"Wrote score to {args.output}") + else: + print(output_json) + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/tests/conftest.py b/scripts/proteomics/spectrum_scoring_hyperscore/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectrum_scoring_hyperscore/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py b/scripts/proteomics/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py new file mode 100644 index 0000000..5cf77c6 --- /dev/null +++ b/scripts/proteomics/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py @@ -0,0 +1,63 @@ +"""Tests for spectrum_scoring_hyperscore.""" + +import json +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestSpectrumScoringHyperscore: + def _get_theoretical_peaks(self, sequence="PEPTIDEK", charge=1): + """Helper to get theoretical peaks for testing.""" + import pyopenms as oms + + aa_seq = oms.AASequence.fromString(sequence) + tsg = oms.TheoreticalSpectrumGenerator() + param = tsg.getParameters() + param.setValue("add_b_ions", "true") + param.setValue("add_y_ions", "true") + param.setValue("add_metainfo", "true") + tsg.setParameters(param) + spec = oms.MSSpectrum() + tsg.getSpectrum(spec, aa_seq, charge, charge) + mzs, _ = spec.get_peaks() + return [float(m) for m in mzs] + + def test_matching_spectrum(self): + from spectrum_scoring_hyperscore import compute_hyperscore + + theo_mzs = self._get_theoretical_peaks() + intensities = [1000.0] * len(theo_mzs) + result = compute_hyperscore(theo_mzs, intensities, "PEPTIDEK", charge=1, tolerance=0.05) + assert result["hyperscore"] > 0 + assert result["matched_total"] > 0 + assert result["sequence"] == "PEPTIDEK" + + def test_no_match(self): + from spectrum_scoring_hyperscore import compute_hyperscore + + result = compute_hyperscore([9999.0], [1000.0], "PEPTIDEK", charge=1, tolerance=0.02) + assert result["hyperscore"] == 0.0 + assert result["matched_total"] == 0 + + def test_result_keys(self): + from spectrum_scoring_hyperscore import compute_hyperscore + + result = compute_hyperscore([100.0], [1000.0], "PEPTIDEK", charge=1) + expected_keys = {"hyperscore", "matched_b", "matched_y", "matched_total", "dot_product", "sequence", "charge"} + assert set(result.keys()) == expected_keys + + def test_output_json(self): + from spectrum_scoring_hyperscore import compute_hyperscore + + result = compute_hyperscore([100.0], [1000.0], "PEPTIDEK", charge=1) + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "score.json") + with open(out, "w") as f: + json.dump(result, f) + assert os.path.exists(out) + with open(out) as f: + loaded = json.load(f) + assert "hyperscore" in loaded diff --git a/scripts/proteomics/spectrum_similarity_scorer/README.md b/scripts/proteomics/spectrum_similarity_scorer/README.md new file mode 100644 index 0000000..7d726d3 --- /dev/null +++ b/scripts/proteomics/spectrum_similarity_scorer/README.md @@ -0,0 +1,10 @@ +# Spectrum Similarity Scorer + +Compute cosine similarity between MS2 spectra from MGF files using custom MGF reader and pyopenms SpectrumAlignment. + +## Usage + +```bash +python spectrum_similarity_scorer.py --query query.mgf --library reference.mgf --tolerance 0.02 +python spectrum_similarity_scorer.py --query query.mgf --library ref.mgf --tolerance 0.02 --output scores.tsv +``` diff --git a/scripts/proteomics/spectrum_similarity_scorer/requirements.txt b/scripts/proteomics/spectrum_similarity_scorer/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/spectrum_similarity_scorer/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/spectrum_similarity_scorer/spectrum_similarity_scorer.py b/scripts/proteomics/spectrum_similarity_scorer/spectrum_similarity_scorer.py new file mode 100644 index 0000000..79df4f8 --- /dev/null +++ b/scripts/proteomics/spectrum_similarity_scorer/spectrum_similarity_scorer.py @@ -0,0 +1,231 @@ +""" +Spectrum Similarity Scorer +========================== +Compute cosine similarity between two MS2 spectra from MGF files. +Uses a custom MGF reader and pyopenms SpectrumAlignment for peak matching. + +Features: +- Custom MGF file parser (no MascotGenericFile dependency) +- Cosine similarity scoring with configurable tolerance +- TSV output with query_id, library_id, score, matched_peaks + +Usage +----- + python spectrum_similarity_scorer.py --query query.mgf --library reference.mgf --tolerance 0.02 + python spectrum_similarity_scorer.py --query query.mgf --library ref.mgf --tolerance 0.02 --output scores.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_mgf(filepath: str) -> list[dict]: + """Parse an MGF file into a list of spectrum dictionaries. + + Parameters + ---------- + filepath : str + Path to the MGF file. + + Returns + ------- + list[dict] + List of dicts with keys: title, pepmass, charge, mz_array, intensity_array. + """ + spectra = [] + current = None + + with open(filepath) as f: + for line in f: + line = line.strip() + if line == "BEGIN IONS": + current = {"title": "", "pepmass": 0.0, "charge": 0, "mz_array": [], "intensity_array": []} + elif line == "END IONS": + if current is not None: + spectra.append(current) + current = None + elif current is not None: + if line.startswith("TITLE="): + current["title"] = line[6:] + elif line.startswith("PEPMASS="): + parts = line[8:].split() + current["pepmass"] = float(parts[0]) + elif line.startswith("CHARGE="): + charge_str = line[7:].replace("+", "").replace("-", "") + current["charge"] = int(charge_str) if charge_str else 0 + elif line and line[0].isdigit(): + parts = line.split() + if len(parts) >= 2: + current["mz_array"].append(float(parts[0])) + current["intensity_array"].append(float(parts[1])) + + return spectra + + +def mgf_to_msspectrum(spec_dict: dict) -> oms.MSSpectrum: + """Convert a parsed MGF spectrum dict to an MSSpectrum object. + + Parameters + ---------- + spec_dict : dict + Parsed spectrum dictionary from parse_mgf. + + Returns + ------- + oms.MSSpectrum + pyopenms MSSpectrum object. + """ + spectrum = oms.MSSpectrum() + spectrum.set_peaks((spec_dict["mz_array"], spec_dict["intensity_array"])) + spectrum.sortByPosition() + return spectrum + + +def cosine_similarity(query: oms.MSSpectrum, library: oms.MSSpectrum, tolerance: float = 0.02) -> dict: + """Compute cosine similarity between two spectra. + + Parameters + ---------- + query : oms.MSSpectrum + Query spectrum. + library : oms.MSSpectrum + Library/reference spectrum. + tolerance : float + Mass tolerance in Da for peak matching (default 0.02). + + Returns + ------- + dict + Dictionary with keys: score, matched_peaks. + """ + aligner = oms.SpectrumAlignment() + param = aligner.getParameters() + param.setValue("tolerance", tolerance) + param.setValue("is_relative_tolerance", "false") + aligner.setParameters(param) + + alignment = [] + aligner.getSpectrumAlignment(alignment, query, library) + + if not alignment: + return {"score": 0.0, "matched_peaks": 0} + + q_mz, q_int = query.get_peaks() + l_mz, l_int = library.get_peaks() + + dot_product = 0.0 + q_norm = 0.0 + l_norm = 0.0 + + matched_q_indices = set() + matched_l_indices = set() + + for qi, li in alignment: + q_i_val = math.sqrt(q_int[qi]) + l_i_val = math.sqrt(l_int[li]) + dot_product += q_i_val * l_i_val + matched_q_indices.add(qi) + matched_l_indices.add(li) + + for i in range(len(q_int)): + q_norm += q_int[i] + for i in range(len(l_int)): + l_norm += l_int[i] + + q_norm = math.sqrt(q_norm) + l_norm = math.sqrt(l_norm) + + if q_norm == 0 or l_norm == 0: + return {"score": 0.0, "matched_peaks": len(alignment)} + + score = dot_product / (q_norm * l_norm) + return {"score": round(score, 6), "matched_peaks": len(alignment)} + + +def score_spectra( + query_path: str, library_path: str, tolerance: float = 0.02 +) -> list[dict]: + """Score all query spectra against all library spectra. + + Parameters + ---------- + query_path : str + Path to query MGF file. + library_path : str + Path to library MGF file. + tolerance : float + Mass tolerance in Da for peak matching. + + Returns + ------- + list[dict] + List of dicts with keys: query_id, library_id, score, matched_peaks. + """ + query_spectra = parse_mgf(query_path) + library_spectra = parse_mgf(library_path) + + results = [] + for qi, q_dict in enumerate(query_spectra): + q_spec = mgf_to_msspectrum(q_dict) + q_id = q_dict["title"] if q_dict["title"] else f"query_{qi}" + for li, l_dict in enumerate(library_spectra): + l_spec = mgf_to_msspectrum(l_dict) + l_id = l_dict["title"] if l_dict["title"] else f"library_{li}" + sim = cosine_similarity(q_spec, l_spec, tolerance) + results.append({ + "query_id": q_id, + "library_id": l_id, + "score": sim["score"], + "matched_peaks": sim["matched_peaks"], + }) + + return results + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write scoring results to TSV file. + + Parameters + ---------- + results : list[dict] + List of scoring result dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["query_id", "library_id", "score", "matched_peaks"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Compute cosine similarity between MS2 spectra from MGF files." + ) + parser.add_argument("--query", required=True, help="Path to query MGF file") + parser.add_argument("--library", required=True, help="Path to library/reference MGF file") + parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") + parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") + args = parser.parse_args() + + results = score_spectra(args.query, args.library, args.tolerance) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} scores to {args.output}") + else: + print("query_id\tlibrary_id\tscore\tmatched_peaks") + for r in results: + print(f"{r['query_id']}\t{r['library_id']}\t{r['score']}\t{r['matched_peaks']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/spectrum_similarity_scorer/tests/conftest.py b/scripts/proteomics/spectrum_similarity_scorer/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/spectrum_similarity_scorer/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py b/scripts/proteomics/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py new file mode 100644 index 0000000..e3d1664 --- /dev/null +++ b/scripts/proteomics/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py @@ -0,0 +1,89 @@ +"""Tests for spectrum_similarity_scorer.""" + +import os +import tempfile + +from conftest import requires_pyopenms + +MGF_TEMPLATE = """BEGIN IONS +TITLE={title} +PEPMASS={pepmass} +CHARGE={charge}+ +{peaks} +END IONS +""" + + +def _write_mgf(path, spectra): + with open(path, "w") as f: + for s in spectra: + peaks = "\n".join(f"{mz} {intensity}" for mz, intensity in zip(s["mzs"], s["intensities"])) + f.write(MGF_TEMPLATE.format(title=s["title"], pepmass=s["pepmass"], charge=s["charge"], peaks=peaks)) + + +@requires_pyopenms +class TestSpectrumSimilarityScorer: + def test_parse_mgf(self): + from spectrum_similarity_scorer import parse_mgf + + with tempfile.TemporaryDirectory() as tmpdir: + mgf_path = os.path.join(tmpdir, "test.mgf") + _write_mgf(mgf_path, [ + {"title": "spec1", "pepmass": 500.0, "charge": 2, + "mzs": [100.0, 200.0, 300.0], "intensities": [1000, 500, 200]}, + ]) + spectra = parse_mgf(mgf_path) + assert len(spectra) == 1 + assert spectra[0]["title"] == "spec1" + assert len(spectra[0]["mz_array"]) == 3 + + def test_cosine_identical(self): + from spectrum_similarity_scorer import cosine_similarity, mgf_to_msspectrum + + spec_dict = {"mz_array": [100.0, 200.0, 300.0], "intensity_array": [1000.0, 500.0, 200.0]} + s1 = mgf_to_msspectrum(spec_dict) + s2 = mgf_to_msspectrum(spec_dict) + result = cosine_similarity(s1, s2, tolerance=0.02) + assert result["score"] > 0.99 + assert result["matched_peaks"] == 3 + + def test_cosine_no_match(self): + from spectrum_similarity_scorer import cosine_similarity, mgf_to_msspectrum + + s1 = mgf_to_msspectrum({"mz_array": [100.0, 200.0], "intensity_array": [1000.0, 500.0]}) + s2 = mgf_to_msspectrum({"mz_array": [500.0, 600.0], "intensity_array": [1000.0, 500.0]}) + result = cosine_similarity(s1, s2, tolerance=0.02) + assert result["score"] == 0.0 + assert result["matched_peaks"] == 0 + + def test_score_spectra(self): + from spectrum_similarity_scorer import score_spectra + + with tempfile.TemporaryDirectory() as tmpdir: + q_path = os.path.join(tmpdir, "query.mgf") + l_path = os.path.join(tmpdir, "library.mgf") + _write_mgf(q_path, [ + {"title": "q1", "pepmass": 500.0, "charge": 2, + "mzs": [100.0, 200.0, 300.0], "intensities": [1000, 500, 200]}, + ]) + _write_mgf(l_path, [ + {"title": "l1", "pepmass": 500.0, "charge": 2, + "mzs": [100.0, 200.0, 300.0], "intensities": [1000, 500, 200]}, + ]) + results = score_spectra(q_path, l_path, tolerance=0.02) + assert len(results) == 1 + assert results[0]["query_id"] == "q1" + assert results[0]["score"] > 0.99 + + def test_write_tsv(self): + from spectrum_similarity_scorer import write_tsv + + results = [{"query_id": "q1", "library_id": "l1", "score": 0.95, "matched_peaks": 5}] + with tempfile.TemporaryDirectory() as tmpdir: + out = os.path.join(tmpdir, "scores.tsv") + write_tsv(results, out) + assert os.path.exists(out) + with open(out) as f: + lines = f.readlines() + assert len(lines) == 2 + assert "query_id" in lines[0] diff --git a/scripts/proteomics/theoretical_spectrum_generator/README.md b/scripts/proteomics/theoretical_spectrum_generator/README.md new file mode 100644 index 0000000..2e0aaca --- /dev/null +++ b/scripts/proteomics/theoretical_spectrum_generator/README.md @@ -0,0 +1,10 @@ +# Theoretical Spectrum Generator + +Generate theoretical b/y/a/c/x/z fragment ion spectra for a peptide sequence with annotated TSV output. + +## Usage + +```bash +python theoretical_spectrum_generator.py --sequence PEPTIDEK --charge 2 --ion-types b,y +python theoretical_spectrum_generator.py --sequence PEPTIDEK --charge 1 --ion-types b,y,a --add-losses --output fragments.tsv +``` diff --git a/scripts/proteomics/theoretical_spectrum_generator/requirements.txt b/scripts/proteomics/theoretical_spectrum_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/theoretical_spectrum_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py b/scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py b/scripts/proteomics/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py new file mode 100644 index 0000000..10681c7 --- /dev/null +++ b/scripts/proteomics/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py @@ -0,0 +1,70 @@ +"""Tests for theoretical_spectrum_generator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestTheoreticalSpectrumGenerator: + def test_generate_by_ions(self): + from theoretical_spectrum_generator import generate_theoretical_spectrum + + results = generate_theoretical_spectrum("PEPTIDEK", charge=1, ion_types=["b", "y"]) + assert len(results) > 0 + ion_types_found = {r["ion_type"] for r in results} + assert "b" in ion_types_found or "y" in ion_types_found + + def test_multiple_ion_types(self): + from theoretical_spectrum_generator import generate_theoretical_spectrum + + results = generate_theoretical_spectrum("PEPTIDEK", charge=1, ion_types=["b", "y", "a"]) + assert len(results) > 0 + + def test_charge_state(self): + from theoretical_spectrum_generator import generate_theoretical_spectrum + + r1 = generate_theoretical_spectrum("PEPTIDEK", charge=1, ion_types=["b", "y"]) + r2 = generate_theoretical_spectrum("PEPTIDEK", charge=2, ion_types=["b", "y"]) + assert len(r2) >= len(r1) + + def test_neutral_losses(self): + from theoretical_spectrum_generator import generate_theoretical_spectrum + + r_no_loss = generate_theoretical_spectrum("PEPTIDEK", charge=1, ion_types=["b", "y"], add_losses=False) + r_loss = generate_theoretical_spectrum("PEPTIDEK", charge=1, ion_types=["b", "y"], add_losses=True) + assert len(r_loss) >= len(r_no_loss) + + def test_result_keys(self): + from theoretical_spectrum_generator import generate_theoretical_spectrum + + results = generate_theoretical_spectrum("PEPTIDEK", charge=1) + assert len(results) > 0 + for r in results: + assert "ion_type" in r + assert "ion_number" in r + assert "charge" in r + assert "mz" in r + assert "annotation" in r + + def test_write_tsv(self): + from theoretical_spectrum_generator import generate_theoretical_spectrum, write_tsv + + results = generate_theoretical_spectrum("PEPTIDEK", charge=1) + with tempfile.TemporaryDirectory() as tmpdir: + out_path = os.path.join(tmpdir, "fragments.tsv") + write_tsv(results, out_path) + assert os.path.exists(out_path) + with open(out_path) as f: + lines = f.readlines() + assert len(lines) > 1 + assert "ion_type" in lines[0] + + def test_parse_annotation(self): + from theoretical_spectrum_generator import _parse_annotation + + result = _parse_annotation("y3+") + assert result["ion_type"] == "y" + assert result["ion_number"] == 3 + assert result["charge"] == 1 diff --git a/scripts/proteomics/theoretical_spectrum_generator/theoretical_spectrum_generator.py b/scripts/proteomics/theoretical_spectrum_generator/theoretical_spectrum_generator.py new file mode 100644 index 0000000..31c05c6 --- /dev/null +++ b/scripts/proteomics/theoretical_spectrum_generator/theoretical_spectrum_generator.py @@ -0,0 +1,195 @@ +""" +Theoretical Spectrum Generator +============================== +Generate theoretical b/y/a/c/x/z fragment ion spectra for a peptide sequence. +Output annotated TSV with ion type, ion number, charge, m/z, and annotation. + +Features: +- Supports b, y, a, c, x, z ion types +- Multiple charge states +- Optional neutral losses (H2O, NH3) +- TSV output with full annotation + +Usage +----- + python theoretical_spectrum_generator.py --sequence PEPTIDEK --charge 2 --ion-types b,y + python theoretical_spectrum_generator.py --sequence PEPTIDEK --charge 1 \ + --ion-types b,y,a --add-losses --output fragments.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def generate_theoretical_spectrum( + sequence: str, + charge: int = 1, + ion_types: list[str] | None = None, + add_losses: bool = False, +) -> list[dict]: + """Generate theoretical fragment ion spectrum for a peptide sequence. + + Parameters + ---------- + sequence : str + Amino acid sequence, e.g. ``"PEPTIDEK"``. + charge : int + Maximum charge state for fragment ions (default 1). + ion_types : list[str] or None + Ion types to generate, e.g. ``["b", "y"]``. Defaults to ``["b", "y"]``. + add_losses : bool + Whether to include neutral losses (H2O, NH3). + + Returns + ------- + list[dict] + List of dicts with keys: ion_type, ion_number, charge, mz, annotation. + """ + if ion_types is None: + ion_types = ["b", "y"] + + aa_seq = oms.AASequence.fromString(sequence) + spec = oms.MSSpectrum() + tsg = oms.TheoreticalSpectrumGenerator() + + param = tsg.getParameters() + param.setValue("add_b_ions", "true" if "b" in ion_types else "false") + param.setValue("add_y_ions", "true" if "y" in ion_types else "false") + param.setValue("add_a_ions", "true" if "a" in ion_types else "false") + param.setValue("add_c_ions", "true" if "c" in ion_types else "false") + param.setValue("add_x_ions", "true" if "x" in ion_types else "false") + param.setValue("add_z_ions", "true" if "z" in ion_types else "false") + param.setValue("add_losses", "true" if add_losses else "false") + param.setValue("add_metainfo", "true") + tsg.setParameters(param) + + tsg.getSpectrum(spec, aa_seq, charge, charge) + + results = [] + for i in range(spec.size()): + mz = spec[i].getMZ() + annotation_str = "" + ion_type_str = "" + ion_number = 0 + ion_charge = 1 + + if spec.getStringDataArrays(): + annotations = spec.getStringDataArrays() + if len(annotations) > 0 and i < annotations[0].size(): + annotation_str = annotations[0][i].decode() if isinstance(annotations[0][i], bytes) else str( + annotations[0][i] + ) + + if annotation_str: + parsed = _parse_annotation(annotation_str) + ion_type_str = parsed["ion_type"] + ion_number = parsed["ion_number"] + ion_charge = parsed["charge"] + else: + ion_type_str = "unknown" + + results.append({ + "ion_type": ion_type_str, + "ion_number": ion_number, + "charge": ion_charge, + "mz": round(mz, 6), + "annotation": annotation_str if annotation_str else f"{mz:.4f}", + }) + + return results + + +def _parse_annotation(annotation: str) -> dict: + """Parse a pyopenms ion annotation string. + + Parameters + ---------- + annotation : str + Annotation string like ``"y3++"`` or ``"b5-H2O+"``. + + Returns + ------- + dict + Dictionary with ion_type, ion_number, charge. + """ + ion_type = "" + ion_number = 0 + charge = 1 + + if not annotation: + return {"ion_type": "unknown", "ion_number": 0, "charge": 1} + + # Extract ion type (first letter) + for c in annotation: + if c.isalpha(): + ion_type = c + break + + # Extract ion number (digits after ion type letter) + num_str = "" + started = False + for c in annotation: + if c.isdigit(): + num_str += c + started = True + elif started: + break + if num_str: + ion_number = int(num_str) + + # Count charge from + signs + charge = annotation.count("+") + if charge == 0: + charge = 1 + + return {"ion_type": ion_type, "ion_number": ion_number, "charge": charge} + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write spectrum results to TSV file. + + Parameters + ---------- + results : list[dict] + List of fragment ion dictionaries. + output_path : str + Path to output TSV file. + """ + fieldnames = ["ion_type", "ion_number", "charge", "mz", "annotation"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Generate theoretical fragment ion spectra for a peptide sequence." + ) + parser.add_argument("--sequence", required=True, help="Amino acid sequence (e.g. PEPTIDEK)") + parser.add_argument("--charge", type=int, default=1, help="Max charge state for fragment ions (default: 1)") + parser.add_argument("--ion-types", default="b,y", help="Comma-separated ion types: b,y,a,c,x,z (default: b,y)") + parser.add_argument("--add-losses", action="store_true", help="Include neutral losses (H2O, NH3)") + parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") + args = parser.parse_args() + + ion_types = [t.strip() for t in args.ion_types.split(",")] + results = generate_theoretical_spectrum(args.sequence, args.charge, ion_types, args.add_losses) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} fragment ions to {args.output}") + else: + print(f"{'ion_type'}\t{'ion_number'}\t{'charge'}\t{'mz'}\t{'annotation'}") + for r in results: + print(f"{r['ion_type']}\t{r['ion_number']}\t{r['charge']}\t{r['mz']}\t{r['annotation']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/tic_bpc_calculator/README.md b/scripts/proteomics/tic_bpc_calculator/README.md new file mode 100644 index 0000000..6272e03 --- /dev/null +++ b/scripts/proteomics/tic_bpc_calculator/README.md @@ -0,0 +1,10 @@ +# TIC/BPC Calculator + +Compute Total Ion Chromatogram (TIC) and Base Peak Chromatogram (BPC) from mzML files. + +## Usage + +```bash +python tic_bpc_calculator.py --input run.mzML --ms-level 1 +python tic_bpc_calculator.py --input run.mzML --ms-level 1 --output chromatograms.tsv +``` diff --git a/scripts/proteomics/tic_bpc_calculator/requirements.txt b/scripts/proteomics/tic_bpc_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/tic_bpc_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/tic_bpc_calculator/tests/conftest.py b/scripts/proteomics/tic_bpc_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/tic_bpc_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py b/scripts/proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py new file mode 100644 index 0000000..1d67fbc --- /dev/null +++ b/scripts/proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py @@ -0,0 +1,66 @@ +"""Tests for tic_bpc_calculator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestTicBpcCalculator: + def test_compute_tic_bpc(self): + from tic_bpc_calculator import compute_tic_bpc, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_scans=5) + results = compute_tic_bpc(mzml_path, ms_level=1) + assert len(results) == 5 + for r in results: + assert r["tic"] > 0 + assert r["bpc"] > 0 + + def test_tic_equals_sum(self): + from tic_bpc_calculator import compute_tic_bpc, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_scans=1) + results = compute_tic_bpc(mzml_path, ms_level=1) + assert len(results) == 1 + # TIC should be sum of [1000, 2000, 3000, 4000, 5000] = 15000 + assert results[0]["tic"] == 15000.0 + + def test_bpc_is_max(self): + from tic_bpc_calculator import compute_tic_bpc, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_scans=1) + results = compute_tic_bpc(mzml_path, ms_level=1) + assert results[0]["bpc"] == 5000.0 + + def test_result_keys(self): + from tic_bpc_calculator import compute_tic_bpc, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, n_scans=1) + results = compute_tic_bpc(mzml_path) + for r in results: + assert "scan_index" in r + assert "rt" in r + assert "tic" in r + assert "bpc" in r + assert "bpc_mz" in r + + def test_write_tsv(self): + from tic_bpc_calculator import compute_tic_bpc, create_synthetic_mzml, write_tsv + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path) + results = compute_tic_bpc(mzml_path) + out = os.path.join(tmpdir, "chrom.tsv") + write_tsv(results, out) + assert os.path.exists(out) diff --git a/scripts/proteomics/tic_bpc_calculator/tic_bpc_calculator.py b/scripts/proteomics/tic_bpc_calculator/tic_bpc_calculator.py new file mode 100644 index 0000000..f63fd45 --- /dev/null +++ b/scripts/proteomics/tic_bpc_calculator/tic_bpc_calculator.py @@ -0,0 +1,145 @@ +""" +TIC/BPC Calculator +================== +Compute Total Ion Chromatogram (TIC) and Base Peak Chromatogram (BPC) from mzML files. + +Features: +- TIC: sum of all peak intensities per scan +- BPC: maximum peak intensity per scan +- Filter by MS level +- TSV output + +Usage +----- + python tic_bpc_calculator.py --input run.mzML --ms-level 1 + python tic_bpc_calculator.py --input run.mzML --ms-level 1 --output chromatograms.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def compute_tic_bpc( + input_path: str, + ms_level: int = 1, +) -> list[dict]: + """Compute TIC and BPC chromatograms from mzML. + + Parameters + ---------- + input_path : str + Path to mzML file. + ms_level : int + MS level to compute chromatograms for (default 1). + + Returns + ------- + list[dict] + List of dicts with keys: scan_index, rt, tic, bpc, bpc_mz. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + results = [] + for i in range(exp.getNrSpectra()): + spec = exp.getSpectrum(i) + if spec.getMSLevel() != ms_level: + continue + + rt = spec.getRT() + mzs, intensities = spec.get_peaks() + + tic = 0.0 + bpc = 0.0 + bpc_mz = 0.0 + + if len(intensities) > 0: + tic = float(sum(intensities)) + max_idx = 0 + for j in range(len(intensities)): + if intensities[j] > bpc: + bpc = float(intensities[j]) + max_idx = j + if len(mzs) > 0: + bpc_mz = float(mzs[max_idx]) + + results.append({ + "scan_index": i, + "rt": round(rt, 4), + "tic": round(tic, 2), + "bpc": round(bpc, 2), + "bpc_mz": round(bpc_mz, 6), + }) + + return results + + +def create_synthetic_mzml(output_path: str, n_scans: int = 10) -> None: + """Create a synthetic mzML file for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + n_scans : int + Number of MS1 scans to generate. + """ + exp = oms.MSExperiment() + + for i in range(n_scans): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(float(i) * 10.0) + mzs = [100.0 + j * 100 for j in range(5)] + ints = [1000.0 * (j + 1) for j in range(5)] + spec.set_peaks((mzs, ints)) + exp.addSpectrum(spec) + + oms.MzMLFile().store(output_path, exp) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write TIC/BPC results to TSV file. + + Parameters + ---------- + results : list[dict] + List of chromatogram data points. + output_path : str + Path to output TSV file. + """ + fieldnames = ["scan_index", "rt", "tic", "bpc", "bpc_mz"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Compute TIC and BPC chromatograms from mzML." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--ms-level", type=int, default=1, help="MS level (default: 1)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + results = compute_tic_bpc(args.input, args.ms_level) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} chromatogram data points to {args.output}") + else: + print("scan_index\trt\ttic\tbpc\tbpc_mz") + for r in results: + print(f"{r['scan_index']}\t{r['rt']}\t{r['tic']}\t{r['bpc']}\t{r['bpc_mz']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/topdown_coverage_calculator/README.md b/scripts/proteomics/topdown_coverage_calculator/README.md new file mode 100644 index 0000000..2dc0b63 --- /dev/null +++ b/scripts/proteomics/topdown_coverage_calculator/README.md @@ -0,0 +1,35 @@ +# Top-Down Coverage Calculator + +Compute per-residue bond cleavage coverage from fragment ions in top-down proteomics. + +## Installation + +```bash +pip install -r requirements.txt +``` + +## Usage + +```bash +python topdown_coverage_calculator.py --sequence PROTEINSEQ \ + --fragments observed.tsv --tolerance 10 --output coverage.tsv +``` + +### Input format + +The fragments file is a TSV with a `mass` column containing observed fragment ion masses: + +``` +mass +300.1589 +401.2066 +``` + +### Parameters + +| Flag | Description | +|------|-------------| +| `--sequence` | Protein amino acid sequence | +| `--fragments` | TSV with observed fragment ion masses | +| `--tolerance` | Tolerance in ppm (default: 10) | +| `--output` | Output coverage TSV | diff --git a/scripts/proteomics/topdown_coverage_calculator/requirements.txt b/scripts/proteomics/topdown_coverage_calculator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/topdown_coverage_calculator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/topdown_coverage_calculator/tests/conftest.py b/scripts/proteomics/topdown_coverage_calculator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/topdown_coverage_calculator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py b/scripts/proteomics/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py new file mode 100644 index 0000000..8681ef4 --- /dev/null +++ b/scripts/proteomics/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py @@ -0,0 +1,92 @@ +"""Tests for topdown_coverage_calculator.""" + +import csv +import sys + +from conftest import requires_pyopenms + + +@requires_pyopenms +def test_theoretical_fragments(): + from topdown_coverage_calculator import theoretical_fragments + + frags = theoretical_fragments("ACDEFGHIK") + assert "b" in frags and "y" in frags + # For a 9-residue peptide, there are 8 possible b and y ions + assert len(frags["b"]) == 8 + assert len(frags["y"]) == 8 + # b-ion numbers should be 1..8 + assert [f[0] for f in frags["b"]] == list(range(1, 9)) + + +@requires_pyopenms +def test_match_fragments_exact(): + from topdown_coverage_calculator import match_fragments, theoretical_fragments + + seq = "PEPTIDE" + frags = theoretical_fragments(seq) + # Use exact theoretical masses as observed + observed = [f[1] for f in frags["b"][:3]] # first 3 b-ions + matches = match_fragments(frags, observed, tolerance_ppm=10.0) + assert len(matches["b"]) >= 3 + + +@requires_pyopenms +def test_bond_coverage(): + from topdown_coverage_calculator import ( + bond_coverage, + match_fragments, + theoretical_fragments, + ) + + seq = "PEPTIDE" + frags = theoretical_fragments(seq) + # Match all b-ions + observed = [f[1] for f in frags["b"]] + matches = match_fragments(frags, observed, tolerance_ppm=10.0) + cov = bond_coverage(seq, matches) + assert len(cov) == len(seq) - 1 + # All bonds should be covered via b-ions + assert all(c["covered"] for c in cov) + + +@requires_pyopenms +def test_coverage_summary(): + from topdown_coverage_calculator import coverage_summary + + bond_cov = [ + {"covered": True}, {"covered": True}, {"covered": False}, + {"covered": True}, {"covered": False}, + ] + summary = coverage_summary(bond_cov) + assert summary["total_bonds"] == 5 + assert summary["covered_bonds"] == 3 + assert abs(summary["coverage_fraction"] - 0.6) < 0.01 + + +@requires_pyopenms +def test_cli_roundtrip(tmp_path): + from topdown_coverage_calculator import main, theoretical_fragments + + seq = "PEPTIDE" + frags = theoretical_fragments(seq) + + frag_file = tmp_path / "fragments.tsv" + output_file = tmp_path / "coverage.tsv" + + with open(frag_file, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["mass"]) + for _, mass in frags["b"][:3]: + writer.writerow([f"{mass:.6f}"]) + + sys.argv = [ + "topdown_coverage_calculator.py", + "--sequence", seq, + "--fragments", str(frag_file), + "--tolerance", "10", + "--output", str(output_file), + ] + main() + + assert output_file.exists() diff --git a/scripts/proteomics/topdown_coverage_calculator/topdown_coverage_calculator.py b/scripts/proteomics/topdown_coverage_calculator/topdown_coverage_calculator.py new file mode 100644 index 0000000..e2c4b79 --- /dev/null +++ b/scripts/proteomics/topdown_coverage_calculator/topdown_coverage_calculator.py @@ -0,0 +1,213 @@ +""" +Top-Down Coverage Calculator +============================= +Compute per-residue bond cleavage coverage from fragment ions in top-down +proteomics. Given a protein sequence and observed fragment ion masses, the +tool generates theoretical b- and y-ion ladders via pyopenms AASequence, +matches observed masses within a user-specified ppm tolerance, and reports +which backbone bonds were covered. + +Usage +----- + python topdown_coverage_calculator.py --sequence PROTEINSEQ \ + --fragments observed.tsv --tolerance 10 --output coverage.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def theoretical_fragments(sequence: str) -> Dict[str, List[Tuple[int, float]]]: + """Generate theoretical b- and y-ion masses for a protein sequence. + + Parameters + ---------- + sequence: + Amino acid sequence string. + + Returns + ------- + dict + ``"b"`` and ``"y"`` keys each mapping to a list of + ``(ion_number, monoisotopic_mass)`` tuples. + """ + aa = oms.AASequence.fromString(sequence) + n = aa.size() + + b_ions: List[Tuple[int, float]] = [] + y_ions: List[Tuple[int, float]] = [] + + for i in range(1, n): + # b-ion: prefix of length i + prefix = aa.getPrefix(i) + b_mass = prefix.getMonoWeight(oms.Residue.ResidueType.BIon, 1) + b_ions.append((i, b_mass)) + + # y-ion: suffix of length i + suffix = aa.getSuffix(i) + y_mass = suffix.getMonoWeight(oms.Residue.ResidueType.YIon, 1) + y_ions.append((i, y_mass)) + + return {"b": b_ions, "y": y_ions} + + +def match_fragments( + theoretical: Dict[str, List[Tuple[int, float]]], + observed_masses: List[float], + tolerance_ppm: float, +) -> Dict[str, List[Dict[str, object]]]: + """Match observed masses against theoretical fragment ions. + + Parameters + ---------- + theoretical: + Output of :func:`theoretical_fragments`. + observed_masses: + List of observed monoisotopic masses (singly charged). + tolerance_ppm: + Tolerance in parts-per-million. + + Returns + ------- + dict + ``"b"`` and ``"y"`` keys mapping to lists of match dicts with + ``ion_number``, ``theoretical_mass``, ``observed_mass``, ``error_ppm``. + """ + matches: Dict[str, List[Dict[str, object]]] = {"b": [], "y": []} + for ion_type in ("b", "y"): + for ion_num, theo_mass in theoretical[ion_type]: + for obs_mass in observed_masses: + error_ppm = abs(obs_mass - theo_mass) / theo_mass * 1e6 + if error_ppm <= tolerance_ppm: + matches[ion_type].append({ + "ion_number": ion_num, + "theoretical_mass": theo_mass, + "observed_mass": obs_mass, + "error_ppm": error_ppm, + }) + break # take first match per ion + return matches + + +def bond_coverage( + sequence: str, matches: Dict[str, List[Dict[str, object]]] +) -> List[Dict[str, object]]: + """Compute per-bond cleavage coverage. + + Bond *i* (between residues *i* and *i+1*, 1-indexed) is covered if + b_i or y_(n-i) was matched. + + Parameters + ---------- + sequence: + Protein sequence. + matches: + Output of :func:`match_fragments`. + + Returns + ------- + list of dict + One entry per bond with ``bond_index``, ``left_residue``, + ``right_residue``, ``covered``, ``ion_types``. + """ + n = len(sequence) + b_matched = {m["ion_number"] for m in matches["b"]} + y_matched = {m["ion_number"] for m in matches["y"]} + + coverage: List[Dict[str, object]] = [] + for i in range(1, n): + ions = [] + if i in b_matched: + ions.append(f"b{i}") + y_num = n - i + if y_num in y_matched: + ions.append(f"y{y_num}") + coverage.append({ + "bond_index": i, + "left_residue": sequence[i - 1], + "right_residue": sequence[i], + "covered": len(ions) > 0, + "ion_types": ",".join(ions) if ions else "-", + }) + return coverage + + +def coverage_summary(bond_cov: List[Dict[str, object]]) -> Dict[str, object]: + """Summarize bond coverage. + + Returns + ------- + dict + ``total_bonds``, ``covered_bonds``, ``coverage_fraction``. + """ + total = len(bond_cov) + covered = sum(1 for b in bond_cov if b["covered"]) + return { + "total_bonds": total, + "covered_bonds": covered, + "coverage_fraction": covered / total if total > 0 else 0.0, + } + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Compute per-residue bond cleavage coverage from fragment ions." + ) + parser.add_argument("--sequence", required=True, help="Protein amino acid sequence") + parser.add_argument( + "--fragments", required=True, + help="TSV with 'mass' column of observed fragment ion masses", + ) + parser.add_argument( + "--tolerance", type=float, default=10.0, + help="Tolerance in ppm (default: 10)", + ) + parser.add_argument("--output", required=True, help="Output coverage TSV") + args = parser.parse_args() + + # Read observed masses + observed: List[float] = [] + with open(args.fragments, newline="") as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + mass_str = row.get("mass", "").strip() + if mass_str: + observed.append(float(mass_str)) + + if not observed: + sys.exit("No observed masses found in fragments file.") + + theo = theoretical_fragments(args.sequence) + matches = match_fragments(theo, observed, args.tolerance) + cov = bond_coverage(args.sequence, matches) + summary = coverage_summary(cov) + + with open(args.output, "w", newline="") as fh: + writer = csv.writer(fh, delimiter="\t") + writer.writerow(["bond_index", "left_residue", "right_residue", "covered", "ion_types"]) + for entry in cov: + writer.writerow([ + entry["bond_index"], entry["left_residue"], + entry["right_residue"], entry["covered"], entry["ion_types"], + ]) + writer.writerow([]) + writer.writerow(["metric", "value"]) + writer.writerow(["total_bonds", summary["total_bonds"]]) + writer.writerow(["covered_bonds", summary["covered_bonds"]]) + writer.writerow(["coverage_fraction", f"{summary['coverage_fraction']:.4f}"]) + + print(f"Coverage: {summary['covered_bonds']}/{summary['total_bonds']} " + f"({summary['coverage_fraction']:.1%}) -> {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/transition_list_generator/README.md b/scripts/proteomics/transition_list_generator/README.md new file mode 100644 index 0000000..d2ea47f --- /dev/null +++ b/scripts/proteomics/transition_list_generator/README.md @@ -0,0 +1,10 @@ +# Transition List Generator + +Generate SRM/MRM/PRM transition lists from peptide sequences. + +## Usage + +```bash +python transition_list_generator.py --peptides PEPTIDEK,ANOTHERPEPTIDE --charge 2,3 --output transitions.tsv +python transition_list_generator.py --peptides PEPTIDEK --charge 2 --product-ions y3-y8 --output transitions.tsv +``` diff --git a/scripts/proteomics/transition_list_generator/requirements.txt b/scripts/proteomics/transition_list_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/transition_list_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/transition_list_generator/tests/conftest.py b/scripts/proteomics/transition_list_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/transition_list_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/transition_list_generator/tests/test_transition_list_generator.py b/scripts/proteomics/transition_list_generator/tests/test_transition_list_generator.py new file mode 100644 index 0000000..dd80176 --- /dev/null +++ b/scripts/proteomics/transition_list_generator/tests/test_transition_list_generator.py @@ -0,0 +1,55 @@ +"""Tests for transition_list_generator.""" + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestTransitionListGenerator: + def test_basic_transitions(self): + from transition_list_generator import generate_transitions + + transitions = generate_transitions("PEPTIDEK", precursor_charges=[2]) + assert len(transitions) > 0 + for t in transitions: + assert t["precursor_mz"] > 0 + assert t["product_mz"] > 0 + assert t["precursor_charge"] == 2 + + def test_multiple_charges(self): + from transition_list_generator import generate_transitions + + transitions = generate_transitions("PEPTIDEK", precursor_charges=[2, 3]) + charges = set(t["precursor_charge"] for t in transitions) + assert 2 in charges + assert 3 in charges + + def test_ion_range_filter(self): + from transition_list_generator import generate_transitions + + all_trans = generate_transitions("PEPTIDEK", precursor_charges=[2]) + filtered = generate_transitions("PEPTIDEK", precursor_charges=[2], ion_range="y3-y6") + assert len(filtered) <= len(all_trans) + for t in filtered: + assert t["annotation"].startswith("y") + + def test_parse_ion_range(self): + from transition_list_generator import parse_ion_range + + series, start, end = parse_ion_range("y3-y8") + assert series == "y" + assert start == 3 + assert end == 8 + + def test_precursor_mz_decreases_with_charge(self): + from transition_list_generator import generate_transitions + + t2 = generate_transitions("PEPTIDEK", precursor_charges=[2]) + t3 = generate_transitions("PEPTIDEK", precursor_charges=[3]) + assert t2[0]["precursor_mz"] > t3[0]["precursor_mz"] + + def test_transitions_have_annotations(self): + from transition_list_generator import generate_transitions + + transitions = generate_transitions("PEPTIDEK", precursor_charges=[2]) + annotated = [t for t in transitions if t["annotation"]] + assert len(annotated) > 0 diff --git a/scripts/proteomics/transition_list_generator/transition_list_generator.py b/scripts/proteomics/transition_list_generator/transition_list_generator.py new file mode 100644 index 0000000..8023d2d --- /dev/null +++ b/scripts/proteomics/transition_list_generator/transition_list_generator.py @@ -0,0 +1,167 @@ +""" +Transition List Generator +========================== +Generate SRM/MRM/PRM transition lists from peptide sequences. + +Features +-------- +- Generate b and y fragment ion transitions +- Support multiple charge states +- Filter product ions by ion series and range +- Output in standard transition list format + +Usage +----- + python transition_list_generator.py --peptides PEPTIDEK,ANOTHERPEPTIDE --charge 2,3 --output transitions.tsv + python transition_list_generator.py --peptides PEPTIDEK --charge 2 --product-ions y3-y8 --output transitions.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + +PROTON = 1.007276 + + +def parse_ion_range(ion_range: str) -> tuple: + """Parse an ion range string like 'y3-y8' into (series, start, end). + + Parameters + ---------- + ion_range : str + Ion range string (e.g., 'y3-y8', 'b2-b6'). + + Returns + ------- + tuple + (series_letter, start_number, end_number). + """ + parts = ion_range.split("-") + series = parts[0][0] + start = int(parts[0][1:]) + end = int(parts[1][1:]) if len(parts) > 1 else start + return series, start, end + + +def generate_transitions(sequence: str, precursor_charges: list = None, + product_charges: list = None, + ion_range: str = None) -> list: + """Generate SRM/MRM transitions for a peptide. + + Parameters + ---------- + sequence : str + Peptide sequence. + precursor_charges : list + List of precursor charge states. + product_charges : list + List of product ion charge states. + ion_range : str + Optional filter like 'y3-y8' to limit product ions. + + Returns + ------- + list + List of transition dicts. + """ + if precursor_charges is None: + precursor_charges = [2] + if product_charges is None: + product_charges = [1] + + aa_seq = oms.AASequence.fromString(sequence) + mono_mass = aa_seq.getMonoWeight() + + # Generate theoretical spectrum + tsg = oms.TheoreticalSpectrumGenerator() + params = tsg.getParameters() + params.setValue("add_b_ions", "true") + params.setValue("add_y_ions", "true") + params.setValue("add_metainfo", "true") + tsg.setParameters(params) + + transitions = [] + for prec_z in precursor_charges: + prec_mz = (mono_mass + prec_z * PROTON) / prec_z + + for prod_z in product_charges: + spec = oms.MSSpectrum() + tsg.getSpectrum(spec, aa_seq, prod_z, prec_z) + + for i in range(spec.size()): + peak = spec[i] + product_mz = peak.getMZ() + annotation = "" + if spec.getStringDataArrays(): + ann = spec.getStringDataArrays()[0][i] + annotation = ann.decode() if isinstance(ann, bytes) else str(ann) + + # Apply ion range filter if specified + if ion_range: + series, start, end = parse_ion_range(ion_range) + if not annotation.startswith(series): + continue + # Extract ion number from annotation + ion_num_str = "" + for ch in annotation[1:]: + if ch.isdigit(): + ion_num_str += ch + else: + break + if ion_num_str: + ion_num = int(ion_num_str) + if ion_num < start or ion_num > end: + continue + + transitions.append({ + "peptide": sequence, + "precursor_mz": round(prec_mz, 6), + "precursor_charge": prec_z, + "product_mz": round(product_mz, 6), + "product_charge": prod_z, + "annotation": annotation, + }) + + return transitions + + +def main(): + """CLI entry point.""" + parser = argparse.ArgumentParser(description="Generate SRM/MRM/PRM transition lists.") + parser.add_argument("--peptides", required=True, help="Comma-separated peptide sequences.") + parser.add_argument("--charge", type=str, default="2", help="Comma-separated precursor charges (default: 2).") + parser.add_argument("--product-charge", type=str, default="1", + help="Comma-separated product ion charges (default: 1).") + parser.add_argument("--product-ions", type=str, help="Ion range filter (e.g., 'y3-y8').") + parser.add_argument("--output", help="Output TSV file.") + args = parser.parse_args() + + peptide_list = [p.strip() for p in args.peptides.split(",") if p.strip()] + precursor_charges = [int(c.strip()) for c in args.charge.split(",")] + product_charges = [int(c.strip()) for c in args.product_charge.split(",")] + + all_transitions = [] + for pep in peptide_list: + transitions = generate_transitions(pep, precursor_charges, product_charges, args.product_ions) + all_transitions.extend(transitions) + + if args.output: + with open(args.output, "w", newline="") as fh: + fieldnames = ["peptide", "precursor_mz", "precursor_charge", "product_mz", + "product_charge", "annotation"] + writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(all_transitions) + print(f"Generated {len(all_transitions)} transitions -> {args.output}") + else: + for t in all_transitions: + print(f"{t['peptide']}\t{t['precursor_mz']}\t{t['product_mz']}\t{t['annotation']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/volcano_plot_data_generator/README.md b/scripts/proteomics/volcano_plot_data_generator/README.md new file mode 100644 index 0000000..5ecad47 --- /dev/null +++ b/scripts/proteomics/volcano_plot_data_generator/README.md @@ -0,0 +1,17 @@ +# Volcano Plot Data Generator + +Generate volcano plot data from differential expression results. + +## Usage + +```bash +python volcano_plot_data_generator.py --input de_results.tsv --fc-threshold 1.0 --pvalue 0.05 --output volcano.tsv +``` + +## Output Columns + +- `feature` - Feature identifier +- `log2fc` - Log2 fold change +- `pvalue` - P-value +- `neg_log10_pvalue` - -log10(p-value) for plotting +- `regulation` - Classification: `up`, `down`, or `ns` diff --git a/scripts/proteomics/volcano_plot_data_generator/requirements.txt b/scripts/proteomics/volcano_plot_data_generator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/volcano_plot_data_generator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/volcano_plot_data_generator/tests/conftest.py b/scripts/proteomics/volcano_plot_data_generator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/volcano_plot_data_generator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py b/scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py new file mode 100644 index 0000000..10cfdac --- /dev/null +++ b/scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py @@ -0,0 +1,63 @@ +"""Tests for volcano_plot_data_generator.""" + +import math + +from conftest import requires_pyopenms +from volcano_plot_data_generator import generate_volcano_data, read_de_results, summarize_volcano + + +@requires_pyopenms +class TestVolcanoPlotDataGenerator: + def _make_de_results(self): + return [ + {"feature": "prot1", "log2fc": 2.0, "pvalue": 0.001}, # up + {"feature": "prot2", "log2fc": -1.5, "pvalue": 0.01}, # down + {"feature": "prot3", "log2fc": 0.5, "pvalue": 0.001}, # ns (fc too low) + {"feature": "prot4", "log2fc": 2.0, "pvalue": 0.1}, # ns (pval too high) + {"feature": "prot5", "log2fc": float("nan"), "pvalue": float("nan")}, # ns + ] + + def test_classification(self): + results = self._make_de_results() + volcano = generate_volcano_data(results, fc_threshold=1.0, pvalue_threshold=0.05) + regs = {v["feature"]: v["regulation"] for v in volcano} + assert regs["prot1"] == "up" + assert regs["prot2"] == "down" + assert regs["prot3"] == "ns" + assert regs["prot4"] == "ns" + assert regs["prot5"] == "ns" + + def test_neg_log10_pvalue(self): + results = [{"feature": "p1", "log2fc": 1.0, "pvalue": 0.01}] + volcano = generate_volcano_data(results) + assert abs(volcano[0]["neg_log10_pvalue"] - 2.0) < 0.01 + + def test_summarize(self): + results = self._make_de_results() + volcano = generate_volcano_data(results, fc_threshold=1.0, pvalue_threshold=0.05) + counts = summarize_volcano(volcano) + assert counts["up"] == 1 + assert counts["down"] == 1 + assert counts["ns"] == 3 + + def test_custom_thresholds(self): + results = self._make_de_results() + volcano = generate_volcano_data(results, fc_threshold=0.3, pvalue_threshold=0.5) + regs = {v["feature"]: v["regulation"] for v in volcano} + assert regs["prot3"] == "up" # fc=0.5 > 0.3 threshold + assert regs["prot4"] == "up" # pval=0.1 < 0.5 threshold + + def test_read_de_results(self, tmp_path): + infile = str(tmp_path / "de.tsv") + with open(infile, "w") as fh: + fh.write("feature\tlog2fc\tadj_pvalue\n") + fh.write("p1\t1.5\t0.01\n") + fh.write("p2\tNA\tNA\n") + results = read_de_results(infile) + assert len(results) == 2 + assert results[0]["log2fc"] == 1.5 + assert math.isnan(results[1]["log2fc"]) + + def test_empty_input(self): + volcano = generate_volcano_data([]) + assert volcano == [] diff --git a/scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py b/scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py new file mode 100644 index 0000000..3076848 --- /dev/null +++ b/scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py @@ -0,0 +1,154 @@ +""" +Volcano Plot Data Generator +============================ +Generate volcano plot data from differential expression results. + +Annotates features as 'up', 'down', or 'ns' (not significant) based on +fold-change and p-value thresholds, and computes -log10(p-value) for plotting. + +Usage +----- + python volcano_plot_data_generator.py --input de_results.tsv --fc-threshold 1.0 --pvalue 0.05 --output volcano.tsv +""" + +import argparse +import csv +import math +import sys + +try: + import pyopenms as oms # noqa: F401 +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def read_de_results(filepath: str) -> list: + """Read differential expression results. + + Expected columns: feature, log2fc, pvalue (or adj_pvalue). + + Returns + ------- + list + List of dicts with keys: feature, log2fc, pvalue. + """ + results = [] + with open(filepath) as fh: + reader = csv.DictReader(fh, delimiter="\t") + for row in reader: + feature = row.get("feature", row.get("protein", row.get("peptide", ""))) + log2fc_str = row.get("log2fc", "NA") + pval_str = row.get("adj_pvalue", row.get("pvalue", "NA")) + + try: + log2fc = float(log2fc_str) + except (ValueError, TypeError): + log2fc = float("nan") + try: + pval = float(pval_str) + except (ValueError, TypeError): + pval = float("nan") + + results.append({"feature": feature, "log2fc": log2fc, "pvalue": pval}) + return results + + +def generate_volcano_data( + de_results: list, fc_threshold: float = 1.0, pvalue_threshold: float = 0.05 +) -> list: + """Annotate DE results for volcano plotting. + + Parameters + ---------- + de_results: + List of dicts with keys: feature, log2fc, pvalue. + fc_threshold: + Absolute log2 fold-change threshold. + pvalue_threshold: + P-value significance threshold. + + Returns + ------- + list + List of dicts with keys: feature, log2fc, pvalue, neg_log10_pvalue, regulation. + """ + volcano = [] + for r in de_results: + log2fc = r["log2fc"] + pval = r["pvalue"] + + if math.isnan(log2fc) or math.isnan(pval): + neg_log10_p = float("nan") + regulation = "ns" + else: + neg_log10_p = -math.log10(pval) if pval > 0 else float("inf") + if pval < pvalue_threshold and log2fc > fc_threshold: + regulation = "up" + elif pval < pvalue_threshold and log2fc < -fc_threshold: + regulation = "down" + else: + regulation = "ns" + + volcano.append({ + "feature": r["feature"], + "log2fc": log2fc, + "pvalue": pval, + "neg_log10_pvalue": neg_log10_p, + "regulation": regulation, + }) + return volcano + + +def summarize_volcano(volcano_data: list) -> dict: + """Count features by regulation status. + + Returns + ------- + dict + {up: int, down: int, ns: int} + """ + counts = {"up": 0, "down": 0, "ns": 0} + for v in volcano_data: + counts[v["regulation"]] += 1 + return counts + + +def main(): + parser = argparse.ArgumentParser(description="Generate volcano plot data from DE results.") + parser.add_argument("--input", required=True, help="Input DE results TSV") + parser.add_argument("--fc-threshold", type=float, default=1.0, help="Log2 fold-change threshold (default: 1.0)") + parser.add_argument("--pvalue", type=float, default=0.05, help="P-value threshold (default: 0.05)") + parser.add_argument("--output", required=True, help="Output TSV file") + args = parser.parse_args() + + de_results = read_de_results(args.input) + volcano = generate_volcano_data(de_results, fc_threshold=args.fc_threshold, pvalue_threshold=args.pvalue) + + with open(args.output, "w", newline="") as fh: + writer = csv.DictWriter( + fh, + fieldnames=["feature", "log2fc", "pvalue", "neg_log10_pvalue", "regulation"], + delimiter="\t", + ) + writer.writeheader() + for v in volcano: + writer.writerow({ + "feature": v["feature"], + "log2fc": f"{v['log2fc']:.6f}" if not math.isnan(v["log2fc"]) else "NA", + "pvalue": f"{v['pvalue']:.6e}" if not math.isnan(v["pvalue"]) else "NA", + "neg_log10_pvalue": ( + f"{v['neg_log10_pvalue']:.4f}" if not math.isnan(v["neg_log10_pvalue"]) else "NA" + ), + "regulation": v["regulation"], + }) + + counts = summarize_volcano(volcano) + print(f"Total features: {len(volcano)}") + print(f"Up-regulated: {counts['up']}") + print(f"Down-regulated: {counts['down']}") + print(f"Not significant: {counts['ns']}") + print(f"Output written to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/xic_extractor/README.md b/scripts/proteomics/xic_extractor/README.md new file mode 100644 index 0000000..b8924d7 --- /dev/null +++ b/scripts/proteomics/xic_extractor/README.md @@ -0,0 +1,10 @@ +# XIC Extractor + +Extract ion chromatograms for target m/z values from mzML files. + +## Usage + +```bash +python xic_extractor.py --input run.mzML --mz 524.265 --ppm 10 +python xic_extractor.py --input run.mzML --mz 524.265 --ppm 10 --output xic.tsv +``` diff --git a/scripts/proteomics/xic_extractor/requirements.txt b/scripts/proteomics/xic_extractor/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/xic_extractor/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/xic_extractor/tests/conftest.py b/scripts/proteomics/xic_extractor/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/xic_extractor/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/xic_extractor/tests/test_xic_extractor.py b/scripts/proteomics/xic_extractor/tests/test_xic_extractor.py new file mode 100644 index 0000000..4a0b134 --- /dev/null +++ b/scripts/proteomics/xic_extractor/tests/test_xic_extractor.py @@ -0,0 +1,68 @@ +"""Tests for xic_extractor.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestXicExtractor: + def test_extract_xic(self): + from xic_extractor import create_synthetic_mzml, extract_xic + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, target_mz=524.265, n_scans=10) + results = extract_xic(mzml_path, 524.265, ppm=10.0) + assert len(results) == 10 + # Middle scan should have highest intensity + intensities = [r["intensity"] for r in results] + assert max(intensities) > 0 + + def test_xic_no_peaks(self): + from xic_extractor import create_synthetic_mzml, extract_xic + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path, target_mz=524.265, n_scans=5) + results = extract_xic(mzml_path, 999.999, ppm=1.0) + assert len(results) == 5 + assert all(r["intensity"] == 0.0 for r in results) + + def test_result_keys(self): + from xic_extractor import create_synthetic_mzml, extract_xic + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path) + results = extract_xic(mzml_path, 524.265, ppm=10.0) + for r in results: + assert "rt" in r + assert "intensity" in r + assert "mz" in r + + def test_write_tsv(self): + from xic_extractor import create_synthetic_mzml, extract_xic, write_tsv + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "test.mzML") + create_synthetic_mzml(mzml_path) + results = extract_xic(mzml_path, 524.265, ppm=10.0) + out = os.path.join(tmpdir, "xic.tsv") + write_tsv(results, out) + assert os.path.exists(out) + with open(out) as f: + lines = f.readlines() + assert len(lines) > 1 + + def test_create_synthetic(self): + import pyopenms as oms + from xic_extractor import create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmpdir: + mzml_path = os.path.join(tmpdir, "synthetic.mzML") + create_synthetic_mzml(mzml_path, n_scans=5) + exp = oms.MSExperiment() + oms.MzMLFile().load(mzml_path, exp) + assert exp.getNrSpectra() == 5 diff --git a/scripts/proteomics/xic_extractor/xic_extractor.py b/scripts/proteomics/xic_extractor/xic_extractor.py new file mode 100644 index 0000000..e20a01c --- /dev/null +++ b/scripts/proteomics/xic_extractor/xic_extractor.py @@ -0,0 +1,156 @@ +""" +XIC Extractor +============= +Extract ion chromatograms (XIC) for target m/z values from mzML files. +Also computes TIC and BPC as part of the extraction. + +Features: +- Extract XIC for one or more target m/z values +- PPM-based mass tolerance +- TSV output with RT and intensity + +Usage +----- + python xic_extractor.py --input run.mzML --mz 524.265 --ppm 10 + python xic_extractor.py --input run.mzML --mz 524.265 --ppm 10 --output xic.tsv +""" + +import argparse +import csv +import sys + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def extract_xic( + input_path: str, + target_mz: float, + ppm: float = 10.0, + ms_level: int = 1, +) -> list[dict]: + """Extract ion chromatogram for a target m/z from mzML. + + Parameters + ---------- + input_path : str + Path to mzML file. + target_mz : float + Target m/z value. + ppm : float + Mass tolerance in ppm. + ms_level : int + MS level to extract from (default 1). + + Returns + ------- + list[dict] + List of dicts with keys: rt, intensity, mz. + """ + exp = oms.MSExperiment() + oms.MzMLFile().load(input_path, exp) + + tolerance_da = target_mz * ppm / 1e6 + mz_low = target_mz - tolerance_da + mz_high = target_mz + tolerance_da + + results = [] + for i in range(exp.getNrSpectra()): + spec = exp.getSpectrum(i) + if spec.getMSLevel() != ms_level: + continue + + rt = spec.getRT() + mzs, intensities = spec.get_peaks() + + max_intensity = 0.0 + best_mz = target_mz + for j in range(len(mzs)): + if mz_low <= mzs[j] <= mz_high: + if intensities[j] > max_intensity: + max_intensity = float(intensities[j]) + best_mz = float(mzs[j]) + + results.append({ + "rt": round(rt, 4), + "intensity": round(max_intensity, 2), + "mz": round(best_mz, 6), + }) + + return results + + +def create_synthetic_mzml(output_path: str, target_mz: float = 524.265, n_scans: int = 10) -> None: + """Create a synthetic mzML file with known peaks for testing. + + Parameters + ---------- + output_path : str + Path to write the synthetic mzML file. + target_mz : float + Target m/z to embed in spectra. + n_scans : int + Number of MS1 scans to generate. + """ + exp = oms.MSExperiment() + + for i in range(n_scans): + spec = oms.MSSpectrum() + spec.setMSLevel(1) + spec.setRT(float(i) * 10.0) + + # Gaussian-like intensity profile centered at scan n_scans//2 + center = n_scans // 2 + intensity = max(100.0, 10000.0 * max(0, 1 - abs(i - center) / center)) + + mzs = [target_mz - 50, target_mz, target_mz + 50] + ints = [500.0, intensity, 300.0] + spec.set_peaks((mzs, ints)) + exp.addSpectrum(spec) + + oms.MzMLFile().store(output_path, exp) + + +def write_tsv(results: list[dict], output_path: str) -> None: + """Write XIC results to TSV file. + + Parameters + ---------- + results : list[dict] + List of XIC data points. + output_path : str + Path to output TSV file. + """ + fieldnames = ["rt", "intensity", "mz"] + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Extract ion chromatograms for target m/z values from mzML." + ) + parser.add_argument("--input", required=True, help="Path to input mzML file") + parser.add_argument("--mz", type=float, required=True, help="Target m/z value") + parser.add_argument("--ppm", type=float, default=10.0, help="Mass tolerance in ppm (default: 10)") + parser.add_argument("--ms-level", type=int, default=1, help="MS level (default: 1)") + parser.add_argument("--output", default=None, help="Output TSV file path") + args = parser.parse_args() + + results = extract_xic(args.input, args.mz, args.ppm, args.ms_level) + + if args.output: + write_tsv(results, args.output) + print(f"Wrote {len(results)} XIC data points to {args.output}") + else: + print("rt\tintensity\tmz") + for r in results: + print(f"{r['rt']}\t{r['intensity']}\t{r['mz']}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/xl_distance_validator/README.md b/scripts/proteomics/xl_distance_validator/README.md new file mode 100644 index 0000000..f3b6ac4 --- /dev/null +++ b/scripts/proteomics/xl_distance_validator/README.md @@ -0,0 +1,18 @@ +# Crosslink Distance Validator + +Validate crosslinks against PDB structure distances by computing CA-CA distances. + +## Usage + +```bash +python xl_distance_validator.py --crosslinks links.tsv --pdb structure.pdb --max-distance 30 --output distances.tsv +``` + +## Input Format + +- `links.tsv`: columns `peptide1`, `peptide2`, `chain1`, `residue1`, `chain2`, `residue2` +- `structure.pdb`: Standard PDB format file + +## Output + +- `distances.tsv` - Crosslinks with computed distances and satisfaction flags diff --git a/scripts/proteomics/xl_distance_validator/requirements.txt b/scripts/proteomics/xl_distance_validator/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/xl_distance_validator/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/xl_distance_validator/tests/conftest.py b/scripts/proteomics/xl_distance_validator/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/xl_distance_validator/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/xl_distance_validator/tests/test_xl_distance_validator.py b/scripts/proteomics/xl_distance_validator/tests/test_xl_distance_validator.py new file mode 100644 index 0000000..e08a502 --- /dev/null +++ b/scripts/proteomics/xl_distance_validator/tests/test_xl_distance_validator.py @@ -0,0 +1,112 @@ +"""Tests for xl_distance_validator.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestXlDistanceValidator: + def _create_pdb(self, tmpdir): + """Create a minimal PDB file with CA atoms.""" + pdb_path = os.path.join(tmpdir, "structure.pdb") + # PDB ATOM format: columns are fixed-width + # ATOM serial name altLoc resName chainID resSeq x y z + lines = [ + "ATOM 1 CA ALA A 1 0.000 0.000 0.000 1.00 0.00 C ", + "ATOM 2 CA LYS A 5 10.000 0.000 0.000 1.00 0.00 C ", + "ATOM 3 CA ALA A 10 50.000 0.000 0.000 1.00 0.00 C ", + "ATOM 4 CA ALA B 1 5.000 0.000 0.000 1.00 0.00 C ", + "END", + ] + with open(pdb_path, "w") as f: + f.write("\n".join(lines) + "\n") + return pdb_path + + def test_parse_pdb_ca_atoms(self): + from xl_distance_validator import parse_pdb_ca_atoms + with tempfile.TemporaryDirectory() as tmpdir: + pdb_path = self._create_pdb(tmpdir) + ca_atoms = parse_pdb_ca_atoms(pdb_path) + assert ("A", 1) in ca_atoms + assert ("A", 5) in ca_atoms + assert ("B", 1) in ca_atoms + assert ca_atoms[("A", 1)] == (0.0, 0.0, 0.0) + + def test_euclidean_distance(self): + from xl_distance_validator import euclidean_distance + d = euclidean_distance((0, 0, 0), (3, 4, 0)) + assert abs(d - 5.0) < 1e-6 + + def test_euclidean_distance_3d(self): + from xl_distance_validator import euclidean_distance + d = euclidean_distance((1, 2, 3), (4, 6, 3)) + assert abs(d - 5.0) < 1e-6 + + def test_validate_sequence(self): + from xl_distance_validator import validate_sequence + assert validate_sequence("PEPTIDEK") is True + + def test_validate_crosslink_satisfied(self): + from xl_distance_validator import validate_crosslink + ca_atoms = {("A", 1): (0.0, 0.0, 0.0), ("A", 5): (10.0, 0.0, 0.0)} + result = validate_crosslink("A", 1, "A", 5, ca_atoms, max_distance=30.0) + assert result["satisfied"] == "YES" + assert abs(result["distance"] - 10.0) < 0.1 + + def test_validate_crosslink_violated(self): + from xl_distance_validator import validate_crosslink + ca_atoms = {("A", 1): (0.0, 0.0, 0.0), ("A", 10): (50.0, 0.0, 0.0)} + result = validate_crosslink("A", 1, "A", 10, ca_atoms, max_distance=30.0) + assert result["satisfied"] == "NO" + + def test_validate_crosslink_missing_residue(self): + from xl_distance_validator import validate_crosslink + ca_atoms = {("A", 1): (0.0, 0.0, 0.0)} + result = validate_crosslink("A", 1, "A", 99, ca_atoms, max_distance=30.0) + assert result["satisfied"] == "UNKNOWN" + + def test_validate_crosslinks_batch(self): + from xl_distance_validator import validate_crosslinks + ca_atoms = { + ("A", 1): (0.0, 0.0, 0.0), + ("A", 5): (10.0, 0.0, 0.0), + ("A", 10): (50.0, 0.0, 0.0), + } + crosslinks = [ + {"peptide1": "AALK", "peptide2": "KDEF", "chain1": "A", "residue1": "1", + "chain2": "A", "residue2": "5"}, + {"peptide1": "AALK", "peptide2": "GHIK", "chain1": "A", "residue1": "1", + "chain2": "A", "residue2": "10"}, + ] + results = validate_crosslinks(crosslinks, ca_atoms, max_distance=30.0) + assert len(results) == 2 + assert results[0]["satisfied"] == "YES" + assert results[1]["satisfied"] == "NO" + + def test_write_output(self): + from xl_distance_validator import write_output + with tempfile.TemporaryDirectory() as tmpdir: + output_path = os.path.join(tmpdir, "distances.tsv") + results = [{"chain1": "A", "residue1": 1, "distance": 10.0, "satisfied": "YES"}] + write_output(output_path, results) + assert os.path.exists(output_path) + + def test_full_pipeline(self): + from xl_distance_validator import parse_pdb_ca_atoms, read_crosslinks, validate_crosslinks, write_output + + with tempfile.TemporaryDirectory() as tmpdir: + pdb_path = self._create_pdb(tmpdir) + xl_path = os.path.join(tmpdir, "links.tsv") + with open(xl_path, "w") as f: + f.write("peptide1\tpeptide2\tchain1\tresidue1\tchain2\tresidue2\n") + f.write("AALK\tKDEF\tA\t1\tA\t5\n") + + ca_atoms = parse_pdb_ca_atoms(pdb_path) + crosslinks = read_crosslinks(xl_path) + results = validate_crosslinks(crosslinks, ca_atoms, 30.0) + output_path = os.path.join(tmpdir, "out.tsv") + write_output(output_path, results) + assert os.path.exists(output_path) + assert results[0]["satisfied"] == "YES" diff --git a/scripts/proteomics/xl_distance_validator/xl_distance_validator.py b/scripts/proteomics/xl_distance_validator/xl_distance_validator.py new file mode 100644 index 0000000..074237e --- /dev/null +++ b/scripts/proteomics/xl_distance_validator/xl_distance_validator.py @@ -0,0 +1,249 @@ +""" +Crosslink Distance Validator +============================= +Validate crosslinks against PDB structure distances. + +Parses PDB files manually (ATOM lines for CA atoms) to extract residue coordinates, +then computes Euclidean distances between crosslinked residue pairs. + +Usage +----- + python xl_distance_validator.py --crosslinks links.tsv --pdb structure.pdb --max-distance 30 --output distances.tsv +""" + +import argparse +import csv +import math +import sys +from typing import Dict, List, Optional, Tuple + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def parse_pdb_ca_atoms(pdb_path: str) -> Dict[Tuple[str, int], Tuple[float, float, float]]: + """Parse a PDB file and extract CA (alpha carbon) atom coordinates. + + Parameters + ---------- + pdb_path: + Path to the PDB file. + + Returns + ------- + dict + Mapping of (chain_id, residue_number) to (x, y, z) coordinates. + """ + ca_atoms: Dict[Tuple[str, int], Tuple[float, float, float]] = {} + + with open(pdb_path, "r") as f: + for line in f: + if not (line.startswith("ATOM") or line.startswith("HETATM")): + continue + atom_name = line[12:16].strip() + if atom_name != "CA": + continue + + chain_id = line[21].strip() + try: + res_num = int(line[22:26].strip()) + x = float(line[30:38].strip()) + y = float(line[38:46].strip()) + z = float(line[46:54].strip()) + except (ValueError, IndexError): + continue + + ca_atoms[(chain_id, res_num)] = (x, y, z) + + return ca_atoms + + +def euclidean_distance( + p1: Tuple[float, float, float], + p2: Tuple[float, float, float], +) -> float: + """Compute Euclidean distance between two 3D points. + + Parameters + ---------- + p1: + First point (x, y, z). + p2: + Second point (x, y, z). + + Returns + ------- + float + Euclidean distance in Angstroms. + """ + return math.sqrt( + (p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2 + (p1[2] - p2[2]) ** 2 + ) + + +def validate_sequence(sequence: str) -> bool: + """Validate a peptide sequence using AASequence. + + Parameters + ---------- + sequence: + Peptide sequence string. + + Returns + ------- + bool + True if the sequence is parseable. + """ + try: + oms.AASequence.fromString(sequence) + return True + except Exception: + return False + + +def validate_crosslink( + chain1: str, + res1: int, + chain2: str, + res2: int, + ca_atoms: Dict[Tuple[str, int], Tuple[float, float, float]], + max_distance: float = 30.0, +) -> Optional[Dict[str, object]]: + """Validate a single crosslink against PDB structure. + + Parameters + ---------- + chain1: + Chain ID of first residue. + res1: + Residue number of first crosslinked site. + chain2: + Chain ID of second residue. + res2: + Residue number of second crosslinked site. + ca_atoms: + CA atom coordinate dictionary from parse_pdb_ca_atoms. + max_distance: + Maximum allowed CA-CA distance in Angstroms. + + Returns + ------- + dict or None + Validation result dict, or None if residues not found in structure. + """ + key1 = (chain1, res1) + key2 = (chain2, res2) + + if key1 not in ca_atoms or key2 not in ca_atoms: + return { + "chain1": chain1, "residue1": res1, + "chain2": chain2, "residue2": res2, + "distance": None, + "satisfied": "UNKNOWN", + "note": "Residue(s) not found in PDB", + } + + dist = euclidean_distance(ca_atoms[key1], ca_atoms[key2]) + satisfied = "YES" if dist <= max_distance else "NO" + + return { + "chain1": chain1, "residue1": res1, + "chain2": chain2, "residue2": res2, + "distance": round(dist, 2), + "satisfied": satisfied, + "note": "", + } + + +def validate_crosslinks( + crosslinks: List[Dict[str, str]], + ca_atoms: Dict[Tuple[str, int], Tuple[float, float, float]], + max_distance: float = 30.0, +) -> List[Dict[str, object]]: + """Validate a list of crosslinks against a PDB structure. + + Parameters + ---------- + crosslinks: + List of dicts with keys: peptide1, peptide2, chain1, residue1, chain2, residue2. + ca_atoms: + CA atom coordinates. + max_distance: + Maximum distance threshold. + + Returns + ------- + list + List of validation result dicts. + """ + results = [] + for xl in crosslinks: + chain1 = xl.get("chain1", "A") + res1 = int(xl["residue1"]) + chain2 = xl.get("chain2", "A") + res2 = int(xl["residue2"]) + + result = validate_crosslink(chain1, res1, chain2, res2, ca_atoms, max_distance) + if result is not None: + # Add peptide info if available + result["peptide1"] = xl.get("peptide1", "") + result["peptide2"] = xl.get("peptide2", "") + result["valid_peptide1"] = str(validate_sequence(xl.get("peptide1", ""))) if xl.get("peptide1") else "" + result["valid_peptide2"] = str(validate_sequence(xl.get("peptide2", ""))) if xl.get("peptide2") else "" + results.append(result) + + return results + + +def read_crosslinks(crosslinks_path: str) -> List[Dict[str, str]]: + """Read crosslinks TSV file.""" + rows = [] + with open(crosslinks_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output(output_path: str, results: List[Dict[str, object]]) -> None: + """Write validation results to TSV.""" + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Validate crosslinks against PDB structure distances." + ) + parser.add_argument("--crosslinks", required=True, help="Crosslinks TSV file") + parser.add_argument("--pdb", required=True, help="PDB structure file") + parser.add_argument( + "--max-distance", type=float, default=30.0, + help="Maximum allowed CA-CA distance in Angstroms (default: 30)" + ) + parser.add_argument("--output", required=True, help="Output distances TSV file") + args = parser.parse_args() + + ca_atoms = parse_pdb_ca_atoms(args.pdb) + crosslinks = read_crosslinks(args.crosslinks) + results = validate_crosslinks(crosslinks, ca_atoms, args.max_distance) + write_output(args.output, results) + + n_satisfied = sum(1 for r in results if r["satisfied"] == "YES") + n_violated = sum(1 for r in results if r["satisfied"] == "NO") + n_unknown = sum(1 for r in results if r["satisfied"] == "UNKNOWN") + print(f"Total crosslinks: {len(results)}") + print(f" Satisfied (dist <= {args.max_distance} A): {n_satisfied}") + print(f" Violated: {n_violated}") + print(f" Unknown: {n_unknown}") + + +if __name__ == "__main__": + main() diff --git a/scripts/proteomics/xl_link_classifier/README.md b/scripts/proteomics/xl_link_classifier/README.md new file mode 100644 index 0000000..45a3bfb --- /dev/null +++ b/scripts/proteomics/xl_link_classifier/README.md @@ -0,0 +1,18 @@ +# Crosslink Classifier + +Classify crosslinks as intra-protein, inter-protein, or monolink. + +## Usage + +```bash +python xl_link_classifier.py --crosslinks links.tsv --fasta proteome.fasta --output classified.tsv +``` + +## Input Format + +- `links.tsv`: columns `peptide1`, `peptide2` (empty peptide2 for monolinks) +- `proteome.fasta`: FASTA file with protein sequences + +## Output + +- `classified.tsv` - Crosslinks with `link_type`, `proteins1`, `proteins2`, `shared_proteins` diff --git a/scripts/proteomics/xl_link_classifier/requirements.txt b/scripts/proteomics/xl_link_classifier/requirements.txt new file mode 100644 index 0000000..7ce28ec --- /dev/null +++ b/scripts/proteomics/xl_link_classifier/requirements.txt @@ -0,0 +1 @@ +pyopenms diff --git a/scripts/proteomics/xl_link_classifier/tests/conftest.py b/scripts/proteomics/xl_link_classifier/tests/conftest.py new file mode 100644 index 0000000..1a21ede --- /dev/null +++ b/scripts/proteomics/xl_link_classifier/tests/conftest.py @@ -0,0 +1,15 @@ +import os +import sys + +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +try: + import pyopenms # noqa: F401 + + HAS_PYOPENMS = True +except ImportError: + HAS_PYOPENMS = False + +requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/xl_link_classifier/tests/test_xl_link_classifier.py b/scripts/proteomics/xl_link_classifier/tests/test_xl_link_classifier.py new file mode 100644 index 0000000..d37ba24 --- /dev/null +++ b/scripts/proteomics/xl_link_classifier/tests/test_xl_link_classifier.py @@ -0,0 +1,123 @@ +"""Tests for xl_link_classifier.""" + +import os +import tempfile + +from conftest import requires_pyopenms + + +@requires_pyopenms +class TestXlLinkClassifier: + def _create_fasta(self, tmpdir, proteins): + """Helper to create a FASTA file.""" + import pyopenms as oms + + fasta_path = os.path.join(tmpdir, "proteome.fasta") + entries = [] + for acc, seq in proteins.items(): + entry = oms.FASTAEntry() + entry.identifier = acc + entry.sequence = seq + entries.append(entry) + fasta_file = oms.FASTAFile() + fasta_file.store(fasta_path, entries) + return fasta_path + + def test_load_fasta(self): + from xl_link_classifier import load_fasta + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, {"P1": "ACDEFGHIKLMNPQRSTVWY"}) + proteins = load_fasta(fasta_path) + assert "P1" in proteins + + def test_strip_modifications(self): + from xl_link_classifier import strip_modifications + assert strip_modifications("PEPTM[147]IDEK") == "PEPTMIDEK" + assert strip_modifications("PEPT(Oxidation)IDEK") == "PEPTIDEK" + + def test_find_peptide_proteins(self): + from xl_link_classifier import find_peptide_proteins + proteins = {"P1": "ACDEFGHIKLMNPQRSTVWY", "P2": "XXACDEFYYYYY"} + found = find_peptide_proteins("ACDEF", proteins) + assert "P1" in found + assert "P2" in found + + def test_find_peptide_not_found(self): + from xl_link_classifier import find_peptide_proteins + proteins = {"P1": "ACDEFGHIK"} + found = find_peptide_proteins("ZZZZZ", proteins) + assert len(found) == 0 + + def test_classify_monolink(self): + from xl_link_classifier import classify_crosslink + proteins = {"P1": "ACDEFGHIKLMNPQRSTVWY"} + result = classify_crosslink("ACDEF", "", proteins) + assert result["link_type"] == "monolink" + + def test_classify_monolink_dash(self): + from xl_link_classifier import classify_crosslink + proteins = {"P1": "ACDEFGHIKLMNPQRSTVWY"} + result = classify_crosslink("ACDEF", "-", proteins) + assert result["link_type"] == "monolink" + + def test_classify_intra_protein(self): + from xl_link_classifier import classify_crosslink + proteins = {"P1": "ACDEFGHIKLMNPQRSTVWY"} + result = classify_crosslink("ACDEF", "GHIKLM", proteins) + assert result["link_type"] == "intra-protein" + + def test_classify_inter_protein(self): + from xl_link_classifier import classify_crosslink + proteins = {"P1": "ACDEFGHIK", "P2": "LMNPQRSTV"} + result = classify_crosslink("ACDEF", "LMNPQ", proteins) + assert result["link_type"] == "inter-protein" + + def test_classify_unknown_unmapped(self): + from xl_link_classifier import classify_crosslink + proteins = {"P1": "ACDEFGHIK"} + result = classify_crosslink("ZZZZZ", "YYYYY", proteins) + assert result["link_type"] == "unknown" + + def test_classify_crosslinks_batch(self): + from xl_link_classifier import classify_crosslinks + proteins = {"P1": "ACDEFGHIKLMNPQRSTVWY", "P2": "XXXXXXYYYYYY"} + crosslinks = [ + {"peptide1": "ACDEF", "peptide2": "GHIKLM"}, + {"peptide1": "ACDEF", "peptide2": "XXXXXX"}, + {"peptide1": "ACDEF", "peptide2": ""}, + ] + results = classify_crosslinks(crosslinks, proteins) + assert results[0]["link_type"] == "intra-protein" + assert results[1]["link_type"] == "inter-protein" + assert results[2]["link_type"] == "monolink" + + def test_compute_summary(self): + from xl_link_classifier import compute_summary + results = [ + {"link_type": "intra-protein"}, + {"link_type": "intra-protein"}, + {"link_type": "inter-protein"}, + {"link_type": "monolink"}, + ] + summary = compute_summary(results) + assert summary["intra-protein"] == 2 + assert summary["inter-protein"] == 1 + assert summary["monolink"] == 1 + + def test_full_pipeline(self): + from xl_link_classifier import classify_crosslinks, load_fasta, write_output + + with tempfile.TemporaryDirectory() as tmpdir: + fasta_path = self._create_fasta(tmpdir, { + "P1": "ACDEFGHIKLMNPQRSTVWY", + "P2": "XXXXXXYYYYYY", + }) + proteins = load_fasta(fasta_path) + crosslinks = [ + {"peptide1": "ACDEF", "peptide2": "GHIKLM"}, + {"peptide1": "ACDEF", "peptide2": "XXXXXX"}, + ] + results = classify_crosslinks(crosslinks, proteins) + output_path = os.path.join(tmpdir, "classified.tsv") + write_output(output_path, results) + assert os.path.exists(output_path) diff --git a/scripts/proteomics/xl_link_classifier/xl_link_classifier.py b/scripts/proteomics/xl_link_classifier/xl_link_classifier.py new file mode 100644 index 0000000..81ca741 --- /dev/null +++ b/scripts/proteomics/xl_link_classifier/xl_link_classifier.py @@ -0,0 +1,245 @@ +""" +Crosslink Classifier +==================== +Classify crosslinks as intra-protein, inter-protein, or monolink based on +peptide-to-protein mappings from a FASTA database. + +Usage +----- + python xl_link_classifier.py --crosslinks links.tsv --fasta proteome.fasta --output classified.tsv +""" + +import argparse +import csv +import sys +from typing import Dict, List, Set + +try: + import pyopenms as oms +except ImportError: + sys.exit("pyopenms is required. Install it with: pip install pyopenms") + + +def load_fasta(fasta_path: str) -> Dict[str, str]: + """Load a FASTA file into a dictionary mapping accession to sequence. + + Parameters + ---------- + fasta_path: + Path to the FASTA file. + + Returns + ------- + dict + Mapping of protein accession to amino acid sequence. + """ + entries = [] + fasta_file = oms.FASTAFile() + fasta_file.load(fasta_path, entries) + + proteins = {} + for entry in entries: + acc = entry.identifier.split()[0] if entry.identifier else "" + proteins[acc] = entry.sequence + return proteins + + +def find_peptide_proteins(peptide: str, proteins: Dict[str, str]) -> Set[str]: + """Find all proteins that contain a given peptide sequence. + + Parameters + ---------- + peptide: + Peptide amino acid sequence (unmodified). + proteins: + Protein accession to sequence mapping. + + Returns + ------- + set + Set of protein accessions containing the peptide. + """ + matching = set() + # Strip modifications for matching + clean = strip_modifications(peptide) + for acc, seq in proteins.items(): + if clean in seq: + matching.add(acc) + return matching + + +def strip_modifications(sequence: str) -> str: + """Remove modification annotations from a peptide sequence. + + Parameters + ---------- + sequence: + Peptide sequence possibly containing bracket/parenthesis modifications. + + Returns + ------- + str + Clean amino acid sequence. + """ + import re + clean = re.sub(r"\[.*?\]", "", sequence) + clean = re.sub(r"\(.*?\)", "", clean) + return clean + + +def classify_crosslink( + peptide1: str, + peptide2: str, + proteins: Dict[str, str], +) -> Dict[str, object]: + """Classify a crosslink as intra-protein, inter-protein, or monolink. + + Parameters + ---------- + peptide1: + First peptide sequence. + peptide2: + Second peptide sequence (empty string for monolinks). + proteins: + Protein accession to sequence mapping. + + Returns + ------- + dict + Classification result with link_type, proteins1, proteins2. + """ + # Monolink: second peptide is empty or missing + if not peptide2 or peptide2.strip() == "" or peptide2.strip() == "-": + prots1 = find_peptide_proteins(peptide1, proteins) + return { + "peptide1": peptide1, + "peptide2": peptide2, + "link_type": "monolink", + "proteins1": ";".join(sorted(prots1)) if prots1 else "UNMAPPED", + "proteins2": "", + "shared_proteins": "", + } + + prots1 = find_peptide_proteins(peptide1, proteins) + prots2 = find_peptide_proteins(peptide2, proteins) + + shared = prots1 & prots2 + + if not prots1 or not prots2: + link_type = "unknown" + elif shared: + # Both peptides map to at least one common protein + if prots1 == prots2 and len(prots1) == 1: + link_type = "intra-protein" + elif shared: + # Could be intra on shared protein(s), but also maps to others + link_type = "intra-protein" + else: + link_type = "inter-protein" + else: + link_type = "inter-protein" + + return { + "peptide1": peptide1, + "peptide2": peptide2, + "link_type": link_type, + "proteins1": ";".join(sorted(prots1)) if prots1 else "UNMAPPED", + "proteins2": ";".join(sorted(prots2)) if prots2 else "UNMAPPED", + "shared_proteins": ";".join(sorted(shared)) if shared else "", + } + + +def classify_crosslinks( + crosslinks: List[Dict[str, str]], + proteins: Dict[str, str], +) -> List[Dict[str, object]]: + """Classify a batch of crosslinks. + + Parameters + ---------- + crosslinks: + List of dicts with keys: peptide1, peptide2. + proteins: + Protein accession to sequence mapping. + + Returns + ------- + list + List of classification result dicts. + """ + results = [] + for xl in crosslinks: + pep1 = xl.get("peptide1", "") + pep2 = xl.get("peptide2", "") + result = classify_crosslink(pep1, pep2, proteins) + # Preserve extra columns from input + for key, val in xl.items(): + if key not in result: + result[key] = val + results.append(result) + return results + + +def compute_summary(results: List[Dict[str, object]]) -> Dict[str, int]: + """Compute summary counts by link type. + + Parameters + ---------- + results: + List of classification result dicts. + + Returns + ------- + dict + Counts per link type. + """ + summary: Dict[str, int] = {} + for r in results: + lt = str(r["link_type"]) + summary[lt] = summary.get(lt, 0) + 1 + return summary + + +def read_crosslinks(crosslinks_path: str) -> List[Dict[str, str]]: + """Read crosslinks TSV file.""" + rows = [] + with open(crosslinks_path, "r") as f: + reader = csv.DictReader(f, delimiter="\t") + for row in reader: + rows.append(row) + return rows + + +def write_output(output_path: str, results: List[Dict[str, object]]) -> None: + """Write classified crosslinks to TSV.""" + if not results: + return + fieldnames = list(results[0].keys()) + with open(output_path, "w", newline="") as f: + writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t") + writer.writeheader() + writer.writerows(results) + + +def main(): + parser = argparse.ArgumentParser( + description="Classify crosslinks as intra/inter-protein or monolink." + ) + parser.add_argument("--crosslinks", required=True, help="Crosslinks TSV file") + parser.add_argument("--fasta", required=True, help="Proteome FASTA file") + parser.add_argument("--output", required=True, help="Output classified TSV file") + args = parser.parse_args() + + proteins = load_fasta(args.fasta) + crosslinks = read_crosslinks(args.crosslinks) + results = classify_crosslinks(crosslinks, proteins) + write_output(args.output, results) + + summary = compute_summary(results) + print(f"Total crosslinks: {len(results)}") + for lt, count in sorted(summary.items()): + print(f" {lt}: {count}") + + +if __name__ == "__main__": + main() From b3ad1ccae2529f7bccb133a6cd89bcfef4e9b735 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 07:29:57 +0100 Subject: [PATCH 03/15] Reorganize tools by topic and remove 20 non-pyopenms scripts Reorganize 123 tools into topic sub-directories within proteomics/ and metabolomics/: Proteomics (89 tools across 12 topics): spectrum_analysis (7), peptide_analysis (12), protein_analysis (5), fasta_utils (8), file_conversion (8), quality_control (15), targeted_proteomics (7), identification (7), ptm_analysis (5), structural_proteomics (5), specialized (7), rna (3) Metabolomics (34 tools across 8 topics): formula_tools (8), feature_processing (7), spectral_analysis (6), compound_annotation (4), drug_metabolism (2), isotope_labeling (2), lipidomics (2), export (3) Removed 20 tools that had zero pyopenms usage (pure stats/math or format parsers that don't need mass spectrometry libraries): volcano_plot_data_generator, sample_correlation_calculator, biomarker_panel_roc, maxquant_result_converter, diann_result_converter, fragpipe_result_converter, missing_value_imputation, etc. Updated AGENTS.md and CLAUDE.md to reflect new 3-level directory structure: scripts//// Co-Authored-By: Claude Opus 4.6 (1M context) --- .gitignore | 4 + AGENTS.md | 11 +- CLAUDE.md | 20 +- 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targeted_proteomics}/inclusion_list_generator/inclusion_list_generator.py (100%) rename scripts/proteomics/{spectral_counting_quantifier => targeted_proteomics/inclusion_list_generator}/requirements.txt (100%) rename scripts/proteomics/{rna_mass_calculator => targeted_proteomics/inclusion_list_generator}/tests/conftest.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/inclusion_list_generator/tests/test_inclusion_list_generator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/irt_calculator/README.md (100%) rename scripts/proteomics/{ => targeted_proteomics}/irt_calculator/irt_calculator.py (100%) rename scripts/proteomics/{spectral_library_builder => targeted_proteomics/irt_calculator}/requirements.txt (100%) rename scripts/proteomics/{rt_prediction_additive => targeted_proteomics/irt_calculator}/tests/conftest.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/irt_calculator/tests/test_irt_calculator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/library_coverage_estimator/README.md (100%) rename scripts/proteomics/{ => targeted_proteomics}/library_coverage_estimator/library_coverage_estimator.py (100%) rename scripts/proteomics/{spectral_library_format_converter => targeted_proteomics/library_coverage_estimator}/requirements.txt (100%) rename scripts/proteomics/{run_comparison_reporter => targeted_proteomics/library_coverage_estimator}/tests/conftest.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/library_coverage_estimator/tests/test_library_coverage_estimator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/tic_bpc_calculator/README.md (100%) rename scripts/proteomics/{spectrum_annotator => targeted_proteomics/tic_bpc_calculator}/requirements.txt (100%) rename scripts/proteomics/{sample_complexity_estimator => targeted_proteomics/tic_bpc_calculator}/tests/conftest.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/tic_bpc_calculator/tests/test_tic_bpc_calculator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/tic_bpc_calculator/tic_bpc_calculator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/transition_list_generator/README.md (100%) rename scripts/proteomics/{spectrum_file_info => targeted_proteomics/transition_list_generator}/requirements.txt (100%) rename scripts/proteomics/{sample_correlation_calculator => targeted_proteomics/transition_list_generator}/tests/conftest.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/transition_list_generator/tests/test_transition_list_generator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/transition_list_generator/transition_list_generator.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/xic_extractor/README.md (100%) rename scripts/proteomics/{spectrum_scoring_hyperscore => targeted_proteomics/xic_extractor}/requirements.txt (100%) rename scripts/proteomics/{scp_reporter_qc => targeted_proteomics/xic_extractor}/tests/conftest.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/xic_extractor/tests/test_xic_extractor.py (100%) rename scripts/proteomics/{ => targeted_proteomics}/xic_extractor/xic_extractor.py (100%) delete mode 100644 scripts/proteomics/theoretical_spectrum_generator/requirements.txt delete mode 100644 scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py delete mode 100644 scripts/proteomics/tic_bpc_calculator/requirements.txt delete mode 100644 scripts/proteomics/tic_bpc_calculator/tests/conftest.py delete mode 100644 scripts/proteomics/topdown_coverage_calculator/requirements.txt delete mode 100644 scripts/proteomics/topdown_coverage_calculator/tests/conftest.py delete mode 100644 scripts/proteomics/transition_list_generator/requirements.txt delete mode 100644 scripts/proteomics/transition_list_generator/tests/conftest.py delete mode 100644 scripts/proteomics/volcano_plot_data_generator/README.md delete mode 100644 scripts/proteomics/volcano_plot_data_generator/requirements.txt delete mode 100644 scripts/proteomics/volcano_plot_data_generator/tests/conftest.py delete mode 100644 scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py delete mode 100644 scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py delete mode 100644 scripts/proteomics/xic_extractor/requirements.txt delete mode 100644 scripts/proteomics/xic_extractor/tests/conftest.py delete mode 100644 scripts/proteomics/xl_distance_validator/requirements.txt delete mode 100644 scripts/proteomics/xl_distance_validator/tests/conftest.py delete mode 100644 scripts/proteomics/xl_link_classifier/requirements.txt delete mode 100644 scripts/proteomics/xl_link_classifier/tests/conftest.py diff --git a/.gitignore b/.gitignore index b7faf40..9604f3d 100644 --- a/.gitignore +++ b/.gitignore @@ -205,3 +205,7 @@ cython_debug/ marimo/_static/ marimo/_lsp/ __marimo__/ + + +#Ignore vscode AI rules +.github/instructions/codacy.instructions.md diff --git a/AGENTS.md b/AGENTS.md index a8c849d..ec5d9c8 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -8,10 +8,10 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ## Contribution Requirements -Every script must be a **self-contained directory** under `scripts///`: +Every script must be a **self-contained directory** under `scripts////`: ``` -scripts/// +scripts//// ├── .py # The tool itself ├── requirements.txt # pyopenms + any script-specific deps (no version pins) ├── README.md # Brief description + CLI usage examples @@ -20,9 +20,16 @@ scripts/// └── test_.py ``` +### Topics + +**Proteomics topics:** `spectrum_analysis/`, `peptide_analysis/`, `protein_analysis/`, `fasta_utils/`, `file_conversion/`, `quality_control/`, `targeted_proteomics/`, `identification/`, `ptm_analysis/`, `structural_proteomics/`, `specialized/`, `rna/` + +**Metabolomics topics:** `formula_tools/`, `feature_processing/`, `spectral_analysis/`, `compound_annotation/`, `drug_metabolism/`, `isotope_labeling/`, `lipidomics/`, `export/` + ### Rules - `` is `proteomics` or `metabolomics` +- `` is one of the topic directories listed above - `requirements.txt` always includes `pyopenms` with no version pin — builds against latest - No cross-script imports — each script is fully independent - No `__init__.py` files — these are NOT Python packages diff --git a/CLAUDE.md b/CLAUDE.md index d889959..ef8a180 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -10,33 +10,33 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ```bash # Install dependencies for a specific script -pip install -r scripts/proteomics/peptide_mass_calculator/requirements.txt +pip install -r scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt # Lint a specific script -ruff check scripts/proteomics/peptide_mass_calculator/ +ruff check scripts/proteomics/peptide_analysis/peptide_mass_calculator/ # Run tests for a specific script -PYTHONPATH=scripts/proteomics/peptide_mass_calculator python -m pytest scripts/proteomics/peptide_mass_calculator/tests/ -v +PYTHONPATH=scripts/proteomics/peptide_analysis/peptide_mass_calculator python -m pytest scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/ -v # Lint all scripts ruff check scripts/ # Run all tests across all scripts -for d in scripts/*/*/; do PYTHONPATH="$d" python -m pytest "$d/tests/" -v; done +for d in scripts/*/*/*/; do PYTHONPATH="$d" python -m pytest "$d/tests/" -v; done # Run a script directly -python scripts/proteomics/peptide_mass_calculator/peptide_mass_calculator.py --sequence PEPTIDEK --charge 2 -python scripts/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py --formula C6H12O6 +python scripts/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py --sequence PEPTIDEK --charge 2 +python scripts/metabolomics/formula_tools/isotope_pattern_matcher/isotope_pattern_matcher.py --formula C6H12O6 ``` ## Architecture ### Per-Script Directory Structure -Each script is a self-contained directory under `scripts///`: +Each script is a self-contained directory under `scripts////`: ``` -scripts/// +scripts//// ├── .py # The tool (importable functions + argparse CLI) ├── requirements.txt # pyopenms + script-specific deps ├── README.md # Usage examples @@ -47,6 +47,10 @@ scripts/// Domains: `proteomics/`, `metabolomics/` +Proteomics topics: `spectrum_analysis/`, `peptide_analysis/`, `protein_analysis/`, `fasta_utils/`, `file_conversion/`, `quality_control/`, `targeted_proteomics/`, `identification/`, `ptm_analysis/`, `structural_proteomics/`, `specialized/`, `rna/` + +Metabolomics topics: `formula_tools/`, `feature_processing/`, `spectral_analysis/`, `compound_annotation/`, `drug_metabolism/`, `isotope_labeling/`, `lipidomics/`, `export/` + ### Key Patterns - pyopenms import wrapped in try/except with user-friendly error message diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/README.md b/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/README.md similarity index 100% rename from scripts/metabolomics/kendrick_mass_defect_analyzer/README.md rename to scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/README.md diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py b/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py similarity index 100% rename from scripts/metabolomics/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py rename to scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py diff --git a/scripts/metabolomics/adduct_calculator/requirements.txt b/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt similarity index 100% rename from scripts/metabolomics/adduct_calculator/requirements.txt rename to scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt diff --git a/scripts/metabolomics/adduct_calculator/tests/conftest.py b/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/tests/conftest.py similarity index 100% rename from scripts/metabolomics/adduct_calculator/tests/conftest.py rename to scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/tests/conftest.py diff --git a/scripts/metabolomics/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py b/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py similarity index 100% rename from scripts/metabolomics/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py rename to scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/tests/test_kendrick_mass_defect_analyzer.py diff --git a/scripts/metabolomics/metabolite_class_predictor/README.md b/scripts/metabolomics/compound_annotation/metabolite_class_predictor/README.md similarity index 100% rename from scripts/metabolomics/metabolite_class_predictor/README.md rename to scripts/metabolomics/compound_annotation/metabolite_class_predictor/README.md diff --git a/scripts/metabolomics/metabolite_class_predictor/metabolite_class_predictor.py b/scripts/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py similarity index 100% rename from scripts/metabolomics/metabolite_class_predictor/metabolite_class_predictor.py rename to scripts/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py diff --git a/scripts/metabolomics/adduct_group_analyzer/requirements.txt b/scripts/metabolomics/compound_annotation/metabolite_class_predictor/requirements.txt similarity index 100% rename from scripts/metabolomics/adduct_group_analyzer/requirements.txt rename to scripts/metabolomics/compound_annotation/metabolite_class_predictor/requirements.txt diff --git a/scripts/metabolomics/adduct_group_analyzer/tests/conftest.py b/scripts/metabolomics/compound_annotation/metabolite_class_predictor/tests/conftest.py similarity index 100% rename from scripts/metabolomics/adduct_group_analyzer/tests/conftest.py rename to scripts/metabolomics/compound_annotation/metabolite_class_predictor/tests/conftest.py diff --git a/scripts/metabolomics/metabolite_class_predictor/tests/test_metabolite_class_predictor.py b/scripts/metabolomics/compound_annotation/metabolite_class_predictor/tests/test_metabolite_class_predictor.py similarity index 100% rename from scripts/metabolomics/metabolite_class_predictor/tests/test_metabolite_class_predictor.py rename to scripts/metabolomics/compound_annotation/metabolite_class_predictor/tests/test_metabolite_class_predictor.py diff --git a/scripts/metabolomics/suspect_screener/README.md b/scripts/metabolomics/compound_annotation/suspect_screener/README.md similarity index 100% rename from scripts/metabolomics/suspect_screener/README.md rename to scripts/metabolomics/compound_annotation/suspect_screener/README.md diff --git a/scripts/metabolomics/blank_subtraction_tool/requirements.txt b/scripts/metabolomics/compound_annotation/suspect_screener/requirements.txt similarity index 100% rename from scripts/metabolomics/blank_subtraction_tool/requirements.txt rename to scripts/metabolomics/compound_annotation/suspect_screener/requirements.txt diff --git a/scripts/metabolomics/suspect_screener/suspect_screener.py b/scripts/metabolomics/compound_annotation/suspect_screener/suspect_screener.py similarity index 100% rename from scripts/metabolomics/suspect_screener/suspect_screener.py rename to scripts/metabolomics/compound_annotation/suspect_screener/suspect_screener.py diff --git a/scripts/metabolomics/blank_subtraction_tool/tests/conftest.py b/scripts/metabolomics/compound_annotation/suspect_screener/tests/conftest.py similarity index 100% rename from scripts/metabolomics/blank_subtraction_tool/tests/conftest.py rename to scripts/metabolomics/compound_annotation/suspect_screener/tests/conftest.py diff --git a/scripts/metabolomics/suspect_screener/tests/test_suspect_screener.py b/scripts/metabolomics/compound_annotation/suspect_screener/tests/test_suspect_screener.py similarity index 100% rename from scripts/metabolomics/suspect_screener/tests/test_suspect_screener.py rename to scripts/metabolomics/compound_annotation/suspect_screener/tests/test_suspect_screener.py diff --git a/scripts/metabolomics/van_krevelen_data_generator/README.md b/scripts/metabolomics/compound_annotation/van_krevelen_data_generator/README.md similarity index 100% rename from scripts/metabolomics/van_krevelen_data_generator/README.md rename to scripts/metabolomics/compound_annotation/van_krevelen_data_generator/README.md diff --git a/scripts/metabolomics/drug_metabolite_screener/requirements.txt b/scripts/metabolomics/compound_annotation/van_krevelen_data_generator/requirements.txt similarity index 100% rename from scripts/metabolomics/drug_metabolite_screener/requirements.txt rename to scripts/metabolomics/compound_annotation/van_krevelen_data_generator/requirements.txt diff --git a/scripts/metabolomics/drug_metabolite_screener/tests/conftest.py b/scripts/metabolomics/compound_annotation/van_krevelen_data_generator/tests/conftest.py similarity index 100% rename from scripts/metabolomics/drug_metabolite_screener/tests/conftest.py rename to scripts/metabolomics/compound_annotation/van_krevelen_data_generator/tests/conftest.py diff --git a/scripts/metabolomics/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py b/scripts/metabolomics/compound_annotation/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py similarity index 100% rename from scripts/metabolomics/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py rename to scripts/metabolomics/compound_annotation/van_krevelen_data_generator/tests/test_van_krevelen_data_generator.py diff --git a/scripts/metabolomics/van_krevelen_data_generator/van_krevelen_data_generator.py b/scripts/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py similarity index 100% rename from scripts/metabolomics/van_krevelen_data_generator/van_krevelen_data_generator.py rename to scripts/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py diff --git a/scripts/metabolomics/drug_metabolite_screener/README.md b/scripts/metabolomics/drug_metabolism/drug_metabolite_screener/README.md similarity index 100% rename from scripts/metabolomics/drug_metabolite_screener/README.md rename to scripts/metabolomics/drug_metabolism/drug_metabolite_screener/README.md diff --git a/scripts/metabolomics/drug_metabolite_screener/drug_metabolite_screener.py b/scripts/metabolomics/drug_metabolism/drug_metabolite_screener/drug_metabolite_screener.py similarity index 100% rename from scripts/metabolomics/drug_metabolite_screener/drug_metabolite_screener.py rename to scripts/metabolomics/drug_metabolism/drug_metabolite_screener/drug_metabolite_screener.py diff --git a/scripts/metabolomics/duplicate_feature_detector/requirements.txt b/scripts/metabolomics/drug_metabolism/drug_metabolite_screener/requirements.txt similarity index 100% rename from scripts/metabolomics/duplicate_feature_detector/requirements.txt rename to scripts/metabolomics/drug_metabolism/drug_metabolite_screener/requirements.txt diff --git a/scripts/metabolomics/duplicate_feature_detector/tests/conftest.py b/scripts/metabolomics/drug_metabolism/drug_metabolite_screener/tests/conftest.py similarity index 100% rename from scripts/metabolomics/duplicate_feature_detector/tests/conftest.py rename to scripts/metabolomics/drug_metabolism/drug_metabolite_screener/tests/conftest.py diff --git a/scripts/metabolomics/drug_metabolite_screener/tests/test_drug_metabolite_screener.py b/scripts/metabolomics/drug_metabolism/drug_metabolite_screener/tests/test_drug_metabolite_screener.py similarity index 100% rename from scripts/metabolomics/drug_metabolite_screener/tests/test_drug_metabolite_screener.py rename to scripts/metabolomics/drug_metabolism/drug_metabolite_screener/tests/test_drug_metabolite_screener.py diff --git a/scripts/metabolomics/mass_difference_network_builder/mass_difference_network_builder.py b/scripts/metabolomics/drug_metabolism/mass_difference_network_builder/mass_difference_network_builder.py similarity index 100% rename from scripts/metabolomics/mass_difference_network_builder/mass_difference_network_builder.py rename to scripts/metabolomics/drug_metabolism/mass_difference_network_builder/mass_difference_network_builder.py diff --git a/scripts/metabolomics/formula_mass_calculator/requirements.txt b/scripts/metabolomics/drug_metabolism/mass_difference_network_builder/requirements.txt similarity index 100% rename from scripts/metabolomics/formula_mass_calculator/requirements.txt rename to scripts/metabolomics/drug_metabolism/mass_difference_network_builder/requirements.txt diff --git a/scripts/metabolomics/formula_mass_calculator/tests/conftest.py b/scripts/metabolomics/drug_metabolism/mass_difference_network_builder/tests/conftest.py similarity index 100% rename from scripts/metabolomics/formula_mass_calculator/tests/conftest.py rename to scripts/metabolomics/drug_metabolism/mass_difference_network_builder/tests/conftest.py diff --git a/scripts/metabolomics/mass_difference_network_builder/tests/test_mass_difference_network_builder.py b/scripts/metabolomics/drug_metabolism/mass_difference_network_builder/tests/test_mass_difference_network_builder.py similarity index 100% rename from scripts/metabolomics/mass_difference_network_builder/tests/test_mass_difference_network_builder.py rename to scripts/metabolomics/drug_metabolism/mass_difference_network_builder/tests/test_mass_difference_network_builder.py diff --git a/scripts/metabolomics/gnps_fbmn_exporter/README.md b/scripts/metabolomics/export/gnps_fbmn_exporter/README.md similarity index 100% rename from scripts/metabolomics/gnps_fbmn_exporter/README.md rename to scripts/metabolomics/export/gnps_fbmn_exporter/README.md diff --git a/scripts/metabolomics/gnps_fbmn_exporter/gnps_fbmn_exporter.py b/scripts/metabolomics/export/gnps_fbmn_exporter/gnps_fbmn_exporter.py similarity index 100% rename from scripts/metabolomics/gnps_fbmn_exporter/gnps_fbmn_exporter.py rename to scripts/metabolomics/export/gnps_fbmn_exporter/gnps_fbmn_exporter.py diff --git a/scripts/metabolomics/formula_validator_golden_rules/requirements.txt b/scripts/metabolomics/export/gnps_fbmn_exporter/requirements.txt similarity index 100% rename from scripts/metabolomics/formula_validator_golden_rules/requirements.txt rename to scripts/metabolomics/export/gnps_fbmn_exporter/requirements.txt diff --git a/scripts/metabolomics/formula_validator_golden_rules/tests/conftest.py b/scripts/metabolomics/export/gnps_fbmn_exporter/tests/conftest.py similarity index 100% rename from scripts/metabolomics/formula_validator_golden_rules/tests/conftest.py rename to scripts/metabolomics/export/gnps_fbmn_exporter/tests/conftest.py diff --git a/scripts/metabolomics/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py b/scripts/metabolomics/export/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py similarity index 100% rename from scripts/metabolomics/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py rename to scripts/metabolomics/export/gnps_fbmn_exporter/tests/test_gnps_fbmn_exporter.py diff --git a/scripts/metabolomics/kovats_ri_calculator/README.md b/scripts/metabolomics/export/kovats_ri_calculator/README.md similarity index 100% rename from scripts/metabolomics/kovats_ri_calculator/README.md rename to scripts/metabolomics/export/kovats_ri_calculator/README.md diff --git a/scripts/metabolomics/kovats_ri_calculator/kovats_ri_calculator.py b/scripts/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py similarity index 100% rename from scripts/metabolomics/kovats_ri_calculator/kovats_ri_calculator.py rename to scripts/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py diff --git a/scripts/metabolomics/gnps_fbmn_exporter/requirements.txt b/scripts/metabolomics/export/kovats_ri_calculator/requirements.txt similarity index 100% rename from scripts/metabolomics/gnps_fbmn_exporter/requirements.txt rename to scripts/metabolomics/export/kovats_ri_calculator/requirements.txt diff --git a/scripts/metabolomics/gnps_fbmn_exporter/tests/conftest.py b/scripts/metabolomics/export/kovats_ri_calculator/tests/conftest.py similarity index 100% rename from scripts/metabolomics/gnps_fbmn_exporter/tests/conftest.py rename to scripts/metabolomics/export/kovats_ri_calculator/tests/conftest.py diff --git a/scripts/metabolomics/kovats_ri_calculator/tests/test_kovats_ri_calculator.py b/scripts/metabolomics/export/kovats_ri_calculator/tests/test_kovats_ri_calculator.py similarity index 100% rename from scripts/metabolomics/kovats_ri_calculator/tests/test_kovats_ri_calculator.py rename to scripts/metabolomics/export/kovats_ri_calculator/tests/test_kovats_ri_calculator.py diff --git a/scripts/metabolomics/sirius_exporter/README.md b/scripts/metabolomics/export/sirius_exporter/README.md similarity index 100% rename from scripts/metabolomics/sirius_exporter/README.md rename to scripts/metabolomics/export/sirius_exporter/README.md diff --git a/scripts/metabolomics/isf_detector/requirements.txt b/scripts/metabolomics/export/sirius_exporter/requirements.txt similarity index 100% rename from scripts/metabolomics/isf_detector/requirements.txt rename to 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a/scripts/metabolomics/adduct_group_analyzer/adduct_group_analyzer.py b/scripts/metabolomics/feature_processing/adduct_group_analyzer/adduct_group_analyzer.py similarity index 100% rename from scripts/metabolomics/adduct_group_analyzer/adduct_group_analyzer.py rename to scripts/metabolomics/feature_processing/adduct_group_analyzer/adduct_group_analyzer.py diff --git a/scripts/metabolomics/isotope_label_detector/requirements.txt b/scripts/metabolomics/feature_processing/adduct_group_analyzer/requirements.txt similarity index 100% rename from scripts/metabolomics/isotope_label_detector/requirements.txt rename to scripts/metabolomics/feature_processing/adduct_group_analyzer/requirements.txt diff --git a/scripts/metabolomics/isotope_label_detector/tests/conftest.py b/scripts/metabolomics/feature_processing/adduct_group_analyzer/tests/conftest.py similarity index 100% rename from scripts/metabolomics/isotope_label_detector/tests/conftest.py rename to 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b/scripts/metabolomics/isotope_labeling/isotope_label_detector/tests/test_isotope_label_detector.py similarity index 100% rename from scripts/metabolomics/isotope_label_detector/tests/test_isotope_label_detector.py rename to scripts/metabolomics/isotope_labeling/isotope_label_detector/tests/test_isotope_label_detector.py diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/README.md b/scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/README.md similarity index 100% rename from scripts/metabolomics/mid_natural_abundance_corrector/README.md rename to scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/README.md diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py b/scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py similarity index 100% rename from scripts/metabolomics/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py rename to scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/requirements.txt b/scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/requirements.txt similarity index 100% rename from scripts/metabolomics/mid_natural_abundance_corrector/requirements.txt rename to scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/requirements.txt diff --git a/scripts/metabolomics/metabolite_formula_annotator/tests/conftest.py b/scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/tests/conftest.py similarity index 100% rename from scripts/metabolomics/metabolite_formula_annotator/tests/conftest.py rename to scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/tests/conftest.py diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py b/scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py similarity index 100% rename from scripts/metabolomics/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py rename to scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/tests/test_mid_natural_abundance_corrector.py diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/README.md b/scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/README.md similarity index 100% rename from scripts/metabolomics/lipid_ecn_rt_predictor/README.md rename to scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/README.md diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py b/scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py similarity index 100% rename from scripts/metabolomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py rename to scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/requirements.txt b/scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/requirements.txt similarity index 100% rename from scripts/metabolomics/lipid_ecn_rt_predictor/requirements.txt rename to scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/requirements.txt diff --git a/scripts/metabolomics/mid_natural_abundance_corrector/tests/conftest.py b/scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/tests/conftest.py similarity index 100% rename from scripts/metabolomics/mid_natural_abundance_corrector/tests/conftest.py rename to scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/tests/conftest.py diff --git a/scripts/metabolomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py b/scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py similarity index 100% rename from scripts/metabolomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py rename to scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/tests/test_lipid_ecn_rt_predictor.py diff --git a/scripts/metabolomics/lipid_species_resolver/README.md b/scripts/metabolomics/lipidomics/lipid_species_resolver/README.md similarity index 100% rename from scripts/metabolomics/lipid_species_resolver/README.md rename to scripts/metabolomics/lipidomics/lipid_species_resolver/README.md diff --git a/scripts/metabolomics/lipid_species_resolver/lipid_species_resolver.py b/scripts/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py similarity index 100% rename from scripts/metabolomics/lipid_species_resolver/lipid_species_resolver.py rename to scripts/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py diff --git a/scripts/metabolomics/molecular_formula_finder/requirements.txt b/scripts/metabolomics/lipidomics/lipid_species_resolver/requirements.txt similarity index 100% rename from scripts/metabolomics/molecular_formula_finder/requirements.txt rename to scripts/metabolomics/lipidomics/lipid_species_resolver/requirements.txt diff --git a/scripts/metabolomics/molecular_formula_finder/tests/conftest.py b/scripts/metabolomics/lipidomics/lipid_species_resolver/tests/conftest.py similarity index 100% rename from scripts/metabolomics/molecular_formula_finder/tests/conftest.py rename to scripts/metabolomics/lipidomics/lipid_species_resolver/tests/conftest.py diff --git a/scripts/metabolomics/lipid_species_resolver/tests/test_lipid_species_resolver.py b/scripts/metabolomics/lipidomics/lipid_species_resolver/tests/test_lipid_species_resolver.py similarity index 100% rename from scripts/metabolomics/lipid_species_resolver/tests/test_lipid_species_resolver.py rename to scripts/metabolomics/lipidomics/lipid_species_resolver/tests/test_lipid_species_resolver.py diff --git a/scripts/metabolomics/metabolite_class_annotator/metabolite_class_annotator.py b/scripts/metabolomics/metabolite_class_annotator/metabolite_class_annotator.py deleted file mode 100644 index e133722..0000000 --- a/scripts/metabolomics/metabolite_class_annotator/metabolite_class_annotator.py +++ /dev/null @@ -1,188 +0,0 @@ -""" -Metabolite Class Annotator -============================ -Annotate features with putative compound classes based on mass defect -analysis and elemental ratio heuristics. - -Mass defect (fractional part of mass) and Kendrick mass defect are -used to classify features into broad compound families such as lipids, -peptides, sugars, and polyketides. - -Usage ------ - python metabolite_class_annotator.py --input features.tsv --output annotated.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -PROTON = 1.007276 - -# Compound class rules based on mass defect ranges (fractional mass) -# These are simplified heuristic boundaries. -CLASS_RULES = [ - { - "name": "Lipid", - "mass_defect_range": (0.0, 0.4), - "mass_range": (200, 1200), - "description": "Fatty acids, glycerolipids, sphingolipids", - }, - { - "name": "Peptide", - "mass_defect_range": (0.0, 0.7), - "mass_range": (400, 5000), - "description": "Di- to oligopeptides", - }, - { - "name": "Sugar/Carbohydrate", - "mass_defect_range": (0.0, 0.15), - "mass_range": (100, 2000), - "description": "Mono- to oligosaccharides", - }, - { - "name": "Polyketide", - "mass_defect_range": (0.0, 0.35), - "mass_range": (150, 800), - "description": "Polyketide-derived natural products", - }, - { - "name": "Terpenoid", - "mass_defect_range": (0.1, 0.5), - "mass_range": (100, 800), - "description": "Mono-, sesqui-, diterpenoids", - }, - { - "name": "Nucleoside", - "mass_defect_range": (0.0, 0.2), - "mass_range": (200, 600), - "description": "Nucleosides and nucleotides", - }, -] - - -def compute_mass_defect(mass: float) -> float: - """Compute the fractional mass defect. - - Parameters - ---------- - mass: - Monoisotopic mass in Da. - - Returns - ------- - float - Fractional part of the mass. - """ - return mass - int(mass) - - -def compute_kendrick_mass_defect(mass: float, base_unit: float = 14.01565) -> float: - """Compute the Kendrick mass defect (CH2-based). - - Parameters - ---------- - mass: - Monoisotopic mass in Da. - base_unit: - Kendrick base mass (default: CH2 = 14.01565 Da). - - Returns - ------- - float - Kendrick mass defect. - """ - kendrick_mass = mass * (14.0 / base_unit) - return round(kendrick_mass) - kendrick_mass - - -def annotate_class(mass: float) -> list[str]: - """Annotate a mass with candidate compound classes. - - Parameters - ---------- - mass: - Neutral monoisotopic mass in Da. - - Returns - ------- - list[str] - List of candidate class names. - """ - md = compute_mass_defect(mass) - candidates = [] - - for rule in CLASS_RULES: - md_lo, md_hi = rule["mass_defect_range"] - m_lo, m_hi = rule["mass_range"] - if md_lo <= md <= md_hi and m_lo <= mass <= m_hi: - candidates.append(rule["name"]) - - return candidates if candidates else ["Unknown"] - - -def annotate_features(features: list[dict]) -> list[dict]: - """Annotate a list of features with compound classes. - - Parameters - ---------- - features: - List of dicts with at least key ``mz``. - - Returns - ------- - list[dict] - Each feature augmented with mass_defect, kendrick_md, and compound_class. - """ - results = [] - for feat in features: - mz = float(feat["mz"]) - neutral_mass = mz - PROTON # assume [M+H]+ - - md = compute_mass_defect(neutral_mass) - kmd = compute_kendrick_mass_defect(neutral_mass) - classes = annotate_class(neutral_mass) - - feat_copy = dict(feat) - feat_copy["neutral_mass"] = round(neutral_mass, 6) - feat_copy["mass_defect"] = round(md, 6) - feat_copy["kendrick_md"] = round(kmd, 6) - feat_copy["compound_class"] = ";".join(classes) - results.append(feat_copy) - - return results - - -def main(): - parser = argparse.ArgumentParser( - description="Annotate features with compound classes by mass defect analysis." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (must have mz column)") - parser.add_argument("--output", required=True, metavar="FILE", help="Output annotated TSV") - args = parser.parse_args() - - features = [] - with open(args.input) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - features.append(row) - - annotated = annotate_features(features) - - fieldnames = list(features[0].keys()) if features else ["mz"] - fieldnames += ["neutral_mass", "mass_defect", "kendrick_md", "compound_class"] - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") - writer.writeheader() - writer.writerows(annotated) - - print(f"Annotated {len(annotated)} features, written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/metabolomics/metabolite_class_annotator/tests/test_metabolite_class_annotator.py b/scripts/metabolomics/metabolite_class_annotator/tests/test_metabolite_class_annotator.py deleted file mode 100644 index 2a18e93..0000000 --- a/scripts/metabolomics/metabolite_class_annotator/tests/test_metabolite_class_annotator.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Tests for metabolite_class_annotator.""" - -from conftest import requires_pyopenms - - -@requires_pyopenms -class TestMetaboliteClassAnnotator: - def test_compute_mass_defect(self): - from metabolite_class_annotator import compute_mass_defect - - md = compute_mass_defect(180.0634) - assert abs(md - 0.0634) < 0.001 - - def test_compute_kendrick_md(self): - from metabolite_class_annotator import compute_kendrick_mass_defect - - kmd = compute_kendrick_mass_defect(180.0634) - assert isinstance(kmd, float) - - def test_annotate_class_small_molecule(self): - from metabolite_class_annotator import annotate_class - - classes = annotate_class(180.0634) # glucose - assert len(classes) > 0 - assert isinstance(classes[0], str) - - def test_annotate_class_lipid_range(self): - from metabolite_class_annotator import annotate_class - - classes = annotate_class(700.2) - assert "Lipid" in classes - - def test_annotate_features(self): - from metabolite_class_annotator import annotate_features - - features = [ - {"mz": "181.0707", "rt": "60.0", "intensity": "1000"}, - {"mz": "701.2", "rt": "300.0", "intensity": "5000"}, - ] - results = annotate_features(features) - assert len(results) == 2 - assert "compound_class" in results[0] - assert "mass_defect" in results[0] - assert "kendrick_md" in results[0] - - def test_unknown_class(self): - from metabolite_class_annotator import annotate_class - - # Very large mass outside normal ranges - classes = annotate_class(50000.0) - assert "Unknown" in classes diff --git a/scripts/metabolomics/retention_index_calculator/retention_index_calculator.py b/scripts/metabolomics/retention_index_calculator/retention_index_calculator.py deleted file mode 100644 index 7423222..0000000 --- a/scripts/metabolomics/retention_index_calculator/retention_index_calculator.py +++ /dev/null @@ -1,146 +0,0 @@ -""" -Retention Index Calculator -=========================== -Calculate Kovats retention indices from alkane standard retention times. - -Given a set of n-alkane standards with known carbon numbers and their -observed retention times, compute retention indices for unknown compounds. - -Usage ------ - python retention_index_calculator.py --input features.tsv --standards alkanes.tsv --output ri.tsv -""" - -import argparse -import csv -import math -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - - -def load_standards(path: str) -> list[tuple[int, float]]: - """Load alkane standards from a TSV file. - - Parameters - ---------- - path: - TSV with columns: carbon_number, rt (retention time in seconds). - - Returns - ------- - list[tuple[int, float]] - Sorted list of (carbon_number, rt) tuples. - """ - standards = [] - with open(path) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - standards.append((int(row["carbon_number"]), float(row["rt"]))) - standards.sort(key=lambda x: x[1]) - return standards - - -def calculate_kovats_ri( - rt: float, - standards: list[tuple[int, float]], -) -> float | None: - """Calculate Kovats retention index for a given retention time. - - Parameters - ---------- - rt: - Retention time of the unknown compound (seconds). - standards: - Sorted list of (carbon_number, rt) for alkane standards. - - Returns - ------- - float or None - Kovats retention index, or None if RT is outside the standard range. - """ - if len(standards) < 2: - return None - - # Find bracketing standards - for i in range(len(standards) - 1): - cn_z, rt_z = standards[i] - cn_z1, rt_z1 = standards[i + 1] - - if rt_z <= rt <= rt_z1: - if rt_z1 == rt_z: - return float(cn_z * 100) - # Kovats RI (isothermal): RI = 100 * [z + (log(rt_x) - log(rt_z)) / (log(rt_z1) - log(rt_z))] - if rt_z > 0 and rt > 0 and rt_z1 > 0: - ri = 100.0 * (cn_z + (math.log(rt) - math.log(rt_z)) / (math.log(rt_z1) - math.log(rt_z))) - return round(ri, 2) - else: - # Linear interpolation fallback - ri = 100.0 * (cn_z + (rt - rt_z) / (rt_z1 - rt_z)) - return round(ri, 2) - - return None - - -def calculate_all_ri( - features: list[dict], - standards: list[tuple[int, float]], -) -> list[dict]: - """Calculate retention indices for all features. - - Parameters - ---------- - features: - List of dicts with at least key ``rt``. - standards: - Alkane standard reference points. - - Returns - ------- - list[dict] - Each feature dict augmented with ``retention_index``. - """ - results = [] - for feat in features: - rt = float(feat["rt"]) - ri = calculate_kovats_ri(rt, standards) - feat_copy = dict(feat) - feat_copy["retention_index"] = ri if ri is not None else "" - results.append(feat_copy) - return results - - -def main(): - parser = argparse.ArgumentParser( - description="Calculate Kovats retention indices from alkane standards." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (must have rt column)") - parser.add_argument("--standards", required=True, metavar="FILE", help="Alkane standards TSV") - parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV with retention indices") - args = parser.parse_args() - - features = [] - with open(args.input) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - features.append(row) - - standards = load_standards(args.standards) - results = calculate_all_ri(features, standards) - - base_fields = list(features[0].keys()) if features else ["mz", "rt", "intensity"] - fieldnames = base_fields + ["retention_index"] - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") - writer.writeheader() - writer.writerows(results) - - n_annotated = sum(1 for r in results if r["retention_index"] != "") - print(f"RI calculated for {n_annotated}/{len(results)} features, written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/metabolomics/retention_index_calculator/tests/test_retention_index_calculator.py b/scripts/metabolomics/retention_index_calculator/tests/test_retention_index_calculator.py deleted file mode 100644 index 8bf8e98..0000000 --- a/scripts/metabolomics/retention_index_calculator/tests/test_retention_index_calculator.py +++ /dev/null @@ -1,60 +0,0 @@ -"""Tests for retention_index_calculator.""" - -from conftest import requires_pyopenms - - -@requires_pyopenms -class TestRetentionIndexCalculator: - def _make_standards(self): - return [ - (8, 100.0), # C8 at 100s - (9, 200.0), # C9 at 200s - (10, 400.0), # C10 at 400s - (11, 800.0), # C11 at 800s - ] - - def test_exact_standard_ri(self): - from retention_index_calculator import calculate_kovats_ri - - standards = self._make_standards() - # At the C9 standard RT, RI should be 900 - ri = calculate_kovats_ri(200.0, standards) - assert ri == 900.0 - - def test_interpolation(self): - from retention_index_calculator import calculate_kovats_ri - - standards = self._make_standards() - ri = calculate_kovats_ri(150.0, standards) - assert ri is not None - assert 800 < ri < 900 - - def test_out_of_range(self): - from retention_index_calculator import calculate_kovats_ri - - standards = self._make_standards() - ri = calculate_kovats_ri(50.0, standards) # before first standard - assert ri is None - - def test_calculate_all_ri(self): - from retention_index_calculator import calculate_all_ri - - standards = self._make_standards() - features = [ - {"mz": "100.0", "rt": "150.0", "intensity": "1000"}, - {"mz": "200.0", "rt": "300.0", "intensity": "2000"}, - {"mz": "300.0", "rt": "50.0", "intensity": "500"}, # out of range - ] - results = calculate_all_ri(features, standards) - assert len(results) == 3 - assert results[0]["retention_index"] != "" - assert results[1]["retention_index"] != "" - assert results[2]["retention_index"] == "" - - def test_monotonic_ri(self): - from retention_index_calculator import calculate_kovats_ri - - standards = self._make_standards() - ri1 = calculate_kovats_ri(150.0, standards) - ri2 = calculate_kovats_ri(300.0, standards) - assert ri1 < ri2 diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/README.md b/scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/README.md similarity index 100% rename from scripts/metabolomics/isotope_pattern_fit_scorer/README.md rename to scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/README.md diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py similarity index 100% rename from scripts/metabolomics/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py diff --git a/scripts/metabolomics/neutral_loss_scanner/requirements.txt b/scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/requirements.txt similarity index 100% rename from scripts/metabolomics/neutral_loss_scanner/requirements.txt rename to scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/requirements.txt diff --git a/scripts/metabolomics/neutral_loss_scanner/tests/conftest.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/tests/conftest.py similarity index 100% rename from scripts/metabolomics/neutral_loss_scanner/tests/conftest.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/tests/conftest.py diff --git a/scripts/metabolomics/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py similarity index 100% rename from scripts/metabolomics/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/tests/test_isotope_pattern_fit_scorer.py diff --git a/scripts/metabolomics/isotope_pattern_matcher/README.md b/scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/README.md similarity index 100% rename from scripts/metabolomics/isotope_pattern_matcher/README.md rename to scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/README.md diff --git a/scripts/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py similarity index 100% rename from scripts/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py diff --git a/scripts/metabolomics/rdbe_calculator/requirements.txt b/scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/requirements.txt similarity index 100% rename from scripts/metabolomics/rdbe_calculator/requirements.txt rename to scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/requirements.txt diff --git a/scripts/metabolomics/rdbe_calculator/tests/conftest.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/tests/conftest.py similarity index 100% rename from scripts/metabolomics/rdbe_calculator/tests/conftest.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/tests/conftest.py diff --git a/scripts/metabolomics/isotope_pattern_matcher/tests/test_isotope_pattern_matcher.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/tests/test_isotope_pattern_matcher.py similarity index 100% rename from scripts/metabolomics/isotope_pattern_matcher/tests/test_isotope_pattern_matcher.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/tests/test_isotope_pattern_matcher.py diff --git a/scripts/metabolomics/isotope_pattern_scorer/isotope_pattern_scorer.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/isotope_pattern_scorer.py similarity index 100% rename from scripts/metabolomics/isotope_pattern_scorer/isotope_pattern_scorer.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/isotope_pattern_scorer.py diff --git a/scripts/metabolomics/retention_index_calculator/requirements.txt b/scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/requirements.txt similarity index 100% rename from scripts/metabolomics/retention_index_calculator/requirements.txt rename to scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/requirements.txt diff --git a/scripts/metabolomics/retention_index_calculator/tests/conftest.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/tests/conftest.py similarity index 100% rename from scripts/metabolomics/retention_index_calculator/tests/conftest.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/tests/conftest.py diff --git a/scripts/metabolomics/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py b/scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py similarity index 100% rename from scripts/metabolomics/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py rename to scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/tests/test_isotope_pattern_scorer.py diff --git a/scripts/metabolomics/massql_query_tool/massql_query_tool.py b/scripts/metabolomics/spectral_analysis/massql_query_tool/massql_query_tool.py similarity index 100% rename from scripts/metabolomics/massql_query_tool/massql_query_tool.py rename to scripts/metabolomics/spectral_analysis/massql_query_tool/massql_query_tool.py diff --git a/scripts/metabolomics/sirius_exporter/requirements.txt b/scripts/metabolomics/spectral_analysis/massql_query_tool/requirements.txt similarity index 100% rename from scripts/metabolomics/sirius_exporter/requirements.txt rename to scripts/metabolomics/spectral_analysis/massql_query_tool/requirements.txt diff --git a/scripts/metabolomics/sirius_exporter/tests/conftest.py b/scripts/metabolomics/spectral_analysis/massql_query_tool/tests/conftest.py similarity index 100% rename from scripts/metabolomics/sirius_exporter/tests/conftest.py rename to scripts/metabolomics/spectral_analysis/massql_query_tool/tests/conftest.py diff --git a/scripts/metabolomics/massql_query_tool/tests/test_massql_query_tool.py b/scripts/metabolomics/spectral_analysis/massql_query_tool/tests/test_massql_query_tool.py similarity index 100% rename from scripts/metabolomics/massql_query_tool/tests/test_massql_query_tool.py rename to scripts/metabolomics/spectral_analysis/massql_query_tool/tests/test_massql_query_tool.py diff --git a/scripts/metabolomics/neutral_loss_scanner/README.md b/scripts/metabolomics/spectral_analysis/neutral_loss_scanner/README.md similarity index 100% rename from scripts/metabolomics/neutral_loss_scanner/README.md rename to scripts/metabolomics/spectral_analysis/neutral_loss_scanner/README.md diff --git a/scripts/metabolomics/neutral_loss_scanner/neutral_loss_scanner.py b/scripts/metabolomics/spectral_analysis/neutral_loss_scanner/neutral_loss_scanner.py similarity index 100% rename from scripts/metabolomics/neutral_loss_scanner/neutral_loss_scanner.py rename to scripts/metabolomics/spectral_analysis/neutral_loss_scanner/neutral_loss_scanner.py diff --git a/scripts/metabolomics/suspect_screener/requirements.txt b/scripts/metabolomics/spectral_analysis/neutral_loss_scanner/requirements.txt similarity index 100% rename from scripts/metabolomics/suspect_screener/requirements.txt rename to scripts/metabolomics/spectral_analysis/neutral_loss_scanner/requirements.txt diff --git a/scripts/metabolomics/spectral_entropy_scorer/tests/conftest.py b/scripts/metabolomics/spectral_analysis/neutral_loss_scanner/tests/conftest.py similarity index 100% rename from scripts/metabolomics/spectral_entropy_scorer/tests/conftest.py rename to scripts/metabolomics/spectral_analysis/neutral_loss_scanner/tests/conftest.py diff --git a/scripts/metabolomics/neutral_loss_scanner/tests/test_neutral_loss_scanner.py b/scripts/metabolomics/spectral_analysis/neutral_loss_scanner/tests/test_neutral_loss_scanner.py similarity index 100% rename from scripts/metabolomics/neutral_loss_scanner/tests/test_neutral_loss_scanner.py rename to scripts/metabolomics/spectral_analysis/neutral_loss_scanner/tests/test_neutral_loss_scanner.py diff --git a/scripts/metabolomics/spectral_entropy_scorer/README.md b/scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/README.md similarity index 100% rename from scripts/metabolomics/spectral_entropy_scorer/README.md rename to scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/README.md diff --git a/scripts/metabolomics/spectral_entropy_scorer/requirements.txt b/scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/requirements.txt similarity index 100% rename from scripts/metabolomics/spectral_entropy_scorer/requirements.txt rename to scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/requirements.txt diff --git a/scripts/metabolomics/spectral_entropy_scorer/spectral_entropy_scorer.py b/scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/spectral_entropy_scorer.py similarity index 100% rename from scripts/metabolomics/spectral_entropy_scorer/spectral_entropy_scorer.py rename to scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/spectral_entropy_scorer.py diff --git a/scripts/metabolomics/suspect_screener/tests/conftest.py b/scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/tests/conftest.py similarity index 100% rename from scripts/metabolomics/suspect_screener/tests/conftest.py rename to scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/tests/conftest.py diff --git a/scripts/metabolomics/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py b/scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py similarity index 100% rename from scripts/metabolomics/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py rename to scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/tests/test_spectral_entropy_scorer.py diff --git a/scripts/proteomics/biomarker_panel_roc/README.md b/scripts/proteomics/biomarker_panel_roc/README.md deleted file mode 100644 index fadd0e1..0000000 --- a/scripts/proteomics/biomarker_panel_roc/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# Biomarker Panel ROC - -Compute ROC curves and AUC values for individual protein biomarkers and multi-marker panels. - -## Installation - -```bash -pip install -r requirements.txt -``` - -## Usage - -```bash -python biomarker_panel_roc.py --input protein_quant.tsv --groups case,control --output roc.tsv -``` - -### Input format - -Tab-separated protein quantification matrix (rows=proteins, columns=samples): - -``` -protein_id case_1 case_2 control_1 control_2 -P12345 100.5 120.3 50.2 45.8 -``` - -### Parameters - -| Flag | Description | -|------|-------------| -| `--input` | Protein quantification TSV | -| `--groups` | Comma-separated group names: positive,negative | -| `--group-file` | Optional TSV mapping sample_id to group | -| `--output` | Output ROC/AUC TSV | diff --git a/scripts/proteomics/biomarker_panel_roc/biomarker_panel_roc.py b/scripts/proteomics/biomarker_panel_roc/biomarker_panel_roc.py deleted file mode 100644 index 49ad575..0000000 --- a/scripts/proteomics/biomarker_panel_roc/biomarker_panel_roc.py +++ /dev/null @@ -1,268 +0,0 @@ -""" -Biomarker Panel ROC -==================== -Compute ROC curves and AUC values for individual protein biomarkers and -simple multi-marker panels. For each protein, the tool computes a -receiver-operating-characteristic curve (sensitivity vs 1-specificity) -and the area under the curve (AUC) to evaluate discriminatory power -between case and control groups. - -For multi-marker panels, a simple sum-score is used to combine markers. - -Uses numpy and scipy for statistical computations. - -Usage ------ - python biomarker_panel_roc.py --input protein_quant.tsv \ - --groups case,control --output roc.tsv -""" - -import argparse -import csv -import sys -from typing import Dict, List, Tuple - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - - -def compute_roc( - scores: List[float], labels: List[int] -) -> Tuple[List[float], List[float], float]: - """Compute ROC curve and AUC for binary classification. - - Parameters - ---------- - scores: - Numeric scores (higher = more likely positive). - labels: - Binary labels (1 = positive/case, 0 = negative/control). - - Returns - ------- - tuple - (fpr_list, tpr_list, auc) where fpr_list and tpr_list define - the ROC curve points and auc is the area under the curve. - """ - scores_arr = np.array(scores, dtype=float) - labels_arr = np.array(labels, dtype=int) - - # Sort by score descending - order = np.argsort(-scores_arr) - sorted_labels = labels_arr[order] - - n_pos = np.sum(labels_arr == 1) - n_neg = np.sum(labels_arr == 0) - - if n_pos == 0 or n_neg == 0: - return [0.0, 1.0], [0.0, 1.0], 0.5 - - tp = 0 - fp = 0 - fpr_list = [0.0] - tpr_list = [0.0] - - for label in sorted_labels: - if label == 1: - tp += 1 - else: - fp += 1 - fpr_list.append(fp / n_neg) - tpr_list.append(tp / n_pos) - - # AUC via trapezoidal rule - auc = 0.0 - for i in range(1, len(fpr_list)): - auc += (fpr_list[i] - fpr_list[i - 1]) * (tpr_list[i] + tpr_list[i - 1]) / 2.0 - - return fpr_list, tpr_list, auc - - -def analyze_biomarkers( - quant_data: Dict[str, Dict[str, float]], - sample_groups: Dict[str, int], -) -> List[Dict[str, object]]: - """Compute ROC/AUC for each protein. - - Parameters - ---------- - quant_data: - Mapping of protein_id to {sample_id: abundance}. - sample_groups: - Mapping of sample_id to label (1=case, 0=control). - - Returns - ------- - list of dict - One entry per protein with ``protein_id``, ``auc``, ``direction``. - """ - results: List[Dict[str, object]] = [] - - for protein_id, abundances in quant_data.items(): - scores = [] - labels = [] - for sample_id, label in sample_groups.items(): - if sample_id in abundances: - scores.append(abundances[sample_id]) - labels.append(label) - - if len(scores) < 4: - continue - - _, _, auc = compute_roc(scores, labels) - - # If AUC < 0.5, flip direction (lower values = case) - direction = "up" - if auc < 0.5: - flipped_scores = [-s for s in scores] - _, _, auc_flipped = compute_roc(flipped_scores, labels) - auc = auc_flipped - direction = "down" - - results.append({ - "protein_id": protein_id, - "auc": auc, - "direction": direction, - "n_case": sum(1 for lab in labels if lab == 1), - "n_control": sum(1 for lab in labels if lab == 0), - }) - - results.sort(key=lambda x: x["auc"], reverse=True) - return results - - -def panel_score( - quant_data: Dict[str, Dict[str, float]], - sample_groups: Dict[str, int], - top_markers: List[str], -) -> Tuple[List[float], List[float], float]: - """Compute a combined panel ROC using sum-score of top markers. - - Parameters - ---------- - quant_data: - Per-protein abundances. - sample_groups: - Sample labels. - top_markers: - List of protein IDs to include in the panel. - - Returns - ------- - tuple - (fpr, tpr, auc) for the combined panel. - """ - sample_scores: Dict[str, float] = {} - for sample_id in sample_groups: - total = 0.0 - count = 0 - for prot in top_markers: - if prot in quant_data and sample_id in quant_data[prot]: - total += quant_data[prot][sample_id] - count += 1 - if count > 0: - sample_scores[sample_id] = total / count - - scores = [] - labels = [] - for sample_id, score in sample_scores.items(): - scores.append(score) - labels.append(sample_groups[sample_id]) - - if len(scores) < 4: - return [0.0, 1.0], [0.0, 1.0], 0.5 - - return compute_roc(scores, labels) - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Compute ROC/AUC for protein biomarker panels." - ) - parser.add_argument( - "--input", required=True, - help="Input TSV: rows=proteins, columns=samples, first column=protein_id", - ) - parser.add_argument( - "--groups", required=True, - help="Comma-separated group names: positive,negative (e.g. case,control)", - ) - parser.add_argument( - "--group-file", default=None, - help="Optional TSV mapping sample_id to group. If not provided, " - "column names must contain group labels.", - ) - parser.add_argument("--output", required=True, help="Output ROC/AUC TSV") - args = parser.parse_args() - - group_names = [g.strip() for g in args.groups.split(",")] - if len(group_names) != 2: - sys.exit("--groups must specify exactly two groups: positive,negative") - pos_group, neg_group = group_names - - # Read quantification matrix - quant_data: Dict[str, Dict[str, float]] = {} - sample_ids: List[str] = [] - - with open(args.input, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - fields = reader.fieldnames or [] - sample_ids = [f for f in fields if f != "protein_id"] - for row in reader: - pid = row.get("protein_id", "").strip() - if not pid: - continue - abundances: Dict[str, float] = {} - for sid in sample_ids: - val = row.get(sid, "").strip() - try: - abundances[sid] = float(val) - except (ValueError, TypeError): - pass - quant_data[pid] = abundances - - # Determine sample groups - sample_groups: Dict[str, int] = {} - if args.group_file: - with open(args.group_file, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - sid = row.get("sample_id", "").strip() - grp = row.get("group", "").strip() - if sid and grp == pos_group: - sample_groups[sid] = 1 - elif sid and grp == neg_group: - sample_groups[sid] = 0 - else: - # Infer from column names containing group labels - for sid in sample_ids: - if pos_group in sid: - sample_groups[sid] = 1 - elif neg_group in sid: - sample_groups[sid] = 0 - - if not sample_groups: - sys.exit("Could not assign any samples to groups. Check --groups or --group-file.") - - results = analyze_biomarkers(quant_data, sample_groups) - - with open(args.output, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein_id", "auc", "direction", "n_case", "n_control"]) - for r in results: - writer.writerow([ - r["protein_id"], f"{r['auc']:.4f}", - r["direction"], r["n_case"], r["n_control"], - ]) - - if results: - print(f"Top marker: {results[0]['protein_id']} AUC={results[0]['auc']:.4f}") - print(f"Analyzed {len(results)} proteins -> {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/biomarker_panel_roc/requirements.txt b/scripts/proteomics/biomarker_panel_roc/requirements.txt deleted file mode 100644 index ba577e4..0000000 --- a/scripts/proteomics/biomarker_panel_roc/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -pyopenms -numpy -scipy diff --git a/scripts/proteomics/biomarker_panel_roc/tests/test_biomarker_panel_roc.py b/scripts/proteomics/biomarker_panel_roc/tests/test_biomarker_panel_roc.py deleted file mode 100644 index d454d45..0000000 --- a/scripts/proteomics/biomarker_panel_roc/tests/test_biomarker_panel_roc.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Tests for biomarker_panel_roc.""" - -import csv -import sys - -from conftest import requires_pyopenms - - -@requires_pyopenms -def test_compute_roc_perfect(): - from biomarker_panel_roc import compute_roc - - # Perfect separation: all cases have higher scores - scores = [10, 9, 8, 7, 1, 2, 3, 4] - labels = [1, 1, 1, 1, 0, 0, 0, 0] - fpr, tpr, auc = compute_roc(scores, labels) - assert abs(auc - 1.0) < 0.01 - - -@requires_pyopenms -def test_compute_roc_random(): - from biomarker_panel_roc import compute_roc - - # No separation - scores = [1, 2, 3, 4, 5, 6, 7, 8] - labels = [1, 0, 1, 0, 1, 0, 1, 0] - _, _, auc = compute_roc(scores, labels) - assert 0.3 < auc < 0.8 # near 0.5 - - -@requires_pyopenms -def test_compute_roc_all_same_class(): - from biomarker_panel_roc import compute_roc - - scores = [1, 2, 3] - labels = [1, 1, 1] - _, _, auc = compute_roc(scores, labels) - assert auc == 0.5 # fallback - - -@requires_pyopenms -def test_analyze_biomarkers(): - from biomarker_panel_roc import analyze_biomarkers - - quant = { - "P1": {"case_1": 100, "case_2": 110, "control_1": 50, "control_2": 45}, - "P2": {"case_1": 10, "case_2": 12, "control_1": 10, "control_2": 11}, - } - groups = {"case_1": 1, "case_2": 1, "control_1": 0, "control_2": 0} - results = analyze_biomarkers(quant, groups) - assert len(results) == 2 - # P1 should have higher AUC (better separation) - assert results[0]["protein_id"] == "P1" - assert results[0]["auc"] > results[1]["auc"] - - -@requires_pyopenms -def test_cli_roundtrip(tmp_path): - from biomarker_panel_roc import main - - input_file = tmp_path / "input.tsv" - output_file = tmp_path / "output.tsv" - - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein_id", "case_1", "case_2", "control_1", "control_2"]) - writer.writerow(["P1", "100", "110", "50", "45"]) - writer.writerow(["P2", "10", "12", "10", "11"]) - - sys.argv = [ - "biomarker_panel_roc.py", - "--input", str(input_file), - "--groups", "case,control", - "--output", str(output_file), - ] - main() - assert output_file.exists() diff --git a/scripts/proteomics/coefficient_of_variation_calculator/README.md b/scripts/proteomics/coefficient_of_variation_calculator/README.md deleted file mode 100644 index 983cd38..0000000 --- a/scripts/proteomics/coefficient_of_variation_calculator/README.md +++ /dev/null @@ -1,14 +0,0 @@ -# Coefficient of Variation Calculator - -Calculate CV% (coefficient of variation) across replicates for each feature. - -## Usage - -```bash -python coefficient_of_variation_calculator.py --input matrix.tsv --groups groups.tsv --output cv_report.tsv -``` - -## Input Files - -- **matrix.tsv** - Quantification matrix (rows=features, columns=samples) -- **groups.tsv** - Group assignments with columns: `sample`, `group` diff --git a/scripts/proteomics/coefficient_of_variation_calculator/coefficient_of_variation_calculator.py b/scripts/proteomics/coefficient_of_variation_calculator/coefficient_of_variation_calculator.py deleted file mode 100644 index 3a50107..0000000 --- a/scripts/proteomics/coefficient_of_variation_calculator/coefficient_of_variation_calculator.py +++ /dev/null @@ -1,164 +0,0 @@ -""" -Coefficient of Variation Calculator -==================================== -Calculate CV% (coefficient of variation) across replicates for each feature. - -Reads a quantification matrix and a groups file that assigns samples to groups, -then computes the CV within each group for each feature. - -Usage ------ - python coefficient_of_variation_calculator.py --input matrix.tsv --groups groups.tsv --output cv_report.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - - -def read_matrix(filepath: str) -> tuple: - """Read a TSV quantification matrix. - - Returns (row_ids, col_names, data_matrix). - """ - with open(filepath) as fh: - reader = csv.reader(fh, delimiter="\t") - header = next(reader) - col_names = header[1:] - row_ids = [] - rows = [] - for row in reader: - row_ids.append(row[0]) - values = [] - for v in row[1:]: - v = v.strip() - if v == "" or v.upper() in ("NA", "NAN"): - values.append(np.nan) - else: - values.append(float(v)) - rows.append(values) - return row_ids, col_names, np.array(rows, dtype=float) - - -def read_groups(filepath: str) -> dict: - """Read a groups file mapping samples to groups. - - Expected format: TSV with columns 'sample' and 'group'. - - Returns - ------- - dict - {sample_name: group_name} - """ - groups = {} - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - groups[row["sample"]] = row["group"] - return groups - - -def calculate_cv(matrix: np.ndarray, row_ids: list, col_names: list, groups: dict) -> list: - """Calculate CV% for each feature within each group. - - Parameters - ---------- - matrix: - 2D array (features x samples). - row_ids: - Feature identifiers. - col_names: - Sample names. - groups: - {sample: group} mapping. - - Returns - ------- - list - List of dicts with keys: feature, group, mean, sd, cv_percent, n_values. - """ - group_names = sorted(set(groups.values())) - group_indices = {} - for g in group_names: - group_indices[g] = [i for i, s in enumerate(col_names) if groups.get(s) == g] - - results = [] - for row_idx, feature in enumerate(row_ids): - for group in group_names: - indices = group_indices[group] - if not indices: - continue - values = matrix[row_idx, indices] - valid = values[~np.isnan(values)] - n_valid = len(valid) - if n_valid < 2: - mean_val = np.mean(valid) if n_valid > 0 else float("nan") - results.append({ - "feature": feature, - "group": group, - "mean": mean_val, - "sd": float("nan"), - "cv_percent": float("nan"), - "n_values": n_valid, - }) - else: - mean_val = np.mean(valid) - sd_val = np.std(valid, ddof=1) - cv = (sd_val / mean_val * 100.0) if mean_val != 0 else float("nan") - results.append({ - "feature": feature, - "group": group, - "mean": mean_val, - "sd": sd_val, - "cv_percent": cv, - "n_values": n_valid, - }) - return results - - -def main(): - parser = argparse.ArgumentParser(description="Calculate CV% across replicates.") - parser.add_argument("--input", required=True, help="Input TSV matrix file") - parser.add_argument("--groups", required=True, help="Groups TSV (columns: sample, group)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - row_ids, col_names, matrix = read_matrix(args.input) - groups = read_groups(args.groups) - results = calculate_cv(matrix, row_ids, col_names, groups) - - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter( - fh, - fieldnames=["feature", "group", "mean", "sd", "cv_percent", "n_values"], - delimiter="\t", - ) - writer.writeheader() - for r in results: - writer.writerow({ - "feature": r["feature"], - "group": r["group"], - "mean": f"{r['mean']:.6f}" if not np.isnan(r["mean"]) else "NA", - "sd": f"{r['sd']:.6f}" if not np.isnan(r["sd"]) else "NA", - "cv_percent": f"{r['cv_percent']:.2f}" if not np.isnan(r["cv_percent"]) else "NA", - "n_values": r["n_values"], - }) - - valid_cvs = [r["cv_percent"] for r in results if not np.isnan(r["cv_percent"])] - if valid_cvs: - print(f"Features: {len(row_ids)}") - print(f"Groups: {len(set(groups.values()))}") - print(f"Median CV%: {np.median(valid_cvs):.2f}") - print(f"Mean CV%: {np.mean(valid_cvs):.2f}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/coefficient_of_variation_calculator/tests/test_coefficient_of_variation_calculator.py b/scripts/proteomics/coefficient_of_variation_calculator/tests/test_coefficient_of_variation_calculator.py deleted file mode 100644 index 35c2088..0000000 --- a/scripts/proteomics/coefficient_of_variation_calculator/tests/test_coefficient_of_variation_calculator.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Tests for coefficient_of_variation_calculator.""" - -import numpy as np -from coefficient_of_variation_calculator import calculate_cv, read_groups -from conftest import requires_pyopenms - - -@requires_pyopenms -class TestCoefficientOfVariationCalculator: - def _make_data(self): - matrix = np.array([ - [100.0, 110.0, 105.0, 200.0, 220.0, 210.0], - [500.0, 500.0, 500.0, 1000.0, 1000.0, 1000.0], # zero CV - ]) - row_ids = ["prot1", "prot2"] - col_names = ["s1", "s2", "s3", "s4", "s5", "s6"] - groups = {"s1": "A", "s2": "A", "s3": "A", "s4": "B", "s5": "B", "s6": "B"} - return matrix, row_ids, col_names, groups - - def test_basic_cv(self): - matrix, row_ids, col_names, groups = self._make_data() - results = calculate_cv(matrix, row_ids, col_names, groups) - assert len(results) == 4 # 2 features x 2 groups - - def test_zero_cv(self): - matrix, row_ids, col_names, groups = self._make_data() - results = calculate_cv(matrix, row_ids, col_names, groups) - prot2_a = next(r for r in results if r["feature"] == "prot2" and r["group"] == "A") - assert abs(prot2_a["cv_percent"]) < 1e-6 - - def test_cv_positive(self): - matrix, row_ids, col_names, groups = self._make_data() - results = calculate_cv(matrix, row_ids, col_names, groups) - prot1_a = next(r for r in results if r["feature"] == "prot1" and r["group"] == "A") - assert prot1_a["cv_percent"] > 0 - - def test_n_values(self): - matrix, row_ids, col_names, groups = self._make_data() - results = calculate_cv(matrix, row_ids, col_names, groups) - for r in results: - assert r["n_values"] == 3 - - def test_with_nan(self): - matrix = np.array([[100.0, np.nan, 105.0, 200.0, 220.0, 210.0]]) - row_ids = ["prot1"] - col_names = ["s1", "s2", "s3", "s4", "s5", "s6"] - groups = {"s1": "A", "s2": "A", "s3": "A", "s4": "B", "s5": "B", "s6": "B"} - results = calculate_cv(matrix, row_ids, col_names, groups) - prot1_a = next(r for r in results if r["group"] == "A") - assert prot1_a["n_values"] == 2 - - def test_read_groups(self, tmp_path): - gfile = str(tmp_path / "groups.tsv") - with open(gfile, "w") as fh: - fh.write("sample\tgroup\n") - fh.write("s1\tA\n") - fh.write("s2\tB\n") - groups = read_groups(gfile) - assert groups == {"s1": "A", "s2": "B"} - - def test_single_value_per_group(self): - matrix = np.array([[100.0, 200.0]]) - row_ids = ["prot1"] - col_names = ["s1", "s2"] - groups = {"s1": "A", "s2": "B"} - results = calculate_cv(matrix, row_ids, col_names, groups) - # With only 1 value per group, CV should be NaN - for r in results: - assert np.isnan(r["cv_percent"]) diff --git a/scripts/proteomics/diann_result_converter/README.md b/scripts/proteomics/diann_result_converter/README.md deleted file mode 100644 index 60bc41f..0000000 --- a/scripts/proteomics/diann_result_converter/README.md +++ /dev/null @@ -1,22 +0,0 @@ -# DIA-NN Result Converter - -Convert DIA-NN report.tsv to a standardized TSV format. - -## Usage - -```bash -python diann_result_converter.py --input report.tsv --output standardized.tsv -``` - -## Column Mapping - -| DIA-NN Column | Standard Column | -|---|---| -| Stripped.Sequence | peptide | -| Modified.Sequence | modified_peptide | -| Precursor.Charge | charge | -| Precursor.Mz | mz | -| RT | rt | -| Protein.Group | protein | -| Q.Value | qvalue | -| Precursor.Quantity | intensity | diff --git a/scripts/proteomics/diann_result_converter/diann_result_converter.py b/scripts/proteomics/diann_result_converter/diann_result_converter.py deleted file mode 100644 index 41b8d20..0000000 --- a/scripts/proteomics/diann_result_converter/diann_result_converter.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -DIA-NN Result Converter -======================= -Convert DIA-NN report.tsv to a standardized TSV format. - -Maps DIA-NN specific column names to a common schema for downstream analysis. - -Usage ------ - python diann_result_converter.py --input report.tsv --output standardized.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -# Mapping from DIA-NN column names to standard column names -COLUMN_MAP = { - "Stripped.Sequence": "peptide", - "Modified.Sequence": "modified_peptide", - "Precursor.Charge": "charge", - "Precursor.Mz": "mz", - "RT": "rt", - "Protein.Group": "protein", - "Protein.Names": "protein_description", - "Genes": "gene", - "Q.Value": "qvalue", - "PG.Q.Value": "pg_qvalue", - "Global.Q.Value": "global_qvalue", - "Precursor.Quantity": "intensity", - "Run": "raw_file", - "File.Name": "file_name", -} - -STANDARD_FIELDS = [ - "peptide", "modified_peptide", "charge", "mz", "rt", - "protein", "protein_description", "gene", - "qvalue", "pg_qvalue", "global_qvalue", - "intensity", "raw_file", "file_name", "source", -] - - -def convert_diann_report(filepath: str) -> list: - """Convert DIA-NN report.tsv to standardized format. - - Parameters - ---------- - filepath: - Path to DIA-NN report.tsv. - - Returns - ------- - list - List of dicts with standardized column names. - """ - rows = [] - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - std_row = {} - for diann_col, std_col in COLUMN_MAP.items(): - std_row[std_col] = row.get(diann_col, "") - std_row["source"] = "DIA-NN" - rows.append(std_row) - return rows - - -def write_standardized(filepath: str, rows: list) -> None: - """Write standardized results to TSV.""" - with open(filepath, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=STANDARD_FIELDS, delimiter="\t", extrasaction="ignore") - writer.writeheader() - writer.writerows(rows) - - -def main(): - parser = argparse.ArgumentParser(description="Convert DIA-NN report.tsv to standardized TSV.") - parser.add_argument("--input", required=True, help="DIA-NN report.tsv file") - parser.add_argument("--output", required=True, help="Output standardized TSV") - args = parser.parse_args() - - rows = convert_diann_report(args.input) - write_standardized(args.output, rows) - - print("Source: DIA-NN") - print(f"Total precursors: {len(rows)}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/diann_result_converter/tests/test_diann_result_converter.py b/scripts/proteomics/diann_result_converter/tests/test_diann_result_converter.py deleted file mode 100644 index fc1b5c2..0000000 --- a/scripts/proteomics/diann_result_converter/tests/test_diann_result_converter.py +++ /dev/null @@ -1,84 +0,0 @@ -"""Tests for diann_result_converter.""" - -import csv - -from conftest import requires_pyopenms -from diann_result_converter import STANDARD_FIELDS, convert_diann_report, write_standardized - - -@requires_pyopenms -class TestDiannResultConverter: - def _write_report(self, tmp_path, rows): - filepath = str(tmp_path / "report.tsv") - fieldnames = [ - "Stripped.Sequence", "Modified.Sequence", "Precursor.Charge", - "Precursor.Mz", "RT", "Protein.Group", "Protein.Names", - "Genes", "Q.Value", "PG.Q.Value", "Global.Q.Value", - "Precursor.Quantity", "Run", "File.Name", - ] - with open(filepath, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") - writer.writeheader() - writer.writerows(rows) - return filepath - - def test_basic_conversion(self, tmp_path): - filepath = self._write_report(tmp_path, [ - { - "Stripped.Sequence": "PEPTIDEK", "Modified.Sequence": "PEPTIDEK", - "Precursor.Charge": "2", "Precursor.Mz": "450.5", - "RT": "25.5", "Protein.Group": "P12345", - "Protein.Names": "Protein 1", "Genes": "GEN1", - "Q.Value": "0.001", "PG.Q.Value": "0.005", - "Global.Q.Value": "0.01", - "Precursor.Quantity": "1e6", "Run": "run1", - "File.Name": "run1.mzML", - } - ]) - rows = convert_diann_report(filepath) - assert len(rows) == 1 - assert rows[0]["peptide"] == "PEPTIDEK" - assert rows[0]["charge"] == "2" - assert rows[0]["source"] == "DIA-NN" - - def test_multiple_rows(self, tmp_path): - filepath = self._write_report(tmp_path, [ - {"Stripped.Sequence": "PEP1", "Modified.Sequence": "PEP1", - "Precursor.Charge": "2", "Precursor.Mz": "400", - "RT": "20", "Protein.Group": "P1", "Protein.Names": "Prot1", - "Genes": "G1", "Q.Value": "0.01", "PG.Q.Value": "0.02", - "Global.Q.Value": "0.03", "Precursor.Quantity": "1e6", - "Run": "run1", "File.Name": "run1.mzML"}, - {"Stripped.Sequence": "PEP2", "Modified.Sequence": "PEP2", - "Precursor.Charge": "3", "Precursor.Mz": "300", - "RT": "30", "Protein.Group": "P2", "Protein.Names": "Prot2", - "Genes": "G2", "Q.Value": "0.02", "PG.Q.Value": "0.03", - "Global.Q.Value": "0.04", "Precursor.Quantity": "5e5", - "Run": "run1", "File.Name": "run1.mzML"}, - ]) - rows = convert_diann_report(filepath) - assert len(rows) == 2 - - def test_write_standardized(self, tmp_path): - rows = [{"peptide": "PEPTIDEK", "charge": "2", "source": "DIA-NN"}] - outfile = str(tmp_path / "out.tsv") - write_standardized(outfile, rows) - with open(outfile) as fh: - reader = csv.DictReader(fh, delimiter="\t") - result = list(reader) - assert len(result) == 1 - assert result[0]["source"] == "DIA-NN" - - def test_standard_fields(self): - assert "peptide" in STANDARD_FIELDS - assert "source" in STANDARD_FIELDS - assert "qvalue" in STANDARD_FIELDS - - def test_missing_columns_handled(self, tmp_path): - filepath = str(tmp_path / "minimal.tsv") - with open(filepath, "w") as fh: - fh.write("Stripped.Sequence\tPrecursor.Charge\n") - fh.write("PEPTIDEK\t2\n") - rows = convert_diann_report(filepath) - assert rows[0]["peptide"] == "PEPTIDEK" - assert rows[0]["rt"] == "" # Missing column => empty string diff --git a/scripts/proteomics/differential_expression_tester/README.md b/scripts/proteomics/differential_expression_tester/README.md deleted file mode 100644 index e8a8777..0000000 --- a/scripts/proteomics/differential_expression_tester/README.md +++ /dev/null @@ -1,19 +0,0 @@ -# Differential Expression Tester - -Perform t-tests with Benjamini-Hochberg FDR correction on quantification matrices. - -## Usage - -```bash -python differential_expression_tester.py --input matrix.tsv --design design.tsv --test ttest --output de_results.tsv -python differential_expression_tester.py --input matrix.tsv --design design.tsv --test welch --output de_results.tsv -``` - -## Input Files - -- **matrix.tsv** - Quantification matrix (rows=features, columns=samples) -- **design.tsv** - Experimental design with columns: `sample`, `condition` - -## Output - -TSV with columns: `feature`, `log2fc`, `pvalue`, `adj_pvalue` diff --git a/scripts/proteomics/differential_expression_tester/differential_expression_tester.py b/scripts/proteomics/differential_expression_tester/differential_expression_tester.py deleted file mode 100644 index deb8189..0000000 --- a/scripts/proteomics/differential_expression_tester/differential_expression_tester.py +++ /dev/null @@ -1,206 +0,0 @@ -""" -Differential Expression Tester -============================== -Perform t-tests with Benjamini-Hochberg correction on quantification matrices. - -Reads a quantification matrix and an experimental design file that maps samples -to conditions, then computes per-feature differential expression statistics. - -Usage ------ - python differential_expression_tester.py --input matrix.tsv --design design.tsv --test ttest --output de.tsv - python differential_expression_tester.py --input matrix.tsv --design design.tsv --test welch --output de.tsv -""" - -import argparse -import csv -import math -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np -from scipy import stats - - -def read_matrix(filepath: str) -> tuple: - """Read a TSV quantification matrix. - - Returns (row_ids, col_names, data_matrix). - """ - with open(filepath) as fh: - reader = csv.reader(fh, delimiter="\t") - header = next(reader) - col_names = header[1:] - row_ids = [] - rows = [] - for row in reader: - row_ids.append(row[0]) - values = [] - for v in row[1:]: - v = v.strip() - if v == "" or v.upper() in ("NA", "NAN"): - values.append(np.nan) - else: - values.append(float(v)) - rows.append(values) - return row_ids, col_names, np.array(rows, dtype=float) - - -def read_design(filepath: str) -> dict: - """Read experimental design file mapping samples to conditions. - - Expected format: TSV with columns 'sample' and 'condition'. - - Returns - ------- - dict - {sample_name: condition_name} - """ - design = {} - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - design[row["sample"]] = row["condition"] - return design - - -def benjamini_hochberg(pvalues: list) -> list: - """Apply Benjamini-Hochberg FDR correction. - - Parameters - ---------- - pvalues: - List of p-values (may contain NaN). - - Returns - ------- - list - Adjusted p-values. - """ - n = len(pvalues) - valid_indices = [i for i in range(n) if not math.isnan(pvalues[i])] - if not valid_indices: - return [float("nan")] * n - - sorted_valid = sorted(valid_indices, key=lambda i: pvalues[i]) - adjusted = [float("nan")] * n - m = len(sorted_valid) - - prev = 1.0 - for rank_idx in range(m - 1, -1, -1): - i = sorted_valid[rank_idx] - rank = rank_idx + 1 - adj = min(pvalues[i] * m / rank, prev) - adj = min(adj, 1.0) - adjusted[i] = adj - prev = adj - - return adjusted - - -def differential_expression( - matrix: np.ndarray, row_ids: list, col_names: list, design: dict, test: str = "ttest" -) -> list: - """Compute differential expression statistics. - - Parameters - ---------- - matrix: - 2D array (features x samples). - row_ids: - Feature identifiers. - col_names: - Sample names matching matrix columns. - design: - {sample: condition} mapping. Expects exactly two conditions. - test: - Statistical test: 'ttest' (equal variance) or 'welch' (unequal variance). - - Returns - ------- - list - List of dicts with keys: feature, log2fc, pvalue, adj_pvalue. - """ - conditions = sorted(set(design.values())) - if len(conditions) != 2: - raise ValueError(f"Exactly 2 conditions required, got {len(conditions)}: {conditions}") - - cond_a, cond_b = conditions - idx_a = [i for i, s in enumerate(col_names) if design.get(s) == cond_a] - idx_b = [i for i, s in enumerate(col_names) if design.get(s) == cond_b] - - if not idx_a or not idx_b: - raise ValueError("No samples found for one or both conditions.") - - equal_var = test.lower() == "ttest" - - results = [] - pvalues = [] - for row_idx in range(len(row_ids)): - vals_a = matrix[row_idx, idx_a] - vals_b = matrix[row_idx, idx_b] - valid_a = vals_a[~np.isnan(vals_a)] - valid_b = vals_b[~np.isnan(vals_b)] - - if len(valid_a) < 2 or len(valid_b) < 2: - log2fc = float("nan") - pval = float("nan") - else: - mean_a = np.mean(valid_a) - mean_b = np.mean(valid_b) - if mean_a > 0 and mean_b > 0: - log2fc = np.log2(mean_b / mean_a) - else: - log2fc = float("nan") - _, pval = stats.ttest_ind(valid_a, valid_b, equal_var=equal_var) - - results.append({ - "feature": row_ids[row_idx], - "log2fc": log2fc, - "pvalue": pval, - }) - pvalues.append(pval) - - adj_pvalues = benjamini_hochberg(pvalues) - for i, r in enumerate(results): - r["adj_pvalue"] = adj_pvalues[i] - - return results - - -def main(): - parser = argparse.ArgumentParser(description="T-test + BH correction on quantification matrices.") - parser.add_argument("--input", required=True, help="Input TSV matrix file") - parser.add_argument("--design", required=True, help="Experimental design TSV (columns: sample, condition)") - parser.add_argument("--test", default="ttest", choices=["ttest", "welch"], help="Test type (default: ttest)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - row_ids, col_names, matrix = read_matrix(args.input) - design = read_design(args.design) - results = differential_expression(matrix, row_ids, col_names, design, test=args.test) - - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=["feature", "log2fc", "pvalue", "adj_pvalue"], delimiter="\t") - writer.writeheader() - for r in results: - writer.writerow({ - "feature": r["feature"], - "log2fc": f"{r['log2fc']:.6f}" if not math.isnan(r["log2fc"]) else "NA", - "pvalue": f"{r['pvalue']:.6e}" if not math.isnan(r["pvalue"]) else "NA", - "adj_pvalue": f"{r['adj_pvalue']:.6e}" if not math.isnan(r["adj_pvalue"]) else "NA", - }) - - n_sig = sum(1 for r in results if not math.isnan(r["adj_pvalue"]) and r["adj_pvalue"] < 0.05) - print(f"Test: {args.test}") - print(f"Features tested: {len(results)}") - print(f"Significant (adj_pvalue < 0.05): {n_sig}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/differential_expression_tester/requirements.txt b/scripts/proteomics/differential_expression_tester/requirements.txt deleted file mode 100644 index ba577e4..0000000 --- a/scripts/proteomics/differential_expression_tester/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -pyopenms -numpy -scipy diff --git a/scripts/proteomics/differential_expression_tester/tests/test_differential_expression_tester.py b/scripts/proteomics/differential_expression_tester/tests/test_differential_expression_tester.py deleted file mode 100644 index d017413..0000000 --- a/scripts/proteomics/differential_expression_tester/tests/test_differential_expression_tester.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Tests for differential_expression_tester.""" - -import math - -import numpy as np -import pytest -from conftest import requires_pyopenms -from differential_expression_tester import ( - benjamini_hochberg, - differential_expression, - read_design, -) - - -@requires_pyopenms -class TestDifferentialExpressionTester: - def _make_data(self): - # 3 features, 6 samples (3 per condition) - np.random.seed(42) - matrix = np.array([ - [100, 110, 105, 200, 210, 205], # clearly different - [100, 105, 102, 103, 108, 101], # not different - [50, 55, 52, 500, 510, 505], # very different - ], dtype=float) - row_ids = ["prot1", "prot2", "prot3"] - col_names = ["s1", "s2", "s3", "s4", "s5", "s6"] - design = {"s1": "A", "s2": "A", "s3": "A", "s4": "B", "s5": "B", "s6": "B"} - return matrix, row_ids, col_names, design - - def test_basic_ttest(self): - matrix, row_ids, col_names, design = self._make_data() - results = differential_expression(matrix, row_ids, col_names, design, test="ttest") - assert len(results) == 3 - assert all("pvalue" in r for r in results) - assert all("log2fc" in r for r in results) - assert all("adj_pvalue" in r for r in results) - - def test_significant_feature(self): - matrix, row_ids, col_names, design = self._make_data() - results = differential_expression(matrix, row_ids, col_names, design) - # prot3 should be highly significant - prot3 = next(r for r in results if r["feature"] == "prot3") - assert prot3["pvalue"] < 0.01 - assert prot3["log2fc"] > 2.0 # ~10x increase - - def test_nonsignificant_feature(self): - matrix, row_ids, col_names, design = self._make_data() - results = differential_expression(matrix, row_ids, col_names, design) - prot2 = next(r for r in results if r["feature"] == "prot2") - assert prot2["pvalue"] > 0.05 - - def test_welch_test(self): - matrix, row_ids, col_names, design = self._make_data() - results = differential_expression(matrix, row_ids, col_names, design, test="welch") - assert len(results) == 3 - - def test_bh_correction(self): - pvalues = [0.01, 0.04, 0.03, 0.2] - adjusted = benjamini_hochberg(pvalues) - assert len(adjusted) == 4 - # Adjusted should be >= original - for orig, adj in zip(pvalues, adjusted): - assert adj >= orig or math.isnan(adj) - - def test_bh_all_nan(self): - pvalues = [float("nan"), float("nan")] - adjusted = benjamini_hochberg(pvalues) - assert all(math.isnan(a) for a in adjusted) - - def test_wrong_conditions(self): - matrix, row_ids, col_names, _ = self._make_data() - design_3 = {"s1": "A", "s2": "B", "s3": "C", "s4": "A", "s5": "B", "s6": "C"} - with pytest.raises(ValueError, match="Exactly 2 conditions"): - differential_expression(matrix, row_ids, col_names, design_3) - - def test_design_file_read(self, tmp_path): - design_file = str(tmp_path / "design.tsv") - with open(design_file, "w") as fh: - fh.write("sample\tcondition\n") - fh.write("s1\tA\n") - fh.write("s2\tB\n") - design = read_design(design_file) - assert design == {"s1": "A", "s2": "B"} diff --git a/scripts/proteomics/contaminant_database_merger/README.md b/scripts/proteomics/fasta_utils/contaminant_database_merger/README.md similarity index 100% rename from scripts/proteomics/contaminant_database_merger/README.md rename to scripts/proteomics/fasta_utils/contaminant_database_merger/README.md diff --git a/scripts/proteomics/contaminant_database_merger/contaminant_database_merger.py b/scripts/proteomics/fasta_utils/contaminant_database_merger/contaminant_database_merger.py similarity index 100% rename from scripts/proteomics/contaminant_database_merger/contaminant_database_merger.py rename to scripts/proteomics/fasta_utils/contaminant_database_merger/contaminant_database_merger.py diff --git a/scripts/metabolomics/targeted_feature_extractor/requirements.txt b/scripts/proteomics/fasta_utils/contaminant_database_merger/requirements.txt similarity index 100% rename from scripts/metabolomics/targeted_feature_extractor/requirements.txt rename to scripts/proteomics/fasta_utils/contaminant_database_merger/requirements.txt diff --git a/scripts/metabolomics/targeted_feature_extractor/tests/conftest.py b/scripts/proteomics/fasta_utils/contaminant_database_merger/tests/conftest.py similarity index 100% rename from scripts/metabolomics/targeted_feature_extractor/tests/conftest.py rename to scripts/proteomics/fasta_utils/contaminant_database_merger/tests/conftest.py diff --git a/scripts/proteomics/contaminant_database_merger/tests/test_contaminant_database_merger.py b/scripts/proteomics/fasta_utils/contaminant_database_merger/tests/test_contaminant_database_merger.py similarity index 100% rename from scripts/proteomics/contaminant_database_merger/tests/test_contaminant_database_merger.py rename to scripts/proteomics/fasta_utils/contaminant_database_merger/tests/test_contaminant_database_merger.py diff --git a/scripts/proteomics/fasta_cleaner/README.md b/scripts/proteomics/fasta_utils/fasta_cleaner/README.md similarity index 100% rename from scripts/proteomics/fasta_cleaner/README.md rename to scripts/proteomics/fasta_utils/fasta_cleaner/README.md diff --git a/scripts/proteomics/fasta_cleaner/fasta_cleaner.py b/scripts/proteomics/fasta_utils/fasta_cleaner/fasta_cleaner.py similarity index 100% rename from scripts/proteomics/fasta_cleaner/fasta_cleaner.py rename to scripts/proteomics/fasta_utils/fasta_cleaner/fasta_cleaner.py diff --git a/scripts/metabolomics/van_krevelen_data_generator/requirements.txt b/scripts/proteomics/fasta_utils/fasta_cleaner/requirements.txt similarity index 100% rename from scripts/metabolomics/van_krevelen_data_generator/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_cleaner/requirements.txt diff --git a/scripts/metabolomics/van_krevelen_data_generator/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_cleaner/tests/conftest.py similarity index 100% rename from scripts/metabolomics/van_krevelen_data_generator/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_cleaner/tests/conftest.py diff --git a/scripts/proteomics/fasta_cleaner/tests/test_fasta_cleaner.py b/scripts/proteomics/fasta_utils/fasta_cleaner/tests/test_fasta_cleaner.py similarity index 100% rename from scripts/proteomics/fasta_cleaner/tests/test_fasta_cleaner.py rename to scripts/proteomics/fasta_utils/fasta_cleaner/tests/test_fasta_cleaner.py diff --git a/scripts/proteomics/fasta_decoy_validator/README.md b/scripts/proteomics/fasta_utils/fasta_decoy_validator/README.md similarity index 100% rename from scripts/proteomics/fasta_decoy_validator/README.md rename to scripts/proteomics/fasta_utils/fasta_decoy_validator/README.md diff --git a/scripts/proteomics/fasta_decoy_validator/fasta_decoy_validator.py b/scripts/proteomics/fasta_utils/fasta_decoy_validator/fasta_decoy_validator.py similarity index 100% rename from scripts/proteomics/fasta_decoy_validator/fasta_decoy_validator.py rename to scripts/proteomics/fasta_utils/fasta_decoy_validator/fasta_decoy_validator.py diff --git a/scripts/proteomics/acquisition_rate_analyzer/requirements.txt b/scripts/proteomics/fasta_utils/fasta_decoy_validator/requirements.txt similarity index 100% rename from scripts/proteomics/acquisition_rate_analyzer/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_decoy_validator/requirements.txt diff --git a/scripts/proteomics/acquisition_rate_analyzer/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_decoy_validator/tests/conftest.py similarity index 100% rename from scripts/proteomics/acquisition_rate_analyzer/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_decoy_validator/tests/conftest.py diff --git a/scripts/proteomics/fasta_decoy_validator/tests/test_fasta_decoy_validator.py b/scripts/proteomics/fasta_utils/fasta_decoy_validator/tests/test_fasta_decoy_validator.py similarity index 100% rename from scripts/proteomics/fasta_decoy_validator/tests/test_fasta_decoy_validator.py rename to scripts/proteomics/fasta_utils/fasta_decoy_validator/tests/test_fasta_decoy_validator.py diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/README.md b/scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/README.md similarity index 100% rename from scripts/proteomics/fasta_in_silico_digest_stats/README.md rename to scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/README.md diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py b/scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py similarity index 100% rename from scripts/proteomics/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py rename to scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py diff --git a/scripts/proteomics/amino_acid_composition_analyzer/requirements.txt b/scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/requirements.txt similarity index 100% rename from scripts/proteomics/amino_acid_composition_analyzer/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/requirements.txt diff --git a/scripts/proteomics/amino_acid_composition_analyzer/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/tests/conftest.py similarity index 100% rename from scripts/proteomics/amino_acid_composition_analyzer/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/tests/conftest.py diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py b/scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py similarity index 100% rename from scripts/proteomics/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py rename to scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/tests/test_fasta_in_silico_digest_stats.py diff --git a/scripts/proteomics/fasta_merger/README.md b/scripts/proteomics/fasta_utils/fasta_merger/README.md similarity index 100% rename from scripts/proteomics/fasta_merger/README.md rename to scripts/proteomics/fasta_utils/fasta_merger/README.md diff --git a/scripts/proteomics/fasta_merger/fasta_merger.py b/scripts/proteomics/fasta_utils/fasta_merger/fasta_merger.py similarity index 100% rename from scripts/proteomics/fasta_merger/fasta_merger.py rename to scripts/proteomics/fasta_utils/fasta_merger/fasta_merger.py diff --git a/scripts/proteomics/charge_state_predictor/requirements.txt b/scripts/proteomics/fasta_utils/fasta_merger/requirements.txt similarity index 100% rename from scripts/proteomics/charge_state_predictor/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_merger/requirements.txt diff --git a/scripts/proteomics/biomarker_panel_roc/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_merger/tests/conftest.py similarity index 100% rename from scripts/proteomics/biomarker_panel_roc/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_merger/tests/conftest.py diff --git a/scripts/proteomics/fasta_merger/tests/test_fasta_merger.py b/scripts/proteomics/fasta_utils/fasta_merger/tests/test_fasta_merger.py similarity index 100% rename from scripts/proteomics/fasta_merger/tests/test_fasta_merger.py rename to scripts/proteomics/fasta_utils/fasta_merger/tests/test_fasta_merger.py diff --git a/scripts/proteomics/fasta_statistics_reporter/README.md b/scripts/proteomics/fasta_utils/fasta_statistics_reporter/README.md similarity index 100% rename from scripts/proteomics/fasta_statistics_reporter/README.md rename to scripts/proteomics/fasta_utils/fasta_statistics_reporter/README.md diff --git a/scripts/proteomics/fasta_statistics_reporter/fasta_statistics_reporter.py b/scripts/proteomics/fasta_utils/fasta_statistics_reporter/fasta_statistics_reporter.py similarity index 100% rename from scripts/proteomics/fasta_statistics_reporter/fasta_statistics_reporter.py rename to scripts/proteomics/fasta_utils/fasta_statistics_reporter/fasta_statistics_reporter.py diff --git a/scripts/proteomics/cleavage_site_profiler/requirements.txt b/scripts/proteomics/fasta_utils/fasta_statistics_reporter/requirements.txt similarity index 100% rename from scripts/proteomics/cleavage_site_profiler/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_statistics_reporter/requirements.txt diff --git a/scripts/proteomics/charge_state_predictor/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_statistics_reporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/charge_state_predictor/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_statistics_reporter/tests/conftest.py diff --git a/scripts/proteomics/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py b/scripts/proteomics/fasta_utils/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py similarity index 100% rename from scripts/proteomics/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py rename to scripts/proteomics/fasta_utils/fasta_statistics_reporter/tests/test_fasta_statistics_reporter.py diff --git a/scripts/proteomics/fasta_subset_extractor/README.md b/scripts/proteomics/fasta_utils/fasta_subset_extractor/README.md similarity index 100% rename from scripts/proteomics/fasta_subset_extractor/README.md rename to scripts/proteomics/fasta_utils/fasta_subset_extractor/README.md diff --git a/scripts/proteomics/fasta_subset_extractor/fasta_subset_extractor.py b/scripts/proteomics/fasta_utils/fasta_subset_extractor/fasta_subset_extractor.py similarity index 100% rename from scripts/proteomics/fasta_subset_extractor/fasta_subset_extractor.py rename to scripts/proteomics/fasta_utils/fasta_subset_extractor/fasta_subset_extractor.py diff --git a/scripts/proteomics/collision_energy_analyzer/requirements.txt b/scripts/proteomics/fasta_utils/fasta_subset_extractor/requirements.txt similarity index 100% rename from scripts/proteomics/collision_energy_analyzer/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_subset_extractor/requirements.txt diff --git a/scripts/proteomics/cleavage_site_profiler/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_subset_extractor/tests/conftest.py similarity index 100% rename from scripts/proteomics/cleavage_site_profiler/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_subset_extractor/tests/conftest.py diff --git a/scripts/proteomics/fasta_subset_extractor/tests/test_fasta_subset_extractor.py b/scripts/proteomics/fasta_utils/fasta_subset_extractor/tests/test_fasta_subset_extractor.py similarity index 100% rename from scripts/proteomics/fasta_subset_extractor/tests/test_fasta_subset_extractor.py rename to scripts/proteomics/fasta_utils/fasta_subset_extractor/tests/test_fasta_subset_extractor.py diff --git a/scripts/proteomics/fasta_taxonomy_splitter/README.md b/scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/README.md similarity index 100% rename from scripts/proteomics/fasta_taxonomy_splitter/README.md rename to scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/README.md diff --git a/scripts/proteomics/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py b/scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py similarity index 100% rename from scripts/proteomics/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py rename to scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py diff --git a/scripts/proteomics/consensus_map_to_matrix/requirements.txt b/scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/requirements.txt similarity index 100% rename from scripts/proteomics/consensus_map_to_matrix/requirements.txt rename to scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/requirements.txt diff --git a/scripts/proteomics/coefficient_of_variation_calculator/tests/conftest.py b/scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/tests/conftest.py similarity index 100% rename from scripts/proteomics/coefficient_of_variation_calculator/tests/conftest.py rename to scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/tests/conftest.py diff --git a/scripts/proteomics/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py b/scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py similarity index 100% rename from scripts/proteomics/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py rename to scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/tests/test_fasta_taxonomy_splitter.py diff --git a/scripts/proteomics/consensus_map_to_matrix/README.md b/scripts/proteomics/file_conversion/consensus_map_to_matrix/README.md similarity index 100% rename from scripts/proteomics/consensus_map_to_matrix/README.md rename to scripts/proteomics/file_conversion/consensus_map_to_matrix/README.md diff --git a/scripts/proteomics/consensus_map_to_matrix/consensus_map_to_matrix.py b/scripts/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py similarity index 100% rename from scripts/proteomics/consensus_map_to_matrix/consensus_map_to_matrix.py rename to scripts/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py diff --git a/scripts/proteomics/contaminant_database_merger/requirements.txt b/scripts/proteomics/file_conversion/consensus_map_to_matrix/requirements.txt similarity index 100% rename from scripts/proteomics/contaminant_database_merger/requirements.txt rename to scripts/proteomics/file_conversion/consensus_map_to_matrix/requirements.txt diff --git a/scripts/proteomics/collision_energy_analyzer/tests/conftest.py b/scripts/proteomics/file_conversion/consensus_map_to_matrix/tests/conftest.py similarity index 100% rename from scripts/proteomics/collision_energy_analyzer/tests/conftest.py rename to scripts/proteomics/file_conversion/consensus_map_to_matrix/tests/conftest.py diff --git a/scripts/proteomics/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py b/scripts/proteomics/file_conversion/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py similarity index 100% rename from scripts/proteomics/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py rename to scripts/proteomics/file_conversion/consensus_map_to_matrix/tests/test_consensus_map_to_matrix.py diff --git a/scripts/proteomics/featurexml_merger/README.md b/scripts/proteomics/file_conversion/featurexml_merger/README.md similarity index 100% rename from scripts/proteomics/featurexml_merger/README.md rename to scripts/proteomics/file_conversion/featurexml_merger/README.md diff --git a/scripts/proteomics/featurexml_merger/featurexml_merger.py b/scripts/proteomics/file_conversion/featurexml_merger/featurexml_merger.py similarity index 100% rename from scripts/proteomics/featurexml_merger/featurexml_merger.py rename to scripts/proteomics/file_conversion/featurexml_merger/featurexml_merger.py diff --git a/scripts/proteomics/crosslink_mass_calculator/requirements.txt b/scripts/proteomics/file_conversion/featurexml_merger/requirements.txt similarity index 100% rename from scripts/proteomics/crosslink_mass_calculator/requirements.txt rename to scripts/proteomics/file_conversion/featurexml_merger/requirements.txt diff --git a/scripts/proteomics/consensus_map_to_matrix/tests/conftest.py b/scripts/proteomics/file_conversion/featurexml_merger/tests/conftest.py similarity index 100% rename from scripts/proteomics/consensus_map_to_matrix/tests/conftest.py rename to scripts/proteomics/file_conversion/featurexml_merger/tests/conftest.py diff --git a/scripts/proteomics/featurexml_merger/tests/test_featurexml_merger.py b/scripts/proteomics/file_conversion/featurexml_merger/tests/test_featurexml_merger.py similarity index 100% rename from scripts/proteomics/featurexml_merger/tests/test_featurexml_merger.py rename to scripts/proteomics/file_conversion/featurexml_merger/tests/test_featurexml_merger.py diff --git a/scripts/proteomics/idxml_to_tsv_exporter/README.md b/scripts/proteomics/file_conversion/idxml_to_tsv_exporter/README.md similarity index 100% rename from scripts/proteomics/idxml_to_tsv_exporter/README.md rename to scripts/proteomics/file_conversion/idxml_to_tsv_exporter/README.md diff --git a/scripts/proteomics/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py b/scripts/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py similarity index 100% rename from scripts/proteomics/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py rename to scripts/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py diff --git a/scripts/proteomics/dia_window_analyzer/requirements.txt b/scripts/proteomics/file_conversion/idxml_to_tsv_exporter/requirements.txt similarity index 100% rename from scripts/proteomics/dia_window_analyzer/requirements.txt rename to scripts/proteomics/file_conversion/idxml_to_tsv_exporter/requirements.txt diff --git a/scripts/proteomics/contaminant_database_merger/tests/conftest.py b/scripts/proteomics/file_conversion/idxml_to_tsv_exporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/contaminant_database_merger/tests/conftest.py rename to scripts/proteomics/file_conversion/idxml_to_tsv_exporter/tests/conftest.py diff --git a/scripts/proteomics/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py b/scripts/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py similarity index 100% rename from scripts/proteomics/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py rename to scripts/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py diff --git a/scripts/proteomics/mgf_to_mzml_converter/README.md b/scripts/proteomics/file_conversion/mgf_to_mzml_converter/README.md similarity index 100% rename from scripts/proteomics/mgf_to_mzml_converter/README.md rename to scripts/proteomics/file_conversion/mgf_to_mzml_converter/README.md diff --git a/scripts/proteomics/mgf_to_mzml_converter/mgf_to_mzml_converter.py b/scripts/proteomics/file_conversion/mgf_to_mzml_converter/mgf_to_mzml_converter.py similarity index 100% rename from scripts/proteomics/mgf_to_mzml_converter/mgf_to_mzml_converter.py rename to scripts/proteomics/file_conversion/mgf_to_mzml_converter/mgf_to_mzml_converter.py diff --git a/scripts/proteomics/diann_result_converter/requirements.txt b/scripts/proteomics/file_conversion/mgf_to_mzml_converter/requirements.txt similarity index 100% rename from scripts/proteomics/diann_result_converter/requirements.txt rename to scripts/proteomics/file_conversion/mgf_to_mzml_converter/requirements.txt diff --git a/scripts/proteomics/crosslink_mass_calculator/tests/conftest.py b/scripts/proteomics/file_conversion/mgf_to_mzml_converter/tests/conftest.py similarity index 100% rename from scripts/proteomics/crosslink_mass_calculator/tests/conftest.py rename to scripts/proteomics/file_conversion/mgf_to_mzml_converter/tests/conftest.py diff --git a/scripts/proteomics/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py b/scripts/proteomics/file_conversion/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py similarity index 100% rename from scripts/proteomics/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py rename to scripts/proteomics/file_conversion/mgf_to_mzml_converter/tests/test_mgf_to_mzml_converter.py diff --git a/scripts/proteomics/ms_data_ml_exporter/README.md b/scripts/proteomics/file_conversion/ms_data_ml_exporter/README.md similarity index 100% rename from scripts/proteomics/ms_data_ml_exporter/README.md rename to scripts/proteomics/file_conversion/ms_data_ml_exporter/README.md diff --git a/scripts/proteomics/ms_data_ml_exporter/ms_data_ml_exporter.py b/scripts/proteomics/file_conversion/ms_data_ml_exporter/ms_data_ml_exporter.py similarity index 100% rename from scripts/proteomics/ms_data_ml_exporter/ms_data_ml_exporter.py rename to scripts/proteomics/file_conversion/ms_data_ml_exporter/ms_data_ml_exporter.py diff --git a/scripts/proteomics/fasta_cleaner/requirements.txt b/scripts/proteomics/file_conversion/ms_data_ml_exporter/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_cleaner/requirements.txt rename to scripts/proteomics/file_conversion/ms_data_ml_exporter/requirements.txt diff --git a/scripts/proteomics/dia_window_analyzer/tests/conftest.py b/scripts/proteomics/file_conversion/ms_data_ml_exporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/dia_window_analyzer/tests/conftest.py rename to scripts/proteomics/file_conversion/ms_data_ml_exporter/tests/conftest.py diff --git a/scripts/proteomics/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py b/scripts/proteomics/file_conversion/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py similarity index 100% rename from scripts/proteomics/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py rename to scripts/proteomics/file_conversion/ms_data_ml_exporter/tests/test_ms_data_ml_exporter.py diff --git a/scripts/proteomics/ms_data_to_csv_exporter/README.md b/scripts/proteomics/file_conversion/ms_data_to_csv_exporter/README.md similarity index 100% rename from scripts/proteomics/ms_data_to_csv_exporter/README.md rename to scripts/proteomics/file_conversion/ms_data_to_csv_exporter/README.md diff --git a/scripts/proteomics/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py b/scripts/proteomics/file_conversion/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py similarity index 100% rename from scripts/proteomics/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py rename to scripts/proteomics/file_conversion/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py diff --git a/scripts/proteomics/fasta_decoy_validator/requirements.txt b/scripts/proteomics/file_conversion/ms_data_to_csv_exporter/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_decoy_validator/requirements.txt rename to scripts/proteomics/file_conversion/ms_data_to_csv_exporter/requirements.txt diff --git a/scripts/proteomics/diann_result_converter/tests/conftest.py b/scripts/proteomics/file_conversion/ms_data_to_csv_exporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/diann_result_converter/tests/conftest.py rename to scripts/proteomics/file_conversion/ms_data_to_csv_exporter/tests/conftest.py diff --git a/scripts/proteomics/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py b/scripts/proteomics/file_conversion/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py similarity index 100% rename from scripts/proteomics/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py rename to scripts/proteomics/file_conversion/ms_data_to_csv_exporter/tests/test_ms_data_to_csv_exporter.py diff --git a/scripts/proteomics/mzml_to_mgf_converter/README.md b/scripts/proteomics/file_conversion/mzml_to_mgf_converter/README.md similarity index 100% rename from scripts/proteomics/mzml_to_mgf_converter/README.md rename to scripts/proteomics/file_conversion/mzml_to_mgf_converter/README.md diff --git a/scripts/proteomics/mzml_to_mgf_converter/mzml_to_mgf_converter.py b/scripts/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py similarity index 100% rename from scripts/proteomics/mzml_to_mgf_converter/mzml_to_mgf_converter.py rename to scripts/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/requirements.txt b/scripts/proteomics/file_conversion/mzml_to_mgf_converter/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_in_silico_digest_stats/requirements.txt rename to scripts/proteomics/file_conversion/mzml_to_mgf_converter/requirements.txt diff --git a/scripts/proteomics/differential_expression_tester/tests/conftest.py b/scripts/proteomics/file_conversion/mzml_to_mgf_converter/tests/conftest.py similarity index 100% rename from scripts/proteomics/differential_expression_tester/tests/conftest.py rename to scripts/proteomics/file_conversion/mzml_to_mgf_converter/tests/conftest.py diff --git a/scripts/proteomics/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py b/scripts/proteomics/file_conversion/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py similarity index 100% rename from scripts/proteomics/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py rename to scripts/proteomics/file_conversion/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py diff --git a/scripts/proteomics/mztab_summarizer/README.md b/scripts/proteomics/file_conversion/mztab_summarizer/README.md similarity index 100% rename from scripts/proteomics/mztab_summarizer/README.md rename to scripts/proteomics/file_conversion/mztab_summarizer/README.md diff --git a/scripts/proteomics/mztab_summarizer/mztab_summarizer.py b/scripts/proteomics/file_conversion/mztab_summarizer/mztab_summarizer.py similarity index 100% rename from scripts/proteomics/mztab_summarizer/mztab_summarizer.py rename to scripts/proteomics/file_conversion/mztab_summarizer/mztab_summarizer.py diff --git a/scripts/proteomics/fasta_merger/requirements.txt b/scripts/proteomics/file_conversion/mztab_summarizer/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_merger/requirements.txt rename to scripts/proteomics/file_conversion/mztab_summarizer/requirements.txt diff --git a/scripts/proteomics/experimental_design_generator/tests/conftest.py b/scripts/proteomics/file_conversion/mztab_summarizer/tests/conftest.py similarity index 100% rename from scripts/proteomics/experimental_design_generator/tests/conftest.py rename to scripts/proteomics/file_conversion/mztab_summarizer/tests/conftest.py diff --git a/scripts/proteomics/mztab_summarizer/tests/test_mztab_summarizer.py b/scripts/proteomics/file_conversion/mztab_summarizer/tests/test_mztab_summarizer.py similarity index 100% rename from scripts/proteomics/mztab_summarizer/tests/test_mztab_summarizer.py rename to scripts/proteomics/file_conversion/mztab_summarizer/tests/test_mztab_summarizer.py diff --git a/scripts/proteomics/fragpipe_result_converter/README.md b/scripts/proteomics/fragpipe_result_converter/README.md deleted file mode 100644 index 0881e63..0000000 --- a/scripts/proteomics/fragpipe_result_converter/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# FragPipe Result Converter - -Convert FragPipe psm.tsv to a standardized TSV format. - -## Usage - -```bash -python fragpipe_result_converter.py --input psm.tsv --output standardized.tsv -``` - -## Column Mapping - -| FragPipe Column | Standard Column | -|---|---| -| Peptide | peptide | -| Modified Peptide | modified_peptide | -| Charge | charge | -| Observed M/Z | mz | -| Retention | rt | -| Protein | protein | -| Hyperscore | score | -| Intensity | intensity | -| Spectrum File | raw_file | diff --git a/scripts/proteomics/fragpipe_result_converter/fragpipe_result_converter.py b/scripts/proteomics/fragpipe_result_converter/fragpipe_result_converter.py deleted file mode 100644 index 17bb21c..0000000 --- a/scripts/proteomics/fragpipe_result_converter/fragpipe_result_converter.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -FragPipe Result Converter -========================= -Convert FragPipe psm.tsv to a standardized TSV format. - -Maps FragPipe-specific column names to a common schema for downstream analysis. - -Usage ------ - python fragpipe_result_converter.py --input psm.tsv --output standardized.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -# Mapping from FragPipe column names to standard column names -COLUMN_MAP = { - "Peptide": "peptide", - "Modified Peptide": "modified_peptide", - "Charge": "charge", - "Calculated Peptide Mass": "mass", - "Calibrated Observed Mass": "observed_mass", - "Observed M/Z": "mz", - "Retention": "rt", - "Protein": "protein", - "Protein Description": "protein_description", - "Gene": "gene", - "Hyperscore": "score", - "Expectation": "expect", - "PeptideProphet Probability": "probability", - "Intensity": "intensity", - "Spectrum": "spectrum", - "Spectrum File": "raw_file", - "Is Unique": "is_unique", - "Mapped Proteins": "mapped_proteins", -} - -STANDARD_FIELDS = [ - "peptide", "modified_peptide", "charge", "mass", "observed_mass", "mz", "rt", - "protein", "protein_description", "gene", - "score", "expect", "probability", - "intensity", "spectrum", "raw_file", - "is_unique", "mapped_proteins", "source", -] - - -def convert_fragpipe_psm(filepath: str) -> list: - """Convert FragPipe psm.tsv to standardized format. - - Parameters - ---------- - filepath: - Path to FragPipe psm.tsv. - - Returns - ------- - list - List of dicts with standardized column names. - """ - rows = [] - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - std_row = {} - for fp_col, std_col in COLUMN_MAP.items(): - std_row[std_col] = row.get(fp_col, "") - std_row["source"] = "FragPipe" - rows.append(std_row) - return rows - - -def write_standardized(filepath: str, rows: list) -> None: - """Write standardized results to TSV.""" - with open(filepath, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=STANDARD_FIELDS, delimiter="\t", extrasaction="ignore") - writer.writeheader() - writer.writerows(rows) - - -def main(): - parser = argparse.ArgumentParser(description="Convert FragPipe psm.tsv to standardized TSV.") - parser.add_argument("--input", required=True, help="FragPipe psm.tsv file") - parser.add_argument("--output", required=True, help="Output standardized TSV") - args = parser.parse_args() - - rows = convert_fragpipe_psm(args.input) - write_standardized(args.output, rows) - - print("Source: FragPipe") - print(f"Total PSMs: {len(rows)}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/fragpipe_result_converter/tests/test_fragpipe_result_converter.py b/scripts/proteomics/fragpipe_result_converter/tests/test_fragpipe_result_converter.py deleted file mode 100644 index bd1c2a2..0000000 --- a/scripts/proteomics/fragpipe_result_converter/tests/test_fragpipe_result_converter.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Tests for fragpipe_result_converter.""" - -import csv - -from conftest import requires_pyopenms -from fragpipe_result_converter import STANDARD_FIELDS, convert_fragpipe_psm, write_standardized - - -@requires_pyopenms -class TestFragpipeResultConverter: - def _write_psm(self, tmp_path, rows): - filepath = str(tmp_path / "psm.tsv") - fieldnames = [ - "Peptide", "Modified Peptide", "Charge", - "Calculated Peptide Mass", "Calibrated Observed Mass", - "Observed M/Z", "Retention", "Protein", "Protein Description", - "Gene", "Hyperscore", "Expectation", - "PeptideProphet Probability", "Intensity", - "Spectrum", "Spectrum File", "Is Unique", "Mapped Proteins", - ] - with open(filepath, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") - writer.writeheader() - writer.writerows(rows) - return filepath - - def test_basic_conversion(self, tmp_path): - filepath = self._write_psm(tmp_path, [ - { - "Peptide": "PEPTIDEK", "Modified Peptide": "PEPTIDEK", - "Charge": "2", "Calculated Peptide Mass": "900.0", - "Calibrated Observed Mass": "900.1", - "Observed M/Z": "450.5", "Retention": "25.5", - "Protein": "sp|P12345|PROT1", "Protein Description": "Test", - "Gene": "GEN1", "Hyperscore": "35.5", - "Expectation": "1e-5", - "PeptideProphet Probability": "0.999", - "Intensity": "1e6", "Spectrum": "scan.1234.1234.2", - "Spectrum File": "run1.mzML", "Is Unique": "true", - "Mapped Proteins": "sp|P12345|PROT1", - } - ]) - rows = convert_fragpipe_psm(filepath) - assert len(rows) == 1 - assert rows[0]["peptide"] == "PEPTIDEK" - assert rows[0]["charge"] == "2" - assert rows[0]["source"] == "FragPipe" - - def test_multiple_rows(self, tmp_path): - filepath = self._write_psm(tmp_path, [ - {"Peptide": "PEP1", "Modified Peptide": "PEP1", "Charge": "2", - "Calculated Peptide Mass": "800", "Calibrated Observed Mass": "800", - "Observed M/Z": "400", "Retention": "20", - "Protein": "P1", "Protein Description": "Prot1", - "Gene": "G1", "Hyperscore": "30", "Expectation": "1e-4", - "PeptideProphet Probability": "0.99", "Intensity": "1e6", - "Spectrum": "scan.1", "Spectrum File": "run1.mzML", - "Is Unique": "true", "Mapped Proteins": "P1"}, - {"Peptide": "PEP2", "Modified Peptide": "PEP2", "Charge": "3", - "Calculated Peptide Mass": "900", "Calibrated Observed Mass": "900", - "Observed M/Z": "300", "Retention": "30", - "Protein": "P2", "Protein Description": "Prot2", - "Gene": "G2", "Hyperscore": "25", "Expectation": "1e-3", - "PeptideProphet Probability": "0.95", "Intensity": "5e5", - "Spectrum": "scan.2", "Spectrum File": "run1.mzML", - "Is Unique": "false", "Mapped Proteins": "P2;P3"}, - ]) - rows = convert_fragpipe_psm(filepath) - assert len(rows) == 2 - - def test_write_standardized(self, tmp_path): - rows = [{"peptide": "PEPTIDEK", "charge": "2", "source": "FragPipe"}] - outfile = str(tmp_path / "out.tsv") - write_standardized(outfile, rows) - with open(outfile) as fh: - reader = csv.DictReader(fh, delimiter="\t") - result = list(reader) - assert len(result) == 1 - assert result[0]["source"] == "FragPipe" - - def test_standard_fields(self): - assert "peptide" in STANDARD_FIELDS - assert "source" in STANDARD_FIELDS - assert "score" in STANDARD_FIELDS - - def test_missing_columns_handled(self, tmp_path): - filepath = str(tmp_path / "minimal.tsv") - with open(filepath, "w") as fh: - fh.write("Peptide\tCharge\n") - fh.write("PEPTIDEK\t2\n") - rows = convert_fragpipe_psm(filepath) - assert rows[0]["peptide"] == "PEPTIDEK" - assert rows[0]["rt"] == "" diff --git a/scripts/proteomics/feature_detection_proteomics/README.md b/scripts/proteomics/identification/feature_detection_proteomics/README.md similarity index 100% rename from scripts/proteomics/feature_detection_proteomics/README.md rename to scripts/proteomics/identification/feature_detection_proteomics/README.md diff --git a/scripts/proteomics/feature_detection_proteomics/feature_detection_proteomics.py b/scripts/proteomics/identification/feature_detection_proteomics/feature_detection_proteomics.py similarity index 100% rename from scripts/proteomics/feature_detection_proteomics/feature_detection_proteomics.py rename to scripts/proteomics/identification/feature_detection_proteomics/feature_detection_proteomics.py diff --git a/scripts/proteomics/fasta_statistics_reporter/requirements.txt b/scripts/proteomics/identification/feature_detection_proteomics/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_statistics_reporter/requirements.txt rename to scripts/proteomics/identification/feature_detection_proteomics/requirements.txt diff --git a/scripts/proteomics/fasta_cleaner/tests/conftest.py b/scripts/proteomics/identification/feature_detection_proteomics/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_cleaner/tests/conftest.py rename to scripts/proteomics/identification/feature_detection_proteomics/tests/conftest.py diff --git a/scripts/proteomics/feature_detection_proteomics/tests/test_feature_detection_proteomics.py b/scripts/proteomics/identification/feature_detection_proteomics/tests/test_feature_detection_proteomics.py similarity index 100% rename from scripts/proteomics/feature_detection_proteomics/tests/test_feature_detection_proteomics.py rename to scripts/proteomics/identification/feature_detection_proteomics/tests/test_feature_detection_proteomics.py diff --git a/scripts/proteomics/mzml_metadata_extractor/README.md b/scripts/proteomics/identification/mzml_metadata_extractor/README.md similarity index 100% rename from scripts/proteomics/mzml_metadata_extractor/README.md rename to scripts/proteomics/identification/mzml_metadata_extractor/README.md diff --git a/scripts/proteomics/mzml_metadata_extractor/mzml_metadata_extractor.py b/scripts/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py similarity index 100% rename from scripts/proteomics/mzml_metadata_extractor/mzml_metadata_extractor.py rename to scripts/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py diff --git a/scripts/proteomics/fasta_subset_extractor/requirements.txt b/scripts/proteomics/identification/mzml_metadata_extractor/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_subset_extractor/requirements.txt rename to scripts/proteomics/identification/mzml_metadata_extractor/requirements.txt diff --git a/scripts/proteomics/fasta_decoy_validator/tests/conftest.py b/scripts/proteomics/identification/mzml_metadata_extractor/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_decoy_validator/tests/conftest.py rename to scripts/proteomics/identification/mzml_metadata_extractor/tests/conftest.py diff --git a/scripts/proteomics/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py b/scripts/proteomics/identification/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py similarity index 100% rename from scripts/proteomics/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py rename to scripts/proteomics/identification/mzml_metadata_extractor/tests/test_mzml_metadata_extractor.py diff --git a/scripts/proteomics/mzml_spectrum_subsetter/README.md b/scripts/proteomics/identification/mzml_spectrum_subsetter/README.md similarity index 100% rename from scripts/proteomics/mzml_spectrum_subsetter/README.md rename to scripts/proteomics/identification/mzml_spectrum_subsetter/README.md diff --git a/scripts/proteomics/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py b/scripts/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py similarity index 100% rename from scripts/proteomics/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py rename to scripts/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py diff --git a/scripts/proteomics/fasta_taxonomy_splitter/requirements.txt b/scripts/proteomics/identification/mzml_spectrum_subsetter/requirements.txt similarity index 100% rename from scripts/proteomics/fasta_taxonomy_splitter/requirements.txt rename to scripts/proteomics/identification/mzml_spectrum_subsetter/requirements.txt diff --git a/scripts/proteomics/fasta_in_silico_digest_stats/tests/conftest.py b/scripts/proteomics/identification/mzml_spectrum_subsetter/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_in_silico_digest_stats/tests/conftest.py rename to scripts/proteomics/identification/mzml_spectrum_subsetter/tests/conftest.py diff --git a/scripts/proteomics/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py b/scripts/proteomics/identification/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py similarity index 100% rename from scripts/proteomics/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py rename to scripts/proteomics/identification/mzml_spectrum_subsetter/tests/test_mzml_spectrum_subsetter.py diff --git a/scripts/proteomics/peptide_spectral_match_validator/README.md b/scripts/proteomics/identification/peptide_spectral_match_validator/README.md similarity index 100% rename from scripts/proteomics/peptide_spectral_match_validator/README.md rename to scripts/proteomics/identification/peptide_spectral_match_validator/README.md diff --git a/scripts/proteomics/peptide_spectral_match_validator/peptide_spectral_match_validator.py b/scripts/proteomics/identification/peptide_spectral_match_validator/peptide_spectral_match_validator.py similarity index 100% rename from scripts/proteomics/peptide_spectral_match_validator/peptide_spectral_match_validator.py rename to scripts/proteomics/identification/peptide_spectral_match_validator/peptide_spectral_match_validator.py diff --git a/scripts/proteomics/feature_detection_proteomics/requirements.txt b/scripts/proteomics/identification/peptide_spectral_match_validator/requirements.txt similarity index 100% rename from scripts/proteomics/feature_detection_proteomics/requirements.txt rename to scripts/proteomics/identification/peptide_spectral_match_validator/requirements.txt diff --git a/scripts/proteomics/fasta_merger/tests/conftest.py b/scripts/proteomics/identification/peptide_spectral_match_validator/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_merger/tests/conftest.py rename to scripts/proteomics/identification/peptide_spectral_match_validator/tests/conftest.py diff --git a/scripts/proteomics/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py b/scripts/proteomics/identification/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py similarity index 100% rename from scripts/proteomics/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py rename to scripts/proteomics/identification/peptide_spectral_match_validator/tests/test_peptide_spectral_match_validator.py diff --git a/scripts/proteomics/psm_feature_extractor/README.md b/scripts/proteomics/identification/psm_feature_extractor/README.md similarity index 100% rename from scripts/proteomics/psm_feature_extractor/README.md rename to scripts/proteomics/identification/psm_feature_extractor/README.md diff --git a/scripts/proteomics/psm_feature_extractor/psm_feature_extractor.py b/scripts/proteomics/identification/psm_feature_extractor/psm_feature_extractor.py similarity index 100% rename from scripts/proteomics/psm_feature_extractor/psm_feature_extractor.py rename to scripts/proteomics/identification/psm_feature_extractor/psm_feature_extractor.py diff --git a/scripts/proteomics/featurexml_merger/requirements.txt b/scripts/proteomics/identification/psm_feature_extractor/requirements.txt similarity index 100% rename from scripts/proteomics/featurexml_merger/requirements.txt rename to scripts/proteomics/identification/psm_feature_extractor/requirements.txt diff --git a/scripts/proteomics/fasta_statistics_reporter/tests/conftest.py b/scripts/proteomics/identification/psm_feature_extractor/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_statistics_reporter/tests/conftest.py rename to scripts/proteomics/identification/psm_feature_extractor/tests/conftest.py diff --git a/scripts/proteomics/psm_feature_extractor/tests/test_psm_feature_extractor.py b/scripts/proteomics/identification/psm_feature_extractor/tests/test_psm_feature_extractor.py similarity index 100% rename from scripts/proteomics/psm_feature_extractor/tests/test_psm_feature_extractor.py rename to scripts/proteomics/identification/psm_feature_extractor/tests/test_psm_feature_extractor.py diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/README.md b/scripts/proteomics/identification/semi_tryptic_peptide_finder/README.md similarity index 100% rename from scripts/proteomics/semi_tryptic_peptide_finder/README.md rename to scripts/proteomics/identification/semi_tryptic_peptide_finder/README.md diff --git a/scripts/proteomics/fragpipe_result_converter/requirements.txt b/scripts/proteomics/identification/semi_tryptic_peptide_finder/requirements.txt similarity index 100% rename from scripts/proteomics/fragpipe_result_converter/requirements.txt rename to scripts/proteomics/identification/semi_tryptic_peptide_finder/requirements.txt diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py b/scripts/proteomics/identification/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py similarity index 100% rename from scripts/proteomics/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py rename to scripts/proteomics/identification/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py diff --git a/scripts/proteomics/fasta_subset_extractor/tests/conftest.py b/scripts/proteomics/identification/semi_tryptic_peptide_finder/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_subset_extractor/tests/conftest.py rename to scripts/proteomics/identification/semi_tryptic_peptide_finder/tests/conftest.py diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py b/scripts/proteomics/identification/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py similarity index 100% rename from scripts/proteomics/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py rename to scripts/proteomics/identification/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py diff --git a/scripts/proteomics/sequence_tag_generator/README.md b/scripts/proteomics/identification/sequence_tag_generator/README.md similarity index 100% rename from scripts/proteomics/sequence_tag_generator/README.md rename to scripts/proteomics/identification/sequence_tag_generator/README.md diff --git a/scripts/proteomics/glycopeptide_mass_calculator/requirements.txt b/scripts/proteomics/identification/sequence_tag_generator/requirements.txt similarity index 100% rename from scripts/proteomics/glycopeptide_mass_calculator/requirements.txt rename to scripts/proteomics/identification/sequence_tag_generator/requirements.txt diff --git a/scripts/proteomics/sequence_tag_generator/sequence_tag_generator.py b/scripts/proteomics/identification/sequence_tag_generator/sequence_tag_generator.py similarity index 100% rename from scripts/proteomics/sequence_tag_generator/sequence_tag_generator.py rename to scripts/proteomics/identification/sequence_tag_generator/sequence_tag_generator.py diff --git a/scripts/proteomics/fasta_taxonomy_splitter/tests/conftest.py b/scripts/proteomics/identification/sequence_tag_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/fasta_taxonomy_splitter/tests/conftest.py rename to scripts/proteomics/identification/sequence_tag_generator/tests/conftest.py diff --git a/scripts/proteomics/sequence_tag_generator/tests/test_sequence_tag_generator.py b/scripts/proteomics/identification/sequence_tag_generator/tests/test_sequence_tag_generator.py similarity index 100% rename from scripts/proteomics/sequence_tag_generator/tests/test_sequence_tag_generator.py rename to scripts/proteomics/identification/sequence_tag_generator/tests/test_sequence_tag_generator.py diff --git a/scripts/proteomics/intensity_distribution_reporter/README.md b/scripts/proteomics/intensity_distribution_reporter/README.md deleted file mode 100644 index c50385e..0000000 --- a/scripts/proteomics/intensity_distribution_reporter/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# Intensity Distribution Reporter - -Report per-sample intensity statistics from a quantification matrix. - -## Usage - -```bash -python intensity_distribution_reporter.py --input matrix.tsv --output intensity_stats.tsv -``` - -## Output Columns - -`sample`, `n_values`, `n_missing`, `mean`, `median`, `sd`, `min`, `max`, `q1`, `q3` diff --git a/scripts/proteomics/intensity_distribution_reporter/intensity_distribution_reporter.py b/scripts/proteomics/intensity_distribution_reporter/intensity_distribution_reporter.py deleted file mode 100644 index 50a7297..0000000 --- a/scripts/proteomics/intensity_distribution_reporter/intensity_distribution_reporter.py +++ /dev/null @@ -1,129 +0,0 @@ -""" -Intensity Distribution Reporter -================================ -Report per-sample intensity statistics from a quantification matrix. - -Computes mean, median, standard deviation, min, max, Q1, Q3, and count of -non-missing values for each sample column. - -Usage ------ - python intensity_distribution_reporter.py --input matrix.tsv --output intensity_stats.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - - -def read_matrix(filepath: str) -> tuple: - """Read a TSV quantification matrix. - - Returns (row_ids, col_names, data_matrix). - """ - with open(filepath) as fh: - reader = csv.reader(fh, delimiter="\t") - header = next(reader) - col_names = header[1:] - row_ids = [] - rows = [] - for row in reader: - row_ids.append(row[0]) - values = [] - for v in row[1:]: - v = v.strip() - if v == "" or v.upper() in ("NA", "NAN"): - values.append(np.nan) - else: - values.append(float(v)) - rows.append(values) - return row_ids, col_names, np.array(rows, dtype=float) - - -def compute_intensity_stats(matrix: np.ndarray, col_names: list) -> list: - """Compute per-sample intensity statistics. - - Parameters - ---------- - matrix: - 2D array (features x samples). - col_names: - Sample names. - - Returns - ------- - list - List of dicts with keys: sample, n_values, n_missing, mean, median, sd, min, max, q1, q3. - """ - n_features = matrix.shape[0] - results = [] - - for col_idx, sample in enumerate(col_names): - col_data = matrix[:, col_idx] - valid = col_data[~np.isnan(col_data)] - n_valid = len(valid) - n_missing = n_features - n_valid - - if n_valid == 0: - results.append({ - "sample": sample, - "n_values": 0, - "n_missing": n_features, - "mean": float("nan"), - "median": float("nan"), - "sd": float("nan"), - "min": float("nan"), - "max": float("nan"), - "q1": float("nan"), - "q3": float("nan"), - }) - else: - results.append({ - "sample": sample, - "n_values": n_valid, - "n_missing": n_missing, - "mean": float(np.mean(valid)), - "median": float(np.median(valid)), - "sd": float(np.std(valid, ddof=1)) if n_valid > 1 else 0.0, - "min": float(np.min(valid)), - "max": float(np.max(valid)), - "q1": float(np.percentile(valid, 25)), - "q3": float(np.percentile(valid, 75)), - }) - - return results - - -def main(): - parser = argparse.ArgumentParser(description="Per-sample intensity statistics.") - parser.add_argument("--input", required=True, help="Input TSV matrix file") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - row_ids, col_names, matrix = read_matrix(args.input) - stats = compute_intensity_stats(matrix, col_names) - - fieldnames = ["sample", "n_values", "n_missing", "mean", "median", "sd", "min", "max", "q1", "q3"] - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") - writer.writeheader() - for s in stats: - row = {"sample": s["sample"], "n_values": s["n_values"], "n_missing": s["n_missing"]} - for key in ["mean", "median", "sd", "min", "max", "q1", "q3"]: - row[key] = f"{s[key]:.6f}" if not np.isnan(s[key]) else "NA" - writer.writerow(row) - - print(f"Samples: {len(col_names)}") - print(f"Features: {len(row_ids)}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/intensity_distribution_reporter/requirements.txt b/scripts/proteomics/intensity_distribution_reporter/requirements.txt deleted file mode 100644 index 1051d92..0000000 --- a/scripts/proteomics/intensity_distribution_reporter/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pyopenms -numpy diff --git a/scripts/proteomics/intensity_distribution_reporter/tests/test_intensity_distribution_reporter.py b/scripts/proteomics/intensity_distribution_reporter/tests/test_intensity_distribution_reporter.py deleted file mode 100644 index 85f9ff4..0000000 --- a/scripts/proteomics/intensity_distribution_reporter/tests/test_intensity_distribution_reporter.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Tests for intensity_distribution_reporter.""" - -import numpy as np -from conftest import requires_pyopenms -from intensity_distribution_reporter import compute_intensity_stats, read_matrix - - -@requires_pyopenms -class TestIntensityDistributionReporter: - def _make_matrix(self): - return np.array([ - [100.0, 200.0, 150.0], - [300.0, 400.0, 350.0], - [500.0, 600.0, 550.0], - [700.0, 800.0, 750.0], - ]) - - def test_basic_stats(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - stats = compute_intensity_stats(matrix, col_names) - assert len(stats) == 3 - - def test_mean_correct(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - stats = compute_intensity_stats(matrix, col_names) - s1 = next(s for s in stats if s["sample"] == "s1") - assert abs(s1["mean"] - 400.0) < 0.01 # mean of [100, 300, 500, 700] - - def test_min_max(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - stats = compute_intensity_stats(matrix, col_names) - s1 = next(s for s in stats if s["sample"] == "s1") - assert s1["min"] == 100.0 - assert s1["max"] == 700.0 - - def test_n_values(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - stats = compute_intensity_stats(matrix, col_names) - for s in stats: - assert s["n_values"] == 4 - assert s["n_missing"] == 0 - - def test_with_nan(self): - matrix = np.array([ - [100.0, np.nan], - [300.0, 400.0], - [np.nan, 600.0], - ]) - col_names = ["s1", "s2"] - stats = compute_intensity_stats(matrix, col_names) - s1 = next(s for s in stats if s["sample"] == "s1") - assert s1["n_values"] == 2 - assert s1["n_missing"] == 1 - - def test_all_nan_column(self): - matrix = np.array([ - [np.nan, 200.0], - [np.nan, 400.0], - ]) - col_names = ["s1", "s2"] - stats = compute_intensity_stats(matrix, col_names) - s1 = next(s for s in stats if s["sample"] == "s1") - assert s1["n_values"] == 0 - assert np.isnan(s1["mean"]) - - def test_quartiles(self): - matrix = np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0], [8.0]]) - col_names = ["s1"] - stats = compute_intensity_stats(matrix, col_names) - s1 = stats[0] - assert s1["q1"] == np.percentile([1, 2, 3, 4, 5, 6, 7, 8], 25) - assert s1["q3"] == np.percentile([1, 2, 3, 4, 5, 6, 7, 8], 75) - - def test_read_matrix_roundtrip(self, tmp_path): - outfile = str(tmp_path / "test.tsv") - with open(outfile, "w") as fh: - fh.write("\ts1\ts2\n") - fh.write("p1\t100.0\t200.0\n") - fh.write("p2\t300.0\tNA\n") - row_ids, col_names, matrix = read_matrix(outfile) - assert row_ids == ["p1", "p2"] - assert col_names == ["s1", "s2"] - assert np.isnan(matrix[1, 1]) diff --git a/scripts/proteomics/isobaric_purity_corrector/README.md b/scripts/proteomics/isobaric_purity_corrector/README.md deleted file mode 100644 index facd3bf..0000000 --- a/scripts/proteomics/isobaric_purity_corrector/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# Isobaric Purity Corrector - -Correct TMT/iTRAQ reporter ion quantification for isotopic impurity using a purity correction matrix. - -## Installation - -```bash -pip install -r requirements.txt -``` - -## Usage - -```bash -python isobaric_purity_corrector.py --input quant.tsv --label TMT16plex \ - --purity-matrix purity.csv --output corrected.tsv -``` - -### Input format - -Quantification TSV with channel columns matching the labeling scheme: - -``` -spectrum_id 126 127N 127C -spec1 1000.0 50.0 30.0 -``` - -Purity matrix CSV (N x N, no headers): - -``` -0.95,0.03,0.02 -0.02,0.94,0.04 -0.01,0.03,0.96 -``` - -### Parameters - -| Flag | Description | -|------|-------------| -| `--input` | Input quantification TSV | -| `--label` | Labeling scheme (TMT6plex, TMT10plex, TMT16plex, iTRAQ4plex, etc.) | -| `--purity-matrix` | Purity correction matrix CSV | -| `--output` | Output corrected TSV | diff --git a/scripts/proteomics/isobaric_purity_corrector/isobaric_purity_corrector.py b/scripts/proteomics/isobaric_purity_corrector/isobaric_purity_corrector.py deleted file mode 100644 index d04d726..0000000 --- a/scripts/proteomics/isobaric_purity_corrector/isobaric_purity_corrector.py +++ /dev/null @@ -1,204 +0,0 @@ -""" -Isobaric Purity Corrector -========================== -Correct TMT or iTRAQ reporter ion quantification for isotopic impurity -using a manufacturer-provided purity correction matrix. The tool reads -a quantification TSV and a purity matrix CSV, solves the linear system -to produce corrected intensities. - -The correction is: corrected = inv(purity_matrix) @ observed - -Usage ------ - python isobaric_purity_corrector.py --input quant.tsv \ - --label TMT16plex --purity-matrix purity.csv --output corrected.tsv -""" - -import argparse -import csv -import sys -from typing import Dict, List, Tuple - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - -# Default channel names for common labeling schemes -LABEL_CHANNELS: Dict[str, List[str]] = { - "TMT6plex": ["126", "127", "128", "129", "130", "131"], - "TMT10plex": ["126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", "130C", "131"], - "TMT11plex": [ - "126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", "130C", "131N", "131C", - ], - "TMT16plex": [ - "126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", - "130C", "131N", "131C", "132N", "132C", "133N", "133C", "134N", - ], - "TMT18plex": [ - "126", "127N", "127C", "128N", "128C", "129N", "129C", "130N", - "130C", "131N", "131C", "132N", "132C", "133N", "133C", "134N", "134C", "135N", - ], - "iTRAQ4plex": ["114", "115", "116", "117"], - "iTRAQ8plex": ["113", "114", "115", "116", "117", "118", "119", "121"], -} - - -def load_purity_matrix(purity_path: str) -> np.ndarray: - """Load purity correction matrix from CSV. - - The CSV should have N rows and N columns (no headers), where N is the - number of channels. Each row represents the contribution of a single - channel to all observed channels. - - Parameters - ---------- - purity_path: - Path to purity matrix CSV. - - Returns - ------- - numpy.ndarray - Square purity matrix of shape (N, N). - """ - rows: List[List[float]] = [] - with open(purity_path, newline="") as fh: - reader = csv.reader(fh) - for row in reader: - values = [float(v.strip()) for v in row if v.strip()] - if values: - rows.append(values) - return np.array(rows, dtype=float) - - -def correct_intensities( - observed: np.ndarray, purity_matrix: np.ndarray -) -> np.ndarray: - """Apply purity correction to observed intensities. - - Parameters - ---------- - observed: - Array of shape (n_spectra, n_channels) with observed intensities. - purity_matrix: - Square purity matrix of shape (n_channels, n_channels). - - Returns - ------- - numpy.ndarray - Corrected intensities, same shape as observed. Negative values - are clipped to zero. - """ - # Solve: purity_matrix @ corrected = observed (for each spectrum) - inv_matrix = np.linalg.inv(purity_matrix) - corrected = observed @ inv_matrix.T - corrected[corrected < 0] = 0.0 - return corrected - - -def get_channels(label: str) -> List[str]: - """Return channel names for a given labeling scheme. - - Parameters - ---------- - label: - Labeling scheme name (e.g. ``"TMT16plex"``). - - Returns - ------- - list of str - Channel names. - """ - if label not in LABEL_CHANNELS: - raise ValueError( - f"Unknown label '{label}'. Supported: {', '.join(sorted(LABEL_CHANNELS.keys()))}" - ) - return LABEL_CHANNELS[label] - - -def process_quant_file( - input_path: str, - purity_matrix: np.ndarray, - channels: List[str], -) -> Tuple[List[str], List[Dict[str, str]], np.ndarray]: - """Read quantification file and apply purity correction. - - Parameters - ---------- - input_path: - Path to input TSV. - purity_matrix: - Purity correction matrix. - channels: - Channel names to correct. - - Returns - ------- - tuple - (non_channel_fields, metadata_rows, corrected_array) - """ - metadata_rows: List[Dict[str, str]] = [] - observed_list: List[List[float]] = [] - - with open(input_path, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - all_fields = reader.fieldnames or [] - non_channel_fields = [f for f in all_fields if f not in channels] - - for row in reader: - meta = {f: row.get(f, "") for f in non_channel_fields} - metadata_rows.append(meta) - intensities = [] - for ch in channels: - val = row.get(ch, "0").strip() - try: - intensities.append(float(val)) - except (ValueError, TypeError): - intensities.append(0.0) - observed_list.append(intensities) - - observed = np.array(observed_list, dtype=float) - corrected = correct_intensities(observed, purity_matrix) - return non_channel_fields, metadata_rows, corrected - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Correct TMT/iTRAQ reporter ion quantification for isotopic impurity." - ) - parser.add_argument("--input", required=True, help="Input quantification TSV") - parser.add_argument( - "--label", required=True, - help="Labeling scheme (e.g. TMT6plex, TMT10plex, TMT16plex, iTRAQ4plex)", - ) - parser.add_argument("--purity-matrix", required=True, help="Purity correction matrix CSV") - parser.add_argument("--output", required=True, help="Output corrected TSV") - args = parser.parse_args() - - channels = get_channels(args.label) - purity_matrix = load_purity_matrix(args.purity_matrix) - - expected_size = len(channels) - if purity_matrix.shape != (expected_size, expected_size): - sys.exit( - f"Purity matrix shape {purity_matrix.shape} does not match " - f"{expected_size} channels for {args.label}" - ) - - non_ch_fields, meta_rows, corrected = process_quant_file(args.input, purity_matrix, channels) - - with open(args.output, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(non_ch_fields + channels) - for i, meta in enumerate(meta_rows): - row = [meta.get(f, "") for f in non_ch_fields] - row += [f"{corrected[i, j]:.4f}" for j in range(len(channels))] - writer.writerow(row) - - print(f"Corrected {len(meta_rows)} spectra across {len(channels)} channels -> {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/isobaric_purity_corrector/requirements.txt b/scripts/proteomics/isobaric_purity_corrector/requirements.txt deleted file mode 100644 index 1051d92..0000000 --- a/scripts/proteomics/isobaric_purity_corrector/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pyopenms -numpy diff --git a/scripts/proteomics/isobaric_purity_corrector/tests/test_isobaric_purity_corrector.py b/scripts/proteomics/isobaric_purity_corrector/tests/test_isobaric_purity_corrector.py deleted file mode 100644 index c9b4365..0000000 --- a/scripts/proteomics/isobaric_purity_corrector/tests/test_isobaric_purity_corrector.py +++ /dev/null @@ -1,125 +0,0 @@ -"""Tests for isobaric_purity_corrector.""" - -import csv -import sys - -import numpy as np -from conftest import requires_pyopenms - - -@requires_pyopenms -def test_get_channels(): - from isobaric_purity_corrector import get_channels - - channels = get_channels("TMT6plex") - assert len(channels) == 6 - assert "126" in channels - - channels_16 = get_channels("TMT16plex") - assert len(channels_16) == 16 - - -@requires_pyopenms -def test_get_channels_unknown(): - import pytest - from isobaric_purity_corrector import get_channels - - with pytest.raises(ValueError): - get_channels("UnknownLabel") - - -@requires_pyopenms -def test_correct_intensities_identity(): - from isobaric_purity_corrector import correct_intensities - - # Identity matrix = no correction - purity = np.eye(3) - observed = np.array([[100.0, 200.0, 300.0]]) - corrected = correct_intensities(observed, purity) - np.testing.assert_allclose(corrected, observed, atol=1e-6) - - -@requires_pyopenms -def test_correct_intensities_with_crosstalk(): - from isobaric_purity_corrector import correct_intensities - - # Purity matrix with some crosstalk - purity = np.array([ - [0.95, 0.03, 0.02], - [0.02, 0.94, 0.04], - [0.01, 0.03, 0.96], - ]) - # True intensities - true = np.array([[100.0, 200.0, 300.0]]) - # Observed = purity @ true (simulate measurement) - observed = true @ purity.T - # Correct should recover true - corrected = correct_intensities(observed, purity) - np.testing.assert_allclose(corrected, true, atol=1.0) - - -@requires_pyopenms -def test_load_purity_matrix(tmp_path): - from isobaric_purity_corrector import load_purity_matrix - - purity_file = tmp_path / "purity.csv" - with open(purity_file, "w", newline="") as fh: - writer = csv.writer(fh) - writer.writerow([0.95, 0.03, 0.02]) - writer.writerow([0.02, 0.94, 0.04]) - writer.writerow([0.01, 0.03, 0.96]) - - matrix = load_purity_matrix(str(purity_file)) - assert matrix.shape == (3, 3) - assert abs(matrix[0, 0] - 0.95) < 0.001 - - -@requires_pyopenms -def test_cli_roundtrip(tmp_path): - from isobaric_purity_corrector import main - - channels = ["126", "127", "128"] - purity = np.eye(3) - - purity_file = tmp_path / "purity.csv" - with open(purity_file, "w", newline="") as fh: - writer = csv.writer(fh) - for row in purity: - writer.writerow(row.tolist()) - - input_file = tmp_path / "quant.tsv" - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["spectrum_id"] + channels) - writer.writerow(["s1", "100.0", "200.0", "300.0"]) - - output_file = tmp_path / "corrected.tsv" - sys.argv = [ - "isobaric_purity_corrector.py", - "--input", str(input_file), - "--label", "TMT6plex", - "--purity-matrix", str(purity_file), - "--output", str(output_file), - ] - # TMT6plex has 6 channels but our test data only has 3 columns + wrong purity size - # Use a proper 6-channel test instead - channels_6 = ["126", "127", "128", "129", "130", "131"] - purity_6 = np.eye(6) - with open(purity_file, "w", newline="") as fh: - writer = csv.writer(fh) - for row in purity_6: - writer.writerow(row.tolist()) - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["spectrum_id"] + channels_6) - writer.writerow(["s1"] + ["100.0"] * 6) - - sys.argv = [ - "isobaric_purity_corrector.py", - "--input", str(input_file), - "--label", "TMT6plex", - "--purity-matrix", str(purity_file), - "--output", str(output_file), - ] - main() - assert output_file.exists() diff --git a/scripts/proteomics/maxquant_result_converter/README.md b/scripts/proteomics/maxquant_result_converter/README.md deleted file mode 100644 index e73b148..0000000 --- a/scripts/proteomics/maxquant_result_converter/README.md +++ /dev/null @@ -1,23 +0,0 @@ -# MaxQuant Result Converter - -Convert MaxQuant evidence.txt to a standardized TSV format. - -## Usage - -```bash -python maxquant_result_converter.py --input evidence.txt --output standardized.tsv -``` - -## Column Mapping - -| MaxQuant Column | Standard Column | -|---|---| -| Sequence | peptide | -| Modified sequence | modified_peptide | -| Charge | charge | -| m/z | mz | -| Retention time | rt | -| Proteins | protein | -| Score | score | -| PEP | pep | -| Intensity | intensity | diff --git a/scripts/proteomics/maxquant_result_converter/maxquant_result_converter.py b/scripts/proteomics/maxquant_result_converter/maxquant_result_converter.py deleted file mode 100644 index 5f226c1..0000000 --- a/scripts/proteomics/maxquant_result_converter/maxquant_result_converter.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -MaxQuant Result Converter -========================= -Convert MaxQuant evidence.txt to a standardized TSV format. - -Maps MaxQuant-specific column names to a common schema suitable for -downstream analysis. - -Usage ------ - python maxquant_result_converter.py --input evidence.txt --output standardized.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -# Mapping from MaxQuant column names to standard column names -COLUMN_MAP = { - "Sequence": "peptide", - "Modified sequence": "modified_peptide", - "Charge": "charge", - "m/z": "mz", - "Mass": "mass", - "Retention time": "rt", - "Proteins": "protein", - "Leading razor protein": "leading_protein", - "Gene names": "gene", - "Score": "score", - "PEP": "pep", - "Intensity": "intensity", - "Raw file": "raw_file", - "Experiment": "experiment", - "MS/MS scan number": "scan_number", - "Reverse": "is_decoy", - "Potential contaminant": "is_contaminant", -} - -STANDARD_FIELDS = [ - "peptide", "modified_peptide", "charge", "mz", "mass", "rt", - "protein", "leading_protein", "gene", "score", "pep", - "intensity", "raw_file", "experiment", "scan_number", - "is_decoy", "is_contaminant", "source", -] - - -def convert_maxquant_evidence(filepath: str) -> list: - """Convert MaxQuant evidence.txt to standardized format. - - Parameters - ---------- - filepath: - Path to MaxQuant evidence.txt. - - Returns - ------- - list - List of dicts with standardized column names. - """ - rows = [] - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - std_row = {} - for mq_col, std_col in COLUMN_MAP.items(): - value = row.get(mq_col, "") - # Normalize decoy/contaminant flags - if std_col == "is_decoy": - value = "true" if value == "+" else "false" - elif std_col == "is_contaminant": - value = "true" if value == "+" else "false" - std_row[std_col] = value - std_row["source"] = "MaxQuant" - rows.append(std_row) - return rows - - -def write_standardized(filepath: str, rows: list) -> None: - """Write standardized results to TSV.""" - with open(filepath, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=STANDARD_FIELDS, delimiter="\t", extrasaction="ignore") - writer.writeheader() - writer.writerows(rows) - - -def main(): - parser = argparse.ArgumentParser(description="Convert MaxQuant evidence.txt to standardized TSV.") - parser.add_argument("--input", required=True, help="MaxQuant evidence.txt file") - parser.add_argument("--output", required=True, help="Output standardized TSV") - args = parser.parse_args() - - rows = convert_maxquant_evidence(args.input) - write_standardized(args.output, rows) - - n_decoy = sum(1 for r in rows if r.get("is_decoy") == "true") - print("Source: MaxQuant") - print(f"Total PSMs: {len(rows)}") - print(f"Decoy PSMs: {n_decoy}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/maxquant_result_converter/tests/test_maxquant_result_converter.py b/scripts/proteomics/maxquant_result_converter/tests/test_maxquant_result_converter.py deleted file mode 100644 index d0ee283..0000000 --- a/scripts/proteomics/maxquant_result_converter/tests/test_maxquant_result_converter.py +++ /dev/null @@ -1,104 +0,0 @@ -"""Tests for maxquant_result_converter.""" - -import csv - -from conftest import requires_pyopenms -from maxquant_result_converter import STANDARD_FIELDS, convert_maxquant_evidence, write_standardized - - -@requires_pyopenms -class TestMaxQuantResultConverter: - def _write_evidence(self, tmp_path, rows): - filepath = str(tmp_path / "evidence.txt") - fieldnames = [ - "Sequence", "Modified sequence", "Charge", "m/z", "Mass", - "Retention time", "Proteins", "Leading razor protein", "Gene names", - "Score", "PEP", "Intensity", "Raw file", "Experiment", - "MS/MS scan number", "Reverse", "Potential contaminant", - ] - with open(filepath, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") - writer.writeheader() - writer.writerows(rows) - return filepath - - def test_basic_conversion(self, tmp_path): - filepath = self._write_evidence(tmp_path, [ - { - "Sequence": "PEPTIDEK", "Modified sequence": "_PEPTIDEK_", - "Charge": "2", "m/z": "450.5", "Mass": "900.0", - "Retention time": "25.5", "Proteins": "P12345", - "Leading razor protein": "P12345", "Gene names": "GEN1", - "Score": "120.5", "PEP": "0.001", "Intensity": "1e6", - "Raw file": "run1", "Experiment": "exp1", - "MS/MS scan number": "1234", "Reverse": "", "Potential contaminant": "", - } - ]) - rows = convert_maxquant_evidence(filepath) - assert len(rows) == 1 - assert rows[0]["peptide"] == "PEPTIDEK" - assert rows[0]["charge"] == "2" - assert rows[0]["source"] == "MaxQuant" - - def test_decoy_flag(self, tmp_path): - filepath = self._write_evidence(tmp_path, [ - { - "Sequence": "PEPTIDEK", "Modified sequence": "_PEPTIDEK_", - "Charge": "2", "m/z": "450.5", "Mass": "900.0", - "Retention time": "25.5", "Proteins": "REV__P12345", - "Leading razor protein": "REV__P12345", "Gene names": "", - "Score": "50.0", "PEP": "0.5", "Intensity": "1e4", - "Raw file": "run1", "Experiment": "exp1", - "MS/MS scan number": "5678", "Reverse": "+", "Potential contaminant": "", - } - ]) - rows = convert_maxquant_evidence(filepath) - assert rows[0]["is_decoy"] == "true" - assert rows[0]["is_contaminant"] == "false" - - def test_contaminant_flag(self, tmp_path): - filepath = self._write_evidence(tmp_path, [ - { - "Sequence": "CONTAM", "Modified sequence": "_CONTAM_", - "Charge": "1", "m/z": "300.0", "Mass": "299.0", - "Retention time": "10.0", "Proteins": "CON__P00001", - "Leading razor protein": "CON__P00001", "Gene names": "", - "Score": "30.0", "PEP": "0.01", "Intensity": "5e5", - "Raw file": "run1", "Experiment": "exp1", - "MS/MS scan number": "999", "Reverse": "", "Potential contaminant": "+", - } - ]) - rows = convert_maxquant_evidence(filepath) - assert rows[0]["is_contaminant"] == "true" - - def test_write_standardized(self, tmp_path): - rows = [{"peptide": "PEPTIDEK", "charge": "2", "source": "MaxQuant"}] - outfile = str(tmp_path / "out.tsv") - write_standardized(outfile, rows) - with open(outfile) as fh: - reader = csv.DictReader(fh, delimiter="\t") - result = list(reader) - assert len(result) == 1 - assert result[0]["peptide"] == "PEPTIDEK" - - def test_standard_fields(self): - assert "peptide" in STANDARD_FIELDS - assert "source" in STANDARD_FIELDS - - def test_multiple_rows(self, tmp_path): - filepath = self._write_evidence(tmp_path, [ - {"Sequence": "PEP1", "Modified sequence": "", "Charge": "2", - "m/z": "400", "Mass": "800", "Retention time": "20", - "Proteins": "P1", "Leading razor protein": "P1", "Gene names": "G1", - "Score": "100", "PEP": "0.01", "Intensity": "1e6", - "Raw file": "run1", "Experiment": "exp1", - "MS/MS scan number": "1", "Reverse": "", "Potential contaminant": ""}, - {"Sequence": "PEP2", "Modified sequence": "", "Charge": "3", - "m/z": "300", "Mass": "900", "Retention time": "30", - "Proteins": "P2", "Leading razor protein": "P2", "Gene names": "G2", - "Score": "90", "PEP": "0.02", "Intensity": "5e5", - "Raw file": "run1", "Experiment": "exp1", - "MS/MS scan number": "2", "Reverse": "", "Potential contaminant": ""}, - ]) - rows = convert_maxquant_evidence(filepath) - assert len(rows) == 2 diff --git a/scripts/proteomics/metapeptide_function_aggregator/README.md b/scripts/proteomics/metapeptide_function_aggregator/README.md deleted file mode 100644 index 1cae96d..0000000 --- a/scripts/proteomics/metapeptide_function_aggregator/README.md +++ /dev/null @@ -1,41 +0,0 @@ -# Metapeptide Function Aggregator - -Aggregate GO/KEGG functional annotations from peptide-to-protein mappings for metaproteomics. - -## Installation - -```bash -pip install -r requirements.txt -``` - -## Usage - -```bash -python metapeptide_function_aggregator.py --peptides identified.tsv \ - --annotations go_terms.tsv --output function.tsv -``` - -### Input format - -Peptides TSV with `peptide` and `protein` columns: - -``` -peptide protein -AGIILTK P12345 -PEPTIDEK P12345;P67890 -``` - -Annotations TSV with `protein`, `term_id`, `term_name` columns: - -``` -protein term_id term_name -P12345 GO:0006412 translation -``` - -### Parameters - -| Flag | Description | -|------|-------------| -| `--peptides` | TSV with peptide-protein mappings | -| `--annotations` | TSV with protein-function annotations | -| `--output` | Output functional aggregation TSV | diff --git a/scripts/proteomics/metapeptide_function_aggregator/metapeptide_function_aggregator.py b/scripts/proteomics/metapeptide_function_aggregator/metapeptide_function_aggregator.py deleted file mode 100644 index b037db3..0000000 --- a/scripts/proteomics/metapeptide_function_aggregator/metapeptide_function_aggregator.py +++ /dev/null @@ -1,200 +0,0 @@ -""" -Metapeptide Function Aggregator -================================ -Aggregate GO/KEGG functional annotations from peptide-to-protein mappings. -Given identified peptides with their protein assignments and a separate -annotation file mapping proteins to functional terms, the tool aggregates -term counts and computes peptide-level functional profiles. - -Useful for metaproteomics where functional characterization of the -community is derived from identified peptides. - -Usage ------ - python metapeptide_function_aggregator.py --peptides identified.tsv \ - --annotations go_terms.tsv --output function.tsv -""" - -import argparse -import csv -import sys -from collections import Counter, defaultdict -from typing import Dict, List, Set, Tuple - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - - -def load_peptide_protein_map(peptides_path: str) -> Dict[str, Set[str]]: - """Load peptide-to-protein mappings from a TSV. - - Expects columns ``peptide`` and ``protein``. A peptide can map to - multiple proteins (one row per mapping or semicolon-separated proteins). - - Returns - ------- - dict - Mapping of peptide sequence to set of protein accessions. - """ - pep_to_prot: Dict[str, Set[str]] = defaultdict(set) - with open(peptides_path, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - peptide = row.get("peptide", "").strip() - proteins_raw = row.get("protein", "").strip() - if not peptide or not proteins_raw: - continue - for prot in proteins_raw.split(";"): - prot = prot.strip() - if prot: - pep_to_prot[peptide].add(prot) - return dict(pep_to_prot) - - -def load_annotations(annotations_path: str) -> Dict[str, List[Tuple[str, str]]]: - """Load protein-to-function annotation mappings. - - Expects columns ``protein``, ``term_id``, and ``term_name``. - - Returns - ------- - dict - Mapping of protein accession to list of (term_id, term_name) tuples. - """ - prot_to_terms: Dict[str, List[Tuple[str, str]]] = defaultdict(list) - with open(annotations_path, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - protein = row.get("protein", "").strip() - term_id = row.get("term_id", "").strip() - term_name = row.get("term_name", "").strip() - if protein and term_id: - prot_to_terms[protein].append((term_id, term_name)) - return dict(prot_to_terms) - - -def aggregate_functions( - pep_to_prot: Dict[str, Set[str]], - prot_to_terms: Dict[str, List[Tuple[str, str]]], -) -> Tuple[List[Dict[str, object]], Counter]: - """Aggregate functional annotations from peptide-protein-term mappings. - - For each peptide, collect all functional terms from all mapped proteins. - Also compute global term frequency counts. - - Parameters - ---------- - pep_to_prot: - Peptide-to-protein mappings. - prot_to_terms: - Protein-to-functional-term mappings. - - Returns - ------- - tuple - (peptide_annotations, term_counts) where peptide_annotations is a list - of dicts with ``peptide``, ``proteins``, ``terms``, and term_counts is - a Counter of term_id occurrences across all peptides. - """ - peptide_annotations: List[Dict[str, object]] = [] - term_counts: Counter = Counter() - - for peptide, proteins in sorted(pep_to_prot.items()): - terms_seen: Set[str] = set() - term_details: List[Tuple[str, str]] = [] - - for prot in proteins: - if prot in prot_to_terms: - for term_id, term_name in prot_to_terms[prot]: - if term_id not in terms_seen: - terms_seen.add(term_id) - term_details.append((term_id, term_name)) - term_counts[term_id] += 1 - - peptide_annotations.append({ - "peptide": peptide, - "n_proteins": len(proteins), - "proteins": ";".join(sorted(proteins)), - "n_terms": len(term_details), - "terms": ";".join(f"{tid}:{tname}" for tid, tname in term_details), - }) - - return peptide_annotations, term_counts - - -def summarize_terms( - term_counts: Counter, - prot_to_terms: Dict[str, List[Tuple[str, str]]], -) -> List[Dict[str, object]]: - """Build a summary table of term frequencies. - - Returns - ------- - list of dict - Sorted by count descending, with ``term_id``, ``term_name``, ``count``. - """ - # Build term_id -> term_name lookup - id_to_name: Dict[str, str] = {} - for terms in prot_to_terms.values(): - for tid, tname in terms: - if tid not in id_to_name: - id_to_name[tid] = tname - - summary: List[Dict[str, object]] = [] - for tid, count in term_counts.most_common(): - summary.append({ - "term_id": tid, - "term_name": id_to_name.get(tid, ""), - "peptide_count": count, - }) - return summary - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Aggregate GO/KEGG annotations from peptide-protein mappings." - ) - parser.add_argument( - "--peptides", required=True, - help="TSV with 'peptide' and 'protein' columns", - ) - parser.add_argument( - "--annotations", required=True, - help="TSV with 'protein', 'term_id', 'term_name' columns", - ) - parser.add_argument("--output", required=True, help="Output functional aggregation TSV") - args = parser.parse_args() - - pep_to_prot = load_peptide_protein_map(args.peptides) - prot_to_terms = load_annotations(args.annotations) - - if not pep_to_prot: - sys.exit("No peptide-protein mappings found.") - - pep_annots, term_counts = aggregate_functions(pep_to_prot, prot_to_terms) - term_summary = summarize_terms(term_counts, prot_to_terms) - - with open(args.output, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - # Peptide-level annotations - writer.writerow(["peptide", "n_proteins", "proteins", "n_terms", "terms"]) - for pa in pep_annots: - writer.writerow([ - pa["peptide"], pa["n_proteins"], pa["proteins"], - pa["n_terms"], pa["terms"], - ]) - writer.writerow([]) - # Term summary - writer.writerow(["term_id", "term_name", "peptide_count"]) - for ts in term_summary: - writer.writerow([ts["term_id"], ts["term_name"], ts["peptide_count"]]) - - annotated = sum(1 for pa in pep_annots if pa["n_terms"] > 0) - print(f"Annotated {annotated}/{len(pep_annots)} peptides with functional terms") - print(f"Unique terms: {len(term_summary)} -> {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/metapeptide_function_aggregator/tests/test_metapeptide_function_aggregator.py b/scripts/proteomics/metapeptide_function_aggregator/tests/test_metapeptide_function_aggregator.py deleted file mode 100644 index 258402f..0000000 --- a/scripts/proteomics/metapeptide_function_aggregator/tests/test_metapeptide_function_aggregator.py +++ /dev/null @@ -1,126 +0,0 @@ -"""Tests for metapeptide_function_aggregator.""" - -import csv -import sys - -from conftest import requires_pyopenms - - -@requires_pyopenms -def test_load_peptide_protein_map(tmp_path): - from metapeptide_function_aggregator import load_peptide_protein_map - - pep_file = tmp_path / "peptides.tsv" - with open(pep_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["peptide", "protein"]) - writer.writerow(["PEPTIDEK", "P1"]) - writer.writerow(["PEPTIDEK", "P2"]) - writer.writerow(["AGIILTK", "P3"]) - - pep_map = load_peptide_protein_map(str(pep_file)) - assert "PEPTIDEK" in pep_map - assert pep_map["PEPTIDEK"] == {"P1", "P2"} - assert pep_map["AGIILTK"] == {"P3"} - - -@requires_pyopenms -def test_load_peptide_protein_map_semicolon(tmp_path): - from metapeptide_function_aggregator import load_peptide_protein_map - - pep_file = tmp_path / "peptides.tsv" - with open(pep_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["peptide", "protein"]) - writer.writerow(["PEPTIDEK", "P1;P2"]) - - pep_map = load_peptide_protein_map(str(pep_file)) - assert pep_map["PEPTIDEK"] == {"P1", "P2"} - - -@requires_pyopenms -def test_load_annotations(tmp_path): - from metapeptide_function_aggregator import load_annotations - - ann_file = tmp_path / "annotations.tsv" - with open(ann_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein", "term_id", "term_name"]) - writer.writerow(["P1", "GO:0006412", "translation"]) - writer.writerow(["P1", "GO:0005840", "ribosome"]) - - annotations = load_annotations(str(ann_file)) - assert "P1" in annotations - assert len(annotations["P1"]) == 2 - - -@requires_pyopenms -def test_aggregate_functions(): - from metapeptide_function_aggregator import aggregate_functions - - pep_to_prot = { - "PEPTIDEK": {"P1", "P2"}, - "AGIILTK": {"P3"}, - } - prot_to_terms = { - "P1": [("GO:0006412", "translation")], - "P2": [("GO:0006412", "translation"), ("GO:0005840", "ribosome")], - "P3": [("KEGG:00010", "glycolysis")], - } - - pep_annots, term_counts = aggregate_functions(pep_to_prot, prot_to_terms) - assert len(pep_annots) == 2 - - # PEPTIDEK maps to P1 and P2, should get 2 unique terms - peptidek_entry = [pa for pa in pep_annots if pa["peptide"] == "PEPTIDEK"][0] - assert peptidek_entry["n_terms"] == 2 - - assert term_counts["GO:0006412"] == 1 # appears once for PEPTIDEK (deduplicated) - assert term_counts["KEGG:00010"] == 1 - - -@requires_pyopenms -def test_summarize_terms(): - from collections import Counter - - from metapeptide_function_aggregator import summarize_terms - - term_counts = Counter({"GO:0006412": 5, "GO:0005840": 3}) - prot_to_terms = { - "P1": [("GO:0006412", "translation"), ("GO:0005840", "ribosome")], - } - - summary = summarize_terms(term_counts, prot_to_terms) - assert len(summary) == 2 - assert summary[0]["term_id"] == "GO:0006412" - assert summary[0]["peptide_count"] == 5 - - -@requires_pyopenms -def test_cli_roundtrip(tmp_path): - from metapeptide_function_aggregator import main - - pep_file = tmp_path / "peptides.tsv" - ann_file = tmp_path / "annotations.tsv" - output_file = tmp_path / "output.tsv" - - with open(pep_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["peptide", "protein"]) - writer.writerow(["PEPTIDEK", "P1"]) - writer.writerow(["AGIILTK", "P2"]) - - with open(ann_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein", "term_id", "term_name"]) - writer.writerow(["P1", "GO:0006412", "translation"]) - writer.writerow(["P2", "KEGG:00010", "glycolysis"]) - - sys.argv = [ - "metapeptide_function_aggregator.py", - "--peptides", str(pep_file), - "--annotations", str(ann_file), - "--output", str(output_file), - ] - main() - assert output_file.exists() diff --git a/scripts/proteomics/missing_value_imputation/README.md b/scripts/proteomics/missing_value_imputation/README.md deleted file mode 100644 index cb4a616..0000000 --- a/scripts/proteomics/missing_value_imputation/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# Missing Value Imputation - -Impute missing values in quantification matrices using MinDet, MinProb, or KNN methods. - -## Usage - -```bash -python missing_value_imputation.py --input matrix.tsv --method mindet --output imputed.tsv -python missing_value_imputation.py --input matrix.tsv --method knn --k 5 --output imputed.tsv -python missing_value_imputation.py --input matrix.tsv --method minprob --output imputed.tsv -``` - -## Methods - -- **mindet** - Replace missing values with the minimum detected value per column -- **minprob** - Random draws from a low-intensity Gaussian distribution -- **knn** - K-nearest-neighbor imputation using observed features diff --git a/scripts/proteomics/missing_value_imputation/missing_value_imputation.py b/scripts/proteomics/missing_value_imputation/missing_value_imputation.py deleted file mode 100644 index a878f10..0000000 --- a/scripts/proteomics/missing_value_imputation/missing_value_imputation.py +++ /dev/null @@ -1,245 +0,0 @@ -""" -Missing Value Imputation -======================== -Impute missing values in quantification matrices using various strategies. - -Supported methods: -- MinDet: Replace missing values with the minimum detected value per column. -- MinProb: Replace missing values with random draws from a low-intensity - Gaussian distribution (1st percentile). -- KNN: K-nearest-neighbor imputation using available features. - -Usage ------ - python missing_value_imputation.py --input matrix.tsv --method mindet --output imputed.tsv - python missing_value_imputation.py --input matrix.tsv --method knn --k 5 --output imputed.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - - -def read_matrix(filepath: str) -> tuple: - """Read a TSV quantification matrix. - - Parameters - ---------- - filepath: - Path to TSV file. First column is row IDs, first row is header. - - Returns - ------- - tuple - (row_ids, col_names, data_matrix) where data_matrix is a numpy array. - """ - with open(filepath) as fh: - reader = csv.reader(fh, delimiter="\t") - header = next(reader) - col_names = header[1:] - row_ids = [] - rows = [] - for row in reader: - row_ids.append(row[0]) - values = [] - for v in row[1:]: - if v.strip() == "" or v.strip().upper() == "NA" or v.strip().upper() == "NAN": - values.append(np.nan) - else: - values.append(float(v)) - rows.append(values) - return row_ids, col_names, np.array(rows, dtype=float) - - -def write_matrix(filepath: str, row_ids: list, col_names: list, matrix: np.ndarray) -> None: - """Write a quantification matrix to TSV.""" - with open(filepath, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow([""] + col_names) - for i, row_id in enumerate(row_ids): - writer.writerow([row_id] + [f"{v:.6f}" for v in matrix[i]]) - - -def impute_mindet(matrix: np.ndarray) -> np.ndarray: - """Impute missing values with minimum detected value per column. - - Parameters - ---------- - matrix: - 2D numpy array with NaN for missing values. - - Returns - ------- - np.ndarray - Imputed matrix. - """ - result = matrix.copy() - for col in range(result.shape[1]): - col_data = result[:, col] - valid = col_data[~np.isnan(col_data)] - if len(valid) > 0: - min_val = np.min(valid) - else: - min_val = 0.0 - col_data[np.isnan(col_data)] = min_val - return result - - -def impute_minprob(matrix: np.ndarray, q: float = 0.01, downshift: float = 1.8) -> np.ndarray: - """Impute missing values from a low-intensity Gaussian distribution. - - For each column, draws from N(mean - downshift*sd, sd*0.3) where mean and - sd are computed from the qth percentile of observed values. - - Parameters - ---------- - matrix: - 2D numpy array with NaN for missing values. - q: - Quantile for determining the low-intensity distribution center. - downshift: - Number of standard deviations to shift the mean down. - - Returns - ------- - np.ndarray - Imputed matrix. - """ - result = matrix.copy() - rng = np.random.default_rng(42) - for col in range(result.shape[1]): - col_data = result[:, col] - valid = col_data[~np.isnan(col_data)] - if len(valid) == 0: - continue - mean_val = np.mean(valid) - sd_val = np.std(valid) if len(valid) > 1 else mean_val * 0.1 - imp_mean = mean_val - downshift * sd_val - imp_sd = sd_val * 0.3 - n_missing = np.sum(np.isnan(col_data)) - if n_missing > 0: - imputed_vals = rng.normal(imp_mean, max(imp_sd, 1e-10), int(n_missing)) - col_data[np.isnan(col_data)] = imputed_vals - return result - - -def impute_knn(matrix: np.ndarray, k: int = 5) -> np.ndarray: - """Impute missing values using K-nearest-neighbor approach. - - For each row with missing values, find the k most similar rows (by - Euclidean distance on shared observed features) and use their mean - for imputation. - - Parameters - ---------- - matrix: - 2D numpy array with NaN for missing values. - k: - Number of neighbors to use. - - Returns - ------- - np.ndarray - Imputed matrix. - """ - result = matrix.copy() - n_rows = result.shape[0] - - for i in range(n_rows): - missing_mask = np.isnan(result[i]) - if not np.any(missing_mask): - continue - - observed_mask = ~missing_mask - if not np.any(observed_mask): - # All missing: use column means - for col in np.where(missing_mask)[0]: - col_valid = result[:, col][~np.isnan(result[:, col])] - result[i, col] = np.mean(col_valid) if len(col_valid) > 0 else 0.0 - continue - - # Find distances to other rows using shared observed features - distances = [] - for j in range(n_rows): - if i == j: - continue - shared = observed_mask & ~np.isnan(result[j]) - if np.sum(shared) == 0: - continue - dist = np.sqrt(np.sum((result[i, shared] - result[j, shared]) ** 2)) - distances.append((j, dist)) - - if not distances: - continue - - distances.sort(key=lambda x: x[1]) - neighbors = [idx for idx, _ in distances[:k]] - - for col in np.where(missing_mask)[0]: - neighbor_vals = [result[j, col] for j in neighbors if not np.isnan(result[j, col])] - if neighbor_vals: - result[i, col] = np.mean(neighbor_vals) - else: - col_valid = result[:, col][~np.isnan(result[:, col])] - result[i, col] = np.mean(col_valid) if len(col_valid) > 0 else 0.0 - - return result - - -def impute(matrix: np.ndarray, method: str = "mindet", **kwargs) -> np.ndarray: - """Impute missing values using the specified method. - - Parameters - ---------- - matrix: - 2D numpy array with NaN for missing values. - method: - One of 'mindet', 'minprob', 'knn'. - - Returns - ------- - np.ndarray - Imputed matrix. - """ - method = method.lower() - if method == "mindet": - return impute_mindet(matrix) - elif method == "minprob": - return impute_minprob(matrix, **kwargs) - elif method == "knn": - k = kwargs.get("k", 5) - return impute_knn(matrix, k=k) - else: - raise ValueError(f"Unknown imputation method: '{method}'. Choose from: mindet, minprob, knn") - - -def main(): - parser = argparse.ArgumentParser(description="Impute missing values in quantification matrices.") - parser.add_argument("--input", required=True, help="Input TSV matrix file") - parser.add_argument("--method", required=True, choices=["mindet", "minprob", "knn"], - help="Imputation method") - parser.add_argument("--k", type=int, default=5, help="Number of neighbors for KNN (default: 5)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - row_ids, col_names, matrix = read_matrix(args.input) - n_missing_before = int(np.sum(np.isnan(matrix))) - imputed = impute(matrix, method=args.method, k=args.k) - n_missing_after = int(np.sum(np.isnan(imputed))) - write_matrix(args.output, row_ids, col_names, imputed) - - print(f"Method: {args.method}") - print(f"Missing values before: {n_missing_before}") - print(f"Missing values after: {n_missing_after}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/missing_value_imputation/requirements.txt b/scripts/proteomics/missing_value_imputation/requirements.txt deleted file mode 100644 index ba577e4..0000000 --- a/scripts/proteomics/missing_value_imputation/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -pyopenms -numpy -scipy diff --git a/scripts/proteomics/missing_value_imputation/tests/test_missing_value_imputation.py b/scripts/proteomics/missing_value_imputation/tests/test_missing_value_imputation.py deleted file mode 100644 index 97b1863..0000000 --- a/scripts/proteomics/missing_value_imputation/tests/test_missing_value_imputation.py +++ /dev/null @@ -1,100 +0,0 @@ -"""Tests for missing_value_imputation.""" - - -import numpy as np -import pytest -from conftest import requires_pyopenms -from missing_value_imputation import ( - impute, - impute_knn, - impute_mindet, - impute_minprob, - read_matrix, - write_matrix, -) - - -@requires_pyopenms -class TestMissingValueImputation: - def _make_matrix_with_missing(self): - matrix = np.array([ - [100.0, 200.0, np.nan], - [150.0, np.nan, 300.0], - [120.0, 180.0, 280.0], - [np.nan, 210.0, 310.0], - ]) - return matrix - - def test_mindet_no_nans(self): - matrix = self._make_matrix_with_missing() - result = impute_mindet(matrix) - assert not np.any(np.isnan(result)) - - def test_mindet_values(self): - matrix = self._make_matrix_with_missing() - result = impute_mindet(matrix) - # Column 0 min = 100, Column 1 min = 180, Column 2 min = 280 - assert result[3, 0] == 100.0 - assert result[1, 1] == 180.0 - assert result[0, 2] == 280.0 - - def test_minprob_no_nans(self): - matrix = self._make_matrix_with_missing() - result = impute_minprob(matrix) - assert not np.any(np.isnan(result)) - - def test_minprob_values_lower(self): - matrix = self._make_matrix_with_missing() - result = impute_minprob(matrix) - # Imputed values should generally be lower than the mean - col0_mean = np.nanmean(matrix[:, 0]) - assert result[3, 0] < col0_mean - - def test_knn_no_nans(self): - matrix = self._make_matrix_with_missing() - result = impute_knn(matrix, k=2) - assert not np.any(np.isnan(result)) - - def test_knn_reasonable_values(self): - matrix = self._make_matrix_with_missing() - result = impute_knn(matrix, k=2) - # Imputed values should be within the range of observed values - assert 50 < result[3, 0] < 500 - assert 50 < result[1, 1] < 500 - - def test_impute_dispatch(self): - matrix = self._make_matrix_with_missing() - for method in ["mindet", "minprob", "knn"]: - result = impute(matrix, method=method) - assert not np.any(np.isnan(result)) - - def test_unknown_method(self): - matrix = self._make_matrix_with_missing() - with pytest.raises(ValueError, match="Unknown imputation method"): - impute(matrix, method="invalid") - - def test_read_write_roundtrip(self, tmp_path): - row_ids = ["prot1", "prot2"] - col_names = ["sample1", "sample2"] - matrix = np.array([[100.0, 200.0], [300.0, 400.0]]) - outfile = str(tmp_path / "test.tsv") - write_matrix(outfile, row_ids, col_names, matrix) - r_ids, c_names, r_matrix = read_matrix(outfile) - assert r_ids == row_ids - assert c_names == col_names - np.testing.assert_allclose(r_matrix, matrix, atol=0.01) - - def test_read_with_missing(self, tmp_path): - outfile = str(tmp_path / "missing.tsv") - with open(outfile, "w") as fh: - fh.write("\tsample1\tsample2\n") - fh.write("prot1\t100.0\tNA\n") - fh.write("prot2\t\t200.0\n") - _, _, matrix = read_matrix(outfile) - assert np.isnan(matrix[0, 1]) - assert np.isnan(matrix[1, 0]) - - def test_no_missing_unchanged(self): - matrix = np.array([[1.0, 2.0], [3.0, 4.0]]) - result = impute_mindet(matrix) - np.testing.assert_array_equal(result, matrix) diff --git a/scripts/proteomics/amino_acid_composition_analyzer/README.md b/scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/README.md similarity index 100% rename from scripts/proteomics/amino_acid_composition_analyzer/README.md rename to scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/README.md diff --git a/scripts/proteomics/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py b/scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py similarity index 100% rename from scripts/proteomics/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py rename to scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py diff --git a/scripts/proteomics/hdx_back_exchange_estimator/requirements.txt b/scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/hdx_back_exchange_estimator/requirements.txt rename to scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/requirements.txt diff --git a/scripts/proteomics/feature_detection_proteomics/tests/conftest.py b/scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/feature_detection_proteomics/tests/conftest.py rename to scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/tests/conftest.py diff --git a/scripts/proteomics/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py b/scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py similarity index 100% rename from scripts/proteomics/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py rename to scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/tests/test_amino_acid_composition_analyzer.py diff --git a/scripts/proteomics/charge_state_predictor/README.md b/scripts/proteomics/peptide_analysis/charge_state_predictor/README.md similarity index 100% rename from scripts/proteomics/charge_state_predictor/README.md rename to scripts/proteomics/peptide_analysis/charge_state_predictor/README.md diff --git a/scripts/proteomics/charge_state_predictor/charge_state_predictor.py b/scripts/proteomics/peptide_analysis/charge_state_predictor/charge_state_predictor.py similarity index 100% rename from scripts/proteomics/charge_state_predictor/charge_state_predictor.py rename to scripts/proteomics/peptide_analysis/charge_state_predictor/charge_state_predictor.py diff --git a/scripts/proteomics/hdx_deuterium_uptake/requirements.txt b/scripts/proteomics/peptide_analysis/charge_state_predictor/requirements.txt similarity index 100% rename from scripts/proteomics/hdx_deuterium_uptake/requirements.txt rename to scripts/proteomics/peptide_analysis/charge_state_predictor/requirements.txt diff --git a/scripts/proteomics/featurexml_merger/tests/conftest.py b/scripts/proteomics/peptide_analysis/charge_state_predictor/tests/conftest.py similarity index 100% rename from scripts/proteomics/featurexml_merger/tests/conftest.py rename to scripts/proteomics/peptide_analysis/charge_state_predictor/tests/conftest.py diff --git a/scripts/proteomics/charge_state_predictor/tests/test_charge_state_predictor.py b/scripts/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py similarity index 100% rename from scripts/proteomics/charge_state_predictor/tests/test_charge_state_predictor.py rename to scripts/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py diff --git a/scripts/proteomics/isoelectric_point_calculator/README.md b/scripts/proteomics/peptide_analysis/isoelectric_point_calculator/README.md similarity index 100% rename from scripts/proteomics/isoelectric_point_calculator/README.md rename to scripts/proteomics/peptide_analysis/isoelectric_point_calculator/README.md diff --git a/scripts/proteomics/isoelectric_point_calculator/isoelectric_point_calculator.py b/scripts/proteomics/peptide_analysis/isoelectric_point_calculator/isoelectric_point_calculator.py similarity index 100% rename from scripts/proteomics/isoelectric_point_calculator/isoelectric_point_calculator.py rename to scripts/proteomics/peptide_analysis/isoelectric_point_calculator/isoelectric_point_calculator.py diff --git a/scripts/proteomics/identification_qc_reporter/requirements.txt b/scripts/proteomics/peptide_analysis/isoelectric_point_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/identification_qc_reporter/requirements.txt rename to scripts/proteomics/peptide_analysis/isoelectric_point_calculator/requirements.txt diff --git a/scripts/proteomics/fragpipe_result_converter/tests/conftest.py b/scripts/proteomics/peptide_analysis/isoelectric_point_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/fragpipe_result_converter/tests/conftest.py rename to scripts/proteomics/peptide_analysis/isoelectric_point_calculator/tests/conftest.py diff --git a/scripts/proteomics/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py b/scripts/proteomics/peptide_analysis/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py similarity index 100% rename from scripts/proteomics/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py rename to scripts/proteomics/peptide_analysis/isoelectric_point_calculator/tests/test_isoelectric_point_calculator.py diff --git a/scripts/proteomics/modification_mass_calculator/README.md b/scripts/proteomics/peptide_analysis/modification_mass_calculator/README.md similarity index 100% rename from scripts/proteomics/modification_mass_calculator/README.md rename to scripts/proteomics/peptide_analysis/modification_mass_calculator/README.md diff --git a/scripts/proteomics/modification_mass_calculator/modification_mass_calculator.py b/scripts/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py similarity index 100% rename from scripts/proteomics/modification_mass_calculator/modification_mass_calculator.py rename to scripts/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py diff --git a/scripts/proteomics/idxml_to_tsv_exporter/requirements.txt b/scripts/proteomics/peptide_analysis/modification_mass_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/idxml_to_tsv_exporter/requirements.txt rename to scripts/proteomics/peptide_analysis/modification_mass_calculator/requirements.txt diff --git a/scripts/proteomics/glycopeptide_mass_calculator/tests/conftest.py b/scripts/proteomics/peptide_analysis/modification_mass_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/glycopeptide_mass_calculator/tests/conftest.py rename to scripts/proteomics/peptide_analysis/modification_mass_calculator/tests/conftest.py diff --git a/scripts/proteomics/modification_mass_calculator/tests/test_modification_mass_calculator.py b/scripts/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py similarity index 100% rename from scripts/proteomics/modification_mass_calculator/tests/test_modification_mass_calculator.py rename to scripts/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py diff --git a/scripts/proteomics/modified_peptide_generator/README.md b/scripts/proteomics/peptide_analysis/modified_peptide_generator/README.md similarity index 100% rename from scripts/proteomics/modified_peptide_generator/README.md rename to scripts/proteomics/peptide_analysis/modified_peptide_generator/README.md diff --git a/scripts/proteomics/modified_peptide_generator/modified_peptide_generator.py b/scripts/proteomics/peptide_analysis/modified_peptide_generator/modified_peptide_generator.py similarity index 100% rename from scripts/proteomics/modified_peptide_generator/modified_peptide_generator.py rename to scripts/proteomics/peptide_analysis/modified_peptide_generator/modified_peptide_generator.py diff --git a/scripts/proteomics/immunopeptide_filter/requirements.txt b/scripts/proteomics/peptide_analysis/modified_peptide_generator/requirements.txt similarity index 100% rename from scripts/proteomics/immunopeptide_filter/requirements.txt rename to scripts/proteomics/peptide_analysis/modified_peptide_generator/requirements.txt diff --git a/scripts/proteomics/hdx_back_exchange_estimator/tests/conftest.py b/scripts/proteomics/peptide_analysis/modified_peptide_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/hdx_back_exchange_estimator/tests/conftest.py rename to scripts/proteomics/peptide_analysis/modified_peptide_generator/tests/conftest.py diff --git a/scripts/proteomics/modified_peptide_generator/tests/test_modified_peptide_generator.py b/scripts/proteomics/peptide_analysis/modified_peptide_generator/tests/test_modified_peptide_generator.py similarity index 100% rename from scripts/proteomics/modified_peptide_generator/tests/test_modified_peptide_generator.py rename to scripts/proteomics/peptide_analysis/modified_peptide_generator/tests/test_modified_peptide_generator.py diff --git a/scripts/proteomics/peptide_detectability_predictor/README.md b/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/README.md similarity index 100% rename from scripts/proteomics/peptide_detectability_predictor/README.md rename to scripts/proteomics/peptide_analysis/peptide_detectability_predictor/README.md diff --git a/scripts/proteomics/peptide_detectability_predictor/peptide_detectability_predictor.py b/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py similarity index 100% rename from scripts/proteomics/peptide_detectability_predictor/peptide_detectability_predictor.py rename to scripts/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py diff --git a/scripts/proteomics/immunopeptidome_qc/requirements.txt b/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/requirements.txt similarity index 100% rename from scripts/proteomics/immunopeptidome_qc/requirements.txt rename to scripts/proteomics/peptide_analysis/peptide_detectability_predictor/requirements.txt diff --git a/scripts/proteomics/hdx_deuterium_uptake/tests/conftest.py b/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/tests/conftest.py similarity index 100% rename from scripts/proteomics/hdx_deuterium_uptake/tests/conftest.py rename to scripts/proteomics/peptide_analysis/peptide_detectability_predictor/tests/conftest.py diff --git a/scripts/proteomics/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py b/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py similarity index 100% rename from scripts/proteomics/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py rename to scripts/proteomics/peptide_analysis/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py diff --git a/scripts/proteomics/peptide_mass_calculator/README.md b/scripts/proteomics/peptide_analysis/peptide_mass_calculator/README.md similarity index 100% rename from scripts/proteomics/peptide_mass_calculator/README.md rename to scripts/proteomics/peptide_analysis/peptide_mass_calculator/README.md diff --git a/scripts/proteomics/peptide_mass_calculator/peptide_mass_calculator.py b/scripts/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py similarity index 100% rename from scripts/proteomics/peptide_mass_calculator/peptide_mass_calculator.py rename to scripts/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py diff --git a/scripts/proteomics/inclusion_list_generator/requirements.txt b/scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/inclusion_list_generator/requirements.txt rename to scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt diff --git a/scripts/proteomics/identification_qc_reporter/tests/conftest.py b/scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/identification_qc_reporter/tests/conftest.py rename to scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/conftest.py diff --git a/scripts/proteomics/peptide_mass_calculator/tests/test_peptide_mass_calculator.py b/scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/test_peptide_mass_calculator.py similarity index 100% rename from scripts/proteomics/peptide_mass_calculator/tests/test_peptide_mass_calculator.py rename to scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/test_peptide_mass_calculator.py diff --git a/scripts/proteomics/peptide_mass_fingerprint/README.md b/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/README.md similarity index 100% rename from scripts/proteomics/peptide_mass_fingerprint/README.md rename to scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/README.md diff --git a/scripts/proteomics/peptide_mass_fingerprint/peptide_mass_fingerprint.py b/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py similarity index 100% rename from scripts/proteomics/peptide_mass_fingerprint/peptide_mass_fingerprint.py rename to scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py diff --git a/scripts/proteomics/injection_time_analyzer/requirements.txt b/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt similarity index 100% rename from scripts/proteomics/injection_time_analyzer/requirements.txt rename to scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt diff --git a/scripts/proteomics/idxml_to_tsv_exporter/tests/conftest.py b/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/conftest.py similarity index 100% rename from scripts/proteomics/idxml_to_tsv_exporter/tests/conftest.py rename to scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/conftest.py diff --git a/scripts/proteomics/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py b/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py similarity index 100% rename from scripts/proteomics/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py rename to scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/test_peptide_mass_fingerprint.py diff --git a/scripts/proteomics/peptide_modification_analyzer/README.md b/scripts/proteomics/peptide_analysis/peptide_modification_analyzer/README.md similarity index 100% rename from scripts/proteomics/peptide_modification_analyzer/README.md rename to scripts/proteomics/peptide_analysis/peptide_modification_analyzer/README.md diff --git a/scripts/proteomics/peptide_modification_analyzer/peptide_modification_analyzer.py b/scripts/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py similarity index 100% rename from scripts/proteomics/peptide_modification_analyzer/peptide_modification_analyzer.py rename to scripts/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py diff --git a/scripts/proteomics/irt_calculator/requirements.txt b/scripts/proteomics/peptide_analysis/peptide_modification_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/irt_calculator/requirements.txt rename to scripts/proteomics/peptide_analysis/peptide_modification_analyzer/requirements.txt diff --git a/scripts/proteomics/immunopeptide_filter/tests/conftest.py b/scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/immunopeptide_filter/tests/conftest.py rename to scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/conftest.py diff --git a/scripts/proteomics/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py b/scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py similarity index 100% rename from scripts/proteomics/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py rename to scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py diff --git a/scripts/proteomics/peptide_property_calculator/README.md b/scripts/proteomics/peptide_analysis/peptide_property_calculator/README.md similarity index 100% rename from scripts/proteomics/peptide_property_calculator/README.md rename to scripts/proteomics/peptide_analysis/peptide_property_calculator/README.md diff --git a/scripts/proteomics/peptide_property_calculator/peptide_property_calculator.py b/scripts/proteomics/peptide_analysis/peptide_property_calculator/peptide_property_calculator.py similarity index 100% rename from scripts/proteomics/peptide_property_calculator/peptide_property_calculator.py rename to scripts/proteomics/peptide_analysis/peptide_property_calculator/peptide_property_calculator.py diff --git a/scripts/proteomics/isoelectric_point_calculator/requirements.txt b/scripts/proteomics/peptide_analysis/peptide_property_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/isoelectric_point_calculator/requirements.txt rename to scripts/proteomics/peptide_analysis/peptide_property_calculator/requirements.txt diff --git a/scripts/proteomics/immunopeptidome_qc/tests/conftest.py b/scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/immunopeptidome_qc/tests/conftest.py rename to scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/conftest.py diff --git a/scripts/proteomics/peptide_property_calculator/tests/test_peptide_property_calculator.py b/scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/test_peptide_property_calculator.py similarity index 100% rename from scripts/proteomics/peptide_property_calculator/tests/test_peptide_property_calculator.py rename to scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/test_peptide_property_calculator.py diff --git a/scripts/proteomics/peptide_uniqueness_checker/README.md b/scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/README.md similarity index 100% rename from scripts/proteomics/peptide_uniqueness_checker/README.md rename to scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/README.md diff --git a/scripts/proteomics/peptide_uniqueness_checker/peptide_uniqueness_checker.py b/scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/peptide_uniqueness_checker.py similarity index 100% rename from scripts/proteomics/peptide_uniqueness_checker/peptide_uniqueness_checker.py rename to scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/peptide_uniqueness_checker.py diff --git a/scripts/proteomics/lc_ms_qc_reporter/requirements.txt b/scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/requirements.txt similarity index 100% rename from scripts/proteomics/lc_ms_qc_reporter/requirements.txt rename to scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/requirements.txt diff --git a/scripts/proteomics/inclusion_list_generator/tests/conftest.py b/scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/tests/conftest.py similarity index 100% rename from scripts/proteomics/inclusion_list_generator/tests/conftest.py rename to scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/tests/conftest.py diff --git a/scripts/proteomics/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py b/scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py similarity index 100% rename from scripts/proteomics/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py rename to scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/tests/test_peptide_uniqueness_checker.py diff --git a/scripts/proteomics/rt_prediction_additive/README.md b/scripts/proteomics/peptide_analysis/rt_prediction_additive/README.md similarity index 100% rename from scripts/proteomics/rt_prediction_additive/README.md rename to scripts/proteomics/peptide_analysis/rt_prediction_additive/README.md diff --git a/scripts/proteomics/library_coverage_estimator/requirements.txt b/scripts/proteomics/peptide_analysis/rt_prediction_additive/requirements.txt similarity index 100% rename from scripts/proteomics/library_coverage_estimator/requirements.txt rename to scripts/proteomics/peptide_analysis/rt_prediction_additive/requirements.txt diff --git a/scripts/proteomics/rt_prediction_additive/rt_prediction_additive.py b/scripts/proteomics/peptide_analysis/rt_prediction_additive/rt_prediction_additive.py similarity index 100% rename from scripts/proteomics/rt_prediction_additive/rt_prediction_additive.py rename to scripts/proteomics/peptide_analysis/rt_prediction_additive/rt_prediction_additive.py diff --git a/scripts/proteomics/injection_time_analyzer/tests/conftest.py b/scripts/proteomics/peptide_analysis/rt_prediction_additive/tests/conftest.py similarity index 100% rename from scripts/proteomics/injection_time_analyzer/tests/conftest.py rename to scripts/proteomics/peptide_analysis/rt_prediction_additive/tests/conftest.py diff --git a/scripts/proteomics/rt_prediction_additive/tests/test_rt_prediction_additive.py b/scripts/proteomics/peptide_analysis/rt_prediction_additive/tests/test_rt_prediction_additive.py similarity index 100% rename from scripts/proteomics/rt_prediction_additive/tests/test_rt_prediction_additive.py rename to scripts/proteomics/peptide_analysis/rt_prediction_additive/tests/test_rt_prediction_additive.py diff --git a/scripts/proteomics/peptide_to_protein_mapper/README.md b/scripts/proteomics/protein_analysis/peptide_to_protein_mapper/README.md similarity index 100% rename from scripts/proteomics/peptide_to_protein_mapper/README.md rename to scripts/proteomics/protein_analysis/peptide_to_protein_mapper/README.md diff --git a/scripts/proteomics/peptide_to_protein_mapper/peptide_to_protein_mapper.py b/scripts/proteomics/protein_analysis/peptide_to_protein_mapper/peptide_to_protein_mapper.py similarity index 100% rename from scripts/proteomics/peptide_to_protein_mapper/peptide_to_protein_mapper.py rename to scripts/proteomics/protein_analysis/peptide_to_protein_mapper/peptide_to_protein_mapper.py diff --git a/scripts/proteomics/mass_error_distribution_analyzer/requirements.txt b/scripts/proteomics/protein_analysis/peptide_to_protein_mapper/requirements.txt similarity index 100% rename from scripts/proteomics/mass_error_distribution_analyzer/requirements.txt rename to scripts/proteomics/protein_analysis/peptide_to_protein_mapper/requirements.txt diff --git a/scripts/proteomics/intensity_distribution_reporter/tests/conftest.py b/scripts/proteomics/protein_analysis/peptide_to_protein_mapper/tests/conftest.py similarity index 100% rename from scripts/proteomics/intensity_distribution_reporter/tests/conftest.py rename to scripts/proteomics/protein_analysis/peptide_to_protein_mapper/tests/conftest.py diff --git a/scripts/proteomics/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py b/scripts/proteomics/protein_analysis/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py similarity index 100% rename from scripts/proteomics/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py rename to scripts/proteomics/protein_analysis/peptide_to_protein_mapper/tests/test_peptide_to_protein_mapper.py diff --git a/scripts/proteomics/protein_coverage_calculator/README.md b/scripts/proteomics/protein_analysis/protein_coverage_calculator/README.md similarity index 100% rename from scripts/proteomics/protein_coverage_calculator/README.md rename to scripts/proteomics/protein_analysis/protein_coverage_calculator/README.md diff --git a/scripts/proteomics/protein_coverage_calculator/protein_coverage_calculator.py b/scripts/proteomics/protein_analysis/protein_coverage_calculator/protein_coverage_calculator.py similarity index 100% rename from scripts/proteomics/protein_coverage_calculator/protein_coverage_calculator.py rename to scripts/proteomics/protein_analysis/protein_coverage_calculator/protein_coverage_calculator.py diff --git a/scripts/proteomics/maxquant_result_converter/requirements.txt b/scripts/proteomics/protein_analysis/protein_coverage_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/maxquant_result_converter/requirements.txt rename to scripts/proteomics/protein_analysis/protein_coverage_calculator/requirements.txt diff --git a/scripts/proteomics/irt_calculator/tests/conftest.py b/scripts/proteomics/protein_analysis/protein_coverage_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/irt_calculator/tests/conftest.py rename to scripts/proteomics/protein_analysis/protein_coverage_calculator/tests/conftest.py diff --git a/scripts/proteomics/protein_coverage_calculator/tests/test_protein_coverage_calculator.py b/scripts/proteomics/protein_analysis/protein_coverage_calculator/tests/test_protein_coverage_calculator.py similarity index 100% rename from scripts/proteomics/protein_coverage_calculator/tests/test_protein_coverage_calculator.py rename to scripts/proteomics/protein_analysis/protein_coverage_calculator/tests/test_protein_coverage_calculator.py diff --git a/scripts/proteomics/protein_digest/README.md b/scripts/proteomics/protein_analysis/protein_digest/README.md similarity index 100% rename from scripts/proteomics/protein_digest/README.md rename to scripts/proteomics/protein_analysis/protein_digest/README.md diff --git a/scripts/proteomics/protein_digest/protein_digest.py b/scripts/proteomics/protein_analysis/protein_digest/protein_digest.py similarity index 100% rename from scripts/proteomics/protein_digest/protein_digest.py rename to scripts/proteomics/protein_analysis/protein_digest/protein_digest.py diff --git a/scripts/proteomics/metapeptide_function_aggregator/requirements.txt b/scripts/proteomics/protein_analysis/protein_digest/requirements.txt similarity index 100% rename from scripts/proteomics/metapeptide_function_aggregator/requirements.txt rename to scripts/proteomics/protein_analysis/protein_digest/requirements.txt diff --git a/scripts/proteomics/isobaric_purity_corrector/tests/conftest.py b/scripts/proteomics/protein_analysis/protein_digest/tests/conftest.py similarity index 100% rename from scripts/proteomics/isobaric_purity_corrector/tests/conftest.py rename to scripts/proteomics/protein_analysis/protein_digest/tests/conftest.py diff --git a/scripts/proteomics/protein_digest/tests/test_protein_digest.py b/scripts/proteomics/protein_analysis/protein_digest/tests/test_protein_digest.py similarity index 100% rename from scripts/proteomics/protein_digest/tests/test_protein_digest.py rename to scripts/proteomics/protein_analysis/protein_digest/tests/test_protein_digest.py diff --git a/scripts/proteomics/protein_group_reporter/README.md b/scripts/proteomics/protein_analysis/protein_group_reporter/README.md similarity index 100% rename from scripts/proteomics/protein_group_reporter/README.md rename to scripts/proteomics/protein_analysis/protein_group_reporter/README.md diff --git a/scripts/proteomics/protein_group_reporter/protein_group_reporter.py b/scripts/proteomics/protein_analysis/protein_group_reporter/protein_group_reporter.py similarity index 100% rename from scripts/proteomics/protein_group_reporter/protein_group_reporter.py rename to scripts/proteomics/protein_analysis/protein_group_reporter/protein_group_reporter.py diff --git a/scripts/proteomics/metapeptide_lca_assigner/requirements.txt b/scripts/proteomics/protein_analysis/protein_group_reporter/requirements.txt similarity index 100% rename from scripts/proteomics/metapeptide_lca_assigner/requirements.txt rename to scripts/proteomics/protein_analysis/protein_group_reporter/requirements.txt diff --git a/scripts/proteomics/isoelectric_point_calculator/tests/conftest.py b/scripts/proteomics/protein_analysis/protein_group_reporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/isoelectric_point_calculator/tests/conftest.py rename to scripts/proteomics/protein_analysis/protein_group_reporter/tests/conftest.py diff --git a/scripts/proteomics/protein_group_reporter/tests/test_protein_group_reporter.py b/scripts/proteomics/protein_analysis/protein_group_reporter/tests/test_protein_group_reporter.py similarity index 100% rename from scripts/proteomics/protein_group_reporter/tests/test_protein_group_reporter.py rename to scripts/proteomics/protein_analysis/protein_group_reporter/tests/test_protein_group_reporter.py diff --git a/scripts/proteomics/spectral_counting_quantifier/README.md b/scripts/proteomics/protein_analysis/spectral_counting_quantifier/README.md similarity index 100% rename from scripts/proteomics/spectral_counting_quantifier/README.md rename to scripts/proteomics/protein_analysis/spectral_counting_quantifier/README.md diff --git a/scripts/proteomics/mgf_to_mzml_converter/requirements.txt b/scripts/proteomics/protein_analysis/spectral_counting_quantifier/requirements.txt similarity index 100% rename from scripts/proteomics/mgf_to_mzml_converter/requirements.txt rename to scripts/proteomics/protein_analysis/spectral_counting_quantifier/requirements.txt diff --git a/scripts/proteomics/spectral_counting_quantifier/spectral_counting_quantifier.py b/scripts/proteomics/protein_analysis/spectral_counting_quantifier/spectral_counting_quantifier.py similarity index 100% rename from scripts/proteomics/spectral_counting_quantifier/spectral_counting_quantifier.py rename to scripts/proteomics/protein_analysis/spectral_counting_quantifier/spectral_counting_quantifier.py diff --git a/scripts/proteomics/lc_ms_qc_reporter/tests/conftest.py b/scripts/proteomics/protein_analysis/spectral_counting_quantifier/tests/conftest.py similarity index 100% rename from scripts/proteomics/lc_ms_qc_reporter/tests/conftest.py rename to scripts/proteomics/protein_analysis/spectral_counting_quantifier/tests/conftest.py diff --git a/scripts/proteomics/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py b/scripts/proteomics/protein_analysis/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py similarity index 100% rename from scripts/proteomics/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py rename to scripts/proteomics/protein_analysis/spectral_counting_quantifier/tests/test_spectral_counting_quantifier.py diff --git a/scripts/proteomics/protein_completeness_matrix/README.md b/scripts/proteomics/protein_completeness_matrix/README.md deleted file mode 100644 index 12c311c..0000000 --- a/scripts/proteomics/protein_completeness_matrix/README.md +++ /dev/null @@ -1,34 +0,0 @@ -# Protein Completeness Matrix - -Compute data completeness per protein and per sample from a quantification matrix. - -## Installation - -```bash -pip install -r requirements.txt -``` - -## Usage - -```bash -python protein_completeness_matrix.py --input quant_matrix.tsv \ - --min-completeness 0.5 --output completeness.tsv -``` - -### Input format - -Tab-separated quantification matrix with `protein_id` as the first column: - -``` -protein_id sample1 sample2 sample3 -P12345 100.5 NA 95.2 -P67890 200.1 180.3 190.7 -``` - -### Parameters - -| Flag | Description | -|------|-------------| -| `--input` | Input quantification matrix TSV | -| `--min-completeness` | Minimum completeness fraction to retain a protein (default: 0.0) | -| `--output` | Output completeness TSV | diff --git a/scripts/proteomics/protein_completeness_matrix/protein_completeness_matrix.py b/scripts/proteomics/protein_completeness_matrix/protein_completeness_matrix.py deleted file mode 100644 index 027161b..0000000 --- a/scripts/proteomics/protein_completeness_matrix/protein_completeness_matrix.py +++ /dev/null @@ -1,242 +0,0 @@ -""" -Protein Completeness Matrix -============================= -Compute data completeness per protein and per sample from a quantification -matrix. Reports the fraction of non-missing values for each protein across -samples and for each sample across proteins, and optionally filters proteins -below a minimum completeness threshold. - -Usage ------ - python protein_completeness_matrix.py --input quant_matrix.tsv \ - --min-completeness 0.5 --output completeness.tsv -""" - -import argparse -import csv -import sys -from typing import Dict, List, Tuple - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - - -def load_quant_matrix(input_path: str) -> Tuple[List[str], List[str], np.ndarray]: - """Load a protein quantification matrix from TSV. - - The first column is expected to be ``protein_id`` and remaining columns - are sample IDs. Missing values (empty strings, ``NA``, ``NaN``) are - treated as missing. - - Parameters - ---------- - input_path: - Path to input TSV. - - Returns - ------- - tuple - (protein_ids, sample_ids, data_matrix) where data_matrix has shape - (n_proteins, n_samples) with NaN for missing values. - """ - protein_ids: List[str] = [] - rows: List[List[float]] = [] - - with open(input_path, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - fields = reader.fieldnames or [] - sample_ids = [f for f in fields if f != "protein_id"] - - for row in reader: - pid = row.get("protein_id", "").strip() - if not pid: - continue - protein_ids.append(pid) - values: List[float] = [] - for sid in sample_ids: - val = row.get(sid, "").strip() - if val in ("", "NA", "NaN", "nan", "null"): - values.append(float("nan")) - else: - try: - v = float(val) - values.append(v if v > 0 else float("nan")) - except (ValueError, TypeError): - values.append(float("nan")) - rows.append(values) - - data = np.array(rows, dtype=float) if rows else np.empty((0, len(sample_ids))) - return protein_ids, sample_ids, data - - -def compute_protein_completeness( - data: np.ndarray, -) -> np.ndarray: - """Compute completeness (fraction of non-NaN values) per protein (row). - - Parameters - ---------- - data: - Matrix of shape (n_proteins, n_samples). - - Returns - ------- - numpy.ndarray - Array of shape (n_proteins,) with completeness fractions. - """ - n_samples = data.shape[1] - if n_samples == 0: - return np.zeros(data.shape[0]) - non_missing = np.sum(~np.isnan(data), axis=1) - return non_missing / n_samples - - -def compute_sample_completeness( - data: np.ndarray, -) -> np.ndarray: - """Compute completeness (fraction of non-NaN values) per sample (column). - - Parameters - ---------- - data: - Matrix of shape (n_proteins, n_samples). - - Returns - ------- - numpy.ndarray - Array of shape (n_samples,) with completeness fractions. - """ - n_proteins = data.shape[0] - if n_proteins == 0: - return np.zeros(data.shape[1]) - non_missing = np.sum(~np.isnan(data), axis=0) - return non_missing / n_proteins - - -def filter_by_completeness( - protein_ids: List[str], - data: np.ndarray, - protein_completeness: np.ndarray, - min_completeness: float, -) -> Tuple[List[str], np.ndarray]: - """Filter proteins by minimum completeness threshold. - - Parameters - ---------- - protein_ids: - Protein identifiers. - data: - Data matrix. - protein_completeness: - Per-protein completeness values. - min_completeness: - Minimum fraction required to keep a protein. - - Returns - ------- - tuple - (filtered_ids, filtered_data) - """ - mask = protein_completeness >= min_completeness - filtered_ids = [pid for pid, keep in zip(protein_ids, mask) if keep] - filtered_data = data[mask] - return filtered_ids, filtered_data - - -def completeness_summary( - protein_completeness: np.ndarray, - sample_completeness: np.ndarray, - data: np.ndarray, -) -> Dict[str, object]: - """Compute overall completeness summary. - - Returns - ------- - dict - Summary statistics. - """ - total_cells = data.size - non_missing = int(np.sum(~np.isnan(data))) - return { - "total_proteins": data.shape[0], - "total_samples": data.shape[1], - "total_cells": total_cells, - "non_missing_cells": non_missing, - "overall_completeness": non_missing / total_cells if total_cells > 0 else 0.0, - "mean_protein_completeness": float(np.mean(protein_completeness)) if len(protein_completeness) > 0 else 0.0, - "median_protein_completeness": float(np.median(protein_completeness)) if len(protein_completeness) > 0 else 0.0, - "mean_sample_completeness": float(np.mean(sample_completeness)) if len(sample_completeness) > 0 else 0.0, - "median_sample_completeness": float(np.median(sample_completeness)) if len(sample_completeness) > 0 else 0.0, - } - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Compute data completeness per protein and sample." - ) - parser.add_argument("--input", required=True, help="Input quantification matrix TSV") - parser.add_argument( - "--min-completeness", type=float, default=0.0, - help="Minimum protein completeness to retain (default: 0.0 = keep all)", - ) - parser.add_argument("--output", required=True, help="Output completeness TSV") - args = parser.parse_args() - - protein_ids, sample_ids, data = load_quant_matrix(args.input) - if len(protein_ids) == 0: - sys.exit("No proteins found in input.") - - prot_comp = compute_protein_completeness(data) - samp_comp = compute_sample_completeness(data) - summary = completeness_summary(prot_comp, samp_comp, data) - - # Filter - filtered_ids, filtered_data = filter_by_completeness( - protein_ids, data, prot_comp, args.min_completeness - ) - - with open(args.output, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - # Per-protein completeness - writer.writerow(["protein_id", "completeness", "n_present", "n_total"]) - for i, pid in enumerate(protein_ids): - n_present = int(np.sum(~np.isnan(data[i]))) - writer.writerow([pid, f"{prot_comp[i]:.4f}", n_present, data.shape[1]]) - writer.writerow([]) - - # Per-sample completeness - writer.writerow(["sample_id", "completeness", "n_present", "n_total"]) - for j, sid in enumerate(sample_ids): - n_present = int(np.sum(~np.isnan(data[:, j]))) - writer.writerow([sid, f"{samp_comp[j]:.4f}", n_present, data.shape[0]]) - writer.writerow([]) - - # Summary - writer.writerow(["metric", "value"]) - for key, val in summary.items(): - if isinstance(val, float): - writer.writerow([key, f"{val:.4f}"]) - else: - writer.writerow([key, val]) - - if args.min_completeness > 0: - writer.writerow([]) - writer.writerow(["filter_threshold", args.min_completeness]) - writer.writerow(["proteins_retained", len(filtered_ids)]) - writer.writerow(["proteins_removed", len(protein_ids) - len(filtered_ids)]) - - print(f"Proteins: {len(protein_ids)}, overall completeness: {summary['overall_completeness']:.1%}") - if args.min_completeness > 0: - print( - f"After filtering (>={args.min_completeness:.0%}): " - f"{len(filtered_ids)}/{len(protein_ids)} proteins retained" - ) - print(f"Output -> {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/protein_completeness_matrix/requirements.txt b/scripts/proteomics/protein_completeness_matrix/requirements.txt deleted file mode 100644 index 1051d92..0000000 --- a/scripts/proteomics/protein_completeness_matrix/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pyopenms -numpy diff --git a/scripts/proteomics/protein_completeness_matrix/tests/test_protein_completeness_matrix.py b/scripts/proteomics/protein_completeness_matrix/tests/test_protein_completeness_matrix.py deleted file mode 100644 index 6e4f2e2..0000000 --- a/scripts/proteomics/protein_completeness_matrix/tests/test_protein_completeness_matrix.py +++ /dev/null @@ -1,111 +0,0 @@ -"""Tests for protein_completeness_matrix.""" - -import csv -import sys - -import numpy as np -from conftest import requires_pyopenms - - -@requires_pyopenms -def test_load_quant_matrix(tmp_path): - from protein_completeness_matrix import load_quant_matrix - - input_file = tmp_path / "quant.tsv" - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein_id", "s1", "s2", "s3"]) - writer.writerow(["P1", "100.0", "NA", "95.0"]) - writer.writerow(["P2", "200.0", "180.0", "190.0"]) - - pids, sids, data = load_quant_matrix(str(input_file)) - assert pids == ["P1", "P2"] - assert sids == ["s1", "s2", "s3"] - assert data.shape == (2, 3) - assert np.isnan(data[0, 1]) # P1, s2 is NA - assert data[1, 0] == 200.0 - - -@requires_pyopenms -def test_compute_protein_completeness(): - from protein_completeness_matrix import compute_protein_completeness - - data = np.array([ - [1.0, np.nan, 1.0], # 2/3 complete - [1.0, 1.0, 1.0], # 3/3 complete - ]) - comp = compute_protein_completeness(data) - assert abs(comp[0] - 2.0 / 3.0) < 0.01 - assert abs(comp[1] - 1.0) < 0.01 - - -@requires_pyopenms -def test_compute_sample_completeness(): - from protein_completeness_matrix import compute_sample_completeness - - data = np.array([ - [1.0, np.nan, 1.0], - [1.0, 1.0, 1.0], - ]) - comp = compute_sample_completeness(data) - assert abs(comp[0] - 1.0) < 0.01 # s1: both proteins present - assert abs(comp[1] - 0.5) < 0.01 # s2: one of two - assert abs(comp[2] - 1.0) < 0.01 # s3: both present - - -@requires_pyopenms -def test_filter_by_completeness(): - from protein_completeness_matrix import filter_by_completeness - - data = np.array([ - [1.0, np.nan, np.nan], # 1/3 = 0.33 - [1.0, 1.0, 1.0], # 3/3 = 1.0 - [1.0, 1.0, np.nan], # 2/3 = 0.67 - ]) - comp = np.array([1.0 / 3, 1.0, 2.0 / 3]) - filtered_ids, filtered_data = filter_by_completeness( - ["P1", "P2", "P3"], data, comp, 0.5 - ) - assert filtered_ids == ["P2", "P3"] - assert filtered_data.shape == (2, 3) - - -@requires_pyopenms -def test_completeness_summary(): - from protein_completeness_matrix import completeness_summary - - data = np.array([ - [1.0, np.nan], - [1.0, 1.0], - ]) - prot_comp = np.array([0.5, 1.0]) - samp_comp = np.array([1.0, 0.5]) - summary = completeness_summary(prot_comp, samp_comp, data) - assert summary["total_proteins"] == 2 - assert summary["total_samples"] == 2 - assert summary["non_missing_cells"] == 3 - assert abs(summary["overall_completeness"] - 0.75) < 0.01 - - -@requires_pyopenms -def test_cli_roundtrip(tmp_path): - from protein_completeness_matrix import main - - input_file = tmp_path / "quant.tsv" - output_file = tmp_path / "completeness.tsv" - - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein_id", "s1", "s2", "s3"]) - writer.writerow(["P1", "100.0", "NA", "95.0"]) - writer.writerow(["P2", "200.0", "180.0", "190.0"]) - writer.writerow(["P3", "NA", "NA", "50.0"]) - - sys.argv = [ - "protein_completeness_matrix.py", - "--input", str(input_file), - "--min-completeness", "0.5", - "--output", str(output_file), - ] - main() - assert output_file.exists() diff --git a/scripts/proteomics/glycopeptide_mass_calculator/README.md b/scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/README.md similarity index 100% rename from scripts/proteomics/glycopeptide_mass_calculator/README.md rename to scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/README.md diff --git a/scripts/proteomics/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py b/scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py similarity index 100% rename from scripts/proteomics/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py rename to scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py diff --git a/scripts/proteomics/missed_cleavage_analyzer/requirements.txt b/scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/missed_cleavage_analyzer/requirements.txt rename to scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/requirements.txt diff --git a/scripts/proteomics/library_coverage_estimator/tests/conftest.py b/scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/library_coverage_estimator/tests/conftest.py rename to scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/tests/conftest.py diff --git a/scripts/proteomics/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py b/scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py similarity index 100% rename from scripts/proteomics/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py rename to scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/tests/test_glycopeptide_mass_calculator.py diff --git a/scripts/proteomics/phospho_enrichment_qc/README.md b/scripts/proteomics/ptm_analysis/phospho_enrichment_qc/README.md similarity index 100% rename from scripts/proteomics/phospho_enrichment_qc/README.md rename to scripts/proteomics/ptm_analysis/phospho_enrichment_qc/README.md diff --git a/scripts/proteomics/phospho_enrichment_qc/phospho_enrichment_qc.py b/scripts/proteomics/ptm_analysis/phospho_enrichment_qc/phospho_enrichment_qc.py similarity index 100% rename from scripts/proteomics/phospho_enrichment_qc/phospho_enrichment_qc.py rename to scripts/proteomics/ptm_analysis/phospho_enrichment_qc/phospho_enrichment_qc.py diff --git a/scripts/proteomics/modification_mass_calculator/requirements.txt b/scripts/proteomics/ptm_analysis/phospho_enrichment_qc/requirements.txt similarity index 100% rename from scripts/proteomics/modification_mass_calculator/requirements.txt rename to scripts/proteomics/ptm_analysis/phospho_enrichment_qc/requirements.txt diff --git a/scripts/proteomics/mass_error_distribution_analyzer/tests/conftest.py b/scripts/proteomics/ptm_analysis/phospho_enrichment_qc/tests/conftest.py similarity index 100% rename from scripts/proteomics/mass_error_distribution_analyzer/tests/conftest.py rename to scripts/proteomics/ptm_analysis/phospho_enrichment_qc/tests/conftest.py diff --git a/scripts/proteomics/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py b/scripts/proteomics/ptm_analysis/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py similarity index 100% rename from scripts/proteomics/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py rename to scripts/proteomics/ptm_analysis/phospho_enrichment_qc/tests/test_phospho_enrichment_qc.py diff --git a/scripts/proteomics/phospho_motif_analyzer/README.md b/scripts/proteomics/ptm_analysis/phospho_motif_analyzer/README.md similarity index 100% rename from scripts/proteomics/phospho_motif_analyzer/README.md rename to scripts/proteomics/ptm_analysis/phospho_motif_analyzer/README.md diff --git a/scripts/proteomics/phospho_motif_analyzer/phospho_motif_analyzer.py b/scripts/proteomics/ptm_analysis/phospho_motif_analyzer/phospho_motif_analyzer.py similarity index 100% rename from scripts/proteomics/phospho_motif_analyzer/phospho_motif_analyzer.py rename to scripts/proteomics/ptm_analysis/phospho_motif_analyzer/phospho_motif_analyzer.py diff --git a/scripts/proteomics/modified_peptide_generator/requirements.txt b/scripts/proteomics/ptm_analysis/phospho_motif_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/modified_peptide_generator/requirements.txt rename to scripts/proteomics/ptm_analysis/phospho_motif_analyzer/requirements.txt diff --git a/scripts/proteomics/maxquant_result_converter/tests/conftest.py b/scripts/proteomics/ptm_analysis/phospho_motif_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/maxquant_result_converter/tests/conftest.py rename to scripts/proteomics/ptm_analysis/phospho_motif_analyzer/tests/conftest.py diff --git a/scripts/proteomics/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py b/scripts/proteomics/ptm_analysis/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py similarity index 100% rename from scripts/proteomics/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py rename to scripts/proteomics/ptm_analysis/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py diff --git a/scripts/proteomics/phosphosite_class_filter/README.md b/scripts/proteomics/ptm_analysis/phosphosite_class_filter/README.md similarity index 100% rename from scripts/proteomics/phosphosite_class_filter/README.md rename to scripts/proteomics/ptm_analysis/phosphosite_class_filter/README.md diff --git a/scripts/proteomics/phosphosite_class_filter/phosphosite_class_filter.py b/scripts/proteomics/ptm_analysis/phosphosite_class_filter/phosphosite_class_filter.py similarity index 100% rename from scripts/proteomics/phosphosite_class_filter/phosphosite_class_filter.py rename to scripts/proteomics/ptm_analysis/phosphosite_class_filter/phosphosite_class_filter.py diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/requirements.txt b/scripts/proteomics/ptm_analysis/phosphosite_class_filter/requirements.txt similarity index 100% rename from scripts/proteomics/ms1_feature_intensity_tracker/requirements.txt rename to scripts/proteomics/ptm_analysis/phosphosite_class_filter/requirements.txt diff --git a/scripts/proteomics/metapeptide_function_aggregator/tests/conftest.py b/scripts/proteomics/ptm_analysis/phosphosite_class_filter/tests/conftest.py similarity index 100% rename from scripts/proteomics/metapeptide_function_aggregator/tests/conftest.py rename to scripts/proteomics/ptm_analysis/phosphosite_class_filter/tests/conftest.py diff --git a/scripts/proteomics/phosphosite_class_filter/tests/test_phosphosite_class_filter.py b/scripts/proteomics/ptm_analysis/phosphosite_class_filter/tests/test_phosphosite_class_filter.py similarity index 100% rename from scripts/proteomics/phosphosite_class_filter/tests/test_phosphosite_class_filter.py rename to scripts/proteomics/ptm_analysis/phosphosite_class_filter/tests/test_phosphosite_class_filter.py diff --git a/scripts/proteomics/ptm_site_localization_scorer/README.md b/scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/README.md similarity index 100% rename from scripts/proteomics/ptm_site_localization_scorer/README.md rename to scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/README.md diff --git a/scripts/proteomics/ptm_site_localization_scorer/ptm_site_localization_scorer.py b/scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py similarity index 100% rename from scripts/proteomics/ptm_site_localization_scorer/ptm_site_localization_scorer.py rename to scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py diff --git a/scripts/proteomics/ms_data_ml_exporter/requirements.txt b/scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/requirements.txt similarity index 100% rename from scripts/proteomics/ms_data_ml_exporter/requirements.txt rename to scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/requirements.txt diff --git a/scripts/proteomics/metapeptide_lca_assigner/tests/conftest.py b/scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/tests/conftest.py similarity index 100% rename from scripts/proteomics/metapeptide_lca_assigner/tests/conftest.py rename to scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/tests/conftest.py diff --git a/scripts/proteomics/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py b/scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py similarity index 100% rename from scripts/proteomics/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py rename to scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/tests/test_ptm_site_localization_scorer.py diff --git a/scripts/proteomics/acquisition_rate_analyzer/acquisition_rate_analyzer.py b/scripts/proteomics/quality_control/acquisition_rate_analyzer/acquisition_rate_analyzer.py similarity index 100% rename from scripts/proteomics/acquisition_rate_analyzer/acquisition_rate_analyzer.py rename to scripts/proteomics/quality_control/acquisition_rate_analyzer/acquisition_rate_analyzer.py diff --git a/scripts/proteomics/ms_data_to_csv_exporter/requirements.txt b/scripts/proteomics/quality_control/acquisition_rate_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/ms_data_to_csv_exporter/requirements.txt rename to scripts/proteomics/quality_control/acquisition_rate_analyzer/requirements.txt diff --git a/scripts/proteomics/mgf_to_mzml_converter/tests/conftest.py b/scripts/proteomics/quality_control/acquisition_rate_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/mgf_to_mzml_converter/tests/conftest.py rename to scripts/proteomics/quality_control/acquisition_rate_analyzer/tests/conftest.py diff --git a/scripts/proteomics/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py b/scripts/proteomics/quality_control/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py similarity index 100% rename from scripts/proteomics/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py rename to scripts/proteomics/quality_control/acquisition_rate_analyzer/tests/test_acquisition_rate_analyzer.py diff --git a/scripts/proteomics/collision_energy_analyzer/README.md b/scripts/proteomics/quality_control/collision_energy_analyzer/README.md similarity index 100% rename from scripts/proteomics/collision_energy_analyzer/README.md rename to scripts/proteomics/quality_control/collision_energy_analyzer/README.md diff --git a/scripts/proteomics/collision_energy_analyzer/collision_energy_analyzer.py b/scripts/proteomics/quality_control/collision_energy_analyzer/collision_energy_analyzer.py similarity index 100% rename from scripts/proteomics/collision_energy_analyzer/collision_energy_analyzer.py rename to scripts/proteomics/quality_control/collision_energy_analyzer/collision_energy_analyzer.py diff --git a/scripts/proteomics/mzml_metadata_extractor/requirements.txt b/scripts/proteomics/quality_control/collision_energy_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/mzml_metadata_extractor/requirements.txt rename to scripts/proteomics/quality_control/collision_energy_analyzer/requirements.txt diff --git a/scripts/proteomics/missed_cleavage_analyzer/tests/conftest.py b/scripts/proteomics/quality_control/collision_energy_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/missed_cleavage_analyzer/tests/conftest.py rename to scripts/proteomics/quality_control/collision_energy_analyzer/tests/conftest.py diff --git a/scripts/proteomics/collision_energy_analyzer/tests/test_collision_energy_analyzer.py b/scripts/proteomics/quality_control/collision_energy_analyzer/tests/test_collision_energy_analyzer.py similarity index 100% rename from scripts/proteomics/collision_energy_analyzer/tests/test_collision_energy_analyzer.py rename to scripts/proteomics/quality_control/collision_energy_analyzer/tests/test_collision_energy_analyzer.py diff --git a/scripts/proteomics/identification_qc_reporter/identification_qc_reporter.py b/scripts/proteomics/quality_control/identification_qc_reporter/identification_qc_reporter.py similarity index 100% rename from scripts/proteomics/identification_qc_reporter/identification_qc_reporter.py rename to scripts/proteomics/quality_control/identification_qc_reporter/identification_qc_reporter.py diff --git a/scripts/proteomics/mzml_spectrum_subsetter/requirements.txt b/scripts/proteomics/quality_control/identification_qc_reporter/requirements.txt similarity index 100% rename from scripts/proteomics/mzml_spectrum_subsetter/requirements.txt rename to scripts/proteomics/quality_control/identification_qc_reporter/requirements.txt diff --git a/scripts/proteomics/missing_value_imputation/tests/conftest.py b/scripts/proteomics/quality_control/identification_qc_reporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/missing_value_imputation/tests/conftest.py rename to scripts/proteomics/quality_control/identification_qc_reporter/tests/conftest.py diff --git a/scripts/proteomics/identification_qc_reporter/tests/test_identification_qc_reporter.py b/scripts/proteomics/quality_control/identification_qc_reporter/tests/test_identification_qc_reporter.py similarity index 100% rename from scripts/proteomics/identification_qc_reporter/tests/test_identification_qc_reporter.py rename to scripts/proteomics/quality_control/identification_qc_reporter/tests/test_identification_qc_reporter.py diff --git a/scripts/proteomics/injection_time_analyzer/injection_time_analyzer.py b/scripts/proteomics/quality_control/injection_time_analyzer/injection_time_analyzer.py similarity index 100% rename from scripts/proteomics/injection_time_analyzer/injection_time_analyzer.py rename to scripts/proteomics/quality_control/injection_time_analyzer/injection_time_analyzer.py diff --git a/scripts/proteomics/mzml_to_mgf_converter/requirements.txt b/scripts/proteomics/quality_control/injection_time_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/mzml_to_mgf_converter/requirements.txt rename to scripts/proteomics/quality_control/injection_time_analyzer/requirements.txt diff --git a/scripts/proteomics/modification_mass_calculator/tests/conftest.py b/scripts/proteomics/quality_control/injection_time_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/modification_mass_calculator/tests/conftest.py rename to scripts/proteomics/quality_control/injection_time_analyzer/tests/conftest.py diff --git a/scripts/proteomics/injection_time_analyzer/tests/test_injection_time_analyzer.py b/scripts/proteomics/quality_control/injection_time_analyzer/tests/test_injection_time_analyzer.py similarity index 100% rename from scripts/proteomics/injection_time_analyzer/tests/test_injection_time_analyzer.py rename to scripts/proteomics/quality_control/injection_time_analyzer/tests/test_injection_time_analyzer.py diff --git a/scripts/proteomics/lc_ms_qc_reporter/lc_ms_qc_reporter.py b/scripts/proteomics/quality_control/lc_ms_qc_reporter/lc_ms_qc_reporter.py similarity index 100% rename from scripts/proteomics/lc_ms_qc_reporter/lc_ms_qc_reporter.py rename to scripts/proteomics/quality_control/lc_ms_qc_reporter/lc_ms_qc_reporter.py diff --git a/scripts/proteomics/mzqc_generator/requirements.txt b/scripts/proteomics/quality_control/lc_ms_qc_reporter/requirements.txt similarity index 100% rename from scripts/proteomics/mzqc_generator/requirements.txt rename to scripts/proteomics/quality_control/lc_ms_qc_reporter/requirements.txt diff --git a/scripts/proteomics/modified_peptide_generator/tests/conftest.py b/scripts/proteomics/quality_control/lc_ms_qc_reporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/modified_peptide_generator/tests/conftest.py rename to scripts/proteomics/quality_control/lc_ms_qc_reporter/tests/conftest.py diff --git a/scripts/proteomics/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py b/scripts/proteomics/quality_control/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py similarity index 100% rename from scripts/proteomics/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py rename to scripts/proteomics/quality_control/lc_ms_qc_reporter/tests/test_lc_ms_qc_reporter.py diff --git a/scripts/proteomics/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py b/scripts/proteomics/quality_control/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py similarity index 100% rename from scripts/proteomics/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py rename to scripts/proteomics/quality_control/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py diff --git a/scripts/proteomics/mztab_summarizer/requirements.txt b/scripts/proteomics/quality_control/mass_error_distribution_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/mztab_summarizer/requirements.txt rename to scripts/proteomics/quality_control/mass_error_distribution_analyzer/requirements.txt diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/tests/conftest.py b/scripts/proteomics/quality_control/mass_error_distribution_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/ms1_feature_intensity_tracker/tests/conftest.py rename to scripts/proteomics/quality_control/mass_error_distribution_analyzer/tests/conftest.py diff --git a/scripts/proteomics/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py b/scripts/proteomics/quality_control/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py similarity index 100% rename from scripts/proteomics/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py rename to scripts/proteomics/quality_control/mass_error_distribution_analyzer/tests/test_mass_error_distribution_analyzer.py diff --git a/scripts/proteomics/missed_cleavage_analyzer/README.md b/scripts/proteomics/quality_control/missed_cleavage_analyzer/README.md similarity index 100% rename from scripts/proteomics/missed_cleavage_analyzer/README.md rename to scripts/proteomics/quality_control/missed_cleavage_analyzer/README.md diff --git a/scripts/proteomics/missed_cleavage_analyzer/missed_cleavage_analyzer.py b/scripts/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py similarity index 100% rename from scripts/proteomics/missed_cleavage_analyzer/missed_cleavage_analyzer.py rename to scripts/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py diff --git a/scripts/proteomics/nterm_modification_annotator/requirements.txt b/scripts/proteomics/quality_control/missed_cleavage_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/nterm_modification_annotator/requirements.txt rename to scripts/proteomics/quality_control/missed_cleavage_analyzer/requirements.txt diff --git a/scripts/proteomics/ms_data_ml_exporter/tests/conftest.py b/scripts/proteomics/quality_control/missed_cleavage_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/ms_data_ml_exporter/tests/conftest.py rename to scripts/proteomics/quality_control/missed_cleavage_analyzer/tests/conftest.py diff --git a/scripts/proteomics/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py b/scripts/proteomics/quality_control/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py similarity index 100% rename from scripts/proteomics/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py rename to scripts/proteomics/quality_control/missed_cleavage_analyzer/tests/test_missed_cleavage_analyzer.py diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/README.md b/scripts/proteomics/quality_control/ms1_feature_intensity_tracker/README.md similarity index 100% rename from scripts/proteomics/ms1_feature_intensity_tracker/README.md rename to scripts/proteomics/quality_control/ms1_feature_intensity_tracker/README.md diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py b/scripts/proteomics/quality_control/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py similarity index 100% rename from scripts/proteomics/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py rename to scripts/proteomics/quality_control/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py diff --git a/scripts/proteomics/peptide_detectability_predictor/requirements.txt b/scripts/proteomics/quality_control/ms1_feature_intensity_tracker/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_detectability_predictor/requirements.txt rename to scripts/proteomics/quality_control/ms1_feature_intensity_tracker/requirements.txt diff --git a/scripts/proteomics/ms_data_to_csv_exporter/tests/conftest.py b/scripts/proteomics/quality_control/ms1_feature_intensity_tracker/tests/conftest.py similarity index 100% rename from scripts/proteomics/ms_data_to_csv_exporter/tests/conftest.py rename to scripts/proteomics/quality_control/ms1_feature_intensity_tracker/tests/conftest.py diff --git a/scripts/proteomics/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py b/scripts/proteomics/quality_control/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py similarity index 100% rename from scripts/proteomics/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py rename to scripts/proteomics/quality_control/ms1_feature_intensity_tracker/tests/test_ms1_feature_intensity_tracker.py diff --git a/scripts/proteomics/mzqc_generator/mzqc_generator.py b/scripts/proteomics/quality_control/mzqc_generator/mzqc_generator.py similarity index 100% rename from scripts/proteomics/mzqc_generator/mzqc_generator.py rename to scripts/proteomics/quality_control/mzqc_generator/mzqc_generator.py diff --git a/scripts/proteomics/peptide_mass_calculator/requirements.txt b/scripts/proteomics/quality_control/mzqc_generator/requirements.txt similarity 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from scripts/proteomics/precursor_charge_distribution/README.md rename to scripts/proteomics/quality_control/precursor_charge_distribution/README.md diff --git a/scripts/proteomics/precursor_charge_distribution/precursor_charge_distribution.py b/scripts/proteomics/quality_control/precursor_charge_distribution/precursor_charge_distribution.py similarity index 100% rename from scripts/proteomics/precursor_charge_distribution/precursor_charge_distribution.py rename to scripts/proteomics/quality_control/precursor_charge_distribution/precursor_charge_distribution.py diff --git a/scripts/proteomics/peptide_mass_fingerprint/requirements.txt b/scripts/proteomics/quality_control/precursor_charge_distribution/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_mass_fingerprint/requirements.txt rename to scripts/proteomics/quality_control/precursor_charge_distribution/requirements.txt diff --git a/scripts/proteomics/mzml_spectrum_subsetter/tests/conftest.py b/scripts/proteomics/quality_control/precursor_charge_distribution/tests/conftest.py similarity index 100% rename from scripts/proteomics/mzml_spectrum_subsetter/tests/conftest.py rename to scripts/proteomics/quality_control/precursor_charge_distribution/tests/conftest.py diff --git a/scripts/proteomics/precursor_charge_distribution/tests/test_precursor_charge_distribution.py b/scripts/proteomics/quality_control/precursor_charge_distribution/tests/test_precursor_charge_distribution.py similarity index 100% rename from scripts/proteomics/precursor_charge_distribution/tests/test_precursor_charge_distribution.py rename to scripts/proteomics/quality_control/precursor_charge_distribution/tests/test_precursor_charge_distribution.py diff --git a/scripts/proteomics/precursor_isolation_purity/precursor_isolation_purity.py b/scripts/proteomics/quality_control/precursor_isolation_purity/precursor_isolation_purity.py similarity index 100% rename from scripts/proteomics/precursor_isolation_purity/precursor_isolation_purity.py rename to scripts/proteomics/quality_control/precursor_isolation_purity/precursor_isolation_purity.py diff --git a/scripts/proteomics/peptide_modification_analyzer/requirements.txt b/scripts/proteomics/quality_control/precursor_isolation_purity/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_modification_analyzer/requirements.txt rename to scripts/proteomics/quality_control/precursor_isolation_purity/requirements.txt diff --git a/scripts/proteomics/mzml_to_mgf_converter/tests/conftest.py b/scripts/proteomics/quality_control/precursor_isolation_purity/tests/conftest.py similarity index 100% rename from scripts/proteomics/mzml_to_mgf_converter/tests/conftest.py rename to scripts/proteomics/quality_control/precursor_isolation_purity/tests/conftest.py diff --git a/scripts/proteomics/precursor_isolation_purity/tests/test_precursor_isolation_purity.py b/scripts/proteomics/quality_control/precursor_isolation_purity/tests/test_precursor_isolation_purity.py similarity index 100% rename from scripts/proteomics/precursor_isolation_purity/tests/test_precursor_isolation_purity.py rename to scripts/proteomics/quality_control/precursor_isolation_purity/tests/test_precursor_isolation_purity.py diff --git a/scripts/proteomics/precursor_recurrence_analyzer/README.md b/scripts/proteomics/quality_control/precursor_recurrence_analyzer/README.md similarity index 100% rename from scripts/proteomics/precursor_recurrence_analyzer/README.md rename to scripts/proteomics/quality_control/precursor_recurrence_analyzer/README.md diff --git a/scripts/proteomics/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py b/scripts/proteomics/quality_control/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py similarity index 100% rename from scripts/proteomics/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py rename to scripts/proteomics/quality_control/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py diff --git a/scripts/proteomics/peptide_property_calculator/requirements.txt b/scripts/proteomics/quality_control/precursor_recurrence_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_property_calculator/requirements.txt rename to scripts/proteomics/quality_control/precursor_recurrence_analyzer/requirements.txt diff --git a/scripts/proteomics/mzqc_generator/tests/conftest.py b/scripts/proteomics/quality_control/precursor_recurrence_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/mzqc_generator/tests/conftest.py rename to scripts/proteomics/quality_control/precursor_recurrence_analyzer/tests/conftest.py diff --git a/scripts/proteomics/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py b/scripts/proteomics/quality_control/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py similarity index 100% rename from scripts/proteomics/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py rename to scripts/proteomics/quality_control/precursor_recurrence_analyzer/tests/test_precursor_recurrence_analyzer.py diff --git a/scripts/proteomics/peptide_spectral_match_validator/requirements.txt b/scripts/proteomics/quality_control/run_comparison_reporter/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_spectral_match_validator/requirements.txt rename to scripts/proteomics/quality_control/run_comparison_reporter/requirements.txt diff --git a/scripts/proteomics/run_comparison_reporter/run_comparison_reporter.py b/scripts/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py similarity index 100% rename from scripts/proteomics/run_comparison_reporter/run_comparison_reporter.py rename to scripts/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py diff --git a/scripts/proteomics/mztab_summarizer/tests/conftest.py b/scripts/proteomics/quality_control/run_comparison_reporter/tests/conftest.py similarity index 100% rename from scripts/proteomics/mztab_summarizer/tests/conftest.py rename to scripts/proteomics/quality_control/run_comparison_reporter/tests/conftest.py diff --git a/scripts/proteomics/run_comparison_reporter/tests/test_run_comparison_reporter.py b/scripts/proteomics/quality_control/run_comparison_reporter/tests/test_run_comparison_reporter.py similarity index 100% rename from scripts/proteomics/run_comparison_reporter/tests/test_run_comparison_reporter.py rename to scripts/proteomics/quality_control/run_comparison_reporter/tests/test_run_comparison_reporter.py diff --git a/scripts/proteomics/sample_complexity_estimator/README.md b/scripts/proteomics/quality_control/sample_complexity_estimator/README.md similarity index 100% rename from scripts/proteomics/sample_complexity_estimator/README.md rename to scripts/proteomics/quality_control/sample_complexity_estimator/README.md diff --git a/scripts/proteomics/peptide_to_protein_mapper/requirements.txt b/scripts/proteomics/quality_control/sample_complexity_estimator/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_to_protein_mapper/requirements.txt rename to scripts/proteomics/quality_control/sample_complexity_estimator/requirements.txt diff --git a/scripts/proteomics/sample_complexity_estimator/sample_complexity_estimator.py b/scripts/proteomics/quality_control/sample_complexity_estimator/sample_complexity_estimator.py similarity index 100% rename from scripts/proteomics/sample_complexity_estimator/sample_complexity_estimator.py rename to scripts/proteomics/quality_control/sample_complexity_estimator/sample_complexity_estimator.py diff --git a/scripts/proteomics/nterm_modification_annotator/tests/conftest.py b/scripts/proteomics/quality_control/sample_complexity_estimator/tests/conftest.py similarity index 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b/scripts/proteomics/quality_control/spectrum_file_info/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_uniqueness_checker/requirements.txt rename to scripts/proteomics/quality_control/spectrum_file_info/requirements.txt diff --git a/scripts/proteomics/spectrum_file_info/spectrum_file_info.py b/scripts/proteomics/quality_control/spectrum_file_info/spectrum_file_info.py similarity index 100% rename from scripts/proteomics/spectrum_file_info/spectrum_file_info.py rename to scripts/proteomics/quality_control/spectrum_file_info/spectrum_file_info.py diff --git a/scripts/proteomics/peptide_detectability_predictor/tests/conftest.py b/scripts/proteomics/quality_control/spectrum_file_info/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_detectability_predictor/tests/conftest.py rename to scripts/proteomics/quality_control/spectrum_file_info/tests/conftest.py diff --git a/scripts/proteomics/spectrum_file_info/tests/test_spectrum_file_info.py b/scripts/proteomics/quality_control/spectrum_file_info/tests/test_spectrum_file_info.py similarity index 100% rename from scripts/proteomics/spectrum_file_info/tests/test_spectrum_file_info.py rename to scripts/proteomics/quality_control/spectrum_file_info/tests/test_spectrum_file_info.py diff --git a/scripts/proteomics/quantification_normalizer/README.md b/scripts/proteomics/quantification_normalizer/README.md deleted file mode 100644 index ce2a8b7..0000000 --- a/scripts/proteomics/quantification_normalizer/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# Quantification Normalizer - -Normalize quantification matrices using median, quantile, or total intensity normalization. - -## Usage - -```bash -python quantification_normalizer.py --input matrix.tsv --method median --output normalized.tsv -python quantification_normalizer.py --input matrix.tsv --method quantile --output normalized.tsv -python quantification_normalizer.py --input matrix.tsv --method total_intensity --output normalized.tsv -``` - -## Methods - -- **median** - Shift columns so all have the same median -- **quantile** - Force all columns to have the same distribution -- **total_intensity** - Scale columns to the same total intensity diff --git a/scripts/proteomics/quantification_normalizer/quantification_normalizer.py b/scripts/proteomics/quantification_normalizer/quantification_normalizer.py deleted file mode 100644 index 5c71505..0000000 --- a/scripts/proteomics/quantification_normalizer/quantification_normalizer.py +++ /dev/null @@ -1,161 +0,0 @@ -""" -Quantification Normalizer -========================= -Normalize quantification matrices using median, quantile, or total intensity methods. - -Usage ------ - python quantification_normalizer.py --input matrix.tsv --method median --output normalized.tsv - python quantification_normalizer.py --input matrix.tsv --method quantile --output normalized.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np - - -def read_matrix(filepath: str) -> tuple: - """Read a TSV quantification matrix. - - Returns - ------- - tuple - (row_ids, col_names, data_matrix). - """ - with open(filepath) as fh: - reader = csv.reader(fh, delimiter="\t") - header = next(reader) - col_names = header[1:] - row_ids = [] - rows = [] - for row in reader: - row_ids.append(row[0]) - rows.append([float(v) if v.strip() else 0.0 for v in row[1:]]) - return row_ids, col_names, np.array(rows, dtype=float) - - -def write_matrix(filepath: str, row_ids: list, col_names: list, matrix: np.ndarray) -> None: - """Write a quantification matrix to TSV.""" - with open(filepath, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow([""] + col_names) - for i, row_id in enumerate(row_ids): - writer.writerow([row_id] + [f"{v:.6f}" for v in matrix[i]]) - - -def normalize_median(matrix: np.ndarray) -> np.ndarray: - """Median normalization: shift each column so all columns have the same median. - - Parameters - ---------- - matrix: - 2D numpy array (rows=features, cols=samples). - - Returns - ------- - np.ndarray - Normalized matrix. - """ - col_medians = np.median(matrix, axis=0) - global_median = np.median(col_medians) - shifts = global_median - col_medians - return matrix + shifts[np.newaxis, :] - - -def normalize_quantile(matrix: np.ndarray) -> np.ndarray: - """Quantile normalization: force all columns to have the same distribution. - - Parameters - ---------- - matrix: - 2D numpy array. - - Returns - ------- - np.ndarray - Quantile-normalized matrix. - """ - n_rows, n_cols = matrix.shape - sorted_indices = np.argsort(matrix, axis=0) - sorted_matrix = np.sort(matrix, axis=0) - row_means = np.mean(sorted_matrix, axis=1) - - result = np.empty_like(matrix) - for col in range(n_cols): - ranks = np.empty(n_rows, dtype=int) - ranks[sorted_indices[:, col]] = np.arange(n_rows) - result[:, col] = row_means[ranks] - return result - - -def normalize_total_intensity(matrix: np.ndarray) -> np.ndarray: - """Total intensity normalization: scale each column to the same total. - - Parameters - ---------- - matrix: - 2D numpy array. - - Returns - ------- - np.ndarray - Normalized matrix. - """ - col_sums = np.sum(matrix, axis=0) - target_sum = np.mean(col_sums) - scale_factors = target_sum / np.where(col_sums > 0, col_sums, 1.0) - return matrix * scale_factors[np.newaxis, :] - - -def normalize(matrix: np.ndarray, method: str = "median") -> np.ndarray: - """Normalize a quantification matrix. - - Parameters - ---------- - matrix: - 2D numpy array. - method: - One of 'median', 'quantile', 'total_intensity'. - - Returns - ------- - np.ndarray - Normalized matrix. - """ - method = method.lower() - if method == "median": - return normalize_median(matrix) - elif method == "quantile": - return normalize_quantile(matrix) - elif method == "total_intensity": - return normalize_total_intensity(matrix) - else: - raise ValueError(f"Unknown normalization method: '{method}'. Choose from: median, quantile, total_intensity") - - -def main(): - parser = argparse.ArgumentParser(description="Normalize quantification matrices.") - parser.add_argument("--input", required=True, help="Input TSV matrix file") - parser.add_argument("--method", required=True, choices=["median", "quantile", "total_intensity"], - help="Normalization method") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - row_ids, col_names, matrix = read_matrix(args.input) - normalized = normalize(matrix, method=args.method) - write_matrix(args.output, row_ids, col_names, normalized) - print(f"Method: {args.method}") - print(f"Samples: {len(col_names)}") - print(f"Features: {len(row_ids)}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/quantification_normalizer/requirements.txt b/scripts/proteomics/quantification_normalizer/requirements.txt deleted file mode 100644 index 1051d92..0000000 --- a/scripts/proteomics/quantification_normalizer/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pyopenms -numpy diff --git a/scripts/proteomics/quantification_normalizer/tests/test_quantification_normalizer.py b/scripts/proteomics/quantification_normalizer/tests/test_quantification_normalizer.py deleted file mode 100644 index c753a32..0000000 --- a/scripts/proteomics/quantification_normalizer/tests/test_quantification_normalizer.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Tests for quantification_normalizer.""" - -import numpy as np -import pytest -from conftest import requires_pyopenms -from quantification_normalizer import ( - normalize, - normalize_median, - normalize_quantile, - normalize_total_intensity, - read_matrix, - write_matrix, -) - - -@requires_pyopenms -class TestQuantificationNormalizer: - def _make_matrix(self): - return np.array([ - [100.0, 200.0, 150.0], - [300.0, 400.0, 350.0], - [500.0, 600.0, 550.0], - [700.0, 800.0, 750.0], - ]) - - def test_median_equal_medians(self): - matrix = self._make_matrix() - result = normalize_median(matrix) - col_medians = np.median(result, axis=0) - np.testing.assert_allclose(col_medians, col_medians[0], atol=1e-6) - - def test_quantile_equal_distributions(self): - matrix = self._make_matrix() - result = normalize_quantile(matrix) - sorted_cols = np.sort(result, axis=0) - for col in range(1, result.shape[1]): - np.testing.assert_allclose(sorted_cols[:, 0], sorted_cols[:, col], atol=1e-6) - - def test_total_intensity_equal_sums(self): - matrix = self._make_matrix() - result = normalize_total_intensity(matrix) - col_sums = np.sum(result, axis=0) - np.testing.assert_allclose(col_sums, col_sums[0], atol=1e-6) - - def test_normalize_dispatch(self): - matrix = self._make_matrix() - for method in ["median", "quantile", "total_intensity"]: - result = normalize(matrix, method=method) - assert result.shape == matrix.shape - - def test_unknown_method(self): - matrix = self._make_matrix() - with pytest.raises(ValueError, match="Unknown normalization method"): - normalize(matrix, method="invalid") - - def test_read_write_roundtrip(self, tmp_path): - row_ids = ["prot1", "prot2"] - col_names = ["s1", "s2"] - matrix = np.array([[100.0, 200.0], [300.0, 400.0]]) - outfile = str(tmp_path / "test.tsv") - write_matrix(outfile, row_ids, col_names, matrix) - r_ids, c_names, r_matrix = read_matrix(outfile) - assert r_ids == row_ids - assert c_names == col_names - np.testing.assert_allclose(r_matrix, matrix, atol=0.01) - - def test_preserves_shape(self): - matrix = self._make_matrix() - for method in ["median", "quantile", "total_intensity"]: - assert normalize(matrix, method).shape == (4, 3) diff --git a/scripts/proteomics/rna_digest/README.md b/scripts/proteomics/rna/rna_digest/README.md similarity index 100% rename from scripts/proteomics/rna_digest/README.md rename to scripts/proteomics/rna/rna_digest/README.md diff --git a/scripts/proteomics/phospho_enrichment_qc/requirements.txt b/scripts/proteomics/rna/rna_digest/requirements.txt similarity index 100% rename from scripts/proteomics/phospho_enrichment_qc/requirements.txt rename to scripts/proteomics/rna/rna_digest/requirements.txt diff --git a/scripts/proteomics/rna_digest/rna_digest.py b/scripts/proteomics/rna/rna_digest/rna_digest.py similarity index 100% rename from scripts/proteomics/rna_digest/rna_digest.py rename to scripts/proteomics/rna/rna_digest/rna_digest.py diff --git a/scripts/proteomics/peptide_mass_calculator/tests/conftest.py b/scripts/proteomics/rna/rna_digest/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_mass_calculator/tests/conftest.py rename to scripts/proteomics/rna/rna_digest/tests/conftest.py diff --git a/scripts/proteomics/rna_digest/tests/test_rna_digest.py b/scripts/proteomics/rna/rna_digest/tests/test_rna_digest.py similarity index 100% rename from scripts/proteomics/rna_digest/tests/test_rna_digest.py rename to scripts/proteomics/rna/rna_digest/tests/test_rna_digest.py diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/README.md b/scripts/proteomics/rna/rna_fragment_spectrum_generator/README.md similarity index 100% rename from scripts/proteomics/rna_fragment_spectrum_generator/README.md rename to scripts/proteomics/rna/rna_fragment_spectrum_generator/README.md diff --git a/scripts/proteomics/phospho_motif_analyzer/requirements.txt b/scripts/proteomics/rna/rna_fragment_spectrum_generator/requirements.txt similarity index 100% rename from scripts/proteomics/phospho_motif_analyzer/requirements.txt rename to scripts/proteomics/rna/rna_fragment_spectrum_generator/requirements.txt diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py b/scripts/proteomics/rna/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py similarity index 100% rename from scripts/proteomics/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py rename to scripts/proteomics/rna/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py diff --git a/scripts/proteomics/peptide_mass_fingerprint/tests/conftest.py b/scripts/proteomics/rna/rna_fragment_spectrum_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_mass_fingerprint/tests/conftest.py rename to scripts/proteomics/rna/rna_fragment_spectrum_generator/tests/conftest.py diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py b/scripts/proteomics/rna/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py similarity index 100% rename from scripts/proteomics/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py rename to scripts/proteomics/rna/rna_fragment_spectrum_generator/tests/test_rna_fragment_spectrum_generator.py diff --git a/scripts/proteomics/rna_mass_calculator/README.md b/scripts/proteomics/rna/rna_mass_calculator/README.md similarity index 100% rename from scripts/proteomics/rna_mass_calculator/README.md rename to scripts/proteomics/rna/rna_mass_calculator/README.md diff --git a/scripts/proteomics/phosphosite_class_filter/requirements.txt b/scripts/proteomics/rna/rna_mass_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/phosphosite_class_filter/requirements.txt rename to scripts/proteomics/rna/rna_mass_calculator/requirements.txt diff --git a/scripts/proteomics/rna_mass_calculator/rna_mass_calculator.py b/scripts/proteomics/rna/rna_mass_calculator/rna_mass_calculator.py similarity index 100% rename from scripts/proteomics/rna_mass_calculator/rna_mass_calculator.py rename to scripts/proteomics/rna/rna_mass_calculator/rna_mass_calculator.py diff --git a/scripts/proteomics/peptide_modification_analyzer/tests/conftest.py b/scripts/proteomics/rna/rna_mass_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_modification_analyzer/tests/conftest.py rename to scripts/proteomics/rna/rna_mass_calculator/tests/conftest.py diff --git a/scripts/proteomics/rna_mass_calculator/tests/test_rna_mass_calculator.py b/scripts/proteomics/rna/rna_mass_calculator/tests/test_rna_mass_calculator.py similarity index 100% rename from scripts/proteomics/rna_mass_calculator/tests/test_rna_mass_calculator.py rename to scripts/proteomics/rna/rna_mass_calculator/tests/test_rna_mass_calculator.py diff --git a/scripts/proteomics/sample_correlation_calculator/README.md b/scripts/proteomics/sample_correlation_calculator/README.md deleted file mode 100644 index b15dde2..0000000 --- a/scripts/proteomics/sample_correlation_calculator/README.md +++ /dev/null @@ -1,10 +0,0 @@ -# Sample Correlation Calculator - -Compute Pearson or Spearman correlations between samples in a quantification matrix. - -## Usage - -```bash -python sample_correlation_calculator.py --input matrix.tsv --method pearson --output correlations.tsv -python sample_correlation_calculator.py --input matrix.tsv --method spearman --output correlations.tsv -``` diff --git a/scripts/proteomics/sample_correlation_calculator/requirements.txt b/scripts/proteomics/sample_correlation_calculator/requirements.txt deleted file mode 100644 index ba577e4..0000000 --- a/scripts/proteomics/sample_correlation_calculator/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -pyopenms -numpy -scipy diff --git a/scripts/proteomics/sample_correlation_calculator/sample_correlation_calculator.py b/scripts/proteomics/sample_correlation_calculator/sample_correlation_calculator.py deleted file mode 100644 index 32f9a37..0000000 --- a/scripts/proteomics/sample_correlation_calculator/sample_correlation_calculator.py +++ /dev/null @@ -1,158 +0,0 @@ -""" -Sample Correlation Calculator -============================= -Compute Pearson or Spearman correlations between samples in a quantification matrix. - -Usage ------ - python sample_correlation_calculator.py --input matrix.tsv --method pearson --output correlations.tsv - python sample_correlation_calculator.py --input matrix.tsv --method spearman --output correlations.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np -from scipy import stats - - -def read_matrix(filepath: str) -> tuple: - """Read a TSV quantification matrix. - - Returns (row_ids, col_names, data_matrix). - """ - with open(filepath) as fh: - reader = csv.reader(fh, delimiter="\t") - header = next(reader) - col_names = header[1:] - row_ids = [] - rows = [] - for row in reader: - row_ids.append(row[0]) - values = [] - for v in row[1:]: - v = v.strip() - if v == "" or v.upper() in ("NA", "NAN"): - values.append(np.nan) - else: - values.append(float(v)) - rows.append(values) - return row_ids, col_names, np.array(rows, dtype=float) - - -def compute_correlations(matrix: np.ndarray, col_names: list, method: str = "pearson") -> list: - """Compute pairwise sample correlations. - - Parameters - ---------- - matrix: - 2D array (features x samples). - col_names: - Sample names. - method: - 'pearson' or 'spearman'. - - Returns - ------- - list - List of dicts with keys: sample_a, sample_b, correlation, pvalue. - """ - method = method.lower() - if method not in ("pearson", "spearman"): - raise ValueError(f"Unknown method: '{method}'. Choose 'pearson' or 'spearman'.") - - n_samples = len(col_names) - results = [] - - for i in range(n_samples): - for j in range(i, n_samples): - col_i = matrix[:, i] - col_j = matrix[:, j] - # Use only rows where both values are non-NaN - mask = ~np.isnan(col_i) & ~np.isnan(col_j) - if np.sum(mask) < 3: - corr = float("nan") - pval = float("nan") - else: - if method == "pearson": - corr, pval = stats.pearsonr(col_i[mask], col_j[mask]) - else: - corr, pval = stats.spearmanr(col_i[mask], col_j[mask]) - - results.append({ - "sample_a": col_names[i], - "sample_b": col_names[j], - "correlation": corr, - "pvalue": pval, - }) - - return results - - -def correlation_matrix(matrix: np.ndarray, col_names: list, method: str = "pearson") -> np.ndarray: - """Compute a full correlation matrix. - - Parameters - ---------- - matrix: - 2D array (features x samples). - col_names: - Sample names. - method: - 'pearson' or 'spearman'. - - Returns - ------- - np.ndarray - Symmetric correlation matrix. - """ - pairs = compute_correlations(matrix, col_names, method) - n = len(col_names) - corr_mat = np.zeros((n, n)) - name_to_idx = {name: i for i, name in enumerate(col_names)} - - for p in pairs: - i = name_to_idx[p["sample_a"]] - j = name_to_idx[p["sample_b"]] - corr_mat[i, j] = p["correlation"] - corr_mat[j, i] = p["correlation"] - - return corr_mat - - -def main(): - parser = argparse.ArgumentParser(description="Compute sample correlations.") - parser.add_argument("--input", required=True, help="Input TSV matrix file") - parser.add_argument("--method", default="pearson", choices=["pearson", "spearman"], - help="Correlation method (default: pearson)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - row_ids, col_names, matrix = read_matrix(args.input) - results = compute_correlations(matrix, col_names, args.method) - - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=["sample_a", "sample_b", "correlation", "pvalue"], delimiter="\t") - writer.writeheader() - for r in results: - writer.writerow({ - "sample_a": r["sample_a"], - "sample_b": r["sample_b"], - "correlation": f"{r['correlation']:.6f}" if not np.isnan(r["correlation"]) else "NA", - "pvalue": f"{r['pvalue']:.6e}" if not np.isnan(r["pvalue"]) else "NA", - }) - - print(f"Method: {args.method}") - print(f"Samples: {len(col_names)}") - print(f"Pairs: {len(results)}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/sample_correlation_calculator/tests/test_sample_correlation_calculator.py b/scripts/proteomics/sample_correlation_calculator/tests/test_sample_correlation_calculator.py deleted file mode 100644 index d50f0f5..0000000 --- a/scripts/proteomics/sample_correlation_calculator/tests/test_sample_correlation_calculator.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Tests for sample_correlation_calculator.""" - -import numpy as np -import pytest -from conftest import requires_pyopenms -from sample_correlation_calculator import compute_correlations, correlation_matrix - - -@requires_pyopenms -class TestSampleCorrelationCalculator: - def _make_matrix(self): - return np.array([ - [100.0, 200.0, 150.0], - [300.0, 600.0, 450.0], - [500.0, 1000.0, 750.0], - [700.0, 1400.0, 1050.0], - ]) - - def test_pearson_self_correlation(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - results = compute_correlations(matrix, col_names, "pearson") - self_corr = [r for r in results if r["sample_a"] == r["sample_b"]] - for r in self_corr: - assert abs(r["correlation"] - 1.0) < 1e-6 - - def test_pearson_perfect_correlation(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - results = compute_correlations(matrix, col_names, "pearson") - # s1 and s2 are perfectly linearly correlated (s2 = 2*s1) - pair = next(r for r in results if r["sample_a"] == "s1" and r["sample_b"] == "s2") - assert abs(pair["correlation"] - 1.0) < 1e-6 - - def test_spearman(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - results = compute_correlations(matrix, col_names, "spearman") - assert len(results) == 6 # 3 choose 2 + 3 diagonal - - def test_correlation_matrix_shape(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - corr_mat = correlation_matrix(matrix, col_names, "pearson") - assert corr_mat.shape == (3, 3) - # Diagonal should be 1 - np.testing.assert_allclose(np.diag(corr_mat), 1.0, atol=1e-6) - - def test_unknown_method(self): - matrix = self._make_matrix() - with pytest.raises(ValueError, match="Unknown method"): - compute_correlations(matrix, ["s1", "s2", "s3"], "invalid") - - def test_with_nan(self): - matrix = np.array([ - [100.0, 200.0], - [np.nan, 400.0], - [300.0, 600.0], - [400.0, 800.0], - ]) - results = compute_correlations(matrix, ["s1", "s2"], "pearson") - # Should still compute using non-NaN rows - pair = next(r for r in results if r["sample_a"] == "s1" and r["sample_b"] == "s2") - assert not np.isnan(pair["correlation"]) - - def test_pair_count(self): - matrix = self._make_matrix() - col_names = ["s1", "s2", "s3"] - results = compute_correlations(matrix, col_names, "pearson") - # n*(n+1)/2 = 6 pairs (including self) - assert len(results) == 6 diff --git a/scripts/proteomics/scp_reporter_qc/README.md b/scripts/proteomics/scp_reporter_qc/README.md deleted file mode 100644 index 54d22d7..0000000 --- a/scripts/proteomics/scp_reporter_qc/README.md +++ /dev/null @@ -1,32 +0,0 @@ -# SCP Reporter QC - -Single-cell proteomics QC: compute sample-to-carrier ratio per spectrum for carrier-based SCP experiments. - -## Installation - -```bash -pip install -r requirements.txt -``` - -## Usage - -```bash -python scp_reporter_qc.py --input reporter_ions.tsv --carrier-channel 131C --output qc.tsv -``` - -### Input format - -Tab-separated file with `spectrum_id` and one column per reporter ion channel: - -``` -spectrum_id 126 127N 127C 128N 131C -spec1 100.5 95.2 110.3 88.7 50000.0 -``` - -### Parameters - -| Flag | Description | -|------|-------------| -| `--input` | Input TSV with reporter ion intensities | -| `--carrier-channel` | Carrier channel name (e.g. `131C`) | -| `--output` | Output QC TSV | diff --git a/scripts/proteomics/scp_reporter_qc/scp_reporter_qc.py b/scripts/proteomics/scp_reporter_qc/scp_reporter_qc.py deleted file mode 100644 index 201e2cc..0000000 --- a/scripts/proteomics/scp_reporter_qc/scp_reporter_qc.py +++ /dev/null @@ -1,187 +0,0 @@ -""" -SCP Reporter QC -================ -Quality control for single-cell proteomics (SCP) data using isobaric -reporter ions. Computes the sample-to-carrier ratio per spectrum, which -is a key QC metric for carrier-based SCP experiments. - -The carrier channel typically has much higher intensity than single-cell -channels. Ratios that are too high indicate insufficient carrier signal; -ratios that are too low suggest excessive carrier relative to single cells. - -Usage ------ - python scp_reporter_qc.py --input reporter_ions.tsv \ - --carrier-channel 131C --output qc.tsv -""" - -import argparse -import csv -import math -import sys -from typing import Dict, List - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - - -def compute_sample_to_carrier_ratios( - spectra: List[Dict[str, float]], carrier_channel: str -) -> List[Dict[str, object]]: - """Compute sample-to-carrier ratio for each spectrum. - - Parameters - ---------- - spectra: - List of dicts mapping channel name to intensity. Must include - a ``spectrum_id`` key (string). - carrier_channel: - Name of the carrier channel (e.g. ``"131C"``). - - Returns - ------- - list of dict - One entry per spectrum with ``spectrum_id``, ``carrier_intensity``, - ``mean_sample_intensity``, ``sample_to_carrier_ratio``, - ``num_nonzero_samples``. - """ - results: List[Dict[str, object]] = [] - for spectrum in spectra: - spec_id = spectrum.get("spectrum_id", "unknown") - carrier_int = spectrum.get(carrier_channel, 0.0) - - sample_intensities = [] - for ch, val in spectrum.items(): - if ch in ("spectrum_id", carrier_channel): - continue - if isinstance(val, (int, float)) and val > 0: - sample_intensities.append(val) - - mean_sample = sum(sample_intensities) / len(sample_intensities) if sample_intensities else 0.0 - ratio = mean_sample / carrier_int if carrier_int > 0 else float("nan") - - results.append({ - "spectrum_id": spec_id, - "carrier_intensity": carrier_int, - "mean_sample_intensity": mean_sample, - "sample_to_carrier_ratio": ratio, - "num_nonzero_samples": len(sample_intensities), - }) - return results - - -def qc_summary(ratios: List[Dict[str, object]]) -> Dict[str, object]: - """Compute summary statistics over sample-to-carrier ratios. - - Returns - ------- - dict - ``n_spectra``, ``median_ratio``, ``mean_ratio``, ``std_ratio``, - ``below_0_01_count`` (spectra with ratio < 0.01, possibly problematic). - """ - valid_ratios = [ - r["sample_to_carrier_ratio"] for r in ratios - if isinstance(r["sample_to_carrier_ratio"], float) - and not math.isnan(r["sample_to_carrier_ratio"]) - ] - if not valid_ratios: - return { - "n_spectra": len(ratios), - "median_ratio": float("nan"), - "mean_ratio": float("nan"), - "std_ratio": float("nan"), - "below_0_01_count": 0, - } - - valid_ratios_sorted = sorted(valid_ratios) - n = len(valid_ratios_sorted) - median = valid_ratios_sorted[n // 2] if n % 2 == 1 else ( - (valid_ratios_sorted[n // 2 - 1] + valid_ratios_sorted[n // 2]) / 2.0 - ) - mean = sum(valid_ratios) / n - variance = sum((r - mean) ** 2 for r in valid_ratios) / n if n > 1 else 0.0 - std = math.sqrt(variance) - - below_threshold = sum(1 for r in valid_ratios if r < 0.01) - - return { - "n_spectra": len(ratios), - "median_ratio": median, - "mean_ratio": mean, - "std_ratio": std, - "below_0_01_count": below_threshold, - } - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Single-cell proteomics QC: sample-to-carrier ratio per spectrum." - ) - parser.add_argument( - "--input", required=True, - help="Input TSV with spectrum_id and reporter ion intensities per channel", - ) - parser.add_argument( - "--carrier-channel", required=True, - help="Name of the carrier channel (e.g. 131C)", - ) - parser.add_argument("--output", required=True, help="Output QC TSV") - args = parser.parse_args() - - spectra: List[Dict[str, float]] = [] - with open(args.input, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - spec: Dict[str, float] = {} - for key, val in row.items(): - if key == "spectrum_id": - spec["spectrum_id"] = val - else: - try: - spec[key] = float(val) - except (ValueError, TypeError): - spec[key] = 0.0 - spectra.append(spec) - - if not spectra: - sys.exit("No spectra found in input.") - - if args.carrier_channel not in (spectra[0] if spectra else {}): - print(f"Warning: carrier channel '{args.carrier_channel}' not found in input columns.") - - ratios = compute_sample_to_carrier_ratios(spectra, args.carrier_channel) - summary = qc_summary(ratios) - - with open(args.output, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow([ - "spectrum_id", "carrier_intensity", "mean_sample_intensity", - "sample_to_carrier_ratio", "num_nonzero_samples", - ]) - for r in ratios: - ratio_str = f"{r['sample_to_carrier_ratio']:.6f}" if not math.isnan( - r["sample_to_carrier_ratio"] - ) else "NA" - writer.writerow([ - r["spectrum_id"], - f"{r['carrier_intensity']:.2f}", - f"{r['mean_sample_intensity']:.2f}", - ratio_str, - r["num_nonzero_samples"], - ]) - writer.writerow([]) - writer.writerow(["metric", "value"]) - for key, val in summary.items(): - if isinstance(val, float) and not math.isnan(val): - writer.writerow([key, f"{val:.6f}"]) - else: - writer.writerow([key, val]) - - median_str = f"{summary['median_ratio']:.4f}" if not math.isnan(summary["median_ratio"]) else "NA" - print(f"Processed {summary['n_spectra']} spectra, median ratio: {median_str}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/scp_reporter_qc/tests/test_scp_reporter_qc.py b/scripts/proteomics/scp_reporter_qc/tests/test_scp_reporter_qc.py deleted file mode 100644 index 9601b24..0000000 --- a/scripts/proteomics/scp_reporter_qc/tests/test_scp_reporter_qc.py +++ /dev/null @@ -1,79 +0,0 @@ -"""Tests for scp_reporter_qc.""" - -import csv -import math -import sys - -from conftest import requires_pyopenms - - -@requires_pyopenms -def test_compute_sample_to_carrier_ratios(): - from scp_reporter_qc import compute_sample_to_carrier_ratios - - spectra = [ - {"spectrum_id": "s1", "126": 100.0, "127N": 120.0, "131C": 50000.0}, - {"spectrum_id": "s2", "126": 200.0, "127N": 180.0, "131C": 40000.0}, - ] - results = compute_sample_to_carrier_ratios(spectra, "131C") - assert len(results) == 2 - # mean of 100,120 = 110, ratio = 110/50000 - assert abs(results[0]["sample_to_carrier_ratio"] - 110.0 / 50000.0) < 1e-6 - assert results[0]["num_nonzero_samples"] == 2 - - -@requires_pyopenms -def test_zero_carrier(): - from scp_reporter_qc import compute_sample_to_carrier_ratios - - spectra = [{"spectrum_id": "s1", "126": 100.0, "131C": 0.0}] - results = compute_sample_to_carrier_ratios(spectra, "131C") - assert math.isnan(results[0]["sample_to_carrier_ratio"]) - - -@requires_pyopenms -def test_qc_summary(): - from scp_reporter_qc import qc_summary - - ratios = [ - {"sample_to_carrier_ratio": 0.002}, - {"sample_to_carrier_ratio": 0.003}, - {"sample_to_carrier_ratio": 0.005}, - {"sample_to_carrier_ratio": 0.001}, - ] - summary = qc_summary(ratios) - assert summary["n_spectra"] == 4 - assert abs(summary["mean_ratio"] - 0.00275) < 1e-6 - assert summary["below_0_01_count"] == 4 # all below 0.01 - - -@requires_pyopenms -def test_qc_summary_empty(): - from scp_reporter_qc import qc_summary - - summary = qc_summary([]) - assert summary["n_spectra"] == 0 - assert math.isnan(summary["median_ratio"]) - - -@requires_pyopenms -def test_cli_roundtrip(tmp_path): - from scp_reporter_qc import main - - input_file = tmp_path / "input.tsv" - output_file = tmp_path / "output.tsv" - - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["spectrum_id", "126", "127N", "131C"]) - writer.writerow(["s1", "100.0", "120.0", "50000.0"]) - writer.writerow(["s2", "200.0", "180.0", "40000.0"]) - - sys.argv = [ - "scp_reporter_qc.py", - "--input", str(input_file), - "--carrier-channel", "131C", - "--output", str(output_file), - ] - main() - assert output_file.exists() diff --git a/scripts/proteomics/search_result_merger/README.md b/scripts/proteomics/search_result_merger/README.md deleted file mode 100644 index fab4161..0000000 --- a/scripts/proteomics/search_result_merger/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Search Result Merger - -Merge multiple identification TSV files with union or intersection consensus. - -## Usage - -```bash -python search_result_merger.py --inputs engine1.tsv engine2.tsv --method union --output merged.tsv -python search_result_merger.py --inputs engine1.tsv engine2.tsv --method intersection --output merged.tsv -``` - -## Methods - -- **union** - Include all PSMs from any search engine -- **intersection** - Only include PSMs found in all search engines diff --git a/scripts/proteomics/search_result_merger/search_result_merger.py b/scripts/proteomics/search_result_merger/search_result_merger.py deleted file mode 100644 index 0618dcc..0000000 --- a/scripts/proteomics/search_result_merger/search_result_merger.py +++ /dev/null @@ -1,145 +0,0 @@ -""" -Search Result Merger -==================== -Merge multiple identification TSV files with union or intersection consensus. - -Each input TSV must have at least a 'peptide' column. Additional columns -(score, protein, etc.) are preserved. The merger identifies PSMs by a -composite key of (peptide, charge, spectrum) or just (peptide) if other -columns are absent. - -Usage ------ - python search_result_merger.py --inputs engine1.tsv engine2.tsv --method union --output merged.tsv - python search_result_merger.py --inputs engine1.tsv engine2.tsv --method intersection --output merged.tsv -""" - -import argparse -import csv -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - - -def read_identification_tsv(filepath: str) -> list: - """Read an identification TSV file. - - Parameters - ---------- - filepath: - Path to TSV with at least a 'peptide' column. - - Returns - ------- - list - List of dicts (one per row). - """ - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - return list(reader) - - -def _make_key(row: dict) -> str: - """Create a composite key for a PSM row.""" - parts = [row.get("peptide", "")] - if "charge" in row: - parts.append(str(row["charge"])) - if "spectrum" in row: - parts.append(row["spectrum"]) - return "||".join(parts) - - -def merge_results(input_files: list, method: str = "union") -> tuple: - """Merge identification results from multiple search engines. - - Parameters - ---------- - input_files: - List of TSV file paths. - method: - 'union' (all PSMs from any engine) or 'intersection' (only PSMs found in all). - - Returns - ------- - tuple - (fieldnames, merged_rows) where merged_rows is a list of dicts. - """ - method = method.lower() - if method not in ("union", "intersection"): - raise ValueError(f"Unknown method: '{method}'. Choose 'union' or 'intersection'.") - - all_results = [] - all_fieldnames = [] - for filepath in input_files: - rows = read_identification_tsv(filepath) - all_results.append(rows) - if rows: - for key in rows[0].keys(): - if key not in all_fieldnames: - all_fieldnames.append(key) - - if "source" not in all_fieldnames: - all_fieldnames.append("source") - if "n_engines" not in all_fieldnames: - all_fieldnames.append("n_engines") - - # Build key -> list of (source_index, row) - key_to_entries = {} - for file_idx, rows in enumerate(all_results): - source = input_files[file_idx] - for row in rows: - key = _make_key(row) - if key not in key_to_entries: - key_to_entries[key] = [] - row_copy = dict(row) - row_copy["_source_idx"] = file_idx - row_copy["_source"] = source - key_to_entries[key].append(row_copy) - - n_files = len(input_files) - merged = [] - - for key, entries in key_to_entries.items(): - source_indices = set(e["_source_idx"] for e in entries) - - if method == "intersection" and len(source_indices) < n_files: - continue - - # Use the first entry as the base row, add source info - base = dict(entries[0]) - base.pop("_source_idx", None) - base.pop("_source", None) - sources = sorted(set(e["_source"] for e in entries)) - base["source"] = ";".join(sources) - base["n_engines"] = str(len(source_indices)) - merged.append(base) - - return all_fieldnames, merged - - -def main(): - parser = argparse.ArgumentParser(description="Merge multiple identification TSV files.") - parser.add_argument("--inputs", nargs="+", required=True, help="Input TSV files") - parser.add_argument("--method", default="union", choices=["union", "intersection"], - help="Merge method (default: union)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - fieldnames, merged = merge_results(args.inputs, method=args.method) - - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") - writer.writeheader() - writer.writerows(merged) - - print(f"Method: {args.method}") - print(f"Input files: {len(args.inputs)}") - print(f"Merged PSMs: {len(merged)}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/search_result_merger/tests/conftest.py b/scripts/proteomics/search_result_merger/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/search_result_merger/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/search_result_merger/tests/test_search_result_merger.py b/scripts/proteomics/search_result_merger/tests/test_search_result_merger.py deleted file mode 100644 index 621820f..0000000 --- a/scripts/proteomics/search_result_merger/tests/test_search_result_merger.py +++ /dev/null @@ -1,78 +0,0 @@ -"""Tests for search_result_merger.""" - -import pytest -from conftest import requires_pyopenms -from search_result_merger import _make_key, merge_results, read_identification_tsv - - -@requires_pyopenms -class TestSearchResultMerger: - def _write_tsv(self, tmp_path, name, rows): - filepath = str(tmp_path / name) - with open(filepath, "w") as fh: - if rows: - keys = list(rows[0].keys()) - fh.write("\t".join(keys) + "\n") - for row in rows: - fh.write("\t".join(str(row[k]) for k in keys) + "\n") - return filepath - - def test_union_merge(self, tmp_path): - f1 = self._write_tsv(tmp_path, "e1.tsv", [ - {"peptide": "PEPTIDEK", "charge": "2", "score": "0.99"}, - {"peptide": "TESTPEP", "charge": "2", "score": "0.95"}, - ]) - f2 = self._write_tsv(tmp_path, "e2.tsv", [ - {"peptide": "PEPTIDEK", "charge": "2", "score": "0.98"}, - {"peptide": "ANOTHERPEP", "charge": "3", "score": "0.90"}, - ]) - _, merged = merge_results([f1, f2], method="union") - peptides = [r["peptide"] for r in merged] - assert "PEPTIDEK" in peptides - assert "TESTPEP" in peptides - assert "ANOTHERPEP" in peptides - assert len(merged) == 3 - - def test_intersection_merge(self, tmp_path): - f1 = self._write_tsv(tmp_path, "e1.tsv", [ - {"peptide": "PEPTIDEK", "charge": "2"}, - {"peptide": "TESTPEP", "charge": "2"}, - ]) - f2 = self._write_tsv(tmp_path, "e2.tsv", [ - {"peptide": "PEPTIDEK", "charge": "2"}, - {"peptide": "ANOTHERPEP", "charge": "3"}, - ]) - _, merged = merge_results([f1, f2], method="intersection") - assert len(merged) == 1 - assert merged[0]["peptide"] == "PEPTIDEK" - - def test_n_engines_count(self, tmp_path): - f1 = self._write_tsv(tmp_path, "e1.tsv", [{"peptide": "PEP", "charge": "2"}]) - f2 = self._write_tsv(tmp_path, "e2.tsv", [{"peptide": "PEP", "charge": "2"}]) - _, merged = merge_results([f1, f2], method="union") - assert merged[0]["n_engines"] == "2" - - def test_unknown_method(self, tmp_path): - f1 = self._write_tsv(tmp_path, "e1.tsv", [{"peptide": "PEP"}]) - with pytest.raises(ValueError, match="Unknown method"): - merge_results([f1], method="invalid") - - def test_make_key(self): - row = {"peptide": "PEPTIDEK", "charge": "2", "spectrum": "scan1"} - key = _make_key(row) - assert "PEPTIDEK" in key - assert "2" in key - assert "scan1" in key - - def test_empty_input(self, tmp_path): - f1 = self._write_tsv(tmp_path, "e1.tsv", []) - _, merged = merge_results([f1], method="union") - assert len(merged) == 0 - - def test_read_tsv(self, tmp_path): - filepath = self._write_tsv(tmp_path, "test.tsv", [ - {"peptide": "PEPTIDEK", "score": "0.99"}, - ]) - rows = read_identification_tsv(filepath) - assert len(rows) == 1 - assert rows[0]["peptide"] == "PEPTIDEK" diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/tests/conftest.py b/scripts/proteomics/semi_tryptic_peptide_finder/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/semi_tryptic_peptide_finder/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/sequence_tag_generator/tests/conftest.py b/scripts/proteomics/sequence_tag_generator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/sequence_tag_generator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/silac_halflife_calculator/README.md b/scripts/proteomics/silac_halflife_calculator/README.md deleted file mode 100644 index e02bca3..0000000 --- a/scripts/proteomics/silac_halflife_calculator/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# SILAC Half-Life Calculator - -Fit exponential decay to SILAC H/L ratios for protein turnover analysis. - -## Installation - -```bash -pip install -r requirements.txt -``` - -## Usage - -```bash -python silac_halflife_calculator.py --input hl_ratios.tsv \ - --timepoints 0,6,12,24,48 --output halflives.tsv -``` - -### Input format - -Tab-separated file with a `protein_id` column and one column per timepoint: - -``` -protein_id t0 t6 t12 t24 t48 -P12345 10.0 7.5 4.2 1.8 0.5 -``` - -### Parameters - -| Flag | Description | -|------|-------------| -| `--input` | Input TSV with protein IDs and H/L ratios | -| `--timepoints` | Comma-separated timepoint values | -| `--output` | Output half-lives TSV | diff --git a/scripts/proteomics/silac_halflife_calculator/requirements.txt b/scripts/proteomics/silac_halflife_calculator/requirements.txt deleted file mode 100644 index ba577e4..0000000 --- a/scripts/proteomics/silac_halflife_calculator/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -pyopenms -numpy -scipy diff --git a/scripts/proteomics/silac_halflife_calculator/silac_halflife_calculator.py b/scripts/proteomics/silac_halflife_calculator/silac_halflife_calculator.py deleted file mode 100644 index cd3704c..0000000 --- a/scripts/proteomics/silac_halflife_calculator/silac_halflife_calculator.py +++ /dev/null @@ -1,206 +0,0 @@ -""" -SILAC Half-Life Calculator -=========================== -Fit exponential decay to SILAC heavy/light (H/L) ratios for protein turnover -analysis. For each protein, the tool fits the model:: - - R(t) = R0 * exp(-k * t) - -where *R(t)* is the H/L ratio at time *t*, *R0* is the initial ratio, and -*k* is the decay rate constant. The half-life is ``ln(2) / k``. - -Uses scipy.optimize.curve_fit for non-linear least-squares fitting. - -Usage ------ - python silac_halflife_calculator.py --input hl_ratios.tsv \ - --timepoints 0,6,12,24,48 --output halflives.tsv -""" - -import argparse -import csv -import math -import sys -from typing import Dict, List, Optional - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - -import numpy as np -from scipy.optimize import curve_fit - - -def exponential_decay(t: np.ndarray, r0: float, k: float) -> np.ndarray: - """Exponential decay model: R(t) = R0 * exp(-k * t).""" - return r0 * np.exp(-k * t) - - -def fit_halflife( - timepoints: List[float], ratios: List[float] -) -> Optional[Dict[str, float]]: - """Fit exponential decay to H/L ratios and compute half-life. - - Parameters - ---------- - timepoints: - Time values (e.g. hours). - ratios: - Corresponding H/L ratios. - - Returns - ------- - dict or None - ``r0``, ``k``, ``halflife``, ``r_squared`` if fit succeeds; None otherwise. - """ - t = np.array(timepoints, dtype=float) - r = np.array(ratios, dtype=float) - - # Filter out NaN/inf - valid = np.isfinite(t) & np.isfinite(r) & (r > 0) - t = t[valid] - r = r[valid] - - if len(t) < 2: - return None - - try: - # Initial guesses - r0_guess = float(r[0]) if r[0] > 0 else 1.0 - k_guess = 0.01 - popt, _ = curve_fit( - exponential_decay, t, r, - p0=[r0_guess, k_guess], - bounds=([0, 0], [np.inf, np.inf]), - maxfev=10000, - ) - r0_fit, k_fit = popt - - if k_fit <= 0: - return None - - halflife = math.log(2) / k_fit - - # R-squared - r_pred = exponential_decay(t, r0_fit, k_fit) - ss_res = np.sum((r - r_pred) ** 2) - ss_tot = np.sum((r - np.mean(r)) ** 2) - r_squared = 1.0 - ss_res / ss_tot if ss_tot > 0 else 0.0 - - return { - "r0": r0_fit, - "k": k_fit, - "halflife": halflife, - "r_squared": r_squared, - } - except (RuntimeError, ValueError): - return None - - -def compute_halflives( - proteins: Dict[str, List[float]], timepoints: List[float] -) -> List[Dict[str, object]]: - """Compute half-lives for multiple proteins. - - Parameters - ---------- - proteins: - Mapping of protein ID to list of H/L ratios (one per timepoint). - timepoints: - Time values corresponding to ratio columns. - - Returns - ------- - list of dict - One entry per protein with fit results. - """ - results: List[Dict[str, object]] = [] - for protein_id, ratios in proteins.items(): - fit = fit_halflife(timepoints, ratios) - if fit is not None: - results.append({ - "protein_id": protein_id, - "r0": fit["r0"], - "k": fit["k"], - "halflife": fit["halflife"], - "r_squared": fit["r_squared"], - "status": "ok", - }) - else: - results.append({ - "protein_id": protein_id, - "r0": float("nan"), - "k": float("nan"), - "halflife": float("nan"), - "r_squared": float("nan"), - "status": "fit_failed", - }) - return results - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Fit exponential decay to SILAC H/L ratios for protein turnover." - ) - parser.add_argument( - "--input", required=True, - help="Input TSV with 'protein_id' and ratio columns (one per timepoint)", - ) - parser.add_argument( - "--timepoints", required=True, - help="Comma-separated timepoint values (e.g. 0,6,12,24,48)", - ) - parser.add_argument("--output", required=True, help="Output half-lives TSV") - args = parser.parse_args() - - timepoints = [float(t) for t in args.timepoints.split(",")] - - proteins: Dict[str, List[float]] = {} - with open(args.input, newline="") as fh: - reader = csv.DictReader(fh, delimiter="\t") - fields = reader.fieldnames or [] - # Ratio columns are all columns except protein_id - ratio_cols = [f for f in fields if f != "protein_id"] - if len(ratio_cols) != len(timepoints): - sys.exit( - f"Number of ratio columns ({len(ratio_cols)}) does not match " - f"number of timepoints ({len(timepoints)})" - ) - for row in reader: - pid = row.get("protein_id", "").strip() - if not pid: - continue - ratios = [] - for col in ratio_cols: - val = row.get(col, "").strip() - try: - ratios.append(float(val)) - except (ValueError, TypeError): - ratios.append(float("nan")) - proteins[pid] = ratios - - if not proteins: - sys.exit("No proteins found in input.") - - results = compute_halflives(proteins, timepoints) - - with open(args.output, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - writer.writerow(["protein_id", "r0", "k", "halflife", "r_squared", "status"]) - for r in results: - writer.writerow([ - r["protein_id"], - f"{r['r0']:.6f}" if not math.isnan(r["r0"]) else "NA", - f"{r['k']:.6f}" if not math.isnan(r["k"]) else "NA", - f"{r['halflife']:.4f}" if not math.isnan(r["halflife"]) else "NA", - f"{r['r_squared']:.4f}" if not math.isnan(r["r_squared"]) else "NA", - r["status"], - ]) - - ok_count = sum(1 for r in results if r["status"] == "ok") - print(f"Fitted {ok_count}/{len(results)} proteins -> {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/silac_halflife_calculator/tests/conftest.py b/scripts/proteomics/silac_halflife_calculator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/silac_halflife_calculator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/silac_halflife_calculator/tests/test_silac_halflife_calculator.py b/scripts/proteomics/silac_halflife_calculator/tests/test_silac_halflife_calculator.py deleted file mode 100644 index 5e39225..0000000 --- a/scripts/proteomics/silac_halflife_calculator/tests/test_silac_halflife_calculator.py +++ /dev/null @@ -1,99 +0,0 @@ -"""Tests for silac_halflife_calculator.""" - -import csv -import math -import sys - -from conftest import requires_pyopenms - - -@requires_pyopenms -def test_exponential_decay(): - import numpy as np - from silac_halflife_calculator import exponential_decay - - t = np.array([0, 1, 2, 3]) - result = exponential_decay(t, r0=10.0, k=0.5) - assert abs(result[0] - 10.0) < 0.01 - assert result[1] < result[0] # decaying - - -@requires_pyopenms -def test_fit_halflife_perfect(): - from silac_halflife_calculator import fit_halflife - - # Generate perfect exponential data - k_true = 0.05 - r0_true = 10.0 - t = [0, 6, 12, 24, 48] - ratios = [r0_true * math.exp(-k_true * ti) for ti in t] - - result = fit_halflife(t, ratios) - assert result is not None - assert abs(result["k"] - k_true) < 0.01 - expected_halflife = math.log(2) / k_true - assert abs(result["halflife"] - expected_halflife) < 1.0 - assert result["r_squared"] > 0.99 - - -@requires_pyopenms -def test_fit_halflife_insufficient_data(): - from silac_halflife_calculator import fit_halflife - - result = fit_halflife([0], [10.0]) - assert result is None - - -@requires_pyopenms -def test_compute_halflives(): - import math - - from silac_halflife_calculator import compute_halflives - - k = 0.05 - timepoints = [0, 6, 12, 24, 48] - proteins = { - "P1": [10.0 * math.exp(-k * t) for t in timepoints], - "P2": [5.0 * math.exp(-0.1 * t) for t in timepoints], - } - - results = compute_halflives(proteins, timepoints) - assert len(results) == 2 - assert results[0]["status"] == "ok" - assert results[1]["status"] == "ok" - # P2 has higher k -> shorter halflife - assert results[1]["halflife"] < results[0]["halflife"] - - -@requires_pyopenms -def test_cli_roundtrip(tmp_path): - import math - - from silac_halflife_calculator import main - - input_file = tmp_path / "input.tsv" - output_file = tmp_path / "output.tsv" - timepoints = [0, 6, 12, 24, 48] - k = 0.05 - - with open(input_file, "w", newline="") as fh: - writer = csv.writer(fh, delimiter="\t") - cols = ["protein_id"] + [f"t{t}" for t in timepoints] - writer.writerow(cols) - ratios = [10.0 * math.exp(-k * t) for t in timepoints] - writer.writerow(["P1"] + [f"{r:.4f}" for r in ratios]) - - sys.argv = [ - "silac_halflife_calculator.py", - "--input", str(input_file), - "--timepoints", ",".join(str(t) for t in timepoints), - "--output", str(output_file), - ] - main() - - assert output_file.exists() - with open(output_file) as fh: - reader = csv.DictReader(fh, delimiter="\t") - rows = list(reader) - assert len(rows) == 1 - assert rows[0]["status"] == "ok" diff --git a/scripts/proteomics/cleavage_site_profiler/README.md b/scripts/proteomics/specialized/cleavage_site_profiler/README.md similarity index 100% rename from scripts/proteomics/cleavage_site_profiler/README.md rename to scripts/proteomics/specialized/cleavage_site_profiler/README.md diff --git a/scripts/proteomics/cleavage_site_profiler/cleavage_site_profiler.py b/scripts/proteomics/specialized/cleavage_site_profiler/cleavage_site_profiler.py similarity index 100% rename from scripts/proteomics/cleavage_site_profiler/cleavage_site_profiler.py rename to scripts/proteomics/specialized/cleavage_site_profiler/cleavage_site_profiler.py diff --git a/scripts/proteomics/precursor_charge_distribution/requirements.txt b/scripts/proteomics/specialized/cleavage_site_profiler/requirements.txt similarity index 100% rename from scripts/proteomics/precursor_charge_distribution/requirements.txt rename to scripts/proteomics/specialized/cleavage_site_profiler/requirements.txt diff --git a/scripts/proteomics/peptide_property_calculator/tests/conftest.py b/scripts/proteomics/specialized/cleavage_site_profiler/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_property_calculator/tests/conftest.py rename to scripts/proteomics/specialized/cleavage_site_profiler/tests/conftest.py diff --git a/scripts/proteomics/cleavage_site_profiler/tests/test_cleavage_site_profiler.py b/scripts/proteomics/specialized/cleavage_site_profiler/tests/test_cleavage_site_profiler.py similarity index 100% rename from scripts/proteomics/cleavage_site_profiler/tests/test_cleavage_site_profiler.py rename to scripts/proteomics/specialized/cleavage_site_profiler/tests/test_cleavage_site_profiler.py diff --git a/scripts/proteomics/immunopeptide_filter/README.md b/scripts/proteomics/specialized/immunopeptide_filter/README.md similarity index 100% rename from scripts/proteomics/immunopeptide_filter/README.md rename to scripts/proteomics/specialized/immunopeptide_filter/README.md diff --git a/scripts/proteomics/immunopeptide_filter/immunopeptide_filter.py b/scripts/proteomics/specialized/immunopeptide_filter/immunopeptide_filter.py similarity index 100% rename from scripts/proteomics/immunopeptide_filter/immunopeptide_filter.py rename to scripts/proteomics/specialized/immunopeptide_filter/immunopeptide_filter.py diff --git a/scripts/proteomics/precursor_isolation_purity/requirements.txt b/scripts/proteomics/specialized/immunopeptide_filter/requirements.txt similarity index 100% rename from scripts/proteomics/precursor_isolation_purity/requirements.txt rename to scripts/proteomics/specialized/immunopeptide_filter/requirements.txt diff --git a/scripts/proteomics/peptide_spectral_match_validator/tests/conftest.py b/scripts/proteomics/specialized/immunopeptide_filter/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_spectral_match_validator/tests/conftest.py rename to scripts/proteomics/specialized/immunopeptide_filter/tests/conftest.py diff --git a/scripts/proteomics/immunopeptide_filter/tests/test_immunopeptide_filter.py b/scripts/proteomics/specialized/immunopeptide_filter/tests/test_immunopeptide_filter.py similarity index 100% rename from scripts/proteomics/immunopeptide_filter/tests/test_immunopeptide_filter.py rename to scripts/proteomics/specialized/immunopeptide_filter/tests/test_immunopeptide_filter.py diff --git a/scripts/proteomics/immunopeptidome_qc/README.md b/scripts/proteomics/specialized/immunopeptidome_qc/README.md similarity index 100% rename from scripts/proteomics/immunopeptidome_qc/README.md rename to scripts/proteomics/specialized/immunopeptidome_qc/README.md diff --git a/scripts/proteomics/immunopeptidome_qc/immunopeptidome_qc.py b/scripts/proteomics/specialized/immunopeptidome_qc/immunopeptidome_qc.py similarity index 100% rename from scripts/proteomics/immunopeptidome_qc/immunopeptidome_qc.py rename to scripts/proteomics/specialized/immunopeptidome_qc/immunopeptidome_qc.py diff --git a/scripts/proteomics/precursor_recurrence_analyzer/requirements.txt b/scripts/proteomics/specialized/immunopeptidome_qc/requirements.txt similarity index 100% rename from scripts/proteomics/precursor_recurrence_analyzer/requirements.txt rename to scripts/proteomics/specialized/immunopeptidome_qc/requirements.txt diff --git a/scripts/proteomics/peptide_to_protein_mapper/tests/conftest.py b/scripts/proteomics/specialized/immunopeptidome_qc/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_to_protein_mapper/tests/conftest.py rename to scripts/proteomics/specialized/immunopeptidome_qc/tests/conftest.py diff --git a/scripts/proteomics/immunopeptidome_qc/tests/test_immunopeptidome_qc.py b/scripts/proteomics/specialized/immunopeptidome_qc/tests/test_immunopeptidome_qc.py similarity index 100% rename from scripts/proteomics/immunopeptidome_qc/tests/test_immunopeptidome_qc.py rename to scripts/proteomics/specialized/immunopeptidome_qc/tests/test_immunopeptidome_qc.py diff --git a/scripts/proteomics/metapeptide_lca_assigner/README.md b/scripts/proteomics/specialized/metapeptide_lca_assigner/README.md similarity index 100% rename from scripts/proteomics/metapeptide_lca_assigner/README.md rename to scripts/proteomics/specialized/metapeptide_lca_assigner/README.md diff --git a/scripts/proteomics/metapeptide_lca_assigner/metapeptide_lca_assigner.py b/scripts/proteomics/specialized/metapeptide_lca_assigner/metapeptide_lca_assigner.py similarity index 100% rename from scripts/proteomics/metapeptide_lca_assigner/metapeptide_lca_assigner.py rename to scripts/proteomics/specialized/metapeptide_lca_assigner/metapeptide_lca_assigner.py diff --git a/scripts/proteomics/protein_coverage_calculator/requirements.txt b/scripts/proteomics/specialized/metapeptide_lca_assigner/requirements.txt similarity index 100% rename from scripts/proteomics/protein_coverage_calculator/requirements.txt rename to scripts/proteomics/specialized/metapeptide_lca_assigner/requirements.txt diff --git a/scripts/proteomics/peptide_uniqueness_checker/tests/conftest.py b/scripts/proteomics/specialized/metapeptide_lca_assigner/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_uniqueness_checker/tests/conftest.py rename to scripts/proteomics/specialized/metapeptide_lca_assigner/tests/conftest.py diff --git a/scripts/proteomics/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py b/scripts/proteomics/specialized/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py similarity index 100% rename from scripts/proteomics/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py rename to scripts/proteomics/specialized/metapeptide_lca_assigner/tests/test_metapeptide_lca_assigner.py diff --git a/scripts/proteomics/nterm_modification_annotator/README.md b/scripts/proteomics/specialized/nterm_modification_annotator/README.md similarity index 100% rename from scripts/proteomics/nterm_modification_annotator/README.md rename to scripts/proteomics/specialized/nterm_modification_annotator/README.md diff --git a/scripts/proteomics/nterm_modification_annotator/nterm_modification_annotator.py b/scripts/proteomics/specialized/nterm_modification_annotator/nterm_modification_annotator.py similarity index 100% rename from scripts/proteomics/nterm_modification_annotator/nterm_modification_annotator.py rename to scripts/proteomics/specialized/nterm_modification_annotator/nterm_modification_annotator.py diff --git a/scripts/proteomics/protein_digest/requirements.txt b/scripts/proteomics/specialized/nterm_modification_annotator/requirements.txt similarity index 100% rename from scripts/proteomics/protein_digest/requirements.txt rename to scripts/proteomics/specialized/nterm_modification_annotator/requirements.txt diff --git a/scripts/proteomics/phospho_enrichment_qc/tests/conftest.py b/scripts/proteomics/specialized/nterm_modification_annotator/tests/conftest.py similarity index 100% rename from scripts/proteomics/phospho_enrichment_qc/tests/conftest.py rename to scripts/proteomics/specialized/nterm_modification_annotator/tests/conftest.py diff --git a/scripts/proteomics/nterm_modification_annotator/tests/test_nterm_modification_annotator.py b/scripts/proteomics/specialized/nterm_modification_annotator/tests/test_nterm_modification_annotator.py similarity index 100% rename from scripts/proteomics/nterm_modification_annotator/tests/test_nterm_modification_annotator.py rename to scripts/proteomics/specialized/nterm_modification_annotator/tests/test_nterm_modification_annotator.py diff --git a/scripts/proteomics/proteoform_delta_annotator/README.md b/scripts/proteomics/specialized/proteoform_delta_annotator/README.md similarity index 100% rename from scripts/proteomics/proteoform_delta_annotator/README.md rename to scripts/proteomics/specialized/proteoform_delta_annotator/README.md diff --git a/scripts/proteomics/proteoform_delta_annotator/proteoform_delta_annotator.py b/scripts/proteomics/specialized/proteoform_delta_annotator/proteoform_delta_annotator.py similarity index 100% rename from scripts/proteomics/proteoform_delta_annotator/proteoform_delta_annotator.py rename to scripts/proteomics/specialized/proteoform_delta_annotator/proteoform_delta_annotator.py diff --git a/scripts/proteomics/protein_group_reporter/requirements.txt b/scripts/proteomics/specialized/proteoform_delta_annotator/requirements.txt similarity index 100% rename from scripts/proteomics/protein_group_reporter/requirements.txt rename to scripts/proteomics/specialized/proteoform_delta_annotator/requirements.txt diff --git a/scripts/proteomics/phospho_motif_analyzer/tests/conftest.py b/scripts/proteomics/specialized/proteoform_delta_annotator/tests/conftest.py similarity index 100% rename from scripts/proteomics/phospho_motif_analyzer/tests/conftest.py rename to scripts/proteomics/specialized/proteoform_delta_annotator/tests/conftest.py diff --git a/scripts/proteomics/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py b/scripts/proteomics/specialized/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py similarity index 100% rename from scripts/proteomics/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py rename to scripts/proteomics/specialized/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py diff --git a/scripts/proteomics/topdown_coverage_calculator/README.md b/scripts/proteomics/specialized/topdown_coverage_calculator/README.md similarity index 100% rename from scripts/proteomics/topdown_coverage_calculator/README.md rename to scripts/proteomics/specialized/topdown_coverage_calculator/README.md diff --git a/scripts/proteomics/proteoform_delta_annotator/requirements.txt b/scripts/proteomics/specialized/topdown_coverage_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/proteoform_delta_annotator/requirements.txt rename to scripts/proteomics/specialized/topdown_coverage_calculator/requirements.txt diff --git a/scripts/proteomics/phosphosite_class_filter/tests/conftest.py b/scripts/proteomics/specialized/topdown_coverage_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/phosphosite_class_filter/tests/conftest.py rename to scripts/proteomics/specialized/topdown_coverage_calculator/tests/conftest.py diff --git a/scripts/proteomics/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py b/scripts/proteomics/specialized/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py similarity index 100% rename from scripts/proteomics/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py rename to scripts/proteomics/specialized/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py diff --git a/scripts/proteomics/topdown_coverage_calculator/topdown_coverage_calculator.py b/scripts/proteomics/specialized/topdown_coverage_calculator/topdown_coverage_calculator.py similarity index 100% rename from scripts/proteomics/topdown_coverage_calculator/topdown_coverage_calculator.py rename to scripts/proteomics/specialized/topdown_coverage_calculator/topdown_coverage_calculator.py diff --git a/scripts/proteomics/spectral_counting_quantifier/tests/conftest.py b/scripts/proteomics/spectral_counting_quantifier/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectral_counting_quantifier/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectral_library_builder/tests/conftest.py b/scripts/proteomics/spectral_library_builder/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectral_library_builder/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectral_library_format_converter/tests/conftest.py b/scripts/proteomics/spectral_library_format_converter/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectral_library_format_converter/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectral_library_builder/README.md b/scripts/proteomics/spectrum_analysis/spectral_library_builder/README.md similarity index 100% rename from scripts/proteomics/spectral_library_builder/README.md rename to scripts/proteomics/spectrum_analysis/spectral_library_builder/README.md diff --git a/scripts/proteomics/psm_feature_extractor/requirements.txt b/scripts/proteomics/spectrum_analysis/spectral_library_builder/requirements.txt similarity index 100% rename from scripts/proteomics/psm_feature_extractor/requirements.txt rename to scripts/proteomics/spectrum_analysis/spectral_library_builder/requirements.txt diff --git a/scripts/proteomics/spectral_library_builder/spectral_library_builder.py b/scripts/proteomics/spectrum_analysis/spectral_library_builder/spectral_library_builder.py similarity index 100% rename from scripts/proteomics/spectral_library_builder/spectral_library_builder.py rename to scripts/proteomics/spectrum_analysis/spectral_library_builder/spectral_library_builder.py diff --git a/scripts/proteomics/precursor_charge_distribution/tests/conftest.py b/scripts/proteomics/spectrum_analysis/spectral_library_builder/tests/conftest.py similarity index 100% rename from scripts/proteomics/precursor_charge_distribution/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/spectral_library_builder/tests/conftest.py diff --git a/scripts/proteomics/spectral_library_builder/tests/test_spectral_library_builder.py b/scripts/proteomics/spectrum_analysis/spectral_library_builder/tests/test_spectral_library_builder.py similarity index 100% rename from scripts/proteomics/spectral_library_builder/tests/test_spectral_library_builder.py rename to scripts/proteomics/spectrum_analysis/spectral_library_builder/tests/test_spectral_library_builder.py diff --git a/scripts/proteomics/spectral_library_format_converter/README.md b/scripts/proteomics/spectrum_analysis/spectral_library_format_converter/README.md similarity index 100% rename from scripts/proteomics/spectral_library_format_converter/README.md rename to scripts/proteomics/spectrum_analysis/spectral_library_format_converter/README.md diff --git a/scripts/proteomics/ptm_site_localization_scorer/requirements.txt b/scripts/proteomics/spectrum_analysis/spectral_library_format_converter/requirements.txt similarity index 100% rename from scripts/proteomics/ptm_site_localization_scorer/requirements.txt rename to scripts/proteomics/spectrum_analysis/spectral_library_format_converter/requirements.txt diff --git a/scripts/proteomics/spectral_library_format_converter/spectral_library_format_converter.py b/scripts/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py similarity index 100% rename from scripts/proteomics/spectral_library_format_converter/spectral_library_format_converter.py rename to scripts/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py diff --git a/scripts/proteomics/precursor_isolation_purity/tests/conftest.py b/scripts/proteomics/spectrum_analysis/spectral_library_format_converter/tests/conftest.py similarity index 100% rename from scripts/proteomics/precursor_isolation_purity/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/spectral_library_format_converter/tests/conftest.py diff --git a/scripts/proteomics/spectral_library_format_converter/tests/test_spectral_library_format_converter.py b/scripts/proteomics/spectrum_analysis/spectral_library_format_converter/tests/test_spectral_library_format_converter.py similarity index 100% rename from scripts/proteomics/spectral_library_format_converter/tests/test_spectral_library_format_converter.py rename to scripts/proteomics/spectrum_analysis/spectral_library_format_converter/tests/test_spectral_library_format_converter.py diff --git a/scripts/proteomics/spectrum_annotator/README.md b/scripts/proteomics/spectrum_analysis/spectrum_annotator/README.md similarity index 100% rename from scripts/proteomics/spectrum_annotator/README.md rename to scripts/proteomics/spectrum_analysis/spectrum_annotator/README.md diff --git a/scripts/proteomics/rna_digest/requirements.txt b/scripts/proteomics/spectrum_analysis/spectrum_annotator/requirements.txt similarity index 100% rename from scripts/proteomics/rna_digest/requirements.txt rename to scripts/proteomics/spectrum_analysis/spectrum_annotator/requirements.txt diff --git a/scripts/proteomics/spectrum_annotator/spectrum_annotator.py b/scripts/proteomics/spectrum_analysis/spectrum_annotator/spectrum_annotator.py similarity index 100% rename from scripts/proteomics/spectrum_annotator/spectrum_annotator.py rename to scripts/proteomics/spectrum_analysis/spectrum_annotator/spectrum_annotator.py diff --git a/scripts/proteomics/precursor_recurrence_analyzer/tests/conftest.py b/scripts/proteomics/spectrum_analysis/spectrum_annotator/tests/conftest.py similarity index 100% rename from scripts/proteomics/precursor_recurrence_analyzer/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/spectrum_annotator/tests/conftest.py diff --git a/scripts/proteomics/spectrum_annotator/tests/test_spectrum_annotator.py b/scripts/proteomics/spectrum_analysis/spectrum_annotator/tests/test_spectrum_annotator.py similarity index 100% rename from scripts/proteomics/spectrum_annotator/tests/test_spectrum_annotator.py rename to scripts/proteomics/spectrum_analysis/spectrum_annotator/tests/test_spectrum_annotator.py diff --git a/scripts/proteomics/spectrum_entropy_calculator/README.md b/scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/README.md similarity index 100% rename from scripts/proteomics/spectrum_entropy_calculator/README.md rename to scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/README.md diff --git a/scripts/proteomics/coefficient_of_variation_calculator/requirements.txt b/scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/coefficient_of_variation_calculator/requirements.txt rename to scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/requirements.txt diff --git a/scripts/proteomics/spectrum_entropy_calculator/spectrum_entropy_calculator.py b/scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/spectrum_entropy_calculator.py similarity index 100% rename from scripts/proteomics/spectrum_entropy_calculator/spectrum_entropy_calculator.py rename to scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/spectrum_entropy_calculator.py diff --git a/scripts/proteomics/protein_completeness_matrix/tests/conftest.py b/scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/protein_completeness_matrix/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/tests/conftest.py diff --git a/scripts/proteomics/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py b/scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py similarity index 100% rename from scripts/proteomics/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py rename to scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/tests/test_spectrum_entropy_calculator.py diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/README.md b/scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/README.md similarity index 100% rename from scripts/proteomics/spectrum_scoring_hyperscore/README.md rename to scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/README.md diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/requirements.txt b/scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/requirements.txt similarity index 100% rename from scripts/proteomics/rna_fragment_spectrum_generator/requirements.txt rename to scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/requirements.txt diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py b/scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py similarity index 100% rename from scripts/proteomics/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py rename to scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py diff --git a/scripts/proteomics/protein_coverage_calculator/tests/conftest.py b/scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/tests/conftest.py similarity index 100% rename from scripts/proteomics/protein_coverage_calculator/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/tests/conftest.py diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py b/scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py similarity index 100% rename from scripts/proteomics/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py rename to scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/tests/test_spectrum_scoring_hyperscore.py diff --git a/scripts/proteomics/spectrum_similarity_scorer/README.md b/scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/README.md similarity index 100% rename from scripts/proteomics/spectrum_similarity_scorer/README.md rename to scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/README.md diff --git a/scripts/proteomics/rna_mass_calculator/requirements.txt b/scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/requirements.txt similarity index 100% rename from scripts/proteomics/rna_mass_calculator/requirements.txt rename to scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/requirements.txt diff --git a/scripts/proteomics/spectrum_similarity_scorer/spectrum_similarity_scorer.py b/scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/spectrum_similarity_scorer.py similarity index 100% rename from scripts/proteomics/spectrum_similarity_scorer/spectrum_similarity_scorer.py rename to scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/spectrum_similarity_scorer.py diff --git a/scripts/proteomics/protein_digest/tests/conftest.py b/scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/tests/conftest.py similarity index 100% rename from scripts/proteomics/protein_digest/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/tests/conftest.py diff --git a/scripts/proteomics/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py b/scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py similarity index 100% rename from scripts/proteomics/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py rename to scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/tests/test_spectrum_similarity_scorer.py diff --git a/scripts/proteomics/theoretical_spectrum_generator/README.md b/scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/README.md similarity index 100% rename from scripts/proteomics/theoretical_spectrum_generator/README.md rename to scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/README.md diff --git a/scripts/proteomics/rt_prediction_additive/requirements.txt b/scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt similarity index 100% rename from scripts/proteomics/rt_prediction_additive/requirements.txt rename to scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt diff --git a/scripts/proteomics/protein_group_reporter/tests/conftest.py b/scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/protein_group_reporter/tests/conftest.py rename to scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/conftest.py diff --git a/scripts/proteomics/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py b/scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py similarity index 100% rename from scripts/proteomics/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py rename to scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/test_theoretical_spectrum_generator.py diff --git a/scripts/proteomics/theoretical_spectrum_generator/theoretical_spectrum_generator.py b/scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py similarity index 100% rename from scripts/proteomics/theoretical_spectrum_generator/theoretical_spectrum_generator.py rename to scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py diff --git a/scripts/proteomics/spectrum_annotator/tests/conftest.py b/scripts/proteomics/spectrum_annotator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectrum_annotator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_entropy_calculator/requirements.txt b/scripts/proteomics/spectrum_entropy_calculator/requirements.txt deleted file mode 100644 index 1051d92..0000000 --- a/scripts/proteomics/spectrum_entropy_calculator/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -pyopenms -numpy diff --git a/scripts/proteomics/spectrum_entropy_calculator/tests/conftest.py b/scripts/proteomics/spectrum_entropy_calculator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectrum_entropy_calculator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_file_info/tests/conftest.py b/scripts/proteomics/spectrum_file_info/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectrum_file_info/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/tests/conftest.py b/scripts/proteomics/spectrum_scoring_hyperscore/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectrum_scoring_hyperscore/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/spectrum_similarity_scorer/requirements.txt b/scripts/proteomics/spectrum_similarity_scorer/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/spectrum_similarity_scorer/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/spectrum_similarity_scorer/tests/conftest.py b/scripts/proteomics/spectrum_similarity_scorer/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/spectrum_similarity_scorer/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/crosslink_mass_calculator/README.md b/scripts/proteomics/structural_proteomics/crosslink_mass_calculator/README.md similarity index 100% rename from scripts/proteomics/crosslink_mass_calculator/README.md rename to scripts/proteomics/structural_proteomics/crosslink_mass_calculator/README.md diff --git a/scripts/proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py b/scripts/proteomics/structural_proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py similarity index 100% rename from scripts/proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py rename to scripts/proteomics/structural_proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py diff --git a/scripts/proteomics/run_comparison_reporter/requirements.txt b/scripts/proteomics/structural_proteomics/crosslink_mass_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/run_comparison_reporter/requirements.txt rename to scripts/proteomics/structural_proteomics/crosslink_mass_calculator/requirements.txt diff --git a/scripts/proteomics/proteoform_delta_annotator/tests/conftest.py b/scripts/proteomics/structural_proteomics/crosslink_mass_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/proteoform_delta_annotator/tests/conftest.py rename to scripts/proteomics/structural_proteomics/crosslink_mass_calculator/tests/conftest.py diff --git a/scripts/proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py b/scripts/proteomics/structural_proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py similarity index 100% rename from scripts/proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py rename to scripts/proteomics/structural_proteomics/crosslink_mass_calculator/tests/test_crosslink_mass_calculator.py diff --git a/scripts/proteomics/hdx_back_exchange_estimator/README.md b/scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/README.md similarity index 100% rename from scripts/proteomics/hdx_back_exchange_estimator/README.md rename to scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/README.md diff --git a/scripts/proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py b/scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py similarity index 100% rename from scripts/proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py rename to scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py diff --git a/scripts/proteomics/sample_complexity_estimator/requirements.txt b/scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/requirements.txt similarity index 100% rename from scripts/proteomics/sample_complexity_estimator/requirements.txt rename to scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/requirements.txt diff --git a/scripts/proteomics/psm_feature_extractor/tests/conftest.py b/scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/tests/conftest.py similarity index 100% rename from scripts/proteomics/psm_feature_extractor/tests/conftest.py rename to scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/tests/conftest.py diff --git a/scripts/proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py b/scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py similarity index 100% rename from scripts/proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py rename to scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/tests/test_hdx_back_exchange_estimator.py diff --git a/scripts/proteomics/hdx_deuterium_uptake/README.md b/scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/README.md similarity index 100% rename from scripts/proteomics/hdx_deuterium_uptake/README.md rename to scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/README.md diff --git a/scripts/proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py b/scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py similarity index 100% rename from scripts/proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py rename to scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py diff --git a/scripts/proteomics/scp_reporter_qc/requirements.txt b/scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/requirements.txt similarity index 100% rename from scripts/proteomics/scp_reporter_qc/requirements.txt rename to scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/requirements.txt diff --git a/scripts/proteomics/ptm_site_localization_scorer/tests/conftest.py b/scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/conftest.py similarity index 100% rename from scripts/proteomics/ptm_site_localization_scorer/tests/conftest.py rename to scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/conftest.py diff --git a/scripts/proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py b/scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py similarity index 100% rename from scripts/proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py rename to scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py diff --git a/scripts/proteomics/xl_distance_validator/README.md b/scripts/proteomics/structural_proteomics/xl_distance_validator/README.md similarity index 100% rename from scripts/proteomics/xl_distance_validator/README.md rename to scripts/proteomics/structural_proteomics/xl_distance_validator/README.md diff --git a/scripts/proteomics/search_result_merger/requirements.txt b/scripts/proteomics/structural_proteomics/xl_distance_validator/requirements.txt similarity index 100% rename from scripts/proteomics/search_result_merger/requirements.txt rename to scripts/proteomics/structural_proteomics/xl_distance_validator/requirements.txt diff --git a/scripts/proteomics/quantification_normalizer/tests/conftest.py b/scripts/proteomics/structural_proteomics/xl_distance_validator/tests/conftest.py similarity index 100% rename from scripts/proteomics/quantification_normalizer/tests/conftest.py rename to scripts/proteomics/structural_proteomics/xl_distance_validator/tests/conftest.py diff --git a/scripts/proteomics/xl_distance_validator/tests/test_xl_distance_validator.py b/scripts/proteomics/structural_proteomics/xl_distance_validator/tests/test_xl_distance_validator.py similarity index 100% rename from scripts/proteomics/xl_distance_validator/tests/test_xl_distance_validator.py rename to scripts/proteomics/structural_proteomics/xl_distance_validator/tests/test_xl_distance_validator.py diff --git a/scripts/proteomics/xl_distance_validator/xl_distance_validator.py b/scripts/proteomics/structural_proteomics/xl_distance_validator/xl_distance_validator.py similarity index 100% rename from scripts/proteomics/xl_distance_validator/xl_distance_validator.py rename to scripts/proteomics/structural_proteomics/xl_distance_validator/xl_distance_validator.py diff --git a/scripts/proteomics/xl_link_classifier/README.md b/scripts/proteomics/structural_proteomics/xl_link_classifier/README.md similarity index 100% rename from scripts/proteomics/xl_link_classifier/README.md rename to scripts/proteomics/structural_proteomics/xl_link_classifier/README.md diff --git a/scripts/proteomics/semi_tryptic_peptide_finder/requirements.txt b/scripts/proteomics/structural_proteomics/xl_link_classifier/requirements.txt similarity index 100% rename from scripts/proteomics/semi_tryptic_peptide_finder/requirements.txt rename to scripts/proteomics/structural_proteomics/xl_link_classifier/requirements.txt diff --git a/scripts/proteomics/rna_digest/tests/conftest.py b/scripts/proteomics/structural_proteomics/xl_link_classifier/tests/conftest.py similarity index 100% rename from scripts/proteomics/rna_digest/tests/conftest.py rename to scripts/proteomics/structural_proteomics/xl_link_classifier/tests/conftest.py diff --git a/scripts/proteomics/xl_link_classifier/tests/test_xl_link_classifier.py b/scripts/proteomics/structural_proteomics/xl_link_classifier/tests/test_xl_link_classifier.py similarity index 100% rename from scripts/proteomics/xl_link_classifier/tests/test_xl_link_classifier.py rename to scripts/proteomics/structural_proteomics/xl_link_classifier/tests/test_xl_link_classifier.py diff --git a/scripts/proteomics/xl_link_classifier/xl_link_classifier.py b/scripts/proteomics/structural_proteomics/xl_link_classifier/xl_link_classifier.py similarity index 100% rename from scripts/proteomics/xl_link_classifier/xl_link_classifier.py rename to scripts/proteomics/structural_proteomics/xl_link_classifier/xl_link_classifier.py diff --git a/scripts/proteomics/dia_window_analyzer/README.md b/scripts/proteomics/targeted_proteomics/dia_window_analyzer/README.md similarity index 100% rename from scripts/proteomics/dia_window_analyzer/README.md rename to scripts/proteomics/targeted_proteomics/dia_window_analyzer/README.md diff --git a/scripts/proteomics/dia_window_analyzer/dia_window_analyzer.py b/scripts/proteomics/targeted_proteomics/dia_window_analyzer/dia_window_analyzer.py similarity index 100% rename from scripts/proteomics/dia_window_analyzer/dia_window_analyzer.py rename to scripts/proteomics/targeted_proteomics/dia_window_analyzer/dia_window_analyzer.py diff --git a/scripts/proteomics/sequence_tag_generator/requirements.txt b/scripts/proteomics/targeted_proteomics/dia_window_analyzer/requirements.txt similarity index 100% rename from scripts/proteomics/sequence_tag_generator/requirements.txt rename to scripts/proteomics/targeted_proteomics/dia_window_analyzer/requirements.txt diff --git a/scripts/proteomics/rna_fragment_spectrum_generator/tests/conftest.py b/scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/rna_fragment_spectrum_generator/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/conftest.py diff --git a/scripts/proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py b/scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py similarity index 100% rename from scripts/proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py rename to scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py diff --git a/scripts/proteomics/inclusion_list_generator/README.md b/scripts/proteomics/targeted_proteomics/inclusion_list_generator/README.md similarity index 100% rename from scripts/proteomics/inclusion_list_generator/README.md rename to scripts/proteomics/targeted_proteomics/inclusion_list_generator/README.md diff --git a/scripts/proteomics/inclusion_list_generator/inclusion_list_generator.py b/scripts/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py similarity index 100% rename from scripts/proteomics/inclusion_list_generator/inclusion_list_generator.py rename to scripts/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py diff --git a/scripts/proteomics/spectral_counting_quantifier/requirements.txt b/scripts/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt similarity index 100% rename from scripts/proteomics/spectral_counting_quantifier/requirements.txt rename to scripts/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt diff --git a/scripts/proteomics/rna_mass_calculator/tests/conftest.py b/scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/rna_mass_calculator/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/conftest.py diff --git a/scripts/proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py b/scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py similarity index 100% rename from scripts/proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py rename to scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py diff --git a/scripts/proteomics/irt_calculator/README.md b/scripts/proteomics/targeted_proteomics/irt_calculator/README.md similarity index 100% rename from scripts/proteomics/irt_calculator/README.md rename to scripts/proteomics/targeted_proteomics/irt_calculator/README.md diff --git a/scripts/proteomics/irt_calculator/irt_calculator.py b/scripts/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py similarity index 100% rename from scripts/proteomics/irt_calculator/irt_calculator.py rename to scripts/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py diff --git a/scripts/proteomics/spectral_library_builder/requirements.txt b/scripts/proteomics/targeted_proteomics/irt_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/spectral_library_builder/requirements.txt rename to scripts/proteomics/targeted_proteomics/irt_calculator/requirements.txt diff --git a/scripts/proteomics/rt_prediction_additive/tests/conftest.py b/scripts/proteomics/targeted_proteomics/irt_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/rt_prediction_additive/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/irt_calculator/tests/conftest.py diff --git a/scripts/proteomics/irt_calculator/tests/test_irt_calculator.py b/scripts/proteomics/targeted_proteomics/irt_calculator/tests/test_irt_calculator.py similarity index 100% rename from scripts/proteomics/irt_calculator/tests/test_irt_calculator.py rename to scripts/proteomics/targeted_proteomics/irt_calculator/tests/test_irt_calculator.py diff --git a/scripts/proteomics/library_coverage_estimator/README.md b/scripts/proteomics/targeted_proteomics/library_coverage_estimator/README.md similarity index 100% rename from scripts/proteomics/library_coverage_estimator/README.md rename to scripts/proteomics/targeted_proteomics/library_coverage_estimator/README.md diff --git a/scripts/proteomics/library_coverage_estimator/library_coverage_estimator.py b/scripts/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py similarity index 100% rename from scripts/proteomics/library_coverage_estimator/library_coverage_estimator.py rename to scripts/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py diff --git a/scripts/proteomics/spectral_library_format_converter/requirements.txt b/scripts/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt similarity index 100% rename from scripts/proteomics/spectral_library_format_converter/requirements.txt rename to scripts/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt diff --git a/scripts/proteomics/run_comparison_reporter/tests/conftest.py b/scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/conftest.py similarity index 100% rename from scripts/proteomics/run_comparison_reporter/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/conftest.py diff --git a/scripts/proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py b/scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py similarity index 100% rename from scripts/proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py rename to scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py diff --git a/scripts/proteomics/tic_bpc_calculator/README.md b/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/README.md similarity index 100% rename from scripts/proteomics/tic_bpc_calculator/README.md rename to scripts/proteomics/targeted_proteomics/tic_bpc_calculator/README.md diff --git a/scripts/proteomics/spectrum_annotator/requirements.txt b/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/spectrum_annotator/requirements.txt rename to scripts/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt diff --git a/scripts/proteomics/sample_complexity_estimator/tests/conftest.py b/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/sample_complexity_estimator/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/conftest.py diff --git a/scripts/proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py b/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py similarity index 100% rename from scripts/proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py rename to scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py diff --git a/scripts/proteomics/tic_bpc_calculator/tic_bpc_calculator.py b/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py similarity index 100% rename from scripts/proteomics/tic_bpc_calculator/tic_bpc_calculator.py rename to scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py diff --git a/scripts/proteomics/transition_list_generator/README.md b/scripts/proteomics/targeted_proteomics/transition_list_generator/README.md similarity index 100% rename from scripts/proteomics/transition_list_generator/README.md rename to scripts/proteomics/targeted_proteomics/transition_list_generator/README.md diff --git a/scripts/proteomics/spectrum_file_info/requirements.txt b/scripts/proteomics/targeted_proteomics/transition_list_generator/requirements.txt similarity index 100% rename from scripts/proteomics/spectrum_file_info/requirements.txt rename to scripts/proteomics/targeted_proteomics/transition_list_generator/requirements.txt diff --git a/scripts/proteomics/sample_correlation_calculator/tests/conftest.py b/scripts/proteomics/targeted_proteomics/transition_list_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/sample_correlation_calculator/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/transition_list_generator/tests/conftest.py diff --git a/scripts/proteomics/transition_list_generator/tests/test_transition_list_generator.py b/scripts/proteomics/targeted_proteomics/transition_list_generator/tests/test_transition_list_generator.py similarity index 100% rename from scripts/proteomics/transition_list_generator/tests/test_transition_list_generator.py rename to scripts/proteomics/targeted_proteomics/transition_list_generator/tests/test_transition_list_generator.py diff --git a/scripts/proteomics/transition_list_generator/transition_list_generator.py b/scripts/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py similarity index 100% rename from scripts/proteomics/transition_list_generator/transition_list_generator.py rename to scripts/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py diff --git a/scripts/proteomics/xic_extractor/README.md b/scripts/proteomics/targeted_proteomics/xic_extractor/README.md similarity index 100% rename from scripts/proteomics/xic_extractor/README.md rename to scripts/proteomics/targeted_proteomics/xic_extractor/README.md diff --git a/scripts/proteomics/spectrum_scoring_hyperscore/requirements.txt b/scripts/proteomics/targeted_proteomics/xic_extractor/requirements.txt similarity index 100% rename from scripts/proteomics/spectrum_scoring_hyperscore/requirements.txt rename to scripts/proteomics/targeted_proteomics/xic_extractor/requirements.txt diff --git a/scripts/proteomics/scp_reporter_qc/tests/conftest.py b/scripts/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py similarity index 100% rename from scripts/proteomics/scp_reporter_qc/tests/conftest.py rename to scripts/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py diff --git a/scripts/proteomics/xic_extractor/tests/test_xic_extractor.py b/scripts/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py similarity index 100% rename from scripts/proteomics/xic_extractor/tests/test_xic_extractor.py rename to scripts/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py diff --git a/scripts/proteomics/xic_extractor/xic_extractor.py b/scripts/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py similarity index 100% rename from scripts/proteomics/xic_extractor/xic_extractor.py rename to scripts/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py diff --git a/scripts/proteomics/theoretical_spectrum_generator/requirements.txt b/scripts/proteomics/theoretical_spectrum_generator/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/theoretical_spectrum_generator/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py b/scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/theoretical_spectrum_generator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/tic_bpc_calculator/requirements.txt b/scripts/proteomics/tic_bpc_calculator/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/tic_bpc_calculator/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/tic_bpc_calculator/tests/conftest.py b/scripts/proteomics/tic_bpc_calculator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/tic_bpc_calculator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/topdown_coverage_calculator/requirements.txt b/scripts/proteomics/topdown_coverage_calculator/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/topdown_coverage_calculator/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/topdown_coverage_calculator/tests/conftest.py b/scripts/proteomics/topdown_coverage_calculator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/topdown_coverage_calculator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/transition_list_generator/requirements.txt b/scripts/proteomics/transition_list_generator/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/transition_list_generator/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/transition_list_generator/tests/conftest.py b/scripts/proteomics/transition_list_generator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/transition_list_generator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/volcano_plot_data_generator/README.md b/scripts/proteomics/volcano_plot_data_generator/README.md deleted file mode 100644 index 5ecad47..0000000 --- a/scripts/proteomics/volcano_plot_data_generator/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# Volcano Plot Data Generator - -Generate volcano plot data from differential expression results. - -## Usage - -```bash -python volcano_plot_data_generator.py --input de_results.tsv --fc-threshold 1.0 --pvalue 0.05 --output volcano.tsv -``` - -## Output Columns - -- `feature` - Feature identifier -- `log2fc` - Log2 fold change -- `pvalue` - P-value -- `neg_log10_pvalue` - -log10(p-value) for plotting -- `regulation` - Classification: `up`, `down`, or `ns` diff --git a/scripts/proteomics/volcano_plot_data_generator/requirements.txt b/scripts/proteomics/volcano_plot_data_generator/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/volcano_plot_data_generator/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/volcano_plot_data_generator/tests/conftest.py b/scripts/proteomics/volcano_plot_data_generator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/volcano_plot_data_generator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py b/scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py deleted file mode 100644 index 10cfdac..0000000 --- a/scripts/proteomics/volcano_plot_data_generator/tests/test_volcano_plot_data_generator.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Tests for volcano_plot_data_generator.""" - -import math - -from conftest import requires_pyopenms -from volcano_plot_data_generator import generate_volcano_data, read_de_results, summarize_volcano - - -@requires_pyopenms -class TestVolcanoPlotDataGenerator: - def _make_de_results(self): - return [ - {"feature": "prot1", "log2fc": 2.0, "pvalue": 0.001}, # up - {"feature": "prot2", "log2fc": -1.5, "pvalue": 0.01}, # down - {"feature": "prot3", "log2fc": 0.5, "pvalue": 0.001}, # ns (fc too low) - {"feature": "prot4", "log2fc": 2.0, "pvalue": 0.1}, # ns (pval too high) - {"feature": "prot5", "log2fc": float("nan"), "pvalue": float("nan")}, # ns - ] - - def test_classification(self): - results = self._make_de_results() - volcano = generate_volcano_data(results, fc_threshold=1.0, pvalue_threshold=0.05) - regs = {v["feature"]: v["regulation"] for v in volcano} - assert regs["prot1"] == "up" - assert regs["prot2"] == "down" - assert regs["prot3"] == "ns" - assert regs["prot4"] == "ns" - assert regs["prot5"] == "ns" - - def test_neg_log10_pvalue(self): - results = [{"feature": "p1", "log2fc": 1.0, "pvalue": 0.01}] - volcano = generate_volcano_data(results) - assert abs(volcano[0]["neg_log10_pvalue"] - 2.0) < 0.01 - - def test_summarize(self): - results = self._make_de_results() - volcano = generate_volcano_data(results, fc_threshold=1.0, pvalue_threshold=0.05) - counts = summarize_volcano(volcano) - assert counts["up"] == 1 - assert counts["down"] == 1 - assert counts["ns"] == 3 - - def test_custom_thresholds(self): - results = self._make_de_results() - volcano = generate_volcano_data(results, fc_threshold=0.3, pvalue_threshold=0.5) - regs = {v["feature"]: v["regulation"] for v in volcano} - assert regs["prot3"] == "up" # fc=0.5 > 0.3 threshold - assert regs["prot4"] == "up" # pval=0.1 < 0.5 threshold - - def test_read_de_results(self, tmp_path): - infile = str(tmp_path / "de.tsv") - with open(infile, "w") as fh: - fh.write("feature\tlog2fc\tadj_pvalue\n") - fh.write("p1\t1.5\t0.01\n") - fh.write("p2\tNA\tNA\n") - results = read_de_results(infile) - assert len(results) == 2 - assert results[0]["log2fc"] == 1.5 - assert math.isnan(results[1]["log2fc"]) - - def test_empty_input(self): - volcano = generate_volcano_data([]) - assert volcano == [] diff --git a/scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py b/scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py deleted file mode 100644 index 3076848..0000000 --- a/scripts/proteomics/volcano_plot_data_generator/volcano_plot_data_generator.py +++ /dev/null @@ -1,154 +0,0 @@ -""" -Volcano Plot Data Generator -============================ -Generate volcano plot data from differential expression results. - -Annotates features as 'up', 'down', or 'ns' (not significant) based on -fold-change and p-value thresholds, and computes -log10(p-value) for plotting. - -Usage ------ - python volcano_plot_data_generator.py --input de_results.tsv --fc-threshold 1.0 --pvalue 0.05 --output volcano.tsv -""" - -import argparse -import csv -import math -import sys - -try: - import pyopenms as oms # noqa: F401 -except ImportError: - sys.exit("pyopenms is required. Install it with: pip install pyopenms") - - -def read_de_results(filepath: str) -> list: - """Read differential expression results. - - Expected columns: feature, log2fc, pvalue (or adj_pvalue). - - Returns - ------- - list - List of dicts with keys: feature, log2fc, pvalue. - """ - results = [] - with open(filepath) as fh: - reader = csv.DictReader(fh, delimiter="\t") - for row in reader: - feature = row.get("feature", row.get("protein", row.get("peptide", ""))) - log2fc_str = row.get("log2fc", "NA") - pval_str = row.get("adj_pvalue", row.get("pvalue", "NA")) - - try: - log2fc = float(log2fc_str) - except (ValueError, TypeError): - log2fc = float("nan") - try: - pval = float(pval_str) - except (ValueError, TypeError): - pval = float("nan") - - results.append({"feature": feature, "log2fc": log2fc, "pvalue": pval}) - return results - - -def generate_volcano_data( - de_results: list, fc_threshold: float = 1.0, pvalue_threshold: float = 0.05 -) -> list: - """Annotate DE results for volcano plotting. - - Parameters - ---------- - de_results: - List of dicts with keys: feature, log2fc, pvalue. - fc_threshold: - Absolute log2 fold-change threshold. - pvalue_threshold: - P-value significance threshold. - - Returns - ------- - list - List of dicts with keys: feature, log2fc, pvalue, neg_log10_pvalue, regulation. - """ - volcano = [] - for r in de_results: - log2fc = r["log2fc"] - pval = r["pvalue"] - - if math.isnan(log2fc) or math.isnan(pval): - neg_log10_p = float("nan") - regulation = "ns" - else: - neg_log10_p = -math.log10(pval) if pval > 0 else float("inf") - if pval < pvalue_threshold and log2fc > fc_threshold: - regulation = "up" - elif pval < pvalue_threshold and log2fc < -fc_threshold: - regulation = "down" - else: - regulation = "ns" - - volcano.append({ - "feature": r["feature"], - "log2fc": log2fc, - "pvalue": pval, - "neg_log10_pvalue": neg_log10_p, - "regulation": regulation, - }) - return volcano - - -def summarize_volcano(volcano_data: list) -> dict: - """Count features by regulation status. - - Returns - ------- - dict - {up: int, down: int, ns: int} - """ - counts = {"up": 0, "down": 0, "ns": 0} - for v in volcano_data: - counts[v["regulation"]] += 1 - return counts - - -def main(): - parser = argparse.ArgumentParser(description="Generate volcano plot data from DE results.") - parser.add_argument("--input", required=True, help="Input DE results TSV") - parser.add_argument("--fc-threshold", type=float, default=1.0, help="Log2 fold-change threshold (default: 1.0)") - parser.add_argument("--pvalue", type=float, default=0.05, help="P-value threshold (default: 0.05)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - de_results = read_de_results(args.input) - volcano = generate_volcano_data(de_results, fc_threshold=args.fc_threshold, pvalue_threshold=args.pvalue) - - with open(args.output, "w", newline="") as fh: - writer = csv.DictWriter( - fh, - fieldnames=["feature", "log2fc", "pvalue", "neg_log10_pvalue", "regulation"], - delimiter="\t", - ) - writer.writeheader() - for v in volcano: - writer.writerow({ - "feature": v["feature"], - "log2fc": f"{v['log2fc']:.6f}" if not math.isnan(v["log2fc"]) else "NA", - "pvalue": f"{v['pvalue']:.6e}" if not math.isnan(v["pvalue"]) else "NA", - "neg_log10_pvalue": ( - f"{v['neg_log10_pvalue']:.4f}" if not math.isnan(v["neg_log10_pvalue"]) else "NA" - ), - "regulation": v["regulation"], - }) - - counts = summarize_volcano(volcano) - print(f"Total features: {len(volcano)}") - print(f"Up-regulated: {counts['up']}") - print(f"Down-regulated: {counts['down']}") - print(f"Not significant: {counts['ns']}") - print(f"Output written to {args.output}") - - -if __name__ == "__main__": - main() diff --git a/scripts/proteomics/xic_extractor/requirements.txt b/scripts/proteomics/xic_extractor/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/xic_extractor/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/xic_extractor/tests/conftest.py b/scripts/proteomics/xic_extractor/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/xic_extractor/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/xl_distance_validator/requirements.txt b/scripts/proteomics/xl_distance_validator/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/xl_distance_validator/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/xl_distance_validator/tests/conftest.py b/scripts/proteomics/xl_distance_validator/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/xl_distance_validator/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") diff --git a/scripts/proteomics/xl_link_classifier/requirements.txt b/scripts/proteomics/xl_link_classifier/requirements.txt deleted file mode 100644 index 7ce28ec..0000000 --- a/scripts/proteomics/xl_link_classifier/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -pyopenms diff --git a/scripts/proteomics/xl_link_classifier/tests/conftest.py b/scripts/proteomics/xl_link_classifier/tests/conftest.py deleted file mode 100644 index 1a21ede..0000000 --- a/scripts/proteomics/xl_link_classifier/tests/conftest.py +++ /dev/null @@ -1,15 +0,0 @@ -import os -import sys - -import pytest - -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - -try: - import pyopenms # noqa: F401 - - HAS_PYOPENMS = True -except ImportError: - HAS_PYOPENMS = False - -requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") From 9029002abb50be2beb48e708533aa467cfe360ba Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 07:43:19 +0100 Subject: [PATCH 04/15] Rewrite README with purpose, AI disclaimer, tool structure docs - Rewrite README.md: explain repo purpose, why it exists, how to contribute agentic, AI-generated disclaimer, tool structure documentation, and full catalog of all 123 tools organized by topic - Update AGENTS.md: use "tool" terminology throughout, add rule against non-pyopenms tools - Update CLAUDE.md: use "tool" terminology, update paths for 3-level structure Co-Authored-By: Claude Opus 4.6 (1M context) --- AGENTS.md | 33 +++--- CLAUDE.md | 14 +-- README.md | 335 +++++++++++++++++++++++++++++++++++++++++++++++++++--- 3 files changed, 341 insertions(+), 41 deletions(-) diff --git a/AGENTS.md b/AGENTS.md index ec5d9c8..1ca3f5b 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,6 +1,6 @@ # AGENTS.md — AI Contributor Guide -This file instructs AI agents (Claude Code, GitHub Copilot, Cursor, Gemini, etc.) how to contribute scripts to the agentomics repository. +This file instructs AI agents (Claude Code, GitHub Copilot, Cursor, Gemini, etc.) how to contribute tools to the agentomics repository. ## Project Purpose @@ -8,12 +8,12 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ## Contribution Requirements -Every script must be a **self-contained directory** under `scripts////`: +Every tool must be a **self-contained directory** under `scripts////`: ``` scripts//// ├── .py # The tool itself -├── requirements.txt # pyopenms + any script-specific deps (no version pins) +├── requirements.txt # pyopenms + any tool-specific deps (no version pins) ├── README.md # Brief description + CLI usage examples └── tests/ ├── conftest.py # Shared test config (see below) @@ -31,15 +31,15 @@ scripts//// - `` is `proteomics` or `metabolomics` - `` is one of the topic directories listed above - `requirements.txt` always includes `pyopenms` with no version pin — builds against latest -- No cross-script imports — each script is fully independent +- No cross-tool imports — each tool is fully independent - No `__init__.py` files — these are NOT Python packages -- No scripts that duplicate functionality already in OpenMS/pyopenms +- No tools that duplicate functionality already in OpenMS/pyopenms TOPP tools ## Code Patterns -### Script structure +### Tool structure -Every script must have: +Every tool must have: 1. **Module docstring** with description, features, and usage examples 2. **pyopenms import guard:** @@ -78,21 +78,21 @@ requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not in Test files: - Decorate test classes with `@requires_pyopenms` from conftest -- Import script functions inside test methods: `from import ` -- For file-I/O scripts: generate synthetic data using pyopenms objects, write to `tempfile.TemporaryDirectory()` +- Import tool functions inside test methods: `from import ` +- For file-I/O tools: generate synthetic data using pyopenms objects, write to `tempfile.TemporaryDirectory()` ## Validation -Every script must pass validation in an **isolated venv** before it can be merged. Run these commands from the repo root: +Every tool must pass validation in an **isolated venv** before it can be merged. Run these commands from the repo root: ```bash -SCRIPT_DIR=scripts// +TOOL_DIR=scripts/// VENV_DIR=$(mktemp -d) python -m venv "$VENV_DIR" -"$VENV_DIR/bin/python" -m pip install -r "$SCRIPT_DIR/requirements.txt" +"$VENV_DIR/bin/python" -m pip install -r "$TOOL_DIR/requirements.txt" "$VENV_DIR/bin/python" -m pip install pytest ruff -"$VENV_DIR/bin/python" -m ruff check "$SCRIPT_DIR/" -PYTHONPATH="$SCRIPT_DIR" "$VENV_DIR/bin/python" -m pytest "$SCRIPT_DIR/tests/" -v +"$VENV_DIR/bin/python" -m ruff check "$TOOL_DIR/" +PYTHONPATH="$TOOL_DIR" "$VENV_DIR/bin/python" -m pytest "$TOOL_DIR/tests/" -v rm -rf "$VENV_DIR" ``` @@ -106,8 +106,9 @@ Ruff is configured in `ruff.toml` at the repo root: ## What NOT to Do -- Do not add cross-script imports +- Do not add cross-tool imports - Do not add dependencies to a shared/root requirements file -- Do not create scripts that duplicate existing pyopenms CLI tools or OpenMS TOPP tools +- Do not create tools that duplicate existing pyopenms CLI tools or OpenMS TOPP tools - Do not pin pyopenms to a specific version - Do not add `__init__.py` files +- Do not add tools that don't actually use pyopenms (pure stats/math tools belong elsewhere) diff --git a/CLAUDE.md b/CLAUDE.md index ef8a180..caffe05 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -9,19 +9,19 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ## Commands ```bash -# Install dependencies for a specific script +# Install dependencies for a specific tool pip install -r scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt -# Lint a specific script +# Lint a specific tool ruff check scripts/proteomics/peptide_analysis/peptide_mass_calculator/ -# Run tests for a specific script +# Run tests for a specific tool PYTHONPATH=scripts/proteomics/peptide_analysis/peptide_mass_calculator python -m pytest scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/ -v -# Lint all scripts +# Lint all tools ruff check scripts/ -# Run all tests across all scripts +# Run all tests across all tools for d in scripts/*/*/*/; do PYTHONPATH="$d" python -m pytest "$d/tests/" -v; done # Run a script directly @@ -31,9 +31,9 @@ python scripts/metabolomics/formula_tools/isotope_pattern_matcher/isotope_patter ## Architecture -### Per-Script Directory Structure +### Per-Tool Directory Structure -Each script is a self-contained directory under `scripts////`: +Each tool is a self-contained directory under `scripts////`: ``` scripts//// diff --git a/README.md b/README.md index 448bbae..5c5046d 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,62 @@ -# agentomics +# Agentomics -A repository of agentic-created tools using [pyopenms](https://pyopenms.readthedocs.io/) for proteomics and metabolomics. +A growing collection of **123 standalone CLI tools** built with [pyopenms](https://pyopenms.readthedocs.io/) for proteomics and metabolomics workflows. Every tool in this repository fills a gap not covered by existing OpenMS TOPP tools — small, focused utilities that researchers need daily but typically write as throwaway scripts. -All code in this repo is written by AI agents. See [AGENTS.md](AGENTS.md) for the contributor guide. +## Why This Exists + +Mass spectrometry researchers constantly need small utilities: extract an XIC from an mzML file, compute adduct m/z values for a metabolite, check peptide uniqueness in a FASTA database, validate crosslink distances against a PDB structure. These tasks are too simple for a full pipeline but too tedious to re-implement from scratch every time. + +Agentomics collects these utilities into a single, organized repository where each tool is: + +- **Self-contained** — no cross-tool dependencies, install and run independently +- **CLI-first** — every tool has an `argparse` interface, usable from the command line or imported as a Python library +- **Tested** — every tool ships with unit tests using synthetic pyopenms data +- **pyopenms-native** — built on the official Python bindings for OpenMS, not reimplementing what already exists + +## AI-Generated Disclaimer + +> **All code in this repository is written entirely by AI agents** (Claude Code, GitHub Copilot, Cursor, Gemini, etc.). This is an agentic-only development project — tool ideas were researched from GitHub repositories, community forums (BioStars, Reddit), published papers, and pyopenms documentation, then implemented by AI. Human review is applied for quality control and direction, but the code itself is machine-generated. Use at your own discretion and always validate results against established tools for critical analyses. + +## Contributing (Agentic Workflow) + +This repo is designed for AI agent contributions. The full contributor guide is in [AGENTS.md](AGENTS.md), but the key idea is: + +1. **Pick a gap** — find a utility task that researchers need but no TOPP tool covers +2. **Follow the structure** — every tool lives in its own directory with a standard layout (see below) +3. **Validate in isolation** — each tool must pass `ruff check` and `pytest` in a fresh venv with only `pyopenms` installed +4. **Do not duplicate TOPP tools** — if `FileConverter`, `PeakPickerHiRes`, `FalseDiscoveryRate`, or any other TOPP command already does it, don't rebuild it here + +Two Claude Code skills are available for contributors: +- **`contribute-script`** — guided workflow for adding a new tool +- **`validate-script`** — validate any tool in an isolated venv (ruff + pytest) + +## Tool Structure + +Every tool follows the same directory layout: + +``` +scripts//// +├── .py # The tool (importable functions + argparse CLI) +├── requirements.txt # pyopenms + tool-specific deps (no version pins) +├── README.md # Brief description + CLI usage examples +└── tests/ + ├── conftest.py # requires_pyopenms marker + sys.path setup + └── test_.py +``` + +**Every `.py` file contains:** + +1. A module docstring describing the tool, its features, and usage +2. A pyopenms import guard with a user-friendly error message +3. Importable functions with type hints and numpy-style docstrings — so the tool works both as a library and as a CLI +4. A `main()` function wiring up `argparse` for command-line usage +5. An `if __name__ == "__main__": main()` guard + +**Domains:** `proteomics/`, `metabolomics/` + +**Proteomics topics:** `spectrum_analysis/`, `peptide_analysis/`, `protein_analysis/`, `fasta_utils/`, `file_conversion/`, `quality_control/`, `targeted_proteomics/`, `identification/`, `ptm_analysis/`, `structural_proteomics/`, `specialized/`, `rna/` + +**Metabolomics topics:** `formula_tools/`, `feature_processing/`, `spectral_analysis/`, `compound_annotation/`, `drug_metabolism/`, `isotope_labeling/`, `lipidomics/`, `export/` ## Requirements @@ -10,28 +64,273 @@ All code in this repo is written by AI agents. See [AGENTS.md](AGENTS.md) for th pip install pyopenms ``` -## Scripts +Some tools require additional dependencies (`numpy`, `scipy`). Check each tool's `requirements.txt`. -### Proteomics +## Running a Tool -| Script | Description | -|--------|-------------| -| [`peptide_mass_calculator`](scripts/proteomics/peptide_mass_calculator/) | Monoisotopic/average masses and b/y fragment ions for peptide sequences | -| [`protein_digest`](scripts/proteomics/protein_digest/) | In-silico enzymatic protein digestion | -| [`spectrum_file_info`](scripts/proteomics/spectrum_file_info/) | Summary statistics for mzML files | -| [`feature_detection_proteomics`](scripts/proteomics/feature_detection_proteomics/) | Peptide feature detection from LC-MS/MS data | +```bash +# Install dependencies +pip install -r scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt -### Metabolomics +# Run via CLI +python scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py --help -| Script | Description | -|--------|-------------| -| [`mass_accuracy_calculator`](scripts/metabolomics/mass_accuracy_calculator/) | m/z mass accuracy (ppm error) for sequences or formulas | -| [`isotope_pattern_matcher`](scripts/metabolomics/isotope_pattern_matcher/) | Theoretical isotope distributions and cosine similarity scoring | -| [`metabolite_feature_detection`](scripts/metabolomics/metabolite_feature_detection/) | Metabolite feature detection from LC-MS data | +# Run tests +PYTHONPATH=scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator \ + python -m pytest scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/ -v +``` ## Validation -Each script is validated in an isolated venv. See [AGENTS.md](AGENTS.md) for validation commands. +Each tool is validated in an isolated venv: + +```bash +TOOL_DIR=scripts/// +VENV_DIR=$(mktemp -d) +python -m venv "$VENV_DIR" +"$VENV_DIR/bin/python" -m pip install -r "$TOOL_DIR/requirements.txt" +"$VENV_DIR/bin/python" -m pip install pytest ruff +"$VENV_DIR/bin/python" -m ruff check "$TOOL_DIR/" +PYTHONPATH="$TOOL_DIR" "$VENV_DIR/bin/python" -m pytest "$TOOL_DIR/tests/" -v +rm -rf "$VENV_DIR" +``` + +Both `ruff` and `pytest` must pass with zero errors. + +--- + +## Tool Catalog + +### Proteomics (89 tools) + +#### Spectrum Analysis (7 tools) + +| Tool | Description | +|------|-------------| +| [`theoretical_spectrum_generator`](scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/) | Generate theoretical b/y/a/c/x/z fragment ion spectra for peptide sequences | +| [`spectrum_similarity_scorer`](scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/) | Compute cosine similarity between MS2 spectra from MGF files | +| [`spectrum_annotator`](scripts/proteomics/spectrum_analysis/spectrum_annotator/) | Annotate observed MS2 peaks with theoretical fragment ion matches | +| [`spectrum_scoring_hyperscore`](scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/) | Score experimental spectra against theoretical using HyperScore | +| [`spectrum_entropy_calculator`](scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/) | Calculate normalized Shannon entropy for MS2 spectra | +| [`spectral_library_builder`](scripts/proteomics/spectrum_analysis/spectral_library_builder/) | Build consensus spectral libraries from mzML + peptide identifications | +| [`spectral_library_format_converter`](scripts/proteomics/spectrum_analysis/spectral_library_format_converter/) | Convert between spectral library formats (MSP, TraML) | + +#### Peptide Analysis (12 tools) + +| Tool | Description | +|------|-------------| +| [`peptide_property_calculator`](scripts/proteomics/peptide_analysis/peptide_property_calculator/) | Calculate pI, hydrophobicity, charge at pH, amino acid composition | +| [`peptide_mass_calculator`](scripts/proteomics/peptide_analysis/peptide_mass_calculator/) | Monoisotopic/average masses and b/y fragment ions | +| [`peptide_uniqueness_checker`](scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/) | Check if peptides are proteotypic within a FASTA database | +| [`modification_mass_calculator`](scripts/proteomics/peptide_analysis/modification_mass_calculator/) | Query Unimod by name or mass shift, compute modified peptide masses | +| [`modified_peptide_generator`](scripts/proteomics/peptide_analysis/modified_peptide_generator/) | Enumerate all modified peptide variants for given variable/fixed mods | +| [`peptide_modification_analyzer`](scripts/proteomics/peptide_analysis/peptide_modification_analyzer/) | Residue-by-residue mass breakdown of modified peptides | +| [`peptide_detectability_predictor`](scripts/proteomics/peptide_analysis/peptide_detectability_predictor/) | Predict peptide detectability from physicochemical heuristics | +| [`isoelectric_point_calculator`](scripts/proteomics/peptide_analysis/isoelectric_point_calculator/) | Calculate pI using Henderson-Hasselbalch with configurable pK sets | +| [`charge_state_predictor`](scripts/proteomics/peptide_analysis/charge_state_predictor/) | Predict charge state distribution based on basic residues | +| [`amino_acid_composition_analyzer`](scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/) | Amino acid frequency and composition statistics | +| [`rt_prediction_additive`](scripts/proteomics/peptide_analysis/rt_prediction_additive/) | Predict peptide RT using additive hydrophobicity models | +| [`peptide_mass_fingerprint`](scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/) | Generate/match peptide mass fingerprints for MALDI-TOF identification | + +#### Protein Analysis (5 tools) + +| Tool | Description | +|------|-------------| +| [`protein_digest`](scripts/proteomics/protein_analysis/protein_digest/) | In-silico enzymatic protein digestion | +| [`protein_coverage_calculator`](scripts/proteomics/protein_analysis/protein_coverage_calculator/) | Map peptides to proteins and calculate sequence coverage | +| [`protein_group_reporter`](scripts/proteomics/protein_analysis/protein_group_reporter/) | Generate clean protein-level reports with group membership | +| [`spectral_counting_quantifier`](scripts/proteomics/protein_analysis/spectral_counting_quantifier/) | Calculate protein abundances using emPAI or NSAF methods | +| [`peptide_to_protein_mapper`](scripts/proteomics/protein_analysis/peptide_to_protein_mapper/) | Map peptide sequences to parent proteins in a FASTA database | + +#### FASTA Utilities (8 tools) + +| Tool | Description | +|------|-------------| +| [`fasta_subset_extractor`](scripts/proteomics/fasta_utils/fasta_subset_extractor/) | Extract proteins by accession list, keyword, or length range | +| [`fasta_statistics_reporter`](scripts/proteomics/fasta_utils/fasta_statistics_reporter/) | Report protein count, lengths, amino acid frequency, tryptic peptide counts | +| [`contaminant_database_merger`](scripts/proteomics/fasta_utils/contaminant_database_merger/) | Append cRAP contaminant sequences with configurable prefix | +| [`fasta_cleaner`](scripts/proteomics/fasta_utils/fasta_cleaner/) | Remove duplicates, fix headers, filter by length | +| [`fasta_merger`](scripts/proteomics/fasta_utils/fasta_merger/) | Merge multiple FASTA files with duplicate removal | +| [`fasta_decoy_validator`](scripts/proteomics/fasta_utils/fasta_decoy_validator/) | Check if a FASTA already contains decoys, validate prefix consistency | +| [`fasta_in_silico_digest_stats`](scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/) | Digest a FASTA and report peptide-level statistics | +| [`fasta_taxonomy_splitter`](scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/) | Split multi-organism FASTA by taxonomy from headers | + +#### File Conversion (8 tools) + +| Tool | Description | +|------|-------------| +| [`mzml_to_mgf_converter`](scripts/proteomics/file_conversion/mzml_to_mgf_converter/) | Convert MS2 spectra from mzML to MGF format | +| [`mgf_to_mzml_converter`](scripts/proteomics/file_conversion/mgf_to_mzml_converter/) | Convert MGF files to mzML format | +| [`consensus_map_to_matrix`](scripts/proteomics/file_conversion/consensus_map_to_matrix/) | Convert consensusXML to flat quantification matrix | +| [`idxml_to_tsv_exporter`](scripts/proteomics/file_conversion/idxml_to_tsv_exporter/) | Export idXML identification results to flat TSV | +| [`ms_data_to_csv_exporter`](scripts/proteomics/file_conversion/ms_data_to_csv_exporter/) | Export mzML/featureXML data to CSV with column selection | +| [`mztab_summarizer`](scripts/proteomics/file_conversion/mztab_summarizer/) | Parse mzTab files and extract summary statistics | +| [`featurexml_merger`](scripts/proteomics/file_conversion/featurexml_merger/) | Merge multiple featureXML files | +| [`ms_data_ml_exporter`](scripts/proteomics/file_conversion/ms_data_ml_exporter/) | Export MS features as ML-ready matrices | + +#### Quality Control (15 tools) + +| Tool | Description | +|------|-------------| +| [`lc_ms_qc_reporter`](scripts/proteomics/quality_control/lc_ms_qc_reporter/) | Comprehensive QC report from mzML (TIC, MS1/MS2 counts, charge distribution) | +| [`mzqc_generator`](scripts/proteomics/quality_control/mzqc_generator/) | Generate mzQC-format (HUPO-PSI standard) quality control files | +| [`identification_qc_reporter`](scripts/proteomics/quality_control/identification_qc_reporter/) | Report identification-level QC metrics from search results | +| [`run_comparison_reporter`](scripts/proteomics/quality_control/run_comparison_reporter/) | Compare mzML files side-by-side (TIC correlation, shared precursors) | +| [`mass_error_distribution_analyzer`](scripts/proteomics/quality_control/mass_error_distribution_analyzer/) | Compute precursor and fragment mass error distributions | +| [`acquisition_rate_analyzer`](scripts/proteomics/quality_control/acquisition_rate_analyzer/) | Analyze MS1/MS2 acquisition rates, cycle time, duty cycle | +| [`precursor_isolation_purity`](scripts/proteomics/quality_control/precursor_isolation_purity/) | Estimate precursor isolation purity and co-isolation interference | +| [`injection_time_analyzer`](scripts/proteomics/quality_control/injection_time_analyzer/) | Extract and analyze injection time values from mzML | +| [`collision_energy_analyzer`](scripts/proteomics/quality_control/collision_energy_analyzer/) | Extract and analyze collision energy values across MS2 spectra | +| [`precursor_charge_distribution`](scripts/proteomics/quality_control/precursor_charge_distribution/) | Analyze charge state distribution across MS2 spectra | +| [`precursor_recurrence_analyzer`](scripts/proteomics/quality_control/precursor_recurrence_analyzer/) | Analyze precursor resampling frequency in DDA runs | +| [`missed_cleavage_analyzer`](scripts/proteomics/quality_control/missed_cleavage_analyzer/) | Analyze missed cleavage distribution as a digestion QC metric | +| [`sample_complexity_estimator`](scripts/proteomics/quality_control/sample_complexity_estimator/) | Estimate sample complexity from MS1 peak density | +| [`spectrum_file_info`](scripts/proteomics/quality_control/spectrum_file_info/) | Summary statistics for mzML files | +| [`ms1_feature_intensity_tracker`](scripts/proteomics/quality_control/ms1_feature_intensity_tracker/) | Track feature intensities across a batch of mzML runs | + +#### Targeted Proteomics (7 tools) + +| Tool | Description | +|------|-------------| +| [`xic_extractor`](scripts/proteomics/targeted_proteomics/xic_extractor/) | Extract ion chromatograms for target m/z values from mzML | +| [`tic_bpc_calculator`](scripts/proteomics/targeted_proteomics/tic_bpc_calculator/) | Compute TIC and base peak chromatograms from mzML | +| [`transition_list_generator`](scripts/proteomics/targeted_proteomics/transition_list_generator/) | Generate SRM/MRM/PRM transition lists from peptide sequences | +| [`irt_calculator`](scripts/proteomics/targeted_proteomics/irt_calculator/) | Convert observed RT to indexed retention time (iRT) values | +| [`inclusion_list_generator`](scripts/proteomics/targeted_proteomics/inclusion_list_generator/) | Generate instrument inclusion lists from identification results | +| [`dia_window_analyzer`](scripts/proteomics/targeted_proteomics/dia_window_analyzer/) | Report DIA isolation window scheme from mzML metadata | +| [`library_coverage_estimator`](scripts/proteomics/targeted_proteomics/library_coverage_estimator/) | Estimate proteome coverage of a spectral library | + +#### Identification (7 tools) + +| Tool | Description | +|------|-------------| +| [`feature_detection_proteomics`](scripts/proteomics/identification/feature_detection_proteomics/) | Peptide feature detection from LC-MS/MS data | +| [`psm_feature_extractor`](scripts/proteomics/identification/psm_feature_extractor/) | Extract rescoring features from PSMs (mass error, coverage, intensity) | +| [`peptide_spectral_match_validator`](scripts/proteomics/identification/peptide_spectral_match_validator/) | Validate individual PSMs by recomputing fragment ion coverage | +| [`semi_tryptic_peptide_finder`](scripts/proteomics/identification/semi_tryptic_peptide_finder/) | Classify peptides as fully/semi/non-tryptic | +| [`sequence_tag_generator`](scripts/proteomics/identification/sequence_tag_generator/) | Generate de novo sequence tags from MS2 fragment ion ladders | +| [`mzml_spectrum_subsetter`](scripts/proteomics/identification/mzml_spectrum_subsetter/) | Extract specific spectra from mzML by scan number list | +| [`mzml_metadata_extractor`](scripts/proteomics/identification/mzml_metadata_extractor/) | Extract instrument metadata from mzML files | + +#### PTM Analysis (5 tools) + +| Tool | Description | +|------|-------------| +| [`ptm_site_localization_scorer`](scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/) | Score PTM site localization confidence using fragment ion coverage | +| [`phosphosite_class_filter`](scripts/proteomics/ptm_analysis/phosphosite_class_filter/) | Classify phosphosites into Class I/II/III by localization probability | +| [`phospho_motif_analyzer`](scripts/proteomics/ptm_analysis/phospho_motif_analyzer/) | Extract sequence windows around phosphosites and analyze kinase motifs | +| [`phospho_enrichment_qc`](scripts/proteomics/ptm_analysis/phospho_enrichment_qc/) | Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios | +| [`glycopeptide_mass_calculator`](scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/) | Calculate glycopeptide masses with glycan compositions | + +#### Structural Proteomics (5 tools) + +| Tool | Description | +|------|-------------| +| [`hdx_deuterium_uptake`](scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/) | Calculate deuterium uptake from HDX-MS time course data | +| [`hdx_back_exchange_estimator`](scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/) | Estimate per-peptide back-exchange rates from fully deuterated controls | +| [`crosslink_mass_calculator`](scripts/proteomics/structural_proteomics/crosslink_mass_calculator/) | Calculate masses for crosslinked peptide pairs (DSS, BS3, DSSO) | +| [`xl_distance_validator`](scripts/proteomics/structural_proteomics/xl_distance_validator/) | Validate crosslink distances against PDB structures | +| [`xl_link_classifier`](scripts/proteomics/structural_proteomics/xl_link_classifier/) | Classify crosslinks as intra-protein, inter-protein, or monolink | + +#### Specialized (7 tools) + +| Tool | Description | +|------|-------------| +| [`immunopeptide_filter`](scripts/proteomics/specialized/immunopeptide_filter/) | Filter peptides for MHC-I/II by length range and motif | +| [`immunopeptidome_qc`](scripts/proteomics/specialized/immunopeptidome_qc/) | QC for immunopeptidomics (length distribution, anchor residues) | +| [`metapeptide_lca_assigner`](scripts/proteomics/specialized/metapeptide_lca_assigner/) | Assign lowest common ancestor taxonomy from peptide-protein mappings | +| [`cleavage_site_profiler`](scripts/proteomics/specialized/cleavage_site_profiler/) | Profile protease cleavage site specificity from N-terminomics data | +| [`nterm_modification_annotator`](scripts/proteomics/specialized/nterm_modification_annotator/) | Classify N-terminal peptides (protein N-term, signal peptide, neo-N-term) | +| [`proteoform_delta_annotator`](scripts/proteomics/specialized/proteoform_delta_annotator/) | Annotate mass differences between proteoforms with known PTMs | +| [`topdown_coverage_calculator`](scripts/proteomics/specialized/topdown_coverage_calculator/) | Compute per-residue bond cleavage coverage for intact proteins | + +#### RNA (3 tools) + +| Tool | Description | +|------|-------------| +| [`rna_mass_calculator`](scripts/proteomics/rna/rna_mass_calculator/) | Calculate mass, formula, and isotopes for RNA/oligonucleotide sequences | +| [`rna_digest`](scripts/proteomics/rna/rna_digest/) | In silico RNA digestion with RNases (T1, U2, etc.) | +| [`rna_fragment_spectrum_generator`](scripts/proteomics/rna/rna_fragment_spectrum_generator/) | Generate theoretical RNA fragment spectra (c/y/w/a-B ions) | + +--- + +### Metabolomics (34 tools) + +#### Formula Tools (8 tools) + +| Tool | Description | +|------|-------------| +| [`adduct_calculator`](scripts/metabolomics/formula_tools/adduct_calculator/) | Compute m/z for all common ESI adducts given a formula or mass | +| [`molecular_formula_finder`](scripts/metabolomics/formula_tools/molecular_formula_finder/) | Enumerate valid molecular formulas for an accurate mass with element constraints | +| [`mass_decomposition_tool`](scripts/metabolomics/formula_tools/mass_decomposition_tool/) | Find molecular formula compositions for a given mass within tolerance | +| [`formula_mass_calculator`](scripts/metabolomics/formula_tools/formula_mass_calculator/) | Calculate exact masses for molecular formulas with adduct support | +| [`formula_validator_golden_rules`](scripts/metabolomics/formula_tools/formula_validator_golden_rules/) | Apply Kind & Fiehn's Seven Golden Rules to filter formula candidates | +| [`rdbe_calculator`](scripts/metabolomics/formula_tools/rdbe_calculator/) | Calculate Ring/Double Bond Equivalence for molecular formulas | +| [`metabolite_formula_annotator`](scripts/metabolomics/formula_tools/metabolite_formula_annotator/) | Annotate features with candidate formulas using mass + isotope fit | +| [`mass_accuracy_calculator`](scripts/metabolomics/formula_tools/mass_accuracy_calculator/) | Compute m/z mass accuracy (ppm error) for sequences or formulas | + +#### Feature Processing (7 tools) + +| Tool | Description | +|------|-------------| +| [`blank_subtraction_tool`](scripts/metabolomics/feature_processing/blank_subtraction_tool/) | Subtract blank/control features from sample features by m/z + RT matching | +| [`duplicate_feature_detector`](scripts/metabolomics/feature_processing/duplicate_feature_detector/) | Detect and flag duplicate features by m/z and RT proximity | +| [`adduct_group_analyzer`](scripts/metabolomics/feature_processing/adduct_group_analyzer/) | Group features by adduct relationships into ion identity groups | +| [`isf_detector`](scripts/metabolomics/feature_processing/isf_detector/) | Detect in-source fragmentation artifacts by coelution and neutral loss | +| [`targeted_feature_extractor`](scripts/metabolomics/feature_processing/targeted_feature_extractor/) | Extract features for known compounds from MS1 data | +| [`mass_defect_filter`](scripts/metabolomics/feature_processing/mass_defect_filter/) | Filter features by mass defect and Kendrick mass defect | +| [`metabolite_feature_detection`](scripts/metabolomics/feature_processing/metabolite_feature_detection/) | Metabolite feature detection from LC-MS data | + +#### Spectral Analysis (6 tools) + +| Tool | Description | +|------|-------------| +| [`spectral_entropy_scorer`](scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/) | Compute spectral entropy similarity (Li & Fiehn 2021) | +| [`neutral_loss_scanner`](scripts/metabolomics/spectral_analysis/neutral_loss_scanner/) | Scan MS2 spectra for characteristic neutral losses | +| [`isotope_pattern_scorer`](scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/) | Score observed vs. theoretical isotope patterns | +| [`isotope_pattern_matcher`](scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/) | Generate theoretical isotope distributions and cosine similarity scoring | +| [`isotope_pattern_fit_scorer`](scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/) | Score isotope pattern fit, detect Cl/Br from M+2 enhancement | +| [`massql_query_tool`](scripts/metabolomics/spectral_analysis/massql_query_tool/) | Query mzML data using MassQL-like syntax | + +#### Compound Annotation (4 tools) + +| Tool | Description | +|------|-------------| +| [`van_krevelen_data_generator`](scripts/metabolomics/compound_annotation/van_krevelen_data_generator/) | Compute H:C and O:C ratios, classify into biochemical compound classes | +| [`kendrick_mass_defect_analyzer`](scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/) | Compute Kendrick mass defect for homologous series detection (CH2, CF2, etc.) | +| [`suspect_screener`](scripts/metabolomics/compound_annotation/suspect_screener/) | Match detected masses against suspect screening lists (CompTox, NORMAN) | +| [`metabolite_class_predictor`](scripts/metabolomics/compound_annotation/metabolite_class_predictor/) | Predict compound class from mass defect, element ratios, and RDBE | + +#### Drug Metabolism (2 tools) + +| Tool | Description | +|------|-------------| +| [`drug_metabolite_screener`](scripts/metabolomics/drug_metabolism/drug_metabolite_screener/) | Predict Phase I/II drug metabolites and screen mzML for matches | +| [`mass_difference_network_builder`](scripts/metabolomics/drug_metabolism/mass_difference_network_builder/) | Connect features by known biotransformation mass differences | + +#### Isotope Labeling (2 tools) + +| Tool | Description | +|------|-------------| +| [`isotope_label_detector`](scripts/metabolomics/isotope_labeling/isotope_label_detector/) | Detect 13C/15N-labeled metabolites by paired feature analysis | +| [`mid_natural_abundance_corrector`](scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/) | Correct mass isotopomer distributions for natural 13C abundance | + +#### Lipidomics (2 tools) + +| Tool | Description | +|------|-------------| +| [`lipid_species_resolver`](scripts/metabolomics/lipidomics/lipid_species_resolver/) | Enumerate acyl chain combinations from sum-composition lipid annotations | +| [`lipid_ecn_rt_predictor`](scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/) | Predict lipid RT from Equivalent Carbon Number | + +#### Export (3 tools) + +| Tool | Description | +|------|-------------| +| [`gnps_fbmn_exporter`](scripts/metabolomics/export/gnps_fbmn_exporter/) | Export MS2 + quantification in GNPS Feature-Based Molecular Networking format | +| [`sirius_exporter`](scripts/metabolomics/export/sirius_exporter/) | Export features + MS2 data to SIRIUS .ms format | +| [`kovats_ri_calculator`](scripts/metabolomics/export/kovats_ri_calculator/) | Calculate Kovats Retention Index from alkane standards for GC-MS | + +--- ## License From 1e0d27a0c5fa0b3a3b126f6cd507e5d999d38dc4 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 09:17:48 +0100 Subject: [PATCH 05/15] Rename scripts/ to tools/ and update all references Rename the top-level scripts/ directory to tools/ to better reflect that these are standalone CLI tools, not scripts. Update all references in CLAUDE.md, AGENTS.md, README.md, CI workflow, Claude Code skills, and design docs. Co-Authored-By: Claude Opus 4.6 (1M context) --- .claude/skills/contribute-script.md | 12 +- .claude/skills/validate-script.md | 2 +- .github/workflows/validate.yml | 42 +-- AGENTS.md | 8 +- CLAUDE.md | 22 +- README.md | 264 +++++++++--------- .../plans/2026-03-24-ai-contributor-skills.md | 194 ++++++------- ...2026-03-24-ai-contributor-skills-design.md | 14 +- ...-25-rename-tools-click-migration-design.md | 81 ++++++ .../kendrick_mass_defect_analyzer/README.md | 0 .../kendrick_mass_defect_analyzer.py | 0 .../requirements.txt | 0 .../tests/conftest.py | 0 .../test_kendrick_mass_defect_analyzer.py | 0 .../metabolite_class_predictor/README.md | 0 .../metabolite_class_predictor.py | 0 .../requirements.txt | 0 .../tests/conftest.py | 0 .../tests/test_metabolite_class_predictor.py | 0 .../suspect_screener/README.md | 0 .../suspect_screener/requirements.txt | 0 .../suspect_screener/suspect_screener.py | 0 .../suspect_screener/tests/conftest.py | 0 .../tests/test_suspect_screener.py | 0 .../van_krevelen_data_generator/README.md | 0 .../requirements.txt | 0 .../tests/conftest.py | 0 .../tests/test_van_krevelen_data_generator.py | 0 .../van_krevelen_data_generator.py | 0 .../drug_metabolite_screener/README.md | 0 .../drug_metabolite_screener.py | 0 .../drug_metabolite_screener/requirements.txt | 0 .../tests/conftest.py | 0 .../tests/test_drug_metabolite_screener.py | 0 .../mass_difference_network_builder.py | 0 .../requirements.txt | 0 .../tests/conftest.py | 0 .../test_mass_difference_network_builder.py | 0 .../export/gnps_fbmn_exporter/README.md | 0 .../gnps_fbmn_exporter/gnps_fbmn_exporter.py | 0 .../gnps_fbmn_exporter/requirements.txt | 0 .../gnps_fbmn_exporter/tests/conftest.py | 0 .../tests/test_gnps_fbmn_exporter.py | 0 .../export/kovats_ri_calculator/README.md | 0 .../kovats_ri_calculator.py | 0 .../kovats_ri_calculator/requirements.txt | 0 .../kovats_ri_calculator/tests/conftest.py | 0 .../tests/test_kovats_ri_calculator.py | 0 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tools}/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py (100%) rename {scripts => tools}/proteomics/targeted_proteomics/xic_extractor/README.md (100%) rename {scripts => tools}/proteomics/targeted_proteomics/xic_extractor/requirements.txt (100%) rename {scripts => tools}/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py (100%) rename {scripts => tools}/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py (100%) rename {scripts => tools}/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py (100%) diff --git a/.claude/skills/contribute-script.md b/.claude/skills/contribute-script.md index f43f7c4..1a69ce3 100644 --- a/.claude/skills/contribute-script.md +++ b/.claude/skills/contribute-script.md @@ -36,7 +36,7 @@ git checkout -b add/ ### 5. Scaffold the directory ```bash -mkdir -p scripts///tests +mkdir -p tools///tests ``` Create these files: @@ -68,7 +68,7 @@ requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not in ### 6. Write the script -Create `scripts///.py` following these patterns: +Create `tools///.py` following these patterns: - Module-level docstring with description, supported features, and CLI usage examples - pyopenms import guard: @@ -80,12 +80,12 @@ Create `scripts///.py` following these patterns: ``` - `PROTON = 1.007276` constant where mass-to-charge calculations are needed - Importable functions as the primary interface (with type hints and numpy-style docstrings) -- `main()` function with argparse CLI +- `main()` function with click CLI - `if __name__ == "__main__": main()` guard ### 7. Write tests -Create `scripts///tests/test_.py`: +Create `tools///tests/test_.py`: - Import `requires_pyopenms` from conftest - Decorate test classes with `@requires_pyopenms` @@ -95,7 +95,7 @@ Create `scripts///tests/test_.py`: ### 8. Write README -Create `scripts///README.md` with a brief description and CLI usage examples. +Create `tools///README.md` with a brief description and CLI usage examples. ### 9. Validate @@ -104,6 +104,6 @@ Invoke the `validate-script` skill on the new script directory. Both ruff and py ### 10. Commit ```bash -git add scripts/// +git add tools/// git commit -m "Add : " ``` diff --git a/.claude/skills/validate-script.md b/.claude/skills/validate-script.md index 5d21baa..ba4a92d 100644 --- a/.claude/skills/validate-script.md +++ b/.claude/skills/validate-script.md @@ -9,7 +9,7 @@ Validate any script in the agentomics repo by running ruff and pytest in a fresh ## Steps (follow exactly — rigid skill) -1. **Identify the script directory.** If the user provided a path, use it. Otherwise, ask which script to validate. The path should be `scripts///`. +1. **Identify the script directory.** If the user provided a path, use it. Otherwise, ask which script to validate. The path should be `tools///`. 2. **Verify the directory structure.** Confirm it contains: - `.py` diff --git a/.github/workflows/validate.yml b/.github/workflows/validate.yml index d545c8b..b587532 100644 --- a/.github/workflows/validate.yml +++ b/.github/workflows/validate.yml @@ -1,9 +1,9 @@ -name: Validate Scripts +name: Validate Tools on: pull_request: paths: - - 'scripts/**' + - 'tools/**' jobs: detect-changes: @@ -17,12 +17,12 @@ jobs: fetch-depth: 0 - id: detect - name: Detect changed script directories + name: Detect changed tool directories run: | # Note: github.base_ref is only available on pull_request events - # Find all script directories that changed in this PR - CHANGED=$(git diff --name-only origin/${{ github.base_ref }}...HEAD -- 'scripts/' \ - | grep -oP 'scripts/[^/]+/[^/]+/' \ + # Find all tool directories that changed in this PR + CHANGED=$(git diff --name-only origin/${{ github.base_ref }}...HEAD -- 'tools/' \ + | grep -oP 'tools/[^/]+/[^/]+/[^/]+/' \ | sort -u \ | jq -R -s -c 'split("\n") | map(select(length > 0))') @@ -38,11 +38,6 @@ jobs: needs: detect-changes if: needs.detect-changes.outputs.has_changes == 'true' runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - script_dir: ${{ fromJson(needs.detect-changes.outputs.matrix) }} - name: Validate ${{ matrix.script_dir }} steps: - uses: actions/checkout@v4 @@ -50,16 +45,27 @@ jobs: with: python-version: '3.11' - - name: Create venv and install dependencies + - name: Create shared venv and install dependencies run: | python -m venv /tmp/validate_venv - /tmp/validate_venv/bin/python -m pip install -r ${{ matrix.script_dir }}requirements.txt - /tmp/validate_venv/bin/python -m pip install pytest ruff + /tmp/validate_venv/bin/python -m pip install pyopenms numpy scipy click pytest ruff - - name: Lint with ruff + - name: Lint changed tools run: | - /tmp/validate_venv/bin/python -m ruff check ${{ matrix.script_dir }} + DIRS='${{ needs.detect-changes.outputs.matrix }}' + echo "$DIRS" | jq -r '.[]' | while read -r dir; do + echo "::group::ruff $dir" + /tmp/validate_venv/bin/python -m ruff check "$dir" + echo "::endgroup::" + done - - name: Run tests + - name: Test changed tools run: | - PYTHONPATH=${{ matrix.script_dir }} /tmp/validate_venv/bin/python -m pytest ${{ matrix.script_dir }}tests/ -v + DIRS='${{ needs.detect-changes.outputs.matrix }}' + echo "$DIRS" | jq -r '.[]' | while read -r dir; do + if [ -d "${dir}tests/" ]; then + echo "::group::pytest $dir" + PYTHONPATH="$dir" /tmp/validate_venv/bin/python -m pytest "${dir}tests/" -v + echo "::endgroup::" + fi + done diff --git a/AGENTS.md b/AGENTS.md index 1ca3f5b..6795643 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -8,10 +8,10 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ## Contribution Requirements -Every tool must be a **self-contained directory** under `scripts////`: +Every tool must be a **self-contained directory** under `tools////`: ``` -scripts//// +tools//// ├── .py # The tool itself ├── requirements.txt # pyopenms + any tool-specific deps (no version pins) ├── README.md # Brief description + CLI usage examples @@ -51,7 +51,7 @@ Every tool must have: sys.exit("pyopenms is required. Install it with: pip install pyopenms") ``` 3. **Importable functions** as the primary interface (with type hints and numpy-style docstrings) -4. **`main()` function** with argparse CLI +4. **`main()` function** with click CLI 5. **`if __name__ == "__main__": main()`** guard 6. **`PROTON = 1.007276`** constant where mass-to-charge calculations are needed @@ -86,7 +86,7 @@ Test files: Every tool must pass validation in an **isolated venv** before it can be merged. Run these commands from the repo root: ```bash -TOOL_DIR=scripts/// +TOOL_DIR=tools/// VENV_DIR=$(mktemp -d) python -m venv "$VENV_DIR" "$VENV_DIR/bin/python" -m pip install -r "$TOOL_DIR/requirements.txt" diff --git a/CLAUDE.md b/CLAUDE.md index caffe05..eacc6f0 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -10,34 +10,34 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ```bash # Install dependencies for a specific tool -pip install -r scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt +pip install -r tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt # Lint a specific tool -ruff check scripts/proteomics/peptide_analysis/peptide_mass_calculator/ +ruff check tools/proteomics/peptide_analysis/peptide_mass_calculator/ # Run tests for a specific tool -PYTHONPATH=scripts/proteomics/peptide_analysis/peptide_mass_calculator python -m pytest scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/ -v +PYTHONPATH=tools/proteomics/peptide_analysis/peptide_mass_calculator python -m pytest tools/proteomics/peptide_analysis/peptide_mass_calculator/tests/ -v # Lint all tools -ruff check scripts/ +ruff check tools/ # Run all tests across all tools -for d in scripts/*/*/*/; do PYTHONPATH="$d" python -m pytest "$d/tests/" -v; done +for d in tools/*/*/*/; do PYTHONPATH="$d" python -m pytest "$d/tests/" -v; done # Run a script directly -python scripts/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py --sequence PEPTIDEK --charge 2 -python scripts/metabolomics/formula_tools/isotope_pattern_matcher/isotope_pattern_matcher.py --formula C6H12O6 +python tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py --sequence PEPTIDEK --charge 2 +python tools/metabolomics/formula_tools/isotope_pattern_matcher/isotope_pattern_matcher.py --formula C6H12O6 ``` ## Architecture ### Per-Tool Directory Structure -Each tool is a self-contained directory under `scripts////`: +Each tool is a self-contained directory under `tools////`: ``` -scripts//// -├── .py # The tool (importable functions + argparse CLI) +tools//// +├── .py # The tool (importable functions + click CLI) ├── requirements.txt # pyopenms + script-specific deps ├── README.md # Usage examples └── tests/ @@ -55,7 +55,7 @@ Metabolomics topics: `formula_tools/`, `feature_processing/`, `spectral_analysis - pyopenms import wrapped in try/except with user-friendly error message - Mass-to-charge: `(mass + charge * PROTON) / charge` with `PROTON = 1.007276` -- Every script has dual interface: importable functions + argparse CLI + `__main__` guard +- Every script has dual interface: importable functions + click CLI + `__main__` guard - Tests use `@requires_pyopenms` skip marker from conftest.py - File-I/O scripts use synthetic test data generated with pyopenms objects diff --git a/README.md b/README.md index 5c5046d..0d0b861 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ Mass spectrometry researchers constantly need small utilities: extract an XIC fr Agentomics collects these utilities into a single, organized repository where each tool is: - **Self-contained** — no cross-tool dependencies, install and run independently -- **CLI-first** — every tool has an `argparse` interface, usable from the command line or imported as a Python library +- **CLI-first** — every tool has an `click` interface, usable from the command line or imported as a Python library - **Tested** — every tool ships with unit tests using synthetic pyopenms data - **pyopenms-native** — built on the official Python bindings for OpenMS, not reimplementing what already exists @@ -35,8 +35,8 @@ Two Claude Code skills are available for contributors: Every tool follows the same directory layout: ``` -scripts//// -├── .py # The tool (importable functions + argparse CLI) +tools//// +├── .py # The tool (importable functions + click CLI) ├── requirements.txt # pyopenms + tool-specific deps (no version pins) ├── README.md # Brief description + CLI usage examples └── tests/ @@ -49,7 +49,7 @@ scripts//// 1. A module docstring describing the tool, its features, and usage 2. A pyopenms import guard with a user-friendly error message 3. Importable functions with type hints and numpy-style docstrings — so the tool works both as a library and as a CLI -4. A `main()` function wiring up `argparse` for command-line usage +4. A `main()` function wiring up `click` for command-line usage 5. An `if __name__ == "__main__": main()` guard **Domains:** `proteomics/`, `metabolomics/` @@ -70,14 +70,14 @@ Some tools require additional dependencies (`numpy`, `scipy`). Check each tool's ```bash # Install dependencies -pip install -r scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt +pip install -r tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt # Run via CLI -python scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py --help +python tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py --help # Run tests -PYTHONPATH=scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator \ - python -m pytest scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/ -v +PYTHONPATH=tools/proteomics/spectrum_analysis/theoretical_spectrum_generator \ + python -m pytest tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/tests/ -v ``` ## Validation @@ -85,7 +85,7 @@ PYTHONPATH=scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator \ Each tool is validated in an isolated venv: ```bash -TOOL_DIR=scripts/// +TOOL_DIR=tools/// VENV_DIR=$(mktemp -d) python -m venv "$VENV_DIR" "$VENV_DIR/bin/python" -m pip install -r "$TOOL_DIR/requirements.txt" @@ -107,150 +107,150 @@ Both `ruff` and `pytest` must pass with zero errors. | Tool | Description | |------|-------------| -| [`theoretical_spectrum_generator`](scripts/proteomics/spectrum_analysis/theoretical_spectrum_generator/) | Generate theoretical b/y/a/c/x/z fragment ion spectra for peptide sequences | -| [`spectrum_similarity_scorer`](scripts/proteomics/spectrum_analysis/spectrum_similarity_scorer/) | Compute cosine similarity between MS2 spectra from MGF files | -| [`spectrum_annotator`](scripts/proteomics/spectrum_analysis/spectrum_annotator/) | Annotate observed MS2 peaks with theoretical fragment ion matches | -| [`spectrum_scoring_hyperscore`](scripts/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/) | Score experimental spectra against theoretical using HyperScore | -| [`spectrum_entropy_calculator`](scripts/proteomics/spectrum_analysis/spectrum_entropy_calculator/) | Calculate normalized Shannon entropy for MS2 spectra | -| [`spectral_library_builder`](scripts/proteomics/spectrum_analysis/spectral_library_builder/) | Build consensus spectral libraries from mzML + peptide identifications | -| [`spectral_library_format_converter`](scripts/proteomics/spectrum_analysis/spectral_library_format_converter/) | Convert between spectral library formats (MSP, TraML) | +| [`theoretical_spectrum_generator`](tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/) | Generate theoretical b/y/a/c/x/z fragment ion spectra for peptide sequences | +| [`spectrum_similarity_scorer`](tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/) | Compute cosine similarity between MS2 spectra from MGF files | +| [`spectrum_annotator`](tools/proteomics/spectrum_analysis/spectrum_annotator/) | Annotate observed MS2 peaks with theoretical fragment ion matches | +| [`spectrum_scoring_hyperscore`](tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/) | Score experimental spectra against theoretical using HyperScore | +| [`spectrum_entropy_calculator`](tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/) | Calculate normalized Shannon entropy for MS2 spectra | +| [`spectral_library_builder`](tools/proteomics/spectrum_analysis/spectral_library_builder/) | Build consensus spectral libraries from mzML + peptide identifications | +| [`spectral_library_format_converter`](tools/proteomics/spectrum_analysis/spectral_library_format_converter/) | Convert between spectral library formats (MSP, TraML) | #### Peptide Analysis (12 tools) | Tool | Description | |------|-------------| -| [`peptide_property_calculator`](scripts/proteomics/peptide_analysis/peptide_property_calculator/) | Calculate pI, hydrophobicity, charge at pH, amino acid composition | -| [`peptide_mass_calculator`](scripts/proteomics/peptide_analysis/peptide_mass_calculator/) | Monoisotopic/average masses and b/y fragment ions | -| [`peptide_uniqueness_checker`](scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/) | Check if peptides are proteotypic within a FASTA database | -| [`modification_mass_calculator`](scripts/proteomics/peptide_analysis/modification_mass_calculator/) | Query Unimod by name or mass shift, compute modified peptide masses | -| [`modified_peptide_generator`](scripts/proteomics/peptide_analysis/modified_peptide_generator/) | Enumerate all modified peptide variants for given variable/fixed mods | -| [`peptide_modification_analyzer`](scripts/proteomics/peptide_analysis/peptide_modification_analyzer/) | Residue-by-residue mass breakdown of modified peptides | -| [`peptide_detectability_predictor`](scripts/proteomics/peptide_analysis/peptide_detectability_predictor/) | Predict peptide detectability from physicochemical heuristics | -| [`isoelectric_point_calculator`](scripts/proteomics/peptide_analysis/isoelectric_point_calculator/) | Calculate pI using Henderson-Hasselbalch with configurable pK sets | -| [`charge_state_predictor`](scripts/proteomics/peptide_analysis/charge_state_predictor/) | Predict charge state distribution based on basic residues | -| [`amino_acid_composition_analyzer`](scripts/proteomics/peptide_analysis/amino_acid_composition_analyzer/) | Amino acid frequency and composition statistics | -| [`rt_prediction_additive`](scripts/proteomics/peptide_analysis/rt_prediction_additive/) | Predict peptide RT using additive hydrophobicity models | -| [`peptide_mass_fingerprint`](scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/) | Generate/match peptide mass fingerprints for MALDI-TOF identification | +| [`peptide_property_calculator`](tools/proteomics/peptide_analysis/peptide_property_calculator/) | Calculate pI, hydrophobicity, charge at pH, amino acid composition | +| [`peptide_mass_calculator`](tools/proteomics/peptide_analysis/peptide_mass_calculator/) | Monoisotopic/average masses and b/y fragment ions | +| [`peptide_uniqueness_checker`](tools/proteomics/peptide_analysis/peptide_uniqueness_checker/) | Check if peptides are proteotypic within a FASTA database | +| [`modification_mass_calculator`](tools/proteomics/peptide_analysis/modification_mass_calculator/) | Query Unimod by name or mass shift, compute modified peptide masses | +| [`modified_peptide_generator`](tools/proteomics/peptide_analysis/modified_peptide_generator/) | Enumerate all modified peptide variants for given variable/fixed mods | +| [`peptide_modification_analyzer`](tools/proteomics/peptide_analysis/peptide_modification_analyzer/) | Residue-by-residue mass breakdown of modified peptides | +| [`peptide_detectability_predictor`](tools/proteomics/peptide_analysis/peptide_detectability_predictor/) | Predict peptide detectability from physicochemical heuristics | +| [`isoelectric_point_calculator`](tools/proteomics/peptide_analysis/isoelectric_point_calculator/) | Calculate pI using Henderson-Hasselbalch with configurable pK sets | +| [`charge_state_predictor`](tools/proteomics/peptide_analysis/charge_state_predictor/) | Predict charge state distribution based on basic residues | +| [`amino_acid_composition_analyzer`](tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/) | Amino acid frequency and composition statistics | +| [`rt_prediction_additive`](tools/proteomics/peptide_analysis/rt_prediction_additive/) | Predict peptide RT using additive hydrophobicity models | +| [`peptide_mass_fingerprint`](tools/proteomics/peptide_analysis/peptide_mass_fingerprint/) | Generate/match peptide mass fingerprints for MALDI-TOF identification | #### Protein Analysis (5 tools) | Tool | Description | |------|-------------| -| [`protein_digest`](scripts/proteomics/protein_analysis/protein_digest/) | In-silico enzymatic protein digestion | -| [`protein_coverage_calculator`](scripts/proteomics/protein_analysis/protein_coverage_calculator/) | Map peptides to proteins and calculate sequence coverage | -| [`protein_group_reporter`](scripts/proteomics/protein_analysis/protein_group_reporter/) | Generate clean protein-level reports with group membership | -| [`spectral_counting_quantifier`](scripts/proteomics/protein_analysis/spectral_counting_quantifier/) | Calculate protein abundances using emPAI or NSAF methods | -| [`peptide_to_protein_mapper`](scripts/proteomics/protein_analysis/peptide_to_protein_mapper/) | Map peptide sequences to parent proteins in a FASTA database | +| [`protein_digest`](tools/proteomics/protein_analysis/protein_digest/) | In-silico enzymatic protein digestion | +| [`protein_coverage_calculator`](tools/proteomics/protein_analysis/protein_coverage_calculator/) | Map peptides to proteins and calculate sequence coverage | +| [`protein_group_reporter`](tools/proteomics/protein_analysis/protein_group_reporter/) | Generate clean protein-level reports with group membership | +| [`spectral_counting_quantifier`](tools/proteomics/protein_analysis/spectral_counting_quantifier/) | Calculate protein abundances using emPAI or NSAF methods | +| [`peptide_to_protein_mapper`](tools/proteomics/protein_analysis/peptide_to_protein_mapper/) | Map peptide sequences to parent proteins in a FASTA database | #### FASTA Utilities (8 tools) | Tool | Description | |------|-------------| -| [`fasta_subset_extractor`](scripts/proteomics/fasta_utils/fasta_subset_extractor/) | Extract proteins by accession list, keyword, or length range | -| [`fasta_statistics_reporter`](scripts/proteomics/fasta_utils/fasta_statistics_reporter/) | Report protein count, lengths, amino acid frequency, tryptic peptide counts | -| [`contaminant_database_merger`](scripts/proteomics/fasta_utils/contaminant_database_merger/) | Append cRAP contaminant sequences with configurable prefix | -| [`fasta_cleaner`](scripts/proteomics/fasta_utils/fasta_cleaner/) | Remove duplicates, fix headers, filter by length | -| [`fasta_merger`](scripts/proteomics/fasta_utils/fasta_merger/) | Merge multiple FASTA files with duplicate removal | -| [`fasta_decoy_validator`](scripts/proteomics/fasta_utils/fasta_decoy_validator/) | Check if a FASTA already contains decoys, validate prefix consistency | -| [`fasta_in_silico_digest_stats`](scripts/proteomics/fasta_utils/fasta_in_silico_digest_stats/) | Digest a FASTA and report peptide-level statistics | -| [`fasta_taxonomy_splitter`](scripts/proteomics/fasta_utils/fasta_taxonomy_splitter/) | Split multi-organism FASTA by taxonomy from headers | +| [`fasta_subset_extractor`](tools/proteomics/fasta_utils/fasta_subset_extractor/) | Extract proteins by accession list, keyword, or length range | +| [`fasta_statistics_reporter`](tools/proteomics/fasta_utils/fasta_statistics_reporter/) | Report protein count, lengths, amino acid frequency, tryptic peptide counts | +| [`contaminant_database_merger`](tools/proteomics/fasta_utils/contaminant_database_merger/) | Append cRAP contaminant sequences with configurable prefix | +| [`fasta_cleaner`](tools/proteomics/fasta_utils/fasta_cleaner/) | Remove duplicates, fix headers, filter by length | +| [`fasta_merger`](tools/proteomics/fasta_utils/fasta_merger/) | Merge multiple FASTA files with duplicate removal | +| [`fasta_decoy_validator`](tools/proteomics/fasta_utils/fasta_decoy_validator/) | Check if a FASTA already contains decoys, validate prefix consistency | +| [`fasta_in_silico_digest_stats`](tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/) | Digest a FASTA and report peptide-level statistics | +| [`fasta_taxonomy_splitter`](tools/proteomics/fasta_utils/fasta_taxonomy_splitter/) | Split multi-organism FASTA by taxonomy from headers | #### File Conversion (8 tools) | Tool | Description | |------|-------------| -| [`mzml_to_mgf_converter`](scripts/proteomics/file_conversion/mzml_to_mgf_converter/) | Convert MS2 spectra from mzML to MGF format | -| [`mgf_to_mzml_converter`](scripts/proteomics/file_conversion/mgf_to_mzml_converter/) | Convert MGF files to mzML format | -| [`consensus_map_to_matrix`](scripts/proteomics/file_conversion/consensus_map_to_matrix/) | Convert consensusXML to flat quantification matrix | -| [`idxml_to_tsv_exporter`](scripts/proteomics/file_conversion/idxml_to_tsv_exporter/) | Export idXML identification results to flat TSV | -| [`ms_data_to_csv_exporter`](scripts/proteomics/file_conversion/ms_data_to_csv_exporter/) | Export mzML/featureXML data to CSV with column selection | -| [`mztab_summarizer`](scripts/proteomics/file_conversion/mztab_summarizer/) | Parse mzTab files and extract summary statistics | -| [`featurexml_merger`](scripts/proteomics/file_conversion/featurexml_merger/) | Merge multiple featureXML files | -| [`ms_data_ml_exporter`](scripts/proteomics/file_conversion/ms_data_ml_exporter/) | Export MS features as ML-ready matrices | +| [`mzml_to_mgf_converter`](tools/proteomics/file_conversion/mzml_to_mgf_converter/) | Convert MS2 spectra from mzML to MGF format | +| [`mgf_to_mzml_converter`](tools/proteomics/file_conversion/mgf_to_mzml_converter/) | Convert MGF files to mzML format | +| [`consensus_map_to_matrix`](tools/proteomics/file_conversion/consensus_map_to_matrix/) | Convert consensusXML to flat quantification matrix | +| [`idxml_to_tsv_exporter`](tools/proteomics/file_conversion/idxml_to_tsv_exporter/) | Export idXML identification results to flat TSV | +| [`ms_data_to_csv_exporter`](tools/proteomics/file_conversion/ms_data_to_csv_exporter/) | Export mzML/featureXML data to CSV with column selection | +| [`mztab_summarizer`](tools/proteomics/file_conversion/mztab_summarizer/) | Parse mzTab files and extract summary statistics | +| [`featurexml_merger`](tools/proteomics/file_conversion/featurexml_merger/) | Merge multiple featureXML files | +| [`ms_data_ml_exporter`](tools/proteomics/file_conversion/ms_data_ml_exporter/) | Export MS features as ML-ready matrices | #### Quality Control (15 tools) | Tool | Description | |------|-------------| -| [`lc_ms_qc_reporter`](scripts/proteomics/quality_control/lc_ms_qc_reporter/) | Comprehensive QC report from mzML (TIC, MS1/MS2 counts, charge distribution) | -| [`mzqc_generator`](scripts/proteomics/quality_control/mzqc_generator/) | Generate mzQC-format (HUPO-PSI standard) quality control files | -| [`identification_qc_reporter`](scripts/proteomics/quality_control/identification_qc_reporter/) | Report identification-level QC metrics from search results | -| [`run_comparison_reporter`](scripts/proteomics/quality_control/run_comparison_reporter/) | Compare mzML files side-by-side (TIC correlation, shared precursors) | -| [`mass_error_distribution_analyzer`](scripts/proteomics/quality_control/mass_error_distribution_analyzer/) | Compute precursor and fragment mass error distributions | -| [`acquisition_rate_analyzer`](scripts/proteomics/quality_control/acquisition_rate_analyzer/) | Analyze MS1/MS2 acquisition rates, cycle time, duty cycle | -| [`precursor_isolation_purity`](scripts/proteomics/quality_control/precursor_isolation_purity/) | Estimate precursor isolation purity and co-isolation interference | -| [`injection_time_analyzer`](scripts/proteomics/quality_control/injection_time_analyzer/) | Extract and analyze injection time values from mzML | -| [`collision_energy_analyzer`](scripts/proteomics/quality_control/collision_energy_analyzer/) | Extract and analyze collision energy values across MS2 spectra | -| [`precursor_charge_distribution`](scripts/proteomics/quality_control/precursor_charge_distribution/) | Analyze charge state distribution across MS2 spectra | -| [`precursor_recurrence_analyzer`](scripts/proteomics/quality_control/precursor_recurrence_analyzer/) | Analyze precursor resampling frequency in DDA runs | -| [`missed_cleavage_analyzer`](scripts/proteomics/quality_control/missed_cleavage_analyzer/) | Analyze missed cleavage distribution as a digestion QC metric | -| [`sample_complexity_estimator`](scripts/proteomics/quality_control/sample_complexity_estimator/) | Estimate sample complexity from MS1 peak density | -| [`spectrum_file_info`](scripts/proteomics/quality_control/spectrum_file_info/) | Summary statistics for mzML files | -| [`ms1_feature_intensity_tracker`](scripts/proteomics/quality_control/ms1_feature_intensity_tracker/) | Track feature intensities across a batch of mzML runs | +| [`lc_ms_qc_reporter`](tools/proteomics/quality_control/lc_ms_qc_reporter/) | Comprehensive QC report from mzML (TIC, MS1/MS2 counts, charge distribution) | +| [`mzqc_generator`](tools/proteomics/quality_control/mzqc_generator/) | Generate mzQC-format (HUPO-PSI standard) quality control files | +| [`identification_qc_reporter`](tools/proteomics/quality_control/identification_qc_reporter/) | Report identification-level QC metrics from search results | +| [`run_comparison_reporter`](tools/proteomics/quality_control/run_comparison_reporter/) | Compare mzML files side-by-side (TIC correlation, shared precursors) | +| [`mass_error_distribution_analyzer`](tools/proteomics/quality_control/mass_error_distribution_analyzer/) | Compute precursor and fragment mass error distributions | +| [`acquisition_rate_analyzer`](tools/proteomics/quality_control/acquisition_rate_analyzer/) | Analyze MS1/MS2 acquisition rates, cycle time, duty cycle | +| [`precursor_isolation_purity`](tools/proteomics/quality_control/precursor_isolation_purity/) | Estimate precursor isolation purity and co-isolation interference | +| [`injection_time_analyzer`](tools/proteomics/quality_control/injection_time_analyzer/) | Extract and analyze injection time values from mzML | +| [`collision_energy_analyzer`](tools/proteomics/quality_control/collision_energy_analyzer/) | Extract and analyze collision energy values across MS2 spectra | +| [`precursor_charge_distribution`](tools/proteomics/quality_control/precursor_charge_distribution/) | Analyze charge state distribution across MS2 spectra | +| [`precursor_recurrence_analyzer`](tools/proteomics/quality_control/precursor_recurrence_analyzer/) | Analyze precursor resampling frequency in DDA runs | +| [`missed_cleavage_analyzer`](tools/proteomics/quality_control/missed_cleavage_analyzer/) | Analyze missed cleavage distribution as a digestion QC metric | +| [`sample_complexity_estimator`](tools/proteomics/quality_control/sample_complexity_estimator/) | Estimate sample complexity from MS1 peak density | +| [`spectrum_file_info`](tools/proteomics/quality_control/spectrum_file_info/) | Summary statistics for mzML files | +| [`ms1_feature_intensity_tracker`](tools/proteomics/quality_control/ms1_feature_intensity_tracker/) | Track feature intensities across a batch of mzML runs | #### Targeted Proteomics (7 tools) | Tool | Description | |------|-------------| -| [`xic_extractor`](scripts/proteomics/targeted_proteomics/xic_extractor/) | Extract ion chromatograms for target m/z values from mzML | -| [`tic_bpc_calculator`](scripts/proteomics/targeted_proteomics/tic_bpc_calculator/) | Compute TIC and base peak chromatograms from mzML | -| [`transition_list_generator`](scripts/proteomics/targeted_proteomics/transition_list_generator/) | Generate SRM/MRM/PRM transition lists from peptide sequences | -| [`irt_calculator`](scripts/proteomics/targeted_proteomics/irt_calculator/) | Convert observed RT to indexed retention time (iRT) values | -| [`inclusion_list_generator`](scripts/proteomics/targeted_proteomics/inclusion_list_generator/) | Generate instrument inclusion lists from identification results | -| [`dia_window_analyzer`](scripts/proteomics/targeted_proteomics/dia_window_analyzer/) | Report DIA isolation window scheme from mzML metadata | -| [`library_coverage_estimator`](scripts/proteomics/targeted_proteomics/library_coverage_estimator/) | Estimate proteome coverage of a spectral library | +| [`xic_extractor`](tools/proteomics/targeted_proteomics/xic_extractor/) | Extract ion chromatograms for target m/z values from mzML | +| [`tic_bpc_calculator`](tools/proteomics/targeted_proteomics/tic_bpc_calculator/) | Compute TIC and base peak chromatograms from mzML | +| [`transition_list_generator`](tools/proteomics/targeted_proteomics/transition_list_generator/) | Generate SRM/MRM/PRM transition lists from peptide sequences | +| [`irt_calculator`](tools/proteomics/targeted_proteomics/irt_calculator/) | Convert observed RT to indexed retention time (iRT) values | +| [`inclusion_list_generator`](tools/proteomics/targeted_proteomics/inclusion_list_generator/) | Generate instrument inclusion lists from identification results | +| [`dia_window_analyzer`](tools/proteomics/targeted_proteomics/dia_window_analyzer/) | Report DIA isolation window scheme from mzML metadata | +| [`library_coverage_estimator`](tools/proteomics/targeted_proteomics/library_coverage_estimator/) | Estimate proteome coverage of a spectral library | #### Identification (7 tools) | Tool | Description | |------|-------------| -| [`feature_detection_proteomics`](scripts/proteomics/identification/feature_detection_proteomics/) | Peptide feature detection from LC-MS/MS data | -| [`psm_feature_extractor`](scripts/proteomics/identification/psm_feature_extractor/) | Extract rescoring features from PSMs (mass error, coverage, intensity) | -| [`peptide_spectral_match_validator`](scripts/proteomics/identification/peptide_spectral_match_validator/) | Validate individual PSMs by recomputing fragment ion coverage | -| [`semi_tryptic_peptide_finder`](scripts/proteomics/identification/semi_tryptic_peptide_finder/) | Classify peptides as fully/semi/non-tryptic | -| [`sequence_tag_generator`](scripts/proteomics/identification/sequence_tag_generator/) | Generate de novo sequence tags from MS2 fragment ion ladders | -| [`mzml_spectrum_subsetter`](scripts/proteomics/identification/mzml_spectrum_subsetter/) | Extract specific spectra from mzML by scan number list | -| [`mzml_metadata_extractor`](scripts/proteomics/identification/mzml_metadata_extractor/) | Extract instrument metadata from mzML files | +| [`feature_detection_proteomics`](tools/proteomics/identification/feature_detection_proteomics/) | Peptide feature detection from LC-MS/MS data | +| [`psm_feature_extractor`](tools/proteomics/identification/psm_feature_extractor/) | Extract rescoring features from PSMs (mass error, coverage, intensity) | +| [`peptide_spectral_match_validator`](tools/proteomics/identification/peptide_spectral_match_validator/) | Validate individual PSMs by recomputing fragment ion coverage | +| [`semi_tryptic_peptide_finder`](tools/proteomics/identification/semi_tryptic_peptide_finder/) | Classify peptides as fully/semi/non-tryptic | +| [`sequence_tag_generator`](tools/proteomics/identification/sequence_tag_generator/) | Generate de novo sequence tags from MS2 fragment ion ladders | +| [`mzml_spectrum_subsetter`](tools/proteomics/identification/mzml_spectrum_subsetter/) | Extract specific spectra from mzML by scan number list | +| [`mzml_metadata_extractor`](tools/proteomics/identification/mzml_metadata_extractor/) | Extract instrument metadata from mzML files | #### PTM Analysis (5 tools) | Tool | Description | |------|-------------| -| [`ptm_site_localization_scorer`](scripts/proteomics/ptm_analysis/ptm_site_localization_scorer/) | Score PTM site localization confidence using fragment ion coverage | -| [`phosphosite_class_filter`](scripts/proteomics/ptm_analysis/phosphosite_class_filter/) | Classify phosphosites into Class I/II/III by localization probability | -| [`phospho_motif_analyzer`](scripts/proteomics/ptm_analysis/phospho_motif_analyzer/) | Extract sequence windows around phosphosites and analyze kinase motifs | -| [`phospho_enrichment_qc`](scripts/proteomics/ptm_analysis/phospho_enrichment_qc/) | Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios | -| [`glycopeptide_mass_calculator`](scripts/proteomics/ptm_analysis/glycopeptide_mass_calculator/) | Calculate glycopeptide masses with glycan compositions | +| [`ptm_site_localization_scorer`](tools/proteomics/ptm_analysis/ptm_site_localization_scorer/) | Score PTM site localization confidence using fragment ion coverage | +| [`phosphosite_class_filter`](tools/proteomics/ptm_analysis/phosphosite_class_filter/) | Classify phosphosites into Class I/II/III by localization probability | +| [`phospho_motif_analyzer`](tools/proteomics/ptm_analysis/phospho_motif_analyzer/) | Extract sequence windows around phosphosites and analyze kinase motifs | +| [`phospho_enrichment_qc`](tools/proteomics/ptm_analysis/phospho_enrichment_qc/) | Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios | +| [`glycopeptide_mass_calculator`](tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/) | Calculate glycopeptide masses with glycan compositions | #### Structural Proteomics (5 tools) | Tool | Description | |------|-------------| -| [`hdx_deuterium_uptake`](scripts/proteomics/structural_proteomics/hdx_deuterium_uptake/) | Calculate deuterium uptake from HDX-MS time course data | -| [`hdx_back_exchange_estimator`](scripts/proteomics/structural_proteomics/hdx_back_exchange_estimator/) | Estimate per-peptide back-exchange rates from fully deuterated controls | -| [`crosslink_mass_calculator`](scripts/proteomics/structural_proteomics/crosslink_mass_calculator/) | Calculate masses for crosslinked peptide pairs (DSS, BS3, DSSO) | -| [`xl_distance_validator`](scripts/proteomics/structural_proteomics/xl_distance_validator/) | Validate crosslink distances against PDB structures | -| [`xl_link_classifier`](scripts/proteomics/structural_proteomics/xl_link_classifier/) | Classify crosslinks as intra-protein, inter-protein, or monolink | +| [`hdx_deuterium_uptake`](tools/proteomics/structural_proteomics/hdx_deuterium_uptake/) | Calculate deuterium uptake from HDX-MS time course data | +| [`hdx_back_exchange_estimator`](tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/) | Estimate per-peptide back-exchange rates from fully deuterated controls | +| [`crosslink_mass_calculator`](tools/proteomics/structural_proteomics/crosslink_mass_calculator/) | Calculate masses for crosslinked peptide pairs (DSS, BS3, DSSO) | +| [`xl_distance_validator`](tools/proteomics/structural_proteomics/xl_distance_validator/) | Validate crosslink distances against PDB structures | +| [`xl_link_classifier`](tools/proteomics/structural_proteomics/xl_link_classifier/) | Classify crosslinks as intra-protein, inter-protein, or monolink | #### Specialized (7 tools) | Tool | Description | |------|-------------| -| [`immunopeptide_filter`](scripts/proteomics/specialized/immunopeptide_filter/) | Filter peptides for MHC-I/II by length range and motif | -| [`immunopeptidome_qc`](scripts/proteomics/specialized/immunopeptidome_qc/) | QC for immunopeptidomics (length distribution, anchor residues) | -| [`metapeptide_lca_assigner`](scripts/proteomics/specialized/metapeptide_lca_assigner/) | Assign lowest common ancestor taxonomy from peptide-protein mappings | -| [`cleavage_site_profiler`](scripts/proteomics/specialized/cleavage_site_profiler/) | Profile protease cleavage site specificity from N-terminomics data | -| [`nterm_modification_annotator`](scripts/proteomics/specialized/nterm_modification_annotator/) | Classify N-terminal peptides (protein N-term, signal peptide, neo-N-term) | -| [`proteoform_delta_annotator`](scripts/proteomics/specialized/proteoform_delta_annotator/) | Annotate mass differences between proteoforms with known PTMs | -| [`topdown_coverage_calculator`](scripts/proteomics/specialized/topdown_coverage_calculator/) | Compute per-residue bond cleavage coverage for intact proteins | +| [`immunopeptide_filter`](tools/proteomics/specialized/immunopeptide_filter/) | Filter peptides for MHC-I/II by length range and motif | +| [`immunopeptidome_qc`](tools/proteomics/specialized/immunopeptidome_qc/) | QC for immunopeptidomics (length distribution, anchor residues) | +| [`metapeptide_lca_assigner`](tools/proteomics/specialized/metapeptide_lca_assigner/) | Assign lowest common ancestor taxonomy from peptide-protein mappings | +| [`cleavage_site_profiler`](tools/proteomics/specialized/cleavage_site_profiler/) | Profile protease cleavage site specificity from N-terminomics data | +| [`nterm_modification_annotator`](tools/proteomics/specialized/nterm_modification_annotator/) | Classify N-terminal peptides (protein N-term, signal peptide, neo-N-term) | +| [`proteoform_delta_annotator`](tools/proteomics/specialized/proteoform_delta_annotator/) | Annotate mass differences between proteoforms with known PTMs | +| [`topdown_coverage_calculator`](tools/proteomics/specialized/topdown_coverage_calculator/) | Compute per-residue bond cleavage coverage for intact proteins | #### RNA (3 tools) | Tool | Description | |------|-------------| -| [`rna_mass_calculator`](scripts/proteomics/rna/rna_mass_calculator/) | Calculate mass, formula, and isotopes for RNA/oligonucleotide sequences | -| [`rna_digest`](scripts/proteomics/rna/rna_digest/) | In silico RNA digestion with RNases (T1, U2, etc.) | -| [`rna_fragment_spectrum_generator`](scripts/proteomics/rna/rna_fragment_spectrum_generator/) | Generate theoretical RNA fragment spectra (c/y/w/a-B ions) | +| [`rna_mass_calculator`](tools/proteomics/rna/rna_mass_calculator/) | Calculate mass, formula, and isotopes for RNA/oligonucleotide sequences | +| [`rna_digest`](tools/proteomics/rna/rna_digest/) | In silico RNA digestion with RNases (T1, U2, etc.) | +| [`rna_fragment_spectrum_generator`](tools/proteomics/rna/rna_fragment_spectrum_generator/) | Generate theoretical RNA fragment spectra (c/y/w/a-B ions) | --- @@ -260,75 +260,75 @@ Both `ruff` and `pytest` must pass with zero errors. | Tool | Description | |------|-------------| -| [`adduct_calculator`](scripts/metabolomics/formula_tools/adduct_calculator/) | Compute m/z for all common ESI adducts given a formula or mass | -| [`molecular_formula_finder`](scripts/metabolomics/formula_tools/molecular_formula_finder/) | Enumerate valid molecular formulas for an accurate mass with element constraints | -| [`mass_decomposition_tool`](scripts/metabolomics/formula_tools/mass_decomposition_tool/) | Find molecular formula compositions for a given mass within tolerance | -| [`formula_mass_calculator`](scripts/metabolomics/formula_tools/formula_mass_calculator/) | Calculate exact masses for molecular formulas with adduct support | -| [`formula_validator_golden_rules`](scripts/metabolomics/formula_tools/formula_validator_golden_rules/) | Apply Kind & Fiehn's Seven Golden Rules to filter formula candidates | -| [`rdbe_calculator`](scripts/metabolomics/formula_tools/rdbe_calculator/) | Calculate Ring/Double Bond Equivalence for molecular formulas | -| [`metabolite_formula_annotator`](scripts/metabolomics/formula_tools/metabolite_formula_annotator/) | Annotate features with candidate formulas using mass + isotope fit | -| [`mass_accuracy_calculator`](scripts/metabolomics/formula_tools/mass_accuracy_calculator/) | Compute m/z mass accuracy (ppm error) for sequences or formulas | +| [`adduct_calculator`](tools/metabolomics/formula_tools/adduct_calculator/) | Compute m/z for all common ESI adducts given a formula or mass | +| [`molecular_formula_finder`](tools/metabolomics/formula_tools/molecular_formula_finder/) | Enumerate valid molecular formulas for an accurate mass with element constraints | +| [`mass_decomposition_tool`](tools/metabolomics/formula_tools/mass_decomposition_tool/) | Find molecular formula compositions for a given mass within tolerance | +| [`formula_mass_calculator`](tools/metabolomics/formula_tools/formula_mass_calculator/) | Calculate exact masses for molecular formulas with adduct support | +| [`formula_validator_golden_rules`](tools/metabolomics/formula_tools/formula_validator_golden_rules/) | Apply Kind & Fiehn's Seven Golden Rules to filter formula candidates | +| [`rdbe_calculator`](tools/metabolomics/formula_tools/rdbe_calculator/) | Calculate Ring/Double Bond Equivalence for molecular formulas | +| [`metabolite_formula_annotator`](tools/metabolomics/formula_tools/metabolite_formula_annotator/) | Annotate features with candidate formulas using mass + isotope fit | +| [`mass_accuracy_calculator`](tools/metabolomics/formula_tools/mass_accuracy_calculator/) | Compute m/z mass accuracy (ppm error) for sequences or formulas | #### Feature Processing (7 tools) | Tool | Description | |------|-------------| -| [`blank_subtraction_tool`](scripts/metabolomics/feature_processing/blank_subtraction_tool/) | Subtract blank/control features from sample features by m/z + RT matching | -| [`duplicate_feature_detector`](scripts/metabolomics/feature_processing/duplicate_feature_detector/) | Detect and flag duplicate features by m/z and RT proximity | -| [`adduct_group_analyzer`](scripts/metabolomics/feature_processing/adduct_group_analyzer/) | Group features by adduct relationships into ion identity groups | -| [`isf_detector`](scripts/metabolomics/feature_processing/isf_detector/) | Detect in-source fragmentation artifacts by coelution and neutral loss | -| [`targeted_feature_extractor`](scripts/metabolomics/feature_processing/targeted_feature_extractor/) | Extract features for known compounds from MS1 data | -| [`mass_defect_filter`](scripts/metabolomics/feature_processing/mass_defect_filter/) | Filter features by mass defect and Kendrick mass defect | -| [`metabolite_feature_detection`](scripts/metabolomics/feature_processing/metabolite_feature_detection/) | Metabolite feature detection from LC-MS data | +| [`blank_subtraction_tool`](tools/metabolomics/feature_processing/blank_subtraction_tool/) | Subtract blank/control features from sample features by m/z + RT matching | +| [`duplicate_feature_detector`](tools/metabolomics/feature_processing/duplicate_feature_detector/) | Detect and flag duplicate features by m/z and RT proximity | +| [`adduct_group_analyzer`](tools/metabolomics/feature_processing/adduct_group_analyzer/) | Group features by adduct relationships into ion identity groups | +| [`isf_detector`](tools/metabolomics/feature_processing/isf_detector/) | Detect in-source fragmentation artifacts by coelution and neutral loss | +| [`targeted_feature_extractor`](tools/metabolomics/feature_processing/targeted_feature_extractor/) | Extract features for known compounds from MS1 data | +| [`mass_defect_filter`](tools/metabolomics/feature_processing/mass_defect_filter/) | Filter features by mass defect and Kendrick mass defect | +| [`metabolite_feature_detection`](tools/metabolomics/feature_processing/metabolite_feature_detection/) | Metabolite feature detection from LC-MS data | #### Spectral Analysis (6 tools) | Tool | Description | |------|-------------| -| [`spectral_entropy_scorer`](scripts/metabolomics/spectral_analysis/spectral_entropy_scorer/) | Compute spectral entropy similarity (Li & Fiehn 2021) | -| [`neutral_loss_scanner`](scripts/metabolomics/spectral_analysis/neutral_loss_scanner/) | Scan MS2 spectra for characteristic neutral losses | -| [`isotope_pattern_scorer`](scripts/metabolomics/spectral_analysis/isotope_pattern_scorer/) | Score observed vs. theoretical isotope patterns | -| [`isotope_pattern_matcher`](scripts/metabolomics/spectral_analysis/isotope_pattern_matcher/) | Generate theoretical isotope distributions and cosine similarity scoring | -| [`isotope_pattern_fit_scorer`](scripts/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/) | Score isotope pattern fit, detect Cl/Br from M+2 enhancement | -| [`massql_query_tool`](scripts/metabolomics/spectral_analysis/massql_query_tool/) | Query mzML data using MassQL-like syntax | +| [`spectral_entropy_scorer`](tools/metabolomics/spectral_analysis/spectral_entropy_scorer/) | Compute spectral entropy similarity (Li & Fiehn 2021) | +| [`neutral_loss_scanner`](tools/metabolomics/spectral_analysis/neutral_loss_scanner/) | Scan MS2 spectra for characteristic neutral losses | +| [`isotope_pattern_scorer`](tools/metabolomics/spectral_analysis/isotope_pattern_scorer/) | Score observed vs. theoretical isotope patterns | +| [`isotope_pattern_matcher`](tools/metabolomics/spectral_analysis/isotope_pattern_matcher/) | Generate theoretical isotope distributions and cosine similarity scoring | +| [`isotope_pattern_fit_scorer`](tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/) | Score isotope pattern fit, detect Cl/Br from M+2 enhancement | +| [`massql_query_tool`](tools/metabolomics/spectral_analysis/massql_query_tool/) | Query mzML data using MassQL-like syntax | #### Compound Annotation (4 tools) | Tool | Description | |------|-------------| -| [`van_krevelen_data_generator`](scripts/metabolomics/compound_annotation/van_krevelen_data_generator/) | Compute H:C and O:C ratios, classify into biochemical compound classes | -| [`kendrick_mass_defect_analyzer`](scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/) | Compute Kendrick mass defect for homologous series detection (CH2, CF2, etc.) | -| [`suspect_screener`](scripts/metabolomics/compound_annotation/suspect_screener/) | Match detected masses against suspect screening lists (CompTox, NORMAN) | -| [`metabolite_class_predictor`](scripts/metabolomics/compound_annotation/metabolite_class_predictor/) | Predict compound class from mass defect, element ratios, and RDBE | +| [`van_krevelen_data_generator`](tools/metabolomics/compound_annotation/van_krevelen_data_generator/) | Compute H:C and O:C ratios, classify into biochemical compound classes | +| [`kendrick_mass_defect_analyzer`](tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/) | Compute Kendrick mass defect for homologous series detection (CH2, CF2, etc.) | +| [`suspect_screener`](tools/metabolomics/compound_annotation/suspect_screener/) | Match detected masses against suspect screening lists (CompTox, NORMAN) | +| [`metabolite_class_predictor`](tools/metabolomics/compound_annotation/metabolite_class_predictor/) | Predict compound class from mass defect, element ratios, and RDBE | #### Drug Metabolism (2 tools) | Tool | Description | |------|-------------| -| [`drug_metabolite_screener`](scripts/metabolomics/drug_metabolism/drug_metabolite_screener/) | Predict Phase I/II drug metabolites and screen mzML for matches | -| [`mass_difference_network_builder`](scripts/metabolomics/drug_metabolism/mass_difference_network_builder/) | Connect features by known biotransformation mass differences | +| [`drug_metabolite_screener`](tools/metabolomics/drug_metabolism/drug_metabolite_screener/) | Predict Phase I/II drug metabolites and screen mzML for matches | +| [`mass_difference_network_builder`](tools/metabolomics/drug_metabolism/mass_difference_network_builder/) | Connect features by known biotransformation mass differences | #### Isotope Labeling (2 tools) | Tool | Description | |------|-------------| -| [`isotope_label_detector`](scripts/metabolomics/isotope_labeling/isotope_label_detector/) | Detect 13C/15N-labeled metabolites by paired feature analysis | -| [`mid_natural_abundance_corrector`](scripts/metabolomics/isotope_labeling/mid_natural_abundance_corrector/) | Correct mass isotopomer distributions for natural 13C abundance | +| [`isotope_label_detector`](tools/metabolomics/isotope_labeling/isotope_label_detector/) | Detect 13C/15N-labeled metabolites by paired feature analysis | +| [`mid_natural_abundance_corrector`](tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/) | Correct mass isotopomer distributions for natural 13C abundance | #### Lipidomics (2 tools) | Tool | Description | |------|-------------| -| [`lipid_species_resolver`](scripts/metabolomics/lipidomics/lipid_species_resolver/) | Enumerate acyl chain combinations from sum-composition lipid annotations | -| [`lipid_ecn_rt_predictor`](scripts/metabolomics/lipidomics/lipid_ecn_rt_predictor/) | Predict lipid RT from Equivalent Carbon Number | +| [`lipid_species_resolver`](tools/metabolomics/lipidomics/lipid_species_resolver/) | Enumerate acyl chain combinations from sum-composition lipid annotations | +| [`lipid_ecn_rt_predictor`](tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/) | Predict lipid RT from Equivalent Carbon Number | #### Export (3 tools) | Tool | Description | |------|-------------| -| [`gnps_fbmn_exporter`](scripts/metabolomics/export/gnps_fbmn_exporter/) | Export MS2 + quantification in GNPS Feature-Based Molecular Networking format | -| [`sirius_exporter`](scripts/metabolomics/export/sirius_exporter/) | Export features + MS2 data to SIRIUS .ms format | -| [`kovats_ri_calculator`](scripts/metabolomics/export/kovats_ri_calculator/) | Calculate Kovats Retention Index from alkane standards for GC-MS | +| [`gnps_fbmn_exporter`](tools/metabolomics/export/gnps_fbmn_exporter/) | Export MS2 + quantification in GNPS Feature-Based Molecular Networking format | +| [`sirius_exporter`](tools/metabolomics/export/sirius_exporter/) | Export features + MS2 data to SIRIUS .ms format | +| [`kovats_ri_calculator`](tools/metabolomics/export/kovats_ri_calculator/) | Calculate Kovats Retention Index from alkane standards for GC-MS | --- diff --git a/docs/superpowers/plans/2026-03-24-ai-contributor-skills.md b/docs/superpowers/plans/2026-03-24-ai-contributor-skills.md index d2e73fe..526a733 100644 --- a/docs/superpowers/plans/2026-03-24-ai-contributor-skills.md +++ b/docs/superpowers/plans/2026-03-24-ai-contributor-skills.md @@ -43,22 +43,22 @@ git commit -m "Add ruff.toml with E/F/W/I rule set, line-length 120" ### Task 2: Migrate peptide_mass_calculator to per-script directory **Files:** -- Create: `scripts/proteomics/peptide_mass_calculator/peptide_mass_calculator.py` -- Create: `scripts/proteomics/peptide_mass_calculator/requirements.txt` -- Create: `scripts/proteomics/peptide_mass_calculator/README.md` -- Create: `scripts/proteomics/peptide_mass_calculator/tests/conftest.py` -- Create: `scripts/proteomics/peptide_mass_calculator/tests/test_peptide_mass_calculator.py` +- Create: `tools/proteomics/peptide_mass_calculator/peptide_mass_calculator.py` +- Create: `tools/proteomics/peptide_mass_calculator/requirements.txt` +- Create: `tools/proteomics/peptide_mass_calculator/README.md` +- Create: `tools/proteomics/peptide_mass_calculator/tests/conftest.py` +- Create: `tools/proteomics/peptide_mass_calculator/tests/test_peptide_mass_calculator.py` - [ ] **Step 1: Create directory structure** ```bash -mkdir -p scripts/proteomics/peptide_mass_calculator/tests +mkdir -p tools/proteomics/peptide_mass_calculator/tests ``` - [ ] **Step 2: Copy script from feature branch** ```bash -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/proteomics/peptide_mass_calculator.py > scripts/proteomics/peptide_mass_calculator/peptide_mass_calculator.py +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/proteomics/peptide_mass_calculator.py > tools/proteomics/peptide_mass_calculator/peptide_mass_calculator.py ``` - [ ] **Step 3: Create requirements.txt** @@ -158,18 +158,18 @@ python peptide_mass_calculator.py --sequence ACDEFGHIK --fragments - [ ] **Step 7: Run ruff** -Run: `ruff check scripts/proteomics/peptide_mass_calculator/` +Run: `ruff check tools/proteomics/peptide_mass_calculator/` Expected: No errors - [ ] **Step 8: Run tests** -Run: `PYTHONPATH=scripts/proteomics/peptide_mass_calculator python -m pytest scripts/proteomics/peptide_mass_calculator/tests/ -v` +Run: `PYTHONPATH=tools/proteomics/peptide_mass_calculator python -m pytest tools/proteomics/peptide_mass_calculator/tests/ -v` Expected: 5 tests pass (or skip if pyopenms not installed) - [ ] **Step 9: Commit** ```bash -git add scripts/proteomics/peptide_mass_calculator/ +git add tools/proteomics/peptide_mass_calculator/ git commit -m "Migrate peptide_mass_calculator to per-script directory structure" ``` @@ -178,17 +178,17 @@ git commit -m "Migrate peptide_mass_calculator to per-script directory structure ### Task 3: Migrate protein_digest to per-script directory **Files:** -- Create: `scripts/proteomics/protein_digest/protein_digest.py` -- Create: `scripts/proteomics/protein_digest/requirements.txt` -- Create: `scripts/proteomics/protein_digest/README.md` -- Create: `scripts/proteomics/protein_digest/tests/conftest.py` -- Create: `scripts/proteomics/protein_digest/tests/test_protein_digest.py` +- Create: `tools/proteomics/protein_digest/protein_digest.py` +- Create: `tools/proteomics/protein_digest/requirements.txt` +- Create: `tools/proteomics/protein_digest/README.md` +- Create: `tools/proteomics/protein_digest/tests/conftest.py` +- Create: `tools/proteomics/protein_digest/tests/test_protein_digest.py` - [ ] **Step 1: Create directory and copy script** ```bash -mkdir -p scripts/proteomics/protein_digest/tests -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/proteomics/protein_digest.py > scripts/proteomics/protein_digest/protein_digest.py +mkdir -p tools/proteomics/protein_digest/tests +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/proteomics/protein_digest.py > tools/proteomics/protein_digest/protein_digest.py ``` - [ ] **Step 2: Create requirements.txt** @@ -273,13 +273,13 @@ python protein_digest.py --list-enzymes - [ ] **Step 6: Run ruff and tests** -Run: `ruff check scripts/proteomics/protein_digest/ && PYTHONPATH=scripts/proteomics/protein_digest python -m pytest scripts/proteomics/protein_digest/tests/ -v` +Run: `ruff check tools/proteomics/protein_digest/ && PYTHONPATH=tools/proteomics/protein_digest python -m pytest tools/proteomics/protein_digest/tests/ -v` Expected: Lint clean, 5 tests pass - [ ] **Step 7: Commit** ```bash -git add scripts/proteomics/protein_digest/ +git add tools/proteomics/protein_digest/ git commit -m "Migrate protein_digest to per-script directory structure" ``` @@ -288,17 +288,17 @@ git commit -m "Migrate protein_digest to per-script directory structure" ### Task 4: Migrate spectrum_file_info to per-script directory **Files:** -- Create: `scripts/proteomics/spectrum_file_info/spectrum_file_info.py` -- Create: `scripts/proteomics/spectrum_file_info/requirements.txt` -- Create: `scripts/proteomics/spectrum_file_info/README.md` -- Create: `scripts/proteomics/spectrum_file_info/tests/conftest.py` -- Create: `scripts/proteomics/spectrum_file_info/tests/test_spectrum_file_info.py` +- Create: `tools/proteomics/spectrum_file_info/spectrum_file_info.py` +- Create: `tools/proteomics/spectrum_file_info/requirements.txt` +- Create: `tools/proteomics/spectrum_file_info/README.md` +- Create: `tools/proteomics/spectrum_file_info/tests/conftest.py` +- Create: `tools/proteomics/spectrum_file_info/tests/test_spectrum_file_info.py` - [ ] **Step 1: Create directory and copy script** ```bash -mkdir -p scripts/proteomics/spectrum_file_info/tests -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/proteomics/spectrum_file_info.py > scripts/proteomics/spectrum_file_info/spectrum_file_info.py +mkdir -p tools/proteomics/spectrum_file_info/tests +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/proteomics/spectrum_file_info.py > tools/proteomics/spectrum_file_info/spectrum_file_info.py ``` - [ ] **Step 2: Create requirements.txt** @@ -392,13 +392,13 @@ python spectrum_file_info.py --input sample.mzML --tic - [ ] **Step 6: Run ruff and tests** -Run: `ruff check scripts/proteomics/spectrum_file_info/ && PYTHONPATH=scripts/proteomics/spectrum_file_info python -m pytest scripts/proteomics/spectrum_file_info/tests/ -v` +Run: `ruff check tools/proteomics/spectrum_file_info/ && PYTHONPATH=tools/proteomics/spectrum_file_info python -m pytest tools/proteomics/spectrum_file_info/tests/ -v` Expected: Lint clean, 4 tests pass - [ ] **Step 7: Commit** ```bash -git add scripts/proteomics/spectrum_file_info/ +git add tools/proteomics/spectrum_file_info/ git commit -m "Migrate spectrum_file_info to per-script directory with synthetic test data" ``` @@ -407,17 +407,17 @@ git commit -m "Migrate spectrum_file_info to per-script directory with synthetic ### Task 5: Migrate feature_detection_proteomics to per-script directory **Files:** -- Create: `scripts/proteomics/feature_detection_proteomics/feature_detection_proteomics.py` -- Create: `scripts/proteomics/feature_detection_proteomics/requirements.txt` -- Create: `scripts/proteomics/feature_detection_proteomics/README.md` -- Create: `scripts/proteomics/feature_detection_proteomics/tests/conftest.py` -- Create: `scripts/proteomics/feature_detection_proteomics/tests/test_feature_detection_proteomics.py` +- Create: `tools/proteomics/feature_detection_proteomics/feature_detection_proteomics.py` +- Create: `tools/proteomics/feature_detection_proteomics/requirements.txt` +- Create: `tools/proteomics/feature_detection_proteomics/README.md` +- Create: `tools/proteomics/feature_detection_proteomics/tests/conftest.py` +- Create: `tools/proteomics/feature_detection_proteomics/tests/test_feature_detection_proteomics.py` - [ ] **Step 1: Create directory and copy script** ```bash -mkdir -p scripts/proteomics/feature_detection_proteomics/tests -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/proteomics/feature_detection_proteomics.py > scripts/proteomics/feature_detection_proteomics/feature_detection_proteomics.py +mkdir -p tools/proteomics/feature_detection_proteomics/tests +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/proteomics/feature_detection_proteomics.py > tools/proteomics/feature_detection_proteomics/feature_detection_proteomics.py ``` - [ ] **Step 2: Create requirements.txt** @@ -489,13 +489,13 @@ python feature_detection_proteomics.py --input sample.mzML --output features.fea - [ ] **Step 6: Run ruff and tests** -Run: `ruff check scripts/proteomics/feature_detection_proteomics/ && PYTHONPATH=scripts/proteomics/feature_detection_proteomics python -m pytest scripts/proteomics/feature_detection_proteomics/tests/ -v` +Run: `ruff check tools/proteomics/feature_detection_proteomics/ && PYTHONPATH=tools/proteomics/feature_detection_proteomics python -m pytest tools/proteomics/feature_detection_proteomics/tests/ -v` Expected: Lint clean, 1 test passes - [ ] **Step 7: Commit** ```bash -git add scripts/proteomics/feature_detection_proteomics/ +git add tools/proteomics/feature_detection_proteomics/ git commit -m "Migrate feature_detection_proteomics to per-script directory with synthetic test data" ``` @@ -504,17 +504,17 @@ git commit -m "Migrate feature_detection_proteomics to per-script directory with ### Task 6: Migrate mass_accuracy_calculator to per-script directory **Files:** -- Create: `scripts/metabolomics/mass_accuracy_calculator/mass_accuracy_calculator.py` -- Create: `scripts/metabolomics/mass_accuracy_calculator/requirements.txt` -- Create: `scripts/metabolomics/mass_accuracy_calculator/README.md` -- Create: `scripts/metabolomics/mass_accuracy_calculator/tests/conftest.py` -- Create: `scripts/metabolomics/mass_accuracy_calculator/tests/test_mass_accuracy_calculator.py` +- Create: `tools/metabolomics/mass_accuracy_calculator/mass_accuracy_calculator.py` +- Create: `tools/metabolomics/mass_accuracy_calculator/requirements.txt` +- Create: `tools/metabolomics/mass_accuracy_calculator/README.md` +- Create: `tools/metabolomics/mass_accuracy_calculator/tests/conftest.py` +- Create: `tools/metabolomics/mass_accuracy_calculator/tests/test_mass_accuracy_calculator.py` - [ ] **Step 1: Create directory and copy script** ```bash -mkdir -p scripts/metabolomics/mass_accuracy_calculator/tests -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/metabolomics/mass_accuracy_calculator.py > scripts/metabolomics/mass_accuracy_calculator/mass_accuracy_calculator.py +mkdir -p tools/metabolomics/mass_accuracy_calculator/tests +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/metabolomics/mass_accuracy_calculator.py > tools/metabolomics/mass_accuracy_calculator/mass_accuracy_calculator.py ``` - [ ] **Step 2: Create requirements.txt** @@ -592,13 +592,13 @@ python mass_accuracy_calculator.py --sequence ACDEFGHIK --charge 2 --observed 55 - [ ] **Step 6: Run ruff and tests** -Run: `ruff check scripts/metabolomics/mass_accuracy_calculator/ && PYTHONPATH=scripts/metabolomics/mass_accuracy_calculator python -m pytest scripts/metabolomics/mass_accuracy_calculator/tests/ -v` +Run: `ruff check tools/metabolomics/mass_accuracy_calculator/ && PYTHONPATH=tools/metabolomics/mass_accuracy_calculator python -m pytest tools/metabolomics/mass_accuracy_calculator/tests/ -v` Expected: Lint clean, 6 tests pass - [ ] **Step 7: Commit** ```bash -git add scripts/metabolomics/mass_accuracy_calculator/ +git add tools/metabolomics/mass_accuracy_calculator/ git commit -m "Migrate mass_accuracy_calculator to per-script directory structure" ``` @@ -607,17 +607,17 @@ git commit -m "Migrate mass_accuracy_calculator to per-script directory structur ### Task 7: Migrate isotope_pattern_matcher to per-script directory **Files:** -- Create: `scripts/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py` -- Create: `scripts/metabolomics/isotope_pattern_matcher/requirements.txt` -- Create: `scripts/metabolomics/isotope_pattern_matcher/README.md` -- Create: `scripts/metabolomics/isotope_pattern_matcher/tests/conftest.py` -- Create: `scripts/metabolomics/isotope_pattern_matcher/tests/test_isotope_pattern_matcher.py` +- Create: `tools/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py` +- Create: `tools/metabolomics/isotope_pattern_matcher/requirements.txt` +- Create: `tools/metabolomics/isotope_pattern_matcher/README.md` +- Create: `tools/metabolomics/isotope_pattern_matcher/tests/conftest.py` +- Create: `tools/metabolomics/isotope_pattern_matcher/tests/test_isotope_pattern_matcher.py` - [ ] **Step 1: Create directory and copy script** ```bash -mkdir -p scripts/metabolomics/isotope_pattern_matcher/tests -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/metabolomics/isotope_pattern_matcher.py > scripts/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py +mkdir -p tools/metabolomics/isotope_pattern_matcher/tests +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/metabolomics/isotope_pattern_matcher.py > tools/metabolomics/isotope_pattern_matcher/isotope_pattern_matcher.py ``` - [ ] **Step 2: Create requirements.txt** @@ -704,13 +704,13 @@ python isotope_pattern_matcher.py --formula C6H12O6 --peaks 181.0709,100.0 182.0 - [ ] **Step 6: Run ruff and tests** -Run: `ruff check scripts/metabolomics/isotope_pattern_matcher/ && PYTHONPATH=scripts/metabolomics/isotope_pattern_matcher python -m pytest scripts/metabolomics/isotope_pattern_matcher/tests/ -v` +Run: `ruff check tools/metabolomics/isotope_pattern_matcher/ && PYTHONPATH=tools/metabolomics/isotope_pattern_matcher python -m pytest tools/metabolomics/isotope_pattern_matcher/tests/ -v` Expected: Lint clean, 6 tests pass - [ ] **Step 7: Commit** ```bash -git add scripts/metabolomics/isotope_pattern_matcher/ +git add tools/metabolomics/isotope_pattern_matcher/ git commit -m "Migrate isotope_pattern_matcher to per-script directory structure" ``` @@ -719,17 +719,17 @@ git commit -m "Migrate isotope_pattern_matcher to per-script directory structure ### Task 8: Migrate metabolite_feature_detection to per-script directory **Files:** -- Create: `scripts/metabolomics/metabolite_feature_detection/metabolite_feature_detection.py` -- Create: `scripts/metabolomics/metabolite_feature_detection/requirements.txt` -- Create: `scripts/metabolomics/metabolite_feature_detection/README.md` -- Create: `scripts/metabolomics/metabolite_feature_detection/tests/conftest.py` -- Create: `scripts/metabolomics/metabolite_feature_detection/tests/test_metabolite_feature_detection.py` +- Create: `tools/metabolomics/metabolite_feature_detection/metabolite_feature_detection.py` +- Create: `tools/metabolomics/metabolite_feature_detection/requirements.txt` +- Create: `tools/metabolomics/metabolite_feature_detection/README.md` +- Create: `tools/metabolomics/metabolite_feature_detection/tests/conftest.py` +- Create: `tools/metabolomics/metabolite_feature_detection/tests/test_metabolite_feature_detection.py` - [ ] **Step 1: Create directory and copy script** ```bash -mkdir -p scripts/metabolomics/metabolite_feature_detection/tests -git show origin/copilot/add-agentic-scripts-for-proteomics:scripts/metabolomics/metabolite_feature_detection.py > scripts/metabolomics/metabolite_feature_detection/metabolite_feature_detection.py +mkdir -p tools/metabolomics/metabolite_feature_detection/tests +git show origin/copilot/add-agentic-scripts-for-proteomics:tools/metabolomics/metabolite_feature_detection.py > tools/metabolomics/metabolite_feature_detection/metabolite_feature_detection.py ``` - [ ] **Step 2: Create requirements.txt** @@ -801,13 +801,13 @@ python metabolite_feature_detection.py --input sample.mzML --output features.fea - [ ] **Step 6: Run ruff and tests** -Run: `ruff check scripts/metabolomics/metabolite_feature_detection/ && PYTHONPATH=scripts/metabolomics/metabolite_feature_detection python -m pytest scripts/metabolomics/metabolite_feature_detection/tests/ -v` +Run: `ruff check tools/metabolomics/metabolite_feature_detection/ && PYTHONPATH=tools/metabolomics/metabolite_feature_detection python -m pytest tools/metabolomics/metabolite_feature_detection/tests/ -v` Expected: Lint clean, 1 test passes - [ ] **Step 7: Commit** ```bash -git add scripts/metabolomics/metabolite_feature_detection/ +git add tools/metabolomics/metabolite_feature_detection/ git commit -m "Migrate metabolite_feature_detection to per-script directory with synthetic test data" ``` @@ -838,7 +838,7 @@ Validate any script in the agentomics repo by running ruff and pytest in a fresh ## Steps (follow exactly — rigid skill) -1. **Identify the script directory.** If the user provided a path, use it. Otherwise, ask which script to validate. The path should be `scripts///`. +1. **Identify the script directory.** If the user provided a path, use it. Otherwise, ask which script to validate. The path should be `tools///`. 2. **Verify the directory structure.** Confirm it contains: - `.py` @@ -918,7 +918,7 @@ git checkout -b add/ ### 5. Scaffold the directory ```bash -mkdir -p scripts///tests +mkdir -p tools///tests ``` Create these files: @@ -950,7 +950,7 @@ requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not in ### 6. Write the script -Create `scripts///.py` following these patterns: +Create `tools///.py` following these patterns: - Module-level docstring with description, supported features, and CLI usage examples - pyopenms import guard: @@ -967,7 +967,7 @@ Create `scripts///.py` following these patterns: ### 7. Write tests -Create `scripts///tests/test_.py`: +Create `tools///tests/test_.py`: - Import `requires_pyopenms` from conftest - Decorate test classes with `@requires_pyopenms` @@ -977,7 +977,7 @@ Create `scripts///tests/test_.py`: ### 8. Write README -Create `scripts///README.md` with a brief description and CLI usage examples. +Create `tools///README.md` with a brief description and CLI usage examples. ### 9. Validate @@ -986,7 +986,7 @@ Invoke the `validate-script` skill on the new script directory. Both ruff and py ### 10. Commit ```bash -git add scripts/// +git add tools/// git commit -m "Add : " ``` ``` @@ -1018,10 +1018,10 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ## Contribution Requirements -Every script must be a **self-contained directory** under `scripts///`: +Every script must be a **self-contained directory** under `tools///`: ``` -scripts/// +tools/// ├── .py # The tool itself ├── requirements.txt # pyopenms + any script-specific deps (no version pins) ├── README.md # Brief description + CLI usage examples @@ -1089,7 +1089,7 @@ Test files: Every script must pass validation in an **isolated venv** before it can be merged. Run these commands from the repo root: ```bash -SCRIPT_DIR=scripts// +SCRIPT_DIR=tools// VENV_DIR=$(mktemp -d) python -m venv "$VENV_DIR" "$VENV_DIR/bin/python" -m pip install -r "$SCRIPT_DIR/requirements.txt" @@ -1138,7 +1138,7 @@ name: Validate Scripts on: pull_request: paths: - - 'scripts/**' + - 'tools/**' jobs: detect-changes: @@ -1156,8 +1156,8 @@ jobs: run: | # Note: github.base_ref is only available on pull_request events # Find all script directories that changed in this PR - CHANGED=$(git diff --name-only origin/${{ github.base_ref }}...HEAD -- 'scripts/' \ - | grep -oP 'scripts/[^/]+/[^/]+/' \ + CHANGED=$(git diff --name-only origin/${{ github.base_ref }}...HEAD -- 'tools/' \ + | grep -oP 'tools/[^/]+/[^/]+/' \ | sort -u \ | jq -R -s -c 'split("\n") | map(select(length > 0))') @@ -1233,16 +1233,16 @@ Agentomics is a collection of standalone CLI tools built with [pyopenms](https:/ ```bash # Install dependencies for a specific script -pip install -r scripts/proteomics/peptide_mass_calculator/requirements.txt +pip install -r tools/proteomics/peptide_mass_calculator/requirements.txt # Lint a specific script -ruff check scripts/proteomics/peptide_mass_calculator/ +ruff check tools/proteomics/peptide_mass_calculator/ # Run tests for a specific script -PYTHONPATH=scripts/proteomics/peptide_mass_calculator python -m pytest scripts/proteomics/peptide_mass_calculator/tests/ -v +PYTHONPATH=tools/proteomics/peptide_mass_calculator python -m pytest tools/proteomics/peptide_mass_calculator/tests/ -v # Lint all scripts -ruff check scripts/ +ruff check tools/ # Run all tests (requires pyopenms installed) find scripts -name 'tests' -type d -exec sh -c 'PYTHONPATH=$(dirname {}) python -m pytest {} -v' \; @@ -1252,10 +1252,10 @@ find scripts -name 'tests' -type d -exec sh -c 'PYTHONPATH=$(dirname {}) python ### Per-Script Directory Structure -Each script is a self-contained directory under `scripts///`: +Each script is a self-contained directory under `tools///`: ``` -scripts/// +tools/// ├── .py # The tool (importable functions + argparse CLI) ├── requirements.txt # pyopenms + script-specific deps ├── README.md # Usage examples @@ -1303,18 +1303,18 @@ pip install pyopenms | Script | Description | |--------|-------------| -| [`peptide_mass_calculator`](scripts/proteomics/peptide_mass_calculator/) | Monoisotopic/average masses and b/y fragment ions for peptide sequences | -| [`protein_digest`](scripts/proteomics/protein_digest/) | In-silico enzymatic protein digestion | -| [`spectrum_file_info`](scripts/proteomics/spectrum_file_info/) | Summary statistics for mzML files | -| [`feature_detection_proteomics`](scripts/proteomics/feature_detection_proteomics/) | Peptide feature detection from LC-MS/MS data | +| [`peptide_mass_calculator`](tools/proteomics/peptide_mass_calculator/) | Monoisotopic/average masses and b/y fragment ions for peptide sequences | +| [`protein_digest`](tools/proteomics/protein_digest/) | In-silico enzymatic protein digestion | +| [`spectrum_file_info`](tools/proteomics/spectrum_file_info/) | Summary statistics for mzML files | +| [`feature_detection_proteomics`](tools/proteomics/feature_detection_proteomics/) | Peptide feature detection from LC-MS/MS data | ### Metabolomics | Script | Description | |--------|-------------| -| [`mass_accuracy_calculator`](scripts/metabolomics/mass_accuracy_calculator/) | m/z mass accuracy (ppm error) for sequences or formulas | -| [`isotope_pattern_matcher`](scripts/metabolomics/isotope_pattern_matcher/) | Theoretical isotope distributions and cosine similarity scoring | -| [`metabolite_feature_detection`](scripts/metabolomics/metabolite_feature_detection/) | Metabolite feature detection from LC-MS data | +| [`mass_accuracy_calculator`](tools/metabolomics/mass_accuracy_calculator/) | m/z mass accuracy (ppm error) for sequences or formulas | +| [`isotope_pattern_matcher`](tools/metabolomics/isotope_pattern_matcher/) | Theoretical isotope distributions and cosine similarity scoring | +| [`metabolite_feature_detection`](tools/metabolomics/metabolite_feature_detection/) | Metabolite feature detection from LC-MS data | ## Validation @@ -1341,7 +1341,7 @@ git commit -m "Update CLAUDE.md and README.md for per-script directory structure Run the validation pipeline on each script directory. For each, execute: ```bash -SCRIPT_DIR=scripts// +SCRIPT_DIR=tools// VENV_DIR=$(mktemp -d) python -m venv "$VENV_DIR" "$VENV_DIR/bin/python" -m pip install -r "$SCRIPT_DIR/requirements.txt" @@ -1352,13 +1352,13 @@ rm -rf "$VENV_DIR" ``` Run for each: -1. `scripts/proteomics/peptide_mass_calculator` -2. `scripts/proteomics/protein_digest` -3. `scripts/proteomics/spectrum_file_info` -4. `scripts/proteomics/feature_detection_proteomics` -5. `scripts/metabolomics/mass_accuracy_calculator` -6. `scripts/metabolomics/isotope_pattern_matcher` -7. `scripts/metabolomics/metabolite_feature_detection` +1. `tools/proteomics/peptide_mass_calculator` +2. `tools/proteomics/protein_digest` +3. `tools/proteomics/spectrum_file_info` +4. `tools/proteomics/feature_detection_proteomics` +5. `tools/metabolomics/mass_accuracy_calculator` +6. `tools/metabolomics/isotope_pattern_matcher` +7. `tools/metabolomics/metabolite_feature_detection` Expected: All 7 pass ruff lint and all tests pass (or skip with `pyopenms not installed`). diff --git a/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md b/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md index 7a3480e..202e35e 100644 --- a/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md +++ b/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md @@ -6,10 +6,10 @@ Define the skills, contributor docs, and CI pipeline that enable AI agents to co ## Per-Script Directory Structure -Every script is a self-contained package under `scripts///`: +Every script is a self-contained package under `tools///`: ``` -scripts/proteomics/peptide_mass_calculator/ +tools/proteomics/peptide_mass_calculator/ ├── peptide_mass_calculator.py ├── requirements.txt ├── README.md @@ -84,7 +84,7 @@ Guides an AI agent through creating a new script end-to-end. Rigid — follow ex 1. **Ask what the tool does** — what pyopenms functionality does it wrap, what gap does it fill 2. **Determine domain** — proteomics or metabolomics (or prompt if a new domain is needed) -3. **Scaffold directory** — create `scripts///` with `requirements.txt`, empty `README.md`, empty test file +3. **Scaffold directory** — create `tools///` with `requirements.txt`, empty `README.md`, empty test file 4. **Write the script** — following established patterns: - pyopenms try/except import with user-friendly error message - `PROTON = 1.007276` constant where mass-to-charge calculations are needed @@ -117,7 +117,7 @@ Platform-agnostic contributor guide at repo root for any AI agent (Copilot, Curs 1. **Project purpose** — agentic-only pyopenms tools for proteomics/metabolomics that don't yet exist in OpenMS 2. **Contribution requirements:** - - Self-contained directory under `scripts///` + - Self-contained directory under `tools///` - Must include: script `.py`, `requirements.txt`, `README.md`, `tests/` with pytest tests - Must use latest pyopenms (no version pinning) - Must pass ruff + pytest in an isolated venv @@ -136,7 +136,7 @@ Platform-agnostic contributor guide at repo root for any AI agent (Copilot, Curs `.github/workflows/validate.yml`: -- **Trigger:** Pull requests that touch anything under `scripts/` +- **Trigger:** Pull requests that touch anything under `tools/` - **Detection job:** Diffs against base branch to identify changed script directories, outputs them as a JSON matrix. Outputs a `has_changes` flag — the validation matrix is conditional on this flag so PRs that only touch non-script files don't produce an empty matrix error. - **Validation matrix:** For each changed script directory, a parallel job that: 1. Checks out the repo @@ -154,10 +154,10 @@ Platform-agnostic contributor guide at repo root for any AI agent (Copilot, Curs After implementation, `CLAUDE.md` must reflect: - New per-script directory structure and how to navigate it -- Per-script test commands: `PYTHONPATH=scripts// python -m pytest scripts///tests/ -v` +- Per-script test commands: `PYTHONPATH=tools// python -m pytest tools///tests/ -v` - Reference to the two Claude Code skills (`contribute-script`, `validate-script`) - Reference to `AGENTS.md` for the full contributor guide -- Ruff lint command: `ruff check scripts/` +- Ruff lint command: `ruff check tools/` ## Deliverables diff --git a/docs/superpowers/specs/2026-03-25-rename-tools-click-migration-design.md b/docs/superpowers/specs/2026-03-25-rename-tools-click-migration-design.md new file mode 100644 index 0000000..1167520 --- /dev/null +++ b/docs/superpowers/specs/2026-03-25-rename-tools-click-migration-design.md @@ -0,0 +1,81 @@ +# Design: Rename scripts/ to tools/ and Migrate argparse to click + +**Date:** 2026-03-25 +**Status:** Approved + +## Summary + +Rename the `scripts/` directory to `tools/` and convert all 123 CLI tools from `argparse` to `click` for a cleaner, more declarative CLI interface. + +## Phase 1 — Directory Rename + +Atomic rename via `git mv scripts/ tools/`. + +Update all references to `scripts/` in: +- `CLAUDE.md` — directory structure, commands, architecture +- `AGENTS.md` — contribution requirements, validation, code patterns +- `README.md` — tool structure, catalog links, examples +- `.github/workflows/validate.yml` — CI paths and grep pattern +- `.claude/skills/contribute-script.md` — scaffolding paths +- `.claude/skills/validate-script.md` — validation paths +- `docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md` +- `docs/superpowers/plans/2026-03-24-ai-contributor-skills.md` + +Single commit: "Rename scripts/ to tools/" + +## Phase 2 — Click Migration + +### Conversion Rules + +Each tool's `main()` function is converted from argparse to click decorators: + +| argparse pattern | click equivalent | +|---|---| +| `import argparse` | `import click` | +| `argparse.ArgumentParser(description="...")` | `@click.command()` | +| `parser.add_argument("--foo", required=True, help="...")` | `@click.option("--foo", required=True, help="...")` | +| `parser.add_argument("--foo", type=int, default=1)` | `@click.option("--foo", type=int, default=1)` | +| `parser.add_argument("--foo", action="store_true")` | `@click.option("--foo", is_flag=True)` | +| `args = parser.parse_args()` | removed — click injects as function params | +| `args.foo` | `foo` (function parameter) | +| `if __name__ == "__main__": main()` | unchanged — click commands are callable | + +### What stays the same + +- Module docstrings +- pyopenms import guard +- Importable functions (the library interface) +- Test structure and conftest.py +- `PROTON` constant where used +- `if __name__ == "__main__": main()` guard + +### Requirements update + +Each tool's `requirements.txt` gets `click` added. + +### Execution + +Two parallel agents, one per domain: +- **Agent 1:** `tools/proteomics/` (~89 tools) +- **Agent 2:** `tools/metabolomics/` (~34 tools) + +No file conflicts since domains don't overlap. + +## Phase 3 — CI and Docs Update + +- Add `click` to the shared venv install in `.github/workflows/validate.yml` +- Update AGENTS.md code pattern: "`main()` function with click CLI" +- Update README.md: "click interface" instead of "argparse interface" +- Update CLAUDE.md: click references in architecture section + +## Directory Structure (after) + +``` +tools//// +├── .py # The tool (importable functions + click CLI) +├── requirements.txt # pyopenms + click + tool-specific deps +├── README.md # Usage examples +└── tests/ + ├── conftest.py # requires_pyopenms marker + sys.path setup + └── test_.py +``` diff --git a/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/README.md b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/README.md similarity index 100% rename from scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/README.md rename to tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/README.md diff --git a/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py similarity index 100% rename from scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py rename to tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py diff --git a/scripts/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt similarity index 100% rename from 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diff --git a/scripts/metabolomics/export/kovats_ri_calculator/README.md b/tools/metabolomics/export/kovats_ri_calculator/README.md similarity index 100% rename from scripts/metabolomics/export/kovats_ri_calculator/README.md rename to tools/metabolomics/export/kovats_ri_calculator/README.md diff --git a/scripts/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py similarity index 100% rename from scripts/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py rename to tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py diff --git a/scripts/metabolomics/export/kovats_ri_calculator/requirements.txt b/tools/metabolomics/export/kovats_ri_calculator/requirements.txt similarity index 100% rename from scripts/metabolomics/export/kovats_ri_calculator/requirements.txt rename to tools/metabolomics/export/kovats_ri_calculator/requirements.txt diff --git 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rename from scripts/proteomics/peptide_analysis/modified_peptide_generator/tests/test_modified_peptide_generator.py rename to tools/proteomics/peptide_analysis/modified_peptide_generator/tests/test_modified_peptide_generator.py diff --git a/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/README.md b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/README.md similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_detectability_predictor/README.md rename to tools/proteomics/peptide_analysis/peptide_detectability_predictor/README.md diff --git a/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py rename to 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a/scripts/proteomics/peptide_analysis/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py rename to tools/proteomics/peptide_analysis/peptide_detectability_predictor/tests/test_peptide_detectability_predictor.py diff --git a/scripts/proteomics/peptide_analysis/peptide_mass_calculator/README.md b/tools/proteomics/peptide_analysis/peptide_mass_calculator/README.md similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_calculator/README.md rename to tools/proteomics/peptide_analysis/peptide_mass_calculator/README.md diff --git a/scripts/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py b/tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py rename to tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py diff --git a/scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt b/tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt rename to tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt diff --git a/scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/conftest.py b/tools/proteomics/peptide_analysis/peptide_mass_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_calculator/tests/conftest.py rename to 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b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py rename to tools/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py diff --git a/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt rename to tools/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt diff --git a/scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/conftest.py b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_mass_fingerprint/tests/conftest.py rename to 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b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/conftest.py rename to tools/proteomics/peptide_analysis/peptide_modification_analyzer/tests/conftest.py diff --git a/scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py rename to tools/proteomics/peptide_analysis/peptide_modification_analyzer/tests/test_peptide_modification_analyzer.py diff --git a/scripts/proteomics/peptide_analysis/peptide_property_calculator/README.md b/tools/proteomics/peptide_analysis/peptide_property_calculator/README.md similarity index 100% rename from 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a/scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/conftest.py b/tools/proteomics/peptide_analysis/peptide_property_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/conftest.py rename to tools/proteomics/peptide_analysis/peptide_property_calculator/tests/conftest.py diff --git a/scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/test_peptide_property_calculator.py b/tools/proteomics/peptide_analysis/peptide_property_calculator/tests/test_peptide_property_calculator.py similarity index 100% rename from scripts/proteomics/peptide_analysis/peptide_property_calculator/tests/test_peptide_property_calculator.py rename to tools/proteomics/peptide_analysis/peptide_property_calculator/tests/test_peptide_property_calculator.py diff --git a/scripts/proteomics/peptide_analysis/peptide_uniqueness_checker/README.md 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--git a/scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/conftest.py b/tools/proteomics/targeted_proteomics/dia_window_analyzer/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/conftest.py rename to tools/proteomics/targeted_proteomics/dia_window_analyzer/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py b/tools/proteomics/targeted_proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py rename to tools/proteomics/targeted_proteomics/dia_window_analyzer/tests/test_dia_window_analyzer.py diff --git a/scripts/proteomics/targeted_proteomics/inclusion_list_generator/README.md b/tools/proteomics/targeted_proteomics/inclusion_list_generator/README.md similarity index 100% rename from scripts/proteomics/targeted_proteomics/inclusion_list_generator/README.md rename to tools/proteomics/targeted_proteomics/inclusion_list_generator/README.md diff --git a/scripts/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py b/tools/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py rename to tools/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py diff --git a/scripts/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt b/tools/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt similarity index 100% rename from scripts/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt rename to tools/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt diff --git a/scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/conftest.py b/tools/proteomics/targeted_proteomics/inclusion_list_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/conftest.py rename to tools/proteomics/targeted_proteomics/inclusion_list_generator/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py b/tools/proteomics/targeted_proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py rename to tools/proteomics/targeted_proteomics/inclusion_list_generator/tests/test_inclusion_list_generator.py diff --git a/scripts/proteomics/targeted_proteomics/irt_calculator/README.md b/tools/proteomics/targeted_proteomics/irt_calculator/README.md similarity index 100% rename from scripts/proteomics/targeted_proteomics/irt_calculator/README.md rename to tools/proteomics/targeted_proteomics/irt_calculator/README.md diff --git a/scripts/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py b/tools/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py rename to tools/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py diff --git a/scripts/proteomics/targeted_proteomics/irt_calculator/requirements.txt b/tools/proteomics/targeted_proteomics/irt_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/targeted_proteomics/irt_calculator/requirements.txt rename to tools/proteomics/targeted_proteomics/irt_calculator/requirements.txt diff --git a/scripts/proteomics/targeted_proteomics/irt_calculator/tests/conftest.py b/tools/proteomics/targeted_proteomics/irt_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/irt_calculator/tests/conftest.py rename to tools/proteomics/targeted_proteomics/irt_calculator/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/irt_calculator/tests/test_irt_calculator.py b/tools/proteomics/targeted_proteomics/irt_calculator/tests/test_irt_calculator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/irt_calculator/tests/test_irt_calculator.py rename to tools/proteomics/targeted_proteomics/irt_calculator/tests/test_irt_calculator.py diff --git a/scripts/proteomics/targeted_proteomics/library_coverage_estimator/README.md b/tools/proteomics/targeted_proteomics/library_coverage_estimator/README.md similarity index 100% rename from scripts/proteomics/targeted_proteomics/library_coverage_estimator/README.md rename to tools/proteomics/targeted_proteomics/library_coverage_estimator/README.md diff --git a/scripts/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py b/tools/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py rename to tools/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py diff --git a/scripts/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt b/tools/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt similarity index 100% rename from scripts/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt rename to tools/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt diff --git a/scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/conftest.py b/tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/conftest.py rename to tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py b/tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py rename to tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py diff --git a/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/README.md b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/README.md similarity index 100% rename from scripts/proteomics/targeted_proteomics/tic_bpc_calculator/README.md rename to tools/proteomics/targeted_proteomics/tic_bpc_calculator/README.md diff --git a/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt similarity index 100% rename from scripts/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt rename to tools/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt diff --git a/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/conftest.py b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/conftest.py rename to tools/proteomics/targeted_proteomics/tic_bpc_calculator/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py rename to tools/proteomics/targeted_proteomics/tic_bpc_calculator/tests/test_tic_bpc_calculator.py diff --git a/scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py rename to tools/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py diff --git a/scripts/proteomics/targeted_proteomics/transition_list_generator/README.md b/tools/proteomics/targeted_proteomics/transition_list_generator/README.md similarity index 100% rename from scripts/proteomics/targeted_proteomics/transition_list_generator/README.md rename to tools/proteomics/targeted_proteomics/transition_list_generator/README.md diff --git a/scripts/proteomics/targeted_proteomics/transition_list_generator/requirements.txt b/tools/proteomics/targeted_proteomics/transition_list_generator/requirements.txt similarity index 100% rename from scripts/proteomics/targeted_proteomics/transition_list_generator/requirements.txt rename to tools/proteomics/targeted_proteomics/transition_list_generator/requirements.txt diff --git a/scripts/proteomics/targeted_proteomics/transition_list_generator/tests/conftest.py b/tools/proteomics/targeted_proteomics/transition_list_generator/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/transition_list_generator/tests/conftest.py rename to tools/proteomics/targeted_proteomics/transition_list_generator/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/transition_list_generator/tests/test_transition_list_generator.py b/tools/proteomics/targeted_proteomics/transition_list_generator/tests/test_transition_list_generator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/transition_list_generator/tests/test_transition_list_generator.py rename to tools/proteomics/targeted_proteomics/transition_list_generator/tests/test_transition_list_generator.py diff --git a/scripts/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py b/tools/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py rename to tools/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py diff --git a/scripts/proteomics/targeted_proteomics/xic_extractor/README.md b/tools/proteomics/targeted_proteomics/xic_extractor/README.md similarity index 100% rename from scripts/proteomics/targeted_proteomics/xic_extractor/README.md rename to tools/proteomics/targeted_proteomics/xic_extractor/README.md diff --git a/scripts/proteomics/targeted_proteomics/xic_extractor/requirements.txt b/tools/proteomics/targeted_proteomics/xic_extractor/requirements.txt similarity index 100% rename from scripts/proteomics/targeted_proteomics/xic_extractor/requirements.txt rename to tools/proteomics/targeted_proteomics/xic_extractor/requirements.txt diff --git a/scripts/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py b/tools/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py rename to tools/proteomics/targeted_proteomics/xic_extractor/tests/conftest.py diff --git a/scripts/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py b/tools/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py rename to tools/proteomics/targeted_proteomics/xic_extractor/tests/test_xic_extractor.py diff --git a/scripts/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py b/tools/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py similarity index 100% rename from scripts/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py rename to tools/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py From f6a0813911e2da27024a9bbac2cb83906f555408 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 09:50:25 +0100 Subject: [PATCH 06/15] Migrate all 123 CLI tools from argparse to click Convert every tool's main() from argparse.ArgumentParser to click decorators (@click.command, @click.option). Add click to all requirements.txt files. Fix 13 line-length violations from long click decorator lines. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../kendrick_mass_defect_analyzer.py | 33 ++++---- .../requirements.txt | 1 + .../metabolite_class_predictor.py | 21 +++-- .../requirements.txt | 1 + .../suspect_screener/requirements.txt | 1 + .../suspect_screener/suspect_screener.py | 31 ++++---- .../requirements.txt | 1 + .../van_krevelen_data_generator.py | 27 +++---- .../drug_metabolite_screener.py | 37 ++++----- .../drug_metabolite_screener/requirements.txt | 1 + .../mass_difference_network_builder.py | 37 ++++----- .../requirements.txt | 1 + .../gnps_fbmn_exporter/gnps_fbmn_exporter.py | 29 ++++--- .../gnps_fbmn_exporter/requirements.txt | 1 + .../kovats_ri_calculator.py | 31 ++++---- .../kovats_ri_calculator/requirements.txt | 1 + .../export/sirius_exporter/requirements.txt | 1 + .../export/sirius_exporter/sirius_exporter.py | 23 +++--- .../adduct_group_analyzer.py | 35 ++++----- .../adduct_group_analyzer/requirements.txt | 1 + .../blank_subtraction_tool.py | 63 +++++++-------- .../blank_subtraction_tool/requirements.txt | 1 + .../duplicate_feature_detector.py | 35 ++++----- .../requirements.txt | 1 + .../isf_detector/isf_detector.py | 33 ++++---- .../isf_detector/requirements.txt | 1 + .../mass_defect_filter/mass_defect_filter.py | 31 ++++---- .../mass_defect_filter/requirements.txt | 1 + .../metabolite_feature_detection.py | 46 +++-------- .../requirements.txt | 1 + .../requirements.txt | 1 + .../targeted_feature_extractor.py | 33 ++++---- .../adduct_calculator/adduct_calculator.py | 42 +++++----- .../adduct_calculator/requirements.txt | 1 + .../formula_mass_calculator.py | 37 ++++----- .../formula_mass_calculator/requirements.txt | 1 + .../formula_validator_golden_rules.py | 25 +++--- .../requirements.txt | 1 + .../mass_accuracy_calculator.py | 57 +++++--------- .../mass_accuracy_calculator/requirements.txt | 1 + .../mass_decomposition_tool.py | 25 +++--- .../mass_decomposition_tool/requirements.txt | 1 + .../metabolite_formula_annotator.py | 29 ++++--- .../requirements.txt | 1 + .../molecular_formula_finder.py | 35 ++++----- .../molecular_formula_finder/requirements.txt | 1 + .../rdbe_calculator/rdbe_calculator.py | 21 +++-- .../rdbe_calculator/requirements.txt | 1 + .../isotope_label_detector.py | 37 ++++----- .../isotope_label_detector/requirements.txt | 1 + .../mid_natural_abundance_corrector.py | 31 ++++---- .../requirements.txt | 1 + .../lipid_ecn_rt_predictor.py | 33 ++++---- .../lipid_ecn_rt_predictor/requirements.txt | 1 + .../lipid_species_resolver.py | 25 +++--- .../lipid_species_resolver/requirements.txt | 1 + .../isotope_pattern_fit_scorer.py | 33 ++++---- .../requirements.txt | 1 + .../isotope_pattern_matcher.py | 54 ++++--------- .../isotope_pattern_matcher/requirements.txt | 1 + .../isotope_pattern_scorer.py | 35 ++++----- .../isotope_pattern_scorer/requirements.txt | 1 + .../massql_query_tool/massql_query_tool.py | 37 ++++----- .../massql_query_tool/requirements.txt | 1 + .../neutral_loss_scanner.py | 29 ++++--- .../neutral_loss_scanner/requirements.txt | 1 + .../spectral_entropy_scorer/requirements.txt | 1 + .../spectral_entropy_scorer.py | 35 ++++----- .../contaminant_database_merger.py | 27 +++---- .../requirements.txt | 1 + .../fasta_cleaner/fasta_cleaner.py | 48 ++++++------ .../fasta_cleaner/requirements.txt | 1 + .../fasta_decoy_validator.py | 31 ++++---- .../fasta_decoy_validator/requirements.txt | 1 + .../fasta_in_silico_digest_stats.py | 29 ++++--- .../requirements.txt | 1 + .../fasta_utils/fasta_merger/fasta_merger.py | 27 ++++--- .../fasta_utils/fasta_merger/requirements.txt | 1 + .../fasta_statistics_reporter.py | 33 ++++---- .../requirements.txt | 1 + .../fasta_subset_extractor.py | 31 ++++---- .../fasta_subset_extractor/requirements.txt | 1 + .../fasta_taxonomy_splitter.py | 25 +++--- .../fasta_taxonomy_splitter/requirements.txt | 1 + .../consensus_map_to_matrix.py | 17 ++-- .../consensus_map_to_matrix/requirements.txt | 1 + .../featurexml_merger/featurexml_merger.py | 17 ++-- .../featurexml_merger/requirements.txt | 1 + .../idxml_to_tsv_exporter.py | 17 ++-- .../idxml_to_tsv_exporter/requirements.txt | 1 + .../mgf_to_mzml_converter.py | 17 ++-- .../mgf_to_mzml_converter/requirements.txt | 1 + .../ms_data_ml_exporter.py | 21 +++-- .../ms_data_ml_exporter/requirements.txt | 1 + .../ms_data_to_csv_exporter.py | 36 ++++----- .../ms_data_to_csv_exporter/requirements.txt | 1 + .../mzml_to_mgf_converter.py | 21 +++-- .../mzml_to_mgf_converter/requirements.txt | 1 + .../mztab_summarizer/mztab_summarizer.py | 21 +++-- .../mztab_summarizer/requirements.txt | 1 + .../feature_detection_proteomics.py | 28 ++----- .../requirements.txt | 1 + .../mzml_metadata_extractor.py | 23 +++--- .../mzml_metadata_extractor/requirements.txt | 1 + .../mzml_spectrum_subsetter.py | 23 +++--- .../mzml_spectrum_subsetter/requirements.txt | 1 + .../peptide_spectral_match_validator.py | 29 ++++--- .../requirements.txt | 1 + .../psm_feature_extractor.py | 25 +++--- .../psm_feature_extractor/requirements.txt | 1 + .../requirements.txt | 1 + .../semi_tryptic_peptide_finder.py | 35 ++++----- .../sequence_tag_generator/requirements.txt | 1 + .../sequence_tag_generator.py | 45 +++++------ .../amino_acid_composition_analyzer.py | 35 ++++----- .../requirements.txt | 1 + .../charge_state_predictor.py | 25 +++--- .../charge_state_predictor/requirements.txt | 1 + .../isoelectric_point_calculator.py | 55 ++++++------- .../requirements.txt | 1 + .../modification_mass_calculator.py | 45 ++++++----- .../requirements.txt | 1 + .../modified_peptide_generator.py | 43 +++++------ .../requirements.txt | 1 + .../peptide_detectability_predictor.py | 39 +++++----- .../requirements.txt | 1 + .../peptide_mass_calculator.py | 36 +++------ .../peptide_mass_calculator/requirements.txt | 1 + .../peptide_mass_fingerprint.py | 49 ++++++------ .../peptide_mass_fingerprint/requirements.txt | 1 + .../peptide_modification_analyzer.py | 27 ++++--- .../requirements.txt | 1 + .../peptide_property_calculator.py | 41 +++++----- .../requirements.txt | 1 + .../peptide_uniqueness_checker.py | 27 ++++--- .../requirements.txt | 1 + .../rt_prediction_additive/requirements.txt | 1 + .../rt_prediction_additive.py | 51 ++++++------ .../peptide_to_protein_mapper.py | 23 +++--- .../requirements.txt | 1 + .../protein_coverage_calculator.py | 25 +++--- .../requirements.txt | 1 + .../protein_digest/protein_digest.py | 77 ++++++------------- .../protein_digest/requirements.txt | 1 + .../protein_group_reporter.py | 31 ++++---- .../protein_group_reporter/requirements.txt | 1 + .../requirements.txt | 1 + .../spectral_counting_quantifier.py | 39 +++++----- .../glycopeptide_mass_calculator.py | 30 +++----- .../requirements.txt | 1 + .../phospho_enrichment_qc.py | 19 ++--- .../phospho_enrichment_qc/requirements.txt | 1 + .../phospho_motif_analyzer.py | 35 ++++----- .../phospho_motif_analyzer/requirements.txt | 1 + .../phosphosite_class_filter.py | 29 ++++--- .../phosphosite_class_filter/requirements.txt | 1 + .../ptm_site_localization_scorer.py | 41 +++++----- .../requirements.txt | 1 + .../acquisition_rate_analyzer.py | 21 +++-- .../requirements.txt | 1 + .../collision_energy_analyzer.py | 23 +++--- .../requirements.txt | 1 + .../identification_qc_reporter.py | 21 +++-- .../requirements.txt | 1 + .../injection_time_analyzer.py | 21 +++-- .../injection_time_analyzer/requirements.txt | 1 + .../lc_ms_qc_reporter/lc_ms_qc_reporter.py | 21 +++-- .../lc_ms_qc_reporter/requirements.txt | 1 + .../mass_error_distribution_analyzer.py | 25 +++--- .../requirements.txt | 1 + .../missed_cleavage_analyzer.py | 29 ++++--- .../missed_cleavage_analyzer/requirements.txt | 1 + .../ms1_feature_intensity_tracker.py | 31 ++++---- .../requirements.txt | 1 + .../mzqc_generator/mzqc_generator.py | 23 +++--- .../mzqc_generator/requirements.txt | 1 + .../precursor_charge_distribution.py | 23 +++--- .../requirements.txt | 1 + .../precursor_isolation_purity.py | 28 +++---- .../requirements.txt | 1 + .../precursor_recurrence_analyzer.py | 29 ++++--- .../requirements.txt | 1 + .../run_comparison_reporter/requirements.txt | 1 + .../run_comparison_reporter.py | 25 +++--- .../requirements.txt | 1 + .../sample_complexity_estimator.py | 30 +++----- .../spectrum_file_info/requirements.txt | 1 + .../spectrum_file_info/spectrum_file_info.py | 32 +++----- .../rna/rna_digest/requirements.txt | 1 + tools/proteomics/rna/rna_digest/rna_digest.py | 41 +++++----- .../requirements.txt | 1 + .../rna_fragment_spectrum_generator.py | 27 ++++--- .../rna/rna_mass_calculator/requirements.txt | 1 + .../rna_mass_calculator.py | 31 ++++---- .../cleavage_site_profiler.py | 31 ++++---- .../cleavage_site_profiler/requirements.txt | 1 + .../immunopeptide_filter.py | 42 +++++----- .../immunopeptide_filter/requirements.txt | 1 + .../immunopeptidome_qc/immunopeptidome_qc.py | 33 ++++---- .../immunopeptidome_qc/requirements.txt | 1 + .../metapeptide_lca_assigner.py | 29 ++++--- .../metapeptide_lca_assigner/requirements.txt | 1 + .../nterm_modification_annotator.py | 23 +++--- .../requirements.txt | 1 + .../proteoform_delta_annotator.py | 31 +++----- .../requirements.txt | 1 + .../requirements.txt | 1 + .../topdown_coverage_calculator.py | 37 ++++----- .../spectral_library_builder/requirements.txt | 1 + .../spectral_library_builder.py | 21 +++-- .../requirements.txt | 1 + .../spectral_library_format_converter.py | 24 +++--- .../spectrum_annotator/requirements.txt | 1 + .../spectrum_annotator/spectrum_annotator.py | 35 ++++----- .../requirements.txt | 1 + .../spectrum_entropy_calculator.py | 25 +++--- .../requirements.txt | 1 + .../spectrum_scoring_hyperscore.py | 35 ++++----- .../requirements.txt | 1 + .../spectrum_similarity_scorer.py | 27 +++---- .../requirements.txt | 1 + .../theoretical_spectrum_generator.py | 31 ++++---- .../crosslink_mass_calculator.py | 39 +++++----- .../requirements.txt | 1 + .../hdx_back_exchange_estimator.py | 39 +++++----- .../requirements.txt | 1 + .../hdx_deuterium_uptake.py | 43 ++++------- .../hdx_deuterium_uptake/requirements.txt | 1 + .../xl_distance_validator/requirements.txt | 1 + .../xl_distance_validator.py | 35 ++++----- .../xl_link_classifier/requirements.txt | 1 + .../xl_link_classifier/xl_link_classifier.py | 25 +++--- .../dia_window_analyzer.py | 23 +++--- .../dia_window_analyzer/requirements.txt | 1 + .../inclusion_list_generator.py | 33 ++++---- .../inclusion_list_generator/requirements.txt | 1 + .../irt_calculator/irt_calculator.py | 29 ++++--- .../irt_calculator/requirements.txt | 1 + .../library_coverage_estimator.py | 30 +++----- .../requirements.txt | 1 + .../tic_bpc_calculator/requirements.txt | 1 + .../tic_bpc_calculator/tic_bpc_calculator.py | 25 +++--- .../requirements.txt | 1 + .../transition_list_generator.py | 34 ++++---- .../xic_extractor/requirements.txt | 1 + .../xic_extractor/xic_extractor.py | 29 ++++--- 246 files changed, 1834 insertions(+), 2191 deletions(-) diff --git a/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py index ce8a5ba..84d0127 100644 --- a/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py +++ b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/kendrick_mass_defect_analyzer.py @@ -9,10 +9,11 @@ python kendrick_mass_defect_analyzer.py --input features.tsv --base CH2 --output kmd.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -126,40 +127,36 @@ def group_homologous_series(kmd_results: list, kmd_tolerance: float = 0.005) -> return sorted_results -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="TSV file with 'mz' or 'formula' column.") +@click.option("--base", default="CH2", help="Base unit formula (default: CH2).") +@click.option("--kmd-tolerance", type=float, default=0.005, + help="KMD tolerance for grouping homologous series (default: 0.005).") +@click.option("--output", required=True, help="Output TSV file with KMD values.") +def main(input_file, base, kmd_tolerance, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Compute Kendrick Mass Defect for configurable base units." - ) - parser.add_argument("--input", required=True, help="TSV file with 'mz' or 'formula' column.") - parser.add_argument("--base", default="CH2", help="Base unit formula (default: CH2).") - parser.add_argument("--kmd-tolerance", type=float, default=0.005, - help="KMD tolerance for grouping homologous series (default: 0.005).") - parser.add_argument("--output", required=True, help="Output TSV file with KMD values.") - args = parser.parse_args() - results = [] - with open(args.input, newline="") as fh: + with open(input_file, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") headers = reader.fieldnames or [] for row in reader: if "formula" in headers: - result = compute_kmd_from_formula(row["formula"], args.base) + result = compute_kmd_from_formula(row["formula"], base) elif "mz" in headers: - result = compute_kmd(float(row["mz"]), args.base) + result = compute_kmd(float(row["mz"]), base) else: sys.exit("Input TSV must have a 'formula' or 'mz' column.") results.append(result) - results = group_homologous_series(results, kmd_tolerance=args.kmd_tolerance) + results = group_homologous_series(results, kmd_tolerance=kmd_tolerance) fieldnames = list(results[0].keys()) if results else [] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Wrote {len(results)} entries to {args.output}") + print(f"Wrote {len(results)} entries to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt +++ b/tools/metabolomics/compound_annotation/kendrick_mass_defect_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py b/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py index e78e8cb..697cbc0 100644 --- a/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py +++ b/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py @@ -15,10 +15,11 @@ python metabolite_class_predictor.py --input formulas.tsv --output predictions.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -267,19 +268,15 @@ def write_predictions(predictions: list[dict], path: str) -> None: writer.writerows(predictions) -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="Formulas table (TSV) with 'formula' column") +@click.option("--output", required=True, help="Output predictions (TSV)") +def main(input_file, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Predict compound class from molecular formula using mass defect, element ratios, and RDBE." - ) - parser.add_argument("--input", required=True, help="Formulas table (TSV) with 'formula' column") - parser.add_argument("--output", required=True, help="Output predictions (TSV)") - args = parser.parse_args() - - formulas = load_formulas(args.input) + formulas = load_formulas(input_file) predictions = classify_batch(formulas) - write_predictions(predictions, args.output) - print(f"Classified {len(predictions)} formulas, written to {args.output}") + write_predictions(predictions, output) + print(f"Classified {len(predictions)} formulas, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/compound_annotation/metabolite_class_predictor/requirements.txt b/tools/metabolomics/compound_annotation/metabolite_class_predictor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/compound_annotation/metabolite_class_predictor/requirements.txt +++ b/tools/metabolomics/compound_annotation/metabolite_class_predictor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/compound_annotation/suspect_screener/requirements.txt b/tools/metabolomics/compound_annotation/suspect_screener/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/compound_annotation/suspect_screener/requirements.txt +++ b/tools/metabolomics/compound_annotation/suspect_screener/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/compound_annotation/suspect_screener/suspect_screener.py b/tools/metabolomics/compound_annotation/suspect_screener/suspect_screener.py index f5c7b5f..1f920e4 100644 --- a/tools/metabolomics/compound_annotation/suspect_screener/suspect_screener.py +++ b/tools/metabolomics/compound_annotation/suspect_screener/suspect_screener.py @@ -10,10 +10,11 @@ python suspect_screener.py --input features.tsv --suspects suspect_list.csv --ppm 5 --output matches.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -192,23 +193,19 @@ def write_matches(matches: list[dict], path: str) -> None: writer.writerows(matches) -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="Feature table (TSV) with mz column") +@click.option("--suspects", required=True, help="Suspect list (CSV) with name, formula, exact_mass") +@click.option("--ppm", type=float, default=5.0, help="PPM tolerance (default: 5)") +@click.option("--output", required=True, help="Output matches (TSV)") +@click.option("--mz-column", default="mz", help="Name of m/z column in features (default: mz)") +def main(input_file, suspects, ppm, output, mz_column) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Match features against a suspect screening list by exact mass." - ) - parser.add_argument("--input", required=True, help="Feature table (TSV) with mz column") - parser.add_argument("--suspects", required=True, help="Suspect list (CSV) with name, formula, exact_mass") - parser.add_argument("--ppm", type=float, default=5.0, help="PPM tolerance (default: 5)") - parser.add_argument("--output", required=True, help="Output matches (TSV)") - parser.add_argument("--mz-column", default="mz", help="Name of m/z column in features (default: mz)") - args = parser.parse_args() - - features = load_features(args.input) - suspects = load_suspects(args.suspects) - matches = screen_suspects(features, suspects, ppm_tolerance=args.ppm, mz_column=args.mz_column) - write_matches(matches, args.output) - print(f"Found {len(matches)} matches, written to {args.output}") + features = load_features(input_file) + suspects_data = load_suspects(suspects) + matches = screen_suspects(features, suspects_data, ppm_tolerance=ppm, mz_column=mz_column) + write_matches(matches, output) + print(f"Found {len(matches)} matches, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/compound_annotation/van_krevelen_data_generator/requirements.txt b/tools/metabolomics/compound_annotation/van_krevelen_data_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/compound_annotation/van_krevelen_data_generator/requirements.txt +++ b/tools/metabolomics/compound_annotation/van_krevelen_data_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py b/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py index 2a81548..c6f7c0e 100644 --- a/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py +++ b/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py @@ -9,10 +9,11 @@ python van_krevelen_data_generator.py --input formulas.tsv --classify --output van_krevelen.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -107,34 +108,30 @@ def process_formulas(formulas: list, classify: bool = False) -> list: return results -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="TSV file with a 'formula' column.") +@click.option("--classify", is_flag=True, help="Add biochemical class assignment.") +@click.option("--output", required=True, help="Output TSV file with ratios.") +def main(input_file, classify, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Compute H:C and O:C ratios from molecular formulas for Van Krevelen diagrams." - ) - parser.add_argument("--input", required=True, help="TSV file with a 'formula' column.") - parser.add_argument("--classify", action="store_true", help="Add biochemical class assignment.") - parser.add_argument("--output", required=True, help="Output TSV file with ratios.") - args = parser.parse_args() - formulas = [] - with open(args.input, newline="") as fh: + with open(input_file, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: formulas.append(row["formula"]) - results = process_formulas(formulas, classify=args.classify) + results = process_formulas(formulas, classify=classify) fieldnames = ["formula", "C", "H", "O", "hc_ratio", "oc_ratio"] - if args.classify: + if classify: fieldnames.append("class") - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Wrote {len(results)} entries to {args.output}") + print(f"Wrote {len(results)} entries to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/drug_metabolism/drug_metabolite_screener/drug_metabolite_screener.py b/tools/metabolomics/drug_metabolism/drug_metabolite_screener/drug_metabolite_screener.py index 5666f40..57aab83 100644 --- a/tools/metabolomics/drug_metabolism/drug_metabolite_screener/drug_metabolite_screener.py +++ b/tools/metabolomics/drug_metabolism/drug_metabolite_screener/drug_metabolite_screener.py @@ -10,10 +10,11 @@ --reactions phase1,phase2 --input run.mzML --ppm 5 --output metabolites.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -146,36 +147,32 @@ def screen_mzml(mzml_path: str, target_masses: list, ppm: float = 5.0) -> list: return matches -def main() -> None: +@click.command() +@click.option("--parent-formula", required=True, help="Molecular formula of the parent drug.") +@click.option("--reactions", default="phase1,phase2", + help="Comma-separated reaction sets: phase1, phase2 (default: phase1,phase2).") +@click.option("--input", "input_file", default=None, help="mzML file to screen (optional).") +@click.option("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5).") +@click.option("--output", required=True, help="Output TSV file.") +def main(parent_formula, reactions, input_file, ppm, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Predict drug metabolites and screen mzML for matches." - ) - parser.add_argument("--parent-formula", required=True, help="Molecular formula of the parent drug.") - parser.add_argument("--reactions", default="phase1,phase2", - help="Comma-separated reaction sets: phase1, phase2 (default: phase1,phase2).") - parser.add_argument("--input", default=None, help="mzML file to screen (optional).") - parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5).") - parser.add_argument("--output", required=True, help="Output TSV file.") - args = parser.parse_args() - - reaction_sets = [r.strip() for r in args.reactions.split(",")] - metabolites = predict_metabolites(args.parent_formula, reaction_sets) - - if args.input: - matches = screen_mzml(args.input, metabolites, ppm=args.ppm) + reaction_sets = [r.strip() for r in reactions.split(",")] + metabolites = predict_metabolites(parent_formula, reaction_sets) + + if input_file: + matches = screen_mzml(input_file, metabolites, ppm=ppm) fieldnames = ["reaction", "expected_mass", "observed_mz", "intensity", "rt", "ppm_error"] output_data = matches else: fieldnames = ["reaction", "formula", "exact_mass", "mass_shift"] output_data = metabolites - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(output_data) - print(f"Wrote {len(output_data)} entries to {args.output}") + print(f"Wrote {len(output_data)} entries to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/drug_metabolism/drug_metabolite_screener/requirements.txt b/tools/metabolomics/drug_metabolism/drug_metabolite_screener/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/drug_metabolism/drug_metabolite_screener/requirements.txt +++ b/tools/metabolomics/drug_metabolism/drug_metabolite_screener/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/drug_metabolism/mass_difference_network_builder/mass_difference_network_builder.py b/tools/metabolomics/drug_metabolism/mass_difference_network_builder/mass_difference_network_builder.py index 140368f..2dc0db8 100644 --- a/tools/metabolomics/drug_metabolism/mass_difference_network_builder/mass_difference_network_builder.py +++ b/tools/metabolomics/drug_metabolism/mass_difference_network_builder/mass_difference_network_builder.py @@ -13,10 +13,11 @@ --tolerance 0.005 --output network.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -123,32 +124,24 @@ def build_network( return edges -def main(): - parser = argparse.ArgumentParser( - description="Build a mass-difference network from features and biotransformation list." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (feature_id, mz)") - parser.add_argument( - "--reactions", default=None, metavar="FILE", - help="Reactions TSV (reaction_name, mass_diff). Uses built-in table if omitted." - ) - parser.add_argument( - "--tolerance", type=float, default=0.005, - help="Mass tolerance in Da (default: 0.005)" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output network TSV") - args = parser.parse_args() - +@click.command() +@click.option("--input", "input_file", required=True, help="Features TSV (feature_id, mz)") +@click.option("--reactions", "reactions_file", default=None, + help="Reactions TSV (reaction_name, mass_diff). Uses built-in table if omitted.") +@click.option("--tolerance", type=float, default=0.005, + help="Mass tolerance in Da (default: 0.005)") +@click.option("--output", required=True, help="Output network TSV") +def main(input_file, reactions_file, tolerance, output): features = [] - with open(args.input) as fh: + with open(input_file) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: features.append(row) - reactions = load_reactions(args.reactions) - edges = build_network(features, reactions, tolerance=args.tolerance) + reactions = load_reactions(reactions_file) + edges = build_network(features, reactions, tolerance=tolerance) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["source_id", "target_id", "reaction", "mass_diff", "error_da"], @@ -157,7 +150,7 @@ def main(): writer.writeheader() writer.writerows(edges) - print(f"Network: {len(edges)} edges from {len(features)} features, written to {args.output}") + print(f"Network: {len(edges)} edges from {len(features)} features, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/drug_metabolism/mass_difference_network_builder/requirements.txt b/tools/metabolomics/drug_metabolism/mass_difference_network_builder/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/drug_metabolism/mass_difference_network_builder/requirements.txt +++ b/tools/metabolomics/drug_metabolism/mass_difference_network_builder/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/export/gnps_fbmn_exporter/gnps_fbmn_exporter.py b/tools/metabolomics/export/gnps_fbmn_exporter/gnps_fbmn_exporter.py index 06ad195..cf14679 100644 --- a/tools/metabolomics/export/gnps_fbmn_exporter/gnps_fbmn_exporter.py +++ b/tools/metabolomics/export/gnps_fbmn_exporter/gnps_fbmn_exporter.py @@ -12,10 +12,11 @@ --output-mgf gnps.mgf --output-quant quant.csv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -241,23 +242,19 @@ def export_fbmn( return n_spectra, len(features) -def main() -> None: +@click.command() +@click.option("--mzml", required=True, help="Input mzML file") +@click.option("--features", required=True, help="Feature table (TSV) with feature_id, mz, rt, intensity") +@click.option("--output-mgf", required=True, help="Output MGF file") +@click.option("--output-quant", required=True, help="Output quantification CSV") +@click.option("--mz-tol", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") +@click.option("--rt-tol", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") +def main(mzml, features, output_mgf, output_quant, mz_tol, rt_tol) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Export MS2 + quant table in GNPS FBMN format." - ) - parser.add_argument("--mzml", required=True, help="Input mzML file") - parser.add_argument("--features", required=True, help="Feature table (TSV) with feature_id, mz, rt, intensity") - parser.add_argument("--output-mgf", required=True, help="Output MGF file") - parser.add_argument("--output-quant", required=True, help="Output quantification CSV") - parser.add_argument("--mz-tol", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") - parser.add_argument("--rt-tol", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") - args = parser.parse_args() - - features = load_features(args.features) + features_data = load_features(features) n_spectra, n_features = export_fbmn( - args.mzml, features, args.output_mgf, args.output_quant, - mz_tol=args.mz_tol, rt_tol=args.rt_tol, + mzml, features_data, output_mgf, output_quant, + mz_tol=mz_tol, rt_tol=rt_tol, ) print(f"Exported {n_spectra} MS2 spectra and {n_features} features for GNPS FBMN") diff --git a/tools/metabolomics/export/gnps_fbmn_exporter/requirements.txt b/tools/metabolomics/export/gnps_fbmn_exporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/export/gnps_fbmn_exporter/requirements.txt +++ b/tools/metabolomics/export/gnps_fbmn_exporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py index 725e403..83ea2ba 100644 --- a/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py +++ b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py @@ -14,11 +14,12 @@ python kovats_ri_calculator.py --input features.tsv --standards alkane_rts.tsv --output ri_values.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -168,23 +169,19 @@ def write_results(results: list[dict], path: str) -> None: writer.writerows(results) -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="Feature table (TSV) with rt column") +@click.option("--standards", required=True, help="Alkane standards (TSV) with carbon_number, rt") +@click.option("--output", required=True, help="Output RI values (TSV)") +@click.option("--rt-column", default="rt", help="Name of RT column (default: rt)") +def main(input_file, standards, output, rt_column) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Calculate Kovats retention indices from alkane standards for GC-MS." - ) - parser.add_argument("--input", required=True, help="Feature table (TSV) with rt column") - parser.add_argument("--standards", required=True, help="Alkane standards (TSV) with carbon_number, rt") - parser.add_argument("--output", required=True, help="Output RI values (TSV)") - parser.add_argument("--rt-column", default="rt", help="Name of RT column (default: rt)") - args = parser.parse_args() - - features = load_tsv(args.input) - standards = load_tsv(args.standards) - alkane_table = build_alkane_table(standards) - results = calculate_ri_batch(features, alkane_table, rt_column=args.rt_column) - write_results(results, args.output) - print(f"Calculated RI for {len(results)} features, written to {args.output}") + features = load_tsv(input_file) + standards_data = load_tsv(standards) + alkane_table = build_alkane_table(standards_data) + results = calculate_ri_batch(features, alkane_table, rt_column=rt_column) + write_results(results, output) + print(f"Calculated RI for {len(results)} features, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/export/kovats_ri_calculator/requirements.txt b/tools/metabolomics/export/kovats_ri_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/export/kovats_ri_calculator/requirements.txt +++ b/tools/metabolomics/export/kovats_ri_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/export/sirius_exporter/requirements.txt b/tools/metabolomics/export/sirius_exporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/export/sirius_exporter/requirements.txt +++ b/tools/metabolomics/export/sirius_exporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/export/sirius_exporter/sirius_exporter.py b/tools/metabolomics/export/sirius_exporter/sirius_exporter.py index 92e53a9..bace632 100644 --- a/tools/metabolomics/export/sirius_exporter/sirius_exporter.py +++ b/tools/metabolomics/export/sirius_exporter/sirius_exporter.py @@ -8,11 +8,12 @@ python sirius_exporter.py --features features.tsv --mzml data.mzML --output sirius_input.ms """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -133,18 +134,16 @@ def export_to_sirius( return write_sirius_ms(features, exp, output_path, mz_tolerance, rt_tolerance) -def main() -> None: - parser = argparse.ArgumentParser(description="Export features + MS2 to SIRIUS .ms format.") - parser.add_argument("--features", required=True, help="Input features TSV (columns: mz, rt, charge, name)") - parser.add_argument("--mzml", required=True, help="Input mzML file") - parser.add_argument("--output", required=True, help="Output SIRIUS .ms file") - parser.add_argument("--mz-tolerance", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") - parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") - args = parser.parse_args() - - stats = export_to_sirius(args.features, args.mzml, args.output, args.mz_tolerance, args.rt_tolerance) +@click.command() +@click.option("--features", required=True, help="Input features TSV (columns: mz, rt, charge, name)") +@click.option("--mzml", required=True, help="Input mzML file") +@click.option("--output", required=True, help="Output SIRIUS .ms file") +@click.option("--mz-tolerance", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") +@click.option("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") +def main(features, mzml, output, mz_tolerance, rt_tolerance) -> None: + stats = export_to_sirius(features, mzml, output, mz_tolerance, rt_tolerance) print(f"Exported {stats['features_exported']} features ({stats['features_with_ms2']} with MS2) " - f"to {args.output}") + f"to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/feature_processing/adduct_group_analyzer/adduct_group_analyzer.py b/tools/metabolomics/feature_processing/adduct_group_analyzer/adduct_group_analyzer.py index ccf4bbe..d4f570f 100644 --- a/tools/metabolomics/feature_processing/adduct_group_analyzer/adduct_group_analyzer.py +++ b/tools/metabolomics/feature_processing/adduct_group_analyzer/adduct_group_analyzer.py @@ -12,10 +12,11 @@ python adduct_group_analyzer.py --input features.tsv --rt-tolerance 5 --output groups.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -112,33 +113,25 @@ def union(i: int, j: int): return results -def main(): - parser = argparse.ArgumentParser( - description="Group features by adduct relationships using m/z+RT proximity." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (mz, rt columns)") - parser.add_argument( - "--rt-tolerance", type=float, default=5.0, - help="RT tolerance in seconds for co-elution (default: 5)" - ) - parser.add_argument( - "--mz-tolerance", type=float, default=0.01, - help="m/z tolerance in Da for adduct matching (default: 0.01)" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output grouped TSV") - args = parser.parse_args() - +@click.command() +@click.option("--input", "input_file", required=True, help="Features TSV (mz, rt columns)") +@click.option("--rt-tolerance", type=float, default=5.0, + help="RT tolerance in seconds for co-elution (default: 5)") +@click.option("--mz-tolerance", type=float, default=0.01, + help="m/z tolerance in Da for adduct matching (default: 0.01)") +@click.option("--output", required=True, help="Output grouped TSV") +def main(input_file, rt_tolerance, mz_tolerance, output): features = [] - with open(args.input) as fh: + with open(input_file) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: features.append(row) groups = find_adduct_groups( - features, rt_tolerance=args.rt_tolerance, mz_tolerance_da=args.mz_tolerance + features, rt_tolerance=rt_tolerance, mz_tolerance_da=mz_tolerance ) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["feature_id", "mz", "rt", "group_id", "adduct_annotation"], @@ -148,7 +141,7 @@ def main(): writer.writerows(groups) n_groups = len(set(g["group_id"] for g in groups)) - print(f"Grouped {len(features)} features into {n_groups} groups, written to {args.output}") + print(f"Grouped {len(features)} features into {n_groups} groups, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/feature_processing/adduct_group_analyzer/requirements.txt b/tools/metabolomics/feature_processing/adduct_group_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/adduct_group_analyzer/requirements.txt +++ b/tools/metabolomics/feature_processing/adduct_group_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/blank_subtraction_tool/blank_subtraction_tool.py b/tools/metabolomics/feature_processing/blank_subtraction_tool/blank_subtraction_tool.py index d7cbea8..0aaa4e2 100644 --- a/tools/metabolomics/feature_processing/blank_subtraction_tool/blank_subtraction_tool.py +++ b/tools/metabolomics/feature_processing/blank_subtraction_tool/blank_subtraction_tool.py @@ -13,10 +13,11 @@ --fold-change 3 --output cleaned.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -77,55 +78,45 @@ def subtract_blanks( return cleaned -def main(): - parser = argparse.ArgumentParser( - description="Subtract blank features from sample features." - ) - parser.add_argument("--sample", required=True, metavar="FILE", help="Sample features TSV") - parser.add_argument("--blank", required=True, metavar="FILE", help="Blank features TSV") - parser.add_argument( - "--fold-change", type=float, default=3.0, - help="Minimum sample/blank fold-change to keep (default: 3)" - ) - parser.add_argument( - "--mz-tolerance", type=float, default=10.0, - help="m/z tolerance in ppm (default: 10)" - ) - parser.add_argument( - "--rt-tolerance", type=float, default=10.0, - help="RT tolerance in seconds (default: 10)" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output cleaned TSV") - args = parser.parse_args() - - sample = [] - with open(args.sample) as fh: +@click.command() +@click.option("--sample", required=True, help="Sample features TSV") +@click.option("--blank", required=True, help="Blank features TSV") +@click.option("--fold-change", type=float, default=3.0, + help="Minimum sample/blank fold-change to keep (default: 3)") +@click.option("--mz-tolerance", type=float, default=10.0, + help="m/z tolerance in ppm (default: 10)") +@click.option("--rt-tolerance", type=float, default=10.0, + help="RT tolerance in seconds (default: 10)") +@click.option("--output", required=True, help="Output cleaned TSV") +def main(sample, blank, fold_change, mz_tolerance, rt_tolerance, output): + sample_data = [] + with open(sample) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: - sample.append(row) + sample_data.append(row) - blank = [] - with open(args.blank) as fh: + blank_data = [] + with open(blank) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: - blank.append(row) + blank_data.append(row) cleaned = subtract_blanks( - sample, blank, - fold_change=args.fold_change, - mz_tolerance_ppm=args.mz_tolerance, - rt_tolerance=args.rt_tolerance, + sample_data, blank_data, + fold_change=fold_change, + mz_tolerance_ppm=mz_tolerance, + rt_tolerance=rt_tolerance, ) fieldnames = list(cleaned[0].keys()) if cleaned else ["mz", "rt", "intensity", "blank_subtracted"] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(cleaned) - removed = len(sample) - len(cleaned) - print(f"Blank subtraction: {len(sample)} input, {removed} removed, {len(cleaned)} kept") - print(f"Output written to {args.output}") + removed = len(sample_data) - len(cleaned) + print(f"Blank subtraction: {len(sample_data)} input, {removed} removed, {len(cleaned)} kept") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/feature_processing/blank_subtraction_tool/requirements.txt b/tools/metabolomics/feature_processing/blank_subtraction_tool/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/blank_subtraction_tool/requirements.txt +++ b/tools/metabolomics/feature_processing/blank_subtraction_tool/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/duplicate_feature_detector/duplicate_feature_detector.py b/tools/metabolomics/feature_processing/duplicate_feature_detector/duplicate_feature_detector.py index 04142b9..1bdc2f8 100644 --- a/tools/metabolomics/feature_processing/duplicate_feature_detector/duplicate_feature_detector.py +++ b/tools/metabolomics/feature_processing/duplicate_feature_detector/duplicate_feature_detector.py @@ -13,10 +13,11 @@ --rt-tolerance 5 --output deduplicated.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -121,32 +122,24 @@ def deduplicate(features: list[dict], mz_tolerance_ppm: float = 10.0, rt_toleran return [f for f in annotated if f["is_duplicate"] == "false"] -def main(): - parser = argparse.ArgumentParser( - description="Detect duplicate features by m/z+RT proximity." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV") - parser.add_argument( - "--mz-tolerance", type=float, default=10.0, - help="m/z tolerance in ppm (default: 10)" - ) - parser.add_argument( - "--rt-tolerance", type=float, default=5.0, - help="RT tolerance in seconds (default: 5)" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output deduplicated TSV") - args = parser.parse_args() - +@click.command() +@click.option("--input", "input_file", required=True, help="Features TSV") +@click.option("--mz-tolerance", type=float, default=10.0, + help="m/z tolerance in ppm (default: 10)") +@click.option("--rt-tolerance", type=float, default=5.0, + help="RT tolerance in seconds (default: 5)") +@click.option("--output", required=True, help="Output deduplicated TSV") +def main(input_file, mz_tolerance, rt_tolerance, output): features = [] - with open(args.input) as fh: + with open(input_file) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: features.append(row) - deduped = deduplicate(features, args.mz_tolerance, args.rt_tolerance) + deduped = deduplicate(features, mz_tolerance, rt_tolerance) fieldnames = list(features[0].keys()) if features else ["mz", "rt", "intensity"] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() # Write without the extra keys @@ -156,7 +149,7 @@ def main(): removed = len(features) - len(deduped) print(f"Deduplication: {len(features)} input, {removed} duplicates removed, {len(deduped)} kept") - print(f"Output written to {args.output}") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/feature_processing/duplicate_feature_detector/requirements.txt b/tools/metabolomics/feature_processing/duplicate_feature_detector/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/duplicate_feature_detector/requirements.txt +++ b/tools/metabolomics/feature_processing/duplicate_feature_detector/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/isf_detector/isf_detector.py b/tools/metabolomics/feature_processing/isf_detector/isf_detector.py index ec795b2..5cea076 100644 --- a/tools/metabolomics/feature_processing/isf_detector/isf_detector.py +++ b/tools/metabolomics/feature_processing/isf_detector/isf_detector.py @@ -9,11 +9,12 @@ python isf_detector.py --input features.tsv --rt-tolerance 3 --output isf_annotated.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -175,37 +176,33 @@ def annotate_features(features: list, isf_pairs: list) -> list: return annotated -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="TSV with columns: id, mz, rt, intensity.") +@click.option("--rt-tolerance", type=float, default=3.0, + help="RT tolerance in seconds (default: 3).") +@click.option("--mass-tolerance", type=float, default=0.01, + help="Mass tolerance in Da for neutral loss matching (default: 0.01).") +@click.option("--output", required=True, help="Output TSV with ISF annotations.") +def main(input_file, rt_tolerance, mass_tolerance, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Detect in-source fragmentation by coelution and common neutral losses." - ) - parser.add_argument("--input", required=True, help="TSV with columns: id, mz, rt, intensity.") - parser.add_argument("--rt-tolerance", type=float, default=3.0, - help="RT tolerance in seconds (default: 3).") - parser.add_argument("--mass-tolerance", type=float, default=0.01, - help="Mass tolerance in Da for neutral loss matching (default: 0.01).") - parser.add_argument("--output", required=True, help="Output TSV with ISF annotations.") - args = parser.parse_args() - features = [] - with open(args.input, newline="") as fh: + with open(input_file, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: features.append(row) - isf_pairs = detect_isf_pairs(features, rt_tolerance=args.rt_tolerance, - mass_tolerance_da=args.mass_tolerance) + isf_pairs = detect_isf_pairs(features, rt_tolerance=rt_tolerance, + mass_tolerance_da=mass_tolerance) annotated = annotate_features(features, isf_pairs) fieldnames = list(annotated[0].keys()) if annotated else [] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(annotated) n_isf = sum(1 for a in annotated if a["isf_flag"]) - print(f"Wrote {len(annotated)} features ({n_isf} ISF-flagged) to {args.output}") + print(f"Wrote {len(annotated)} features ({n_isf} ISF-flagged) to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/feature_processing/isf_detector/requirements.txt b/tools/metabolomics/feature_processing/isf_detector/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/isf_detector/requirements.txt +++ b/tools/metabolomics/feature_processing/isf_detector/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/mass_defect_filter/mass_defect_filter.py b/tools/metabolomics/feature_processing/mass_defect_filter/mass_defect_filter.py index d384d65..981c3ed 100644 --- a/tools/metabolomics/feature_processing/mass_defect_filter/mass_defect_filter.py +++ b/tools/metabolomics/feature_processing/mass_defect_filter/mass_defect_filter.py @@ -16,10 +16,11 @@ --output filtered.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -166,23 +167,19 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Compute mass defect and Kendrick mass defect, filter features." - ) - parser.add_argument("--input", required=True, help="Input TSV with exact_mass column") - parser.add_argument("--mdf-min", type=float, default=0.0, help="Minimum mass defect (default: 0.0)") - parser.add_argument("--mdf-max", type=float, default=1.0, help="Maximum mass defect (default: 1.0)") - parser.add_argument("--kendrick-base", default="CH2", help="Kendrick base formula (default: CH2)") - parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") - args = parser.parse_args() - - features = read_features_tsv(args.input) - results = filter_by_mass_defect(features, args.mdf_min, args.mdf_max, args.kendrick_base) +@click.command() +@click.option("--input", "input_file", required=True, help="Input TSV with exact_mass column") +@click.option("--mdf-min", type=float, default=0.0, help="Minimum mass defect (default: 0.0)") +@click.option("--mdf-max", type=float, default=1.0, help="Maximum mass defect (default: 1.0)") +@click.option("--kendrick-base", default="CH2", help="Kendrick base formula (default: CH2)") +@click.option("--output", default=None, help="Output TSV file path (default: print to stdout)") +def main(input_file, mdf_min, mdf_max, kendrick_base, output): + features = read_features_tsv(input_file) + results = filter_by_mass_defect(features, mdf_min, mdf_max, kendrick_base) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} filtered features to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} filtered features to {output}") else: if results: print("\t".join(results[0].keys())) diff --git a/tools/metabolomics/feature_processing/mass_defect_filter/requirements.txt b/tools/metabolomics/feature_processing/mass_defect_filter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/mass_defect_filter/requirements.txt +++ b/tools/metabolomics/feature_processing/mass_defect_filter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/metabolite_feature_detection/metabolite_feature_detection.py b/tools/metabolomics/feature_processing/metabolite_feature_detection/metabolite_feature_detection.py index 2e88c08..2b52b12 100644 --- a/tools/metabolomics/feature_processing/metabolite_feature_detection/metabolite_feature_detection.py +++ b/tools/metabolomics/feature_processing/metabolite_feature_detection/metabolite_feature_detection.py @@ -11,9 +11,10 @@ python metabolite_feature_detection.py --input sample.mzML --output features.featureXML --noise 1e5 """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -105,40 +106,15 @@ def print_feature_summary(feature_map: oms.FeatureMap, top_n: int = 20) -> None: ) -def main(): - parser = argparse.ArgumentParser( - description="Detect metabolite features in an mzML file using pyopenms." - ) - parser.add_argument( - "--input", - required=True, - metavar="FILE", - help="Centroided mzML input file", - ) - parser.add_argument( - "--output", - metavar="FILE", - help="Output featureXML file (default: .featureXML)", - ) - parser.add_argument( - "--noise", - type=float, - default=1e4, - metavar="THRESHOLD", - help="Noise intensity threshold for mass tracing (default: 1e4)", - ) - parser.add_argument( - "--top", - type=int, - default=20, - metavar="N", - help="Number of top features to print (default: 20)", - ) - args = parser.parse_args() - - output_path = args.output or args.input.replace(".mzML", "_metabolites.featureXML") - feature_map = detect_metabolite_features(args.input, output_path, args.noise) - print_feature_summary(feature_map, args.top) +@click.command() +@click.option("--input", "input_file", required=True, help="Centroided mzML input file") +@click.option("--output", default=None, help="Output featureXML file (default: .featureXML)") +@click.option("--noise", type=float, default=1e4, help="Noise intensity threshold for mass tracing (default: 1e4)") +@click.option("--top", type=int, default=20, help="Number of top features to print (default: 20)") +def main(input_file, output, noise, top): + output_path = output or input_file.replace(".mzML", "_metabolites.featureXML") + feature_map = detect_metabolite_features(input_file, output_path, noise) + print_feature_summary(feature_map, top) if __name__ == "__main__": diff --git a/tools/metabolomics/feature_processing/metabolite_feature_detection/requirements.txt b/tools/metabolomics/feature_processing/metabolite_feature_detection/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/metabolite_feature_detection/requirements.txt +++ b/tools/metabolomics/feature_processing/metabolite_feature_detection/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/targeted_feature_extractor/requirements.txt b/tools/metabolomics/feature_processing/targeted_feature_extractor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/feature_processing/targeted_feature_extractor/requirements.txt +++ b/tools/metabolomics/feature_processing/targeted_feature_extractor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/feature_processing/targeted_feature_extractor/targeted_feature_extractor.py b/tools/metabolomics/feature_processing/targeted_feature_extractor/targeted_feature_extractor.py index 24bed39..a433a08 100644 --- a/tools/metabolomics/feature_processing/targeted_feature_extractor/targeted_feature_extractor.py +++ b/tools/metabolomics/feature_processing/targeted_feature_extractor/targeted_feature_extractor.py @@ -13,10 +13,11 @@ --ppm 5 --output quantified.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -139,28 +140,24 @@ def extract_targets( return results -def main(): - parser = argparse.ArgumentParser( - description="Extract features for known compounds from MS1 data." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="mzML file") - parser.add_argument("--targets", required=True, metavar="FILE", help="Compounds TSV (name, formula)") - parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") - parser.add_argument("--output", required=True, metavar="FILE", help="Output quantified TSV") - args = parser.parse_args() - +@click.command() +@click.option("--input", "input_file", required=True, help="mzML file") +@click.option("--targets", required=True, help="Compounds TSV (name, formula)") +@click.option("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") +@click.option("--output", required=True, help="Output quantified TSV") +def main(input_file, targets, ppm, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input_file, exp) - targets = [] - with open(args.targets) as fh: + target_list = [] + with open(targets) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: - targets.append(row) + target_list.append(row) - results = extract_targets(exp, targets, ppm=args.ppm) + results = extract_targets(exp, target_list, ppm=ppm) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["name", "formula", "target_mz", "peak_area", "max_intensity", "n_points"], @@ -169,7 +166,7 @@ def main(): writer.writeheader() writer.writerows(results) - print(f"Extracted {len(results)} targets, written to {args.output}") + print(f"Extracted {len(results)} targets, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/formula_tools/adduct_calculator/adduct_calculator.py b/tools/metabolomics/formula_tools/adduct_calculator/adduct_calculator.py index 981afb2..68f4fc5 100644 --- a/tools/metabolomics/formula_tools/adduct_calculator/adduct_calculator.py +++ b/tools/metabolomics/formula_tools/adduct_calculator/adduct_calculator.py @@ -12,10 +12,11 @@ python adduct_calculator.py --mass 180.0634 --mode negative --output adducts.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -95,35 +96,32 @@ def formula_to_mass(formula: str) -> float: return ef.getMonoWeight() -def main(): - parser = argparse.ArgumentParser( - description="Compute m/z for all ESI adducts given formula or mass." - ) - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument("--formula", help="Molecular formula (e.g. C6H12O6)") - group.add_argument("--mass", type=float, help="Neutral monoisotopic mass in Da") - parser.add_argument( - "--mode", choices=["positive", "negative"], default="positive", - help="Ionization mode (default: positive)" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV file") - args = parser.parse_args() - - if args.formula: - mass = formula_to_mass(args.formula) - print(f"Formula: {args.formula} Mass: {mass:.6f} Da") +@click.command() +@click.option("--formula", default=None, help="Molecular formula (e.g. C6H12O6)") +@click.option("--mass", type=float, default=None, help="Neutral monoisotopic mass in Da") +@click.option("--mode", type=click.Choice(["positive", "negative"]), default="positive", + help="Ionization mode (default: positive)") +@click.option("--output", required=True, help="Output TSV file") +def main(formula, mass, mode, output): + if not formula and mass is None: + raise click.UsageError("Either --formula or --mass must be provided.") + if formula and mass is not None: + raise click.UsageError("--formula and --mass are mutually exclusive.") + + if formula: + mass = formula_to_mass(formula) + print(f"Formula: {formula} Mass: {mass:.6f} Da") else: - mass = args.mass print(f"Mass: {mass:.6f} Da") - adducts = compute_adducts(mass, args.mode) + adducts = compute_adducts(mass, mode) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["adduct", "mz", "charge"], delimiter="\t") writer.writeheader() writer.writerows(adducts) - print(f"\n{len(adducts)} adducts written to {args.output}") + print(f"\n{len(adducts)} adducts written to {output}") for a in adducts: print(f" {a['adduct']:<20} m/z = {a['mz']:.6f} (z={a['charge']})") diff --git a/tools/metabolomics/formula_tools/adduct_calculator/requirements.txt b/tools/metabolomics/formula_tools/adduct_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/adduct_calculator/requirements.txt +++ b/tools/metabolomics/formula_tools/adduct_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/formula_mass_calculator/formula_mass_calculator.py b/tools/metabolomics/formula_tools/formula_mass_calculator/formula_mass_calculator.py index 4b650fd..2d62003 100644 --- a/tools/metabolomics/formula_tools/formula_mass_calculator/formula_mass_calculator.py +++ b/tools/metabolomics/formula_tools/formula_mass_calculator/formula_mass_calculator.py @@ -10,11 +10,12 @@ python formula_mass_calculator.py --batch formulas.tsv --output masses.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -100,20 +101,20 @@ def batch_calculate(rows: list[dict]) -> list[dict]: return results -def main(): - parser = argparse.ArgumentParser( - description="Calculate masses for molecular formulas with adducts." - ) - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument("--formula", help="Single molecular formula (e.g. C6H12O6)") - group.add_argument("--batch", metavar="FILE", help="Batch TSV file with formula column") - parser.add_argument("--adduct", default="[M]", help='Adduct type (default: "[M]" = neutral)') - parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON or TSV file") - args = parser.parse_args() - - if args.formula: - result = calculate_formula_mass(args.formula, args.adduct) - with open(args.output, "w") as fh: +@click.command() +@click.option("--formula", default=None, help="Single molecular formula (e.g. C6H12O6)") +@click.option("--batch", default=None, help="Batch TSV file with formula column") +@click.option("--adduct", default="[M]", help='Adduct type (default: "[M]" = neutral)') +@click.option("--output", required=True, help="Output JSON or TSV file") +def main(formula, batch, adduct, output): + if not formula and not batch: + raise click.UsageError("Either --formula or --batch must be provided.") + if formula and batch: + raise click.UsageError("--formula and --batch are mutually exclusive.") + + if formula: + result = calculate_formula_mass(formula, adduct) + with open(output, "w") as fh: json.dump(result, fh, indent=2) print(f"Formula: {result['formula']}") print(f"Adduct: {result['adduct']}") @@ -122,14 +123,14 @@ def main(): print(f"m/z: {result['mz']:.6f}") else: rows = [] - with open(args.batch) as fh: + with open(batch) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: rows.append(row) results = batch_calculate(rows) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["formula", "adduct", "monoisotopic_mass", "average_mass", "mz", "charge"], @@ -138,7 +139,7 @@ def main(): writer.writeheader() writer.writerows(results) - print(f"Calculated masses for {len(results)} formulas, written to {args.output}") + print(f"Calculated masses for {len(results)} formulas, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/formula_tools/formula_mass_calculator/requirements.txt b/tools/metabolomics/formula_tools/formula_mass_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/formula_mass_calculator/requirements.txt +++ b/tools/metabolomics/formula_tools/formula_mass_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py b/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py index 36d2a8b..b43c3c3 100644 --- a/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py +++ b/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py @@ -9,10 +9,11 @@ python formula_validator_golden_rules.py --input formulas.tsv --rules all --output validated.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -230,21 +231,17 @@ def validate_formulas(formulas: list, rules: list) -> list: return [validate_formula(f, rules) for f in formulas] -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="TSV file with a 'formula' column.") +@click.option("--rules", default="all", + help="Comma-separated rules: rdbe,hc,nc,oc,sc,pc or 'all' (default: all).") +@click.option("--output", required=True, help="Output TSV with validation results.") +def main(input_file, rules, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Apply Seven Golden Rules to validate molecular formulas." - ) - parser.add_argument("--input", required=True, help="TSV file with a 'formula' column.") - parser.add_argument("--rules", default="all", - help="Comma-separated rules: rdbe,hc,nc,oc,sc,pc or 'all' (default: all).") - parser.add_argument("--output", required=True, help="Output TSV with validation results.") - args = parser.parse_args() - - rules = [r.strip() for r in args.rules.split(",")] + rules = [r.strip() for r in rules.split(",")] formulas = [] - with open(args.input, newline="") as fh: + with open(input_file, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: formulas.append(row["formula"]) @@ -252,7 +249,7 @@ def main() -> None: results = validate_formulas(formulas, rules) fieldnames = list(results[0].keys()) if results else [] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) diff --git a/tools/metabolomics/formula_tools/formula_validator_golden_rules/requirements.txt b/tools/metabolomics/formula_tools/formula_validator_golden_rules/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/formula_validator_golden_rules/requirements.txt +++ b/tools/metabolomics/formula_tools/formula_validator_golden_rules/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py b/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py index b8c1b7a..ddba34b 100644 --- a/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py +++ b/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py @@ -21,9 +21,10 @@ --observed 554.2478 554.2480 554.2482 """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -92,46 +93,28 @@ def ppm_error(theoretical: float, observed: float) -> float: return (observed - theoretical) / theoretical * 1e6 -def main(): - parser = argparse.ArgumentParser( - description="Compute m/z mass accuracy (ppm error) using pyopenms." - ) - group = parser.add_mutually_exclusive_group(required=True) - group.add_argument( - "--sequence", - help="Peptide sequence (e.g. PEPTIDEK)", - ) - group.add_argument( - "--formula", - help="Molecular formula (e.g. C6H12O6)", - ) - parser.add_argument( - "--charge", - type=int, - default=1, - help="Charge state (default: 1)", - ) - parser.add_argument( - "--observed", - nargs="+", - type=float, - required=True, - metavar="MZ", - help="Observed m/z value(s)", - ) - args = parser.parse_args() - - if args.sequence: - theoretical = theoretical_mz_from_sequence(args.sequence, args.charge) - label = f"sequence={args.sequence}" +@click.command() +@click.option("--sequence", default=None, help="Peptide sequence (e.g. PEPTIDEK)") +@click.option("--formula", default=None, help="Molecular formula (e.g. C6H12O6)") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1)") +@click.option("--observed", multiple=True, type=float, required=True, help="Observed m/z value(s)") +def main(sequence, formula, charge, observed): + if not sequence and not formula: + raise click.UsageError("Either --sequence or --formula must be provided.") + if sequence and formula: + raise click.UsageError("--sequence and --formula are mutually exclusive.") + + if sequence: + theoretical = theoretical_mz_from_sequence(sequence, charge) + label = f"sequence={sequence}" else: - theoretical = theoretical_mz_from_formula(args.formula, args.charge) - label = f"formula={args.formula}" + theoretical = theoretical_mz_from_formula(formula, charge) + label = f"formula={formula}" - print(f"Theoretical m/z ({label}, charge {args.charge}+): {theoretical:.6f}") + print(f"Theoretical m/z ({label}, charge {charge}+): {theoretical:.6f}") print(f"\n{'Observed m/z':>14} {'PPM error':>10}") print("-" * 28) - for obs in args.observed: + for obs in observed: ppm = ppm_error(theoretical, obs) print(f"{obs:>14.6f} {ppm:>+10.4f}") diff --git a/tools/metabolomics/formula_tools/mass_accuracy_calculator/requirements.txt b/tools/metabolomics/formula_tools/mass_accuracy_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/mass_accuracy_calculator/requirements.txt +++ b/tools/metabolomics/formula_tools/mass_accuracy_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/mass_decomposition_tool/mass_decomposition_tool.py b/tools/metabolomics/formula_tools/mass_decomposition_tool/mass_decomposition_tool.py index 8508404..f98b723 100644 --- a/tools/metabolomics/formula_tools/mass_decomposition_tool/mass_decomposition_tool.py +++ b/tools/metabolomics/formula_tools/mass_decomposition_tool/mass_decomposition_tool.py @@ -16,10 +16,11 @@ python mass_decomposition_tool.py --mass 180.0634 --tolerance 0.01 --output decompositions.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -190,20 +191,16 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Find molecular formula compositions for a given mass within tolerance." - ) - parser.add_argument("--mass", type=float, required=True, help="Target mass in Da") - parser.add_argument("--tolerance", type=float, default=0.01, help="Mass tolerance in Da (default: 0.01)") - parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") - args = parser.parse_args() - - results = decompose_mass(args.mass, args.tolerance) +@click.command() +@click.option("--mass", type=float, required=True, help="Target mass in Da") +@click.option("--tolerance", type=float, default=0.01, help="Mass tolerance in Da (default: 0.01)") +@click.option("--output", default=None, help="Output TSV file path (default: print to stdout)") +def main(mass, tolerance, output): + results = decompose_mass(mass, tolerance) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} decompositions to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} decompositions to {output}") else: if results: print("formula\tmass\terror_da") diff --git a/tools/metabolomics/formula_tools/mass_decomposition_tool/requirements.txt b/tools/metabolomics/formula_tools/mass_decomposition_tool/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/mass_decomposition_tool/requirements.txt +++ b/tools/metabolomics/formula_tools/mass_decomposition_tool/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/metabolite_formula_annotator/metabolite_formula_annotator.py b/tools/metabolomics/formula_tools/metabolite_formula_annotator/metabolite_formula_annotator.py index 5ea5e4c..307b414 100644 --- a/tools/metabolomics/formula_tools/metabolite_formula_annotator/metabolite_formula_annotator.py +++ b/tools/metabolomics/formula_tools/metabolite_formula_annotator/metabolite_formula_annotator.py @@ -12,11 +12,12 @@ python metabolite_formula_annotator.py --input features.tsv --ppm 5 --elements C,H,N,O --output annotated.tsv """ -import argparse import csv import itertools import sys +import click + try: import pyopenms as oms except ImportError: @@ -166,28 +167,24 @@ def annotate_features( return results -def main(): - parser = argparse.ArgumentParser( - description="Annotate features with candidate molecular formulas." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Features TSV (must have mz column)") - parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") - parser.add_argument("--elements", default="C,H,N,O", help="Comma-separated elements (default: C,H,N,O)") - parser.add_argument("--output", required=True, metavar="FILE", help="Output annotated TSV") - args = parser.parse_args() - - elements = args.elements.split(",") +@click.command() +@click.option("--input", "input_file", required=True, help="Features TSV (must have mz column)") +@click.option("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") +@click.option("--elements", default="C,H,N,O", help="Comma-separated elements (default: C,H,N,O)") +@click.option("--output", required=True, help="Output annotated TSV") +def main(input_file, ppm, elements, output): + elements = elements.split(",") element_ranges = {e.strip(): DEFAULT_ELEMENT_RANGES.get(e.strip(), (0, 10)) for e in elements} features = [] - with open(args.input) as fh: + with open(input_file) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: features.append(row) - annotated = annotate_features(features, ppm=args.ppm, element_ranges=element_ranges) + annotated = annotate_features(features, ppm=ppm, element_ranges=element_ranges) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.writer(fh, delimiter="\t") writer.writerow(["mz", "rt", "intensity", "candidate_formula", "candidate_mass", "error_ppm"]) for feat in annotated: @@ -210,7 +207,7 @@ def main(): "", "", "", ]) - print(f"Annotated {len(annotated)} features, written to {args.output}") + print(f"Annotated {len(annotated)} features, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/formula_tools/metabolite_formula_annotator/requirements.txt b/tools/metabolomics/formula_tools/metabolite_formula_annotator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/metabolite_formula_annotator/requirements.txt +++ b/tools/metabolomics/formula_tools/metabolite_formula_annotator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py b/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py index 6f2052d..06d47f7 100644 --- a/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py +++ b/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py @@ -17,10 +17,11 @@ python molecular_formula_finder.py --mass 180.0634 --ppm 5 --output formulas.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -263,24 +264,20 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Enumerate valid molecular formulas for an accurate mass." - ) - parser.add_argument("--mass", type=float, required=True, help="Target mass in Da") - parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") - parser.add_argument("--elements", default="C:0-12,H:0-30,N:0-5,O:0-10", - help="Element constraints (default: C:0-12,H:0-30,N:0-5,O:0-10)") - parser.add_argument("--no-rules", action="store_true", help="Disable Seven Golden Rules filtering") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - constraints = parse_element_constraints(args.elements) - results = find_formulas(args.mass, args.ppm, constraints, apply_rules=not args.no_rules) - - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} formulas to {args.output}") +@click.command() +@click.option("--mass", type=float, required=True, help="Target mass in Da") +@click.option("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5)") +@click.option("--elements", default="C:0-12,H:0-30,N:0-5,O:0-10", + help="Element constraints (default: C:0-12,H:0-30,N:0-5,O:0-10)") +@click.option("--no-rules", is_flag=True, help="Disable Seven Golden Rules filtering") +@click.option("--output", default=None, help="Output TSV file path") +def main(mass, ppm, elements, no_rules, output): + constraints = parse_element_constraints(elements) + results = find_formulas(mass, ppm, constraints, apply_rules=not no_rules) + + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} formulas to {output}") else: if results: print("formula\tmass\terror_ppm\tpasses_senior\tpasses_hc_ratio\tpasses_nitrogen_rule") diff --git a/tools/metabolomics/formula_tools/molecular_formula_finder/requirements.txt b/tools/metabolomics/formula_tools/molecular_formula_finder/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/molecular_formula_finder/requirements.txt +++ b/tools/metabolomics/formula_tools/molecular_formula_finder/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py b/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py index 5fb3670..076679c 100644 --- a/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py +++ b/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py @@ -9,10 +9,11 @@ python rdbe_calculator.py --input formulas.tsv --output rdbe.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -85,17 +86,13 @@ def calculate_rdbe_batch(formulas: list) -> list: return results -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="TSV file with a 'formula' column.") +@click.option("--output", required=True, help="Output TSV file with RDBE values.") +def main(input_file, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Calculate RDBE (Ring and Double Bond Equivalents) for molecular formulas." - ) - parser.add_argument("--input", required=True, help="TSV file with a 'formula' column.") - parser.add_argument("--output", required=True, help="Output TSV file with RDBE values.") - args = parser.parse_args() - formulas = [] - with open(args.input, newline="") as fh: + with open(input_file, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: formulas.append(row["formula"]) @@ -103,12 +100,12 @@ def main() -> None: results = calculate_rdbe_batch(formulas) fieldnames = ["formula", "C", "H", "N", "P", "rdbe"] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Calculated RDBE for {len(results)} formulas, wrote to {args.output}") + print(f"Calculated RDBE for {len(results)} formulas, wrote to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/formula_tools/rdbe_calculator/requirements.txt b/tools/metabolomics/formula_tools/rdbe_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/formula_tools/rdbe_calculator/requirements.txt +++ b/tools/metabolomics/formula_tools/rdbe_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/isotope_labeling/isotope_label_detector/isotope_label_detector.py b/tools/metabolomics/isotope_labeling/isotope_label_detector/isotope_label_detector.py index 91592d4..e39707f 100644 --- a/tools/metabolomics/isotope_labeling/isotope_label_detector/isotope_label_detector.py +++ b/tools/metabolomics/isotope_labeling/isotope_label_detector/isotope_label_detector.py @@ -10,10 +10,11 @@ --labeled features_13c.tsv --tracer 13C --ppm 5 --output pairs.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -142,38 +143,34 @@ def find_labeled_pairs( return pairs -def main() -> None: +@click.command() +@click.option("--unlabeled", required=True, help="TSV with unlabeled features (id, mz, rt).") +@click.option("--labeled", required=True, help="TSV with labeled features (id, mz, rt).") +@click.option("--tracer", default="13C", help="Tracer type: 13C, 15N, 2H (default: 13C).") +@click.option("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5).") +@click.option("--rt-tolerance", type=float, default=10.0, + help="RT tolerance in seconds (default: 10).") +@click.option("--output", required=True, help="Output TSV with paired features.") +def main(unlabeled, labeled, tracer, ppm, rt_tolerance, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Detect isotope-labeled metabolites by pairing unlabeled and labeled features." - ) - parser.add_argument("--unlabeled", required=True, help="TSV with unlabeled features (id, mz, rt).") - parser.add_argument("--labeled", required=True, help="TSV with labeled features (id, mz, rt).") - parser.add_argument("--tracer", default="13C", help="Tracer type: 13C, 15N, 2H (default: 13C).") - parser.add_argument("--ppm", type=float, default=5.0, help="Mass tolerance in ppm (default: 5).") - parser.add_argument("--rt-tolerance", type=float, default=10.0, - help="RT tolerance in seconds (default: 10).") - parser.add_argument("--output", required=True, help="Output TSV with paired features.") - args = parser.parse_args() - def read_features(path): with open(path, newline="") as fh: return list(csv.DictReader(fh, delimiter="\t")) - unlabeled = read_features(args.unlabeled) - labeled = read_features(args.labeled) + unlabeled_data = read_features(unlabeled) + labeled_data = read_features(labeled) - pairs = find_labeled_pairs(unlabeled, labeled, tracer=args.tracer, - ppm=args.ppm, rt_tolerance=args.rt_tolerance) + pairs = find_labeled_pairs(unlabeled_data, labeled_data, tracer=tracer, + ppm=ppm, rt_tolerance=rt_tolerance) fieldnames = ["unlabeled_id", "unlabeled_mz", "labeled_id", "labeled_mz", "mass_diff", "n_labels", "rt_diff", "ppm_error"] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(pairs) - print(f"Found {len(pairs)} labeled pairs, wrote to {args.output}") + print(f"Found {len(pairs)} labeled pairs, wrote to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/isotope_labeling/isotope_label_detector/requirements.txt b/tools/metabolomics/isotope_labeling/isotope_label_detector/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/isotope_labeling/isotope_label_detector/requirements.txt +++ b/tools/metabolomics/isotope_labeling/isotope_label_detector/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py b/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py index e5c97ba..1b0cd30 100644 --- a/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py +++ b/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/mid_natural_abundance_corrector.py @@ -11,10 +11,11 @@ --formula C6H12O6 --tracer 13C --output corrected.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -141,40 +142,36 @@ def correct_mid(measured_mid: list, formula: str, tracer: str = "13C") -> list: return [round(float(v), 6) for v in corrected] -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, + help="TSV with columns: sample, M0, M1, M2, ... (fractional abundances).") +@click.option("--formula", required=True, help="Molecular formula of the metabolite.") +@click.option("--tracer", default="13C", help="Tracer isotope (default: 13C).") +@click.option("--output", required=True, help="Output TSV with corrected MIDs.") +def main(input_file, formula, tracer, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Correct mass isotopomer distributions for natural 13C abundance." - ) - parser.add_argument("--input", required=True, - help="TSV with columns: sample, M0, M1, M2, ... (fractional abundances).") - parser.add_argument("--formula", required=True, help="Molecular formula of the metabolite.") - parser.add_argument("--tracer", default="13C", help="Tracer isotope (default: 13C).") - parser.add_argument("--output", required=True, help="Output TSV with corrected MIDs.") - args = parser.parse_args() - - n_atoms = get_num_tracer_atoms(args.formula, args.tracer) + n_atoms = get_num_tracer_atoms(formula, tracer) rows_out = [] - with open(args.input, newline="") as fh: + with open(input_file, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") headers = reader.fieldnames or [] mid_cols = [h for h in headers if h.startswith("M")] for row in reader: measured = [float(row[c]) for c in mid_cols] - corrected = correct_mid(measured, args.formula, args.tracer) + corrected = correct_mid(measured, formula, tracer) out_row = {"sample": row.get("sample", "")} for i, val in enumerate(corrected): out_row[f"M{i}_corrected"] = val rows_out.append(out_row) fieldnames = ["sample"] + [f"M{i}_corrected" for i in range(n_atoms + 1)] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(rows_out) - print(f"Wrote {len(rows_out)} corrected MIDs to {args.output}") + print(f"Wrote {len(rows_out)} corrected MIDs to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/requirements.txt b/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/requirements.txt index 1051d92..33e6da2 100644 --- a/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/requirements.txt +++ b/tools/metabolomics/isotope_labeling/mid_natural_abundance_corrector/requirements.txt @@ -1,2 +1,3 @@ pyopenms +click numpy diff --git a/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py b/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py index 07c0c89..cb0159f 100644 --- a/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py +++ b/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/lipid_ecn_rt_predictor.py @@ -11,10 +11,11 @@ python lipid_ecn_rt_predictor.py --input lipids.tsv --calibration standards.tsv --output predictions.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -170,28 +171,20 @@ def write_predictions(predictions: list[dict], path: str) -> None: writer.writerows(predictions) -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, + help="Lipid table (TSV) with lipid_class, total_carbons, double_bonds") +@click.option("--calibration", required=True, + help="Standards table (TSV) with lipid_class, total_carbons, double_bonds, rt") +@click.option("--output", required=True, help="Output predictions (TSV)") +def main(input_file, calibration, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Predict lipid RT from ECN using linear regression per lipid class." - ) - parser.add_argument( - "--input", required=True, - help="Lipid table (TSV) with lipid_class, total_carbons, double_bonds", - ) - parser.add_argument( - "--calibration", required=True, - help="Standards table (TSV) with lipid_class, total_carbons, double_bonds, rt", - ) - parser.add_argument("--output", required=True, help="Output predictions (TSV)") - args = parser.parse_args() - - lipids = load_tsv(args.input) - standards = load_tsv(args.calibration) + lipids = load_tsv(input_file) + standards = load_tsv(calibration) models = build_calibration_models(standards) predictions = predict_rt(lipids, models) - write_predictions(predictions, args.output) - print(f"Predicted RT for {len(predictions)} lipids, written to {args.output}") + write_predictions(predictions, output) + print(f"Predicted RT for {len(predictions)} lipids, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/requirements.txt b/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/requirements.txt index ba577e4..57f09ac 100644 --- a/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/requirements.txt +++ b/tools/metabolomics/lipidomics/lipid_ecn_rt_predictor/requirements.txt @@ -1,3 +1,4 @@ pyopenms +click numpy scipy diff --git a/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py b/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py index 99bc402..ceb38f8 100644 --- a/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py +++ b/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py @@ -10,10 +10,11 @@ python lipid_species_resolver.py --input lipids.tsv --lipid-class PC --output resolved.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -240,20 +241,16 @@ def write_resolved(resolved: list[dict], path: str) -> None: writer.writerows(resolved) -def main() -> None: +@click.command() +@click.option("--input", "input_file", required=True, help="Lipid table (TSV) with 'lipid' column (e.g. 'PC 36:2')") +@click.option("--lipid-class", default=None, help="Override lipid class (e.g. PC)") +@click.option("--output", required=True, help="Output resolved species (TSV)") +def main(input_file, lipid_class, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Resolve sum-composition lipids into acyl chain combinations." - ) - parser.add_argument("--input", required=True, help="Lipid table (TSV) with 'lipid' column (e.g. 'PC 36:2')") - parser.add_argument("--lipid-class", default=None, help="Override lipid class (e.g. PC)") - parser.add_argument("--output", required=True, help="Output resolved species (TSV)") - args = parser.parse_args() - - lipids = load_lipids(args.input) - resolved = resolve_lipids(lipids, lipid_class_override=args.lipid_class) - write_resolved(resolved, args.output) - print(f"Resolved {len(resolved)} species from {len(lipids)} lipid(s), written to {args.output}") + lipids = load_lipids(input_file) + resolved = resolve_lipids(lipids, lipid_class_override=lipid_class) + write_resolved(resolved, output) + print(f"Resolved {len(resolved)} species from {len(lipids)} lipid(s), written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/lipidomics/lipid_species_resolver/requirements.txt b/tools/metabolomics/lipidomics/lipid_species_resolver/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/lipidomics/lipid_species_resolver/requirements.txt +++ b/tools/metabolomics/lipidomics/lipid_species_resolver/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py b/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py index 2df8a4e..d53d9ce 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/isotope_pattern_fit_scorer.py @@ -11,11 +11,12 @@ --formula C6H12O6 --output fit.json """ -import argparse import json import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -229,34 +230,28 @@ def score_pattern( } -def main() -> None: +@click.command() +@click.option("--observed", required=True, + help="Observed peaks as 'mz1:int1,mz2:int2,...'") +@click.option("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") +@click.option("--max-isotopes", type=int, default=6, help="Max isotope peaks (default: 6)") +@click.option("--mz-tolerance", type=float, default=0.05, help="m/z tolerance (default: 0.05)") +@click.option("--output", required=True, help="Output JSON file") +def main(observed, formula, max_isotopes, mz_tolerance, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Score observed vs theoretical isotope patterns and detect halogenation." - ) - parser.add_argument( - "--observed", required=True, - help="Observed peaks as 'mz1:int1,mz2:int2,...'" - ) - parser.add_argument("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") - parser.add_argument("--max-isotopes", type=int, default=6, help="Max isotope peaks (default: 6)") - parser.add_argument("--mz-tolerance", type=float, default=0.05, help="m/z tolerance (default: 0.05)") - parser.add_argument("--output", required=True, help="Output JSON file") - args = parser.parse_args() - result = score_pattern( - args.observed, args.formula, - max_isotopes=args.max_isotopes, mz_tolerance=args.mz_tolerance, + observed, formula, + max_isotopes=max_isotopes, mz_tolerance=mz_tolerance, ) - with open(args.output, "w") as fh: + with open(output, "w") as fh: json.dump(result, fh, indent=2) print(f"Cosine similarity: {result['cosine_similarity']:.4f}") halogen = result["halogen_detection"] if halogen["halogen_flag"]: print(f"Halogen detected: {halogen['possible_halogen']} (M+2 excess: {halogen['m2_excess']}%)") - print(f"Results written to {args.output}") + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/requirements.txt b/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/requirements.txt +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_fit_scorer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py b/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py index f0510d4..24d080c 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py @@ -15,10 +15,11 @@ --peaks 181.0709,100.0 182.0742,6.7 183.0775,0.4 """ -import argparse import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -109,53 +110,30 @@ def parse_peaks(peak_strings: list) -> list: return peaks -def main(): - parser = argparse.ArgumentParser( - description="Generate theoretical isotope patterns and optionally " - "compare them against observed peaks using pyopenms." - ) - parser.add_argument( - "--formula", - required=True, - help="Molecular formula (e.g. C6H12O6)", - ) - parser.add_argument( - "--max-isotopes", - type=int, - default=5, - dest="max_isotopes", - help="Maximum isotope peaks to compute (default: 5)", - ) - parser.add_argument( - "--peaks", - nargs="+", - metavar="MZ,INTENSITY", - help="Observed peaks as 'mz,intensity' pairs for similarity scoring", - ) - parser.add_argument( - "--tolerance", - type=float, - default=0.02, - metavar="DA", - help="m/z tolerance in Da for peak matching (default: 0.02)", - ) - args = parser.parse_args() - - distribution = get_isotope_distribution(args.formula, args.max_isotopes) +@click.command() +@click.option("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") +@click.option("--max-isotopes", type=int, default=5, + help="Maximum isotope peaks to compute (default: 5)") +@click.option("--peaks", multiple=True, + help="Observed peaks as 'mz,intensity' pairs for similarity scoring") +@click.option("--tolerance", type=float, default=0.02, + help="m/z tolerance in Da for peak matching (default: 0.02)") +def main(formula, max_isotopes, peaks, tolerance): + distribution = get_isotope_distribution(formula, max_isotopes) if not distribution: print("Could not compute isotope distribution for the given formula.") return - print(f"Isotope distribution for {args.formula}:") + print(f"Isotope distribution for {formula}:") print(f"\n{'Peak':>5} {'m/z':>12} {'Relative Abundance (%)':>22}") print("-" * 44) for i, (mz, rel_ab) in enumerate(distribution): bar = "#" * int(rel_ab / 5) print(f" M+{i} {mz:>12.4f} {rel_ab:>6.2f} % {bar}") - if args.peaks: - observed = parse_peaks(args.peaks) - sim = cosine_similarity(distribution, observed, args.tolerance) + if peaks: + observed = parse_peaks(list(peaks)) + sim = cosine_similarity(distribution, observed, tolerance) print(f"\nCosine similarity vs. observed peaks: {sim:.4f}") if sim >= 0.9: print(" ✓ Excellent match (≥ 0.90)") diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/requirements.txt b/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/requirements.txt +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/isotope_pattern_scorer.py b/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/isotope_pattern_scorer.py index b3ad299..10e1a75 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/isotope_pattern_scorer.py +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/isotope_pattern_scorer.py @@ -12,10 +12,11 @@ python isotope_pattern_scorer.py --observed "180.063:100,181.067:6.5" --formula C6H12O6 --output fit.json """ -import argparse import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -130,27 +131,21 @@ def score_pattern( } -def main(): - parser = argparse.ArgumentParser( - description="Score observed vs theoretical isotope pattern for a formula." - ) - parser.add_argument( - "--observed", required=True, - help='Observed pattern as "mz:int,mz:int,..." (e.g. "180.063:100,181.067:6.5")' - ) - parser.add_argument("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") - parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON file") - args = parser.parse_args() - - observed = parse_observed(args.observed) - theoretical = get_theoretical_pattern(args.formula, n_peaks=len(observed)) - result = score_pattern(observed, theoretical) - result["formula"] = args.formula - - with open(args.output, "w") as fh: +@click.command() +@click.option("--observed", required=True, + help='Observed pattern as "mz:int,mz:int,..." (e.g. "180.063:100,181.067:6.5")') +@click.option("--formula", required=True, help="Molecular formula (e.g. C6H12O6)") +@click.option("--output", required=True, help="Output JSON file") +def main(observed, formula, output): + observed_peaks = parse_observed(observed) + theoretical = get_theoretical_pattern(formula, n_peaks=len(observed_peaks)) + result = score_pattern(observed_peaks, theoretical) + result["formula"] = formula + + with open(output, "w") as fh: json.dump(result, fh, indent=2) - print(f"Isotope fit written to {args.output}") + print(f"Isotope fit written to {output}") print(f" Cosine score: {result['cosine_score']:.6f}") print(f" Peaks compared: {result['n_peaks_compared']}") diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/requirements.txt b/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/requirements.txt +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_scorer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/spectral_analysis/massql_query_tool/massql_query_tool.py b/tools/metabolomics/spectral_analysis/massql_query_tool/massql_query_tool.py index b6267f0..32f3787 100644 --- a/tools/metabolomics/spectral_analysis/massql_query_tool/massql_query_tool.py +++ b/tools/metabolomics/spectral_analysis/massql_query_tool/massql_query_tool.py @@ -15,11 +15,12 @@ python massql_query_tool.py --input data.mzML --query "MS2PROD=226.18" --output results.tsv """ -import argparse import csv import re import sys +import click + try: import pyopenms as oms except ImportError: @@ -127,36 +128,28 @@ def execute_query( return results -def main(): - parser = argparse.ArgumentParser( - description="Query mzML using MassQL-like syntax." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="mzML file") - parser.add_argument( - "--query", required=True, - help='MassQL query (e.g. "MS2PROD=226.18", "MS1MZ=180.06", "PRECMZ=500.0")' - ) - parser.add_argument( - "--tolerance", type=float, default=0.5, - help="m/z tolerance in Da (default: 0.5)" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output results TSV") - args = parser.parse_args() - - parsed = parse_query(args.query) +@click.command() +@click.option("--input", "input_file", required=True, help="mzML file") +@click.option("--query", required=True, + help='MassQL query (e.g. "MS2PROD=226.18", "MS1MZ=180.06", "PRECMZ=500.0")') +@click.option("--tolerance", type=float, default=0.5, + help="m/z tolerance in Da (default: 0.5)") +@click.option("--output", required=True, help="Output results TSV") +def main(input_file, query, tolerance, output): + parsed = parse_query(query) exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input_file, exp) - results = execute_query(exp, parsed, tolerance_da=args.tolerance) + results = execute_query(exp, parsed, tolerance_da=tolerance) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: fieldnames = ["scan_index", "rt", "ms_level", "precursor_mz", "matched_mz", "matched_intensity"] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Query '{args.query}': {len(results)} matches, written to {args.output}") + print(f"Query '{query}': {len(results)} matches, written to {output}") if __name__ == "__main__": diff --git a/tools/metabolomics/spectral_analysis/massql_query_tool/requirements.txt b/tools/metabolomics/spectral_analysis/massql_query_tool/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/spectral_analysis/massql_query_tool/requirements.txt +++ b/tools/metabolomics/spectral_analysis/massql_query_tool/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/spectral_analysis/neutral_loss_scanner/neutral_loss_scanner.py b/tools/metabolomics/spectral_analysis/neutral_loss_scanner/neutral_loss_scanner.py index e5d3cb3..20b72d5 100644 --- a/tools/metabolomics/spectral_analysis/neutral_loss_scanner/neutral_loss_scanner.py +++ b/tools/metabolomics/spectral_analysis/neutral_loss_scanner/neutral_loss_scanner.py @@ -16,10 +16,11 @@ python neutral_loss_scanner.py --input file.mzML --losses 97.977 --tolerance 0.05 --output matches.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -145,22 +146,18 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Scan MS2 spectra for characteristic neutral losses." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--losses", required=True, help="Comma-separated neutral loss masses in Da") - parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") - parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") - args = parser.parse_args() - - losses = [float(x.strip()) for x in args.losses.split(",")] - results = scan_neutral_losses(args.input, losses, args.tolerance) +@click.command() +@click.option("--input", "input_file", required=True, help="Path to input mzML file") +@click.option("--losses", required=True, help="Comma-separated neutral loss masses in Da") +@click.option("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") +@click.option("--output", default=None, help="Output TSV file path (default: print to stdout)") +def main(input_file, losses, tolerance, output): + losses_list = [float(x.strip()) for x in losses.split(",")] + results = scan_neutral_losses(input_file, losses_list, tolerance) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} neutral loss matches to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} neutral loss matches to {output}") else: print("scan_index\trt\tprecursor_mz\tneutral_loss\tfragment_mz\tintensity\tdelta_da") for r in results: diff --git a/tools/metabolomics/spectral_analysis/neutral_loss_scanner/requirements.txt b/tools/metabolomics/spectral_analysis/neutral_loss_scanner/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/metabolomics/spectral_analysis/neutral_loss_scanner/requirements.txt +++ b/tools/metabolomics/spectral_analysis/neutral_loss_scanner/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/requirements.txt b/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/requirements.txt index 1051d92..33e6da2 100644 --- a/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/requirements.txt +++ b/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/requirements.txt @@ -1,2 +1,3 @@ pyopenms +click numpy diff --git a/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/spectral_entropy_scorer.py b/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/spectral_entropy_scorer.py index 47c16e2..1886e77 100644 --- a/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/spectral_entropy_scorer.py +++ b/tools/metabolomics/spectral_analysis/spectral_entropy_scorer/spectral_entropy_scorer.py @@ -10,11 +10,12 @@ --tolerance 0.02 --output scores.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -220,22 +221,18 @@ def read_peaks_file(path: str) -> list: return list(spectra.values()) -def main() -> None: +@click.command() +@click.option("--query", required=True, + help="TSV with query peaks (spectrum_id, mz, intensity).") +@click.option("--library", required=True, + help="TSV with library peaks (spectrum_id, mz, intensity).") +@click.option("--tolerance", type=float, default=0.02, + help="m/z tolerance in Da (default: 0.02).") +@click.option("--output", required=True, help="Output TSV with similarity scores.") +def main(query, library, tolerance, output) -> None: """CLI entry point.""" - parser = argparse.ArgumentParser( - description="Compute spectral entropy similarity between query and library spectra." - ) - parser.add_argument("--query", required=True, - help="TSV with query peaks (spectrum_id, mz, intensity).") - parser.add_argument("--library", required=True, - help="TSV with library peaks (spectrum_id, mz, intensity).") - parser.add_argument("--tolerance", type=float, default=0.02, - help="m/z tolerance in Da (default: 0.02).") - parser.add_argument("--output", required=True, help="Output TSV with similarity scores.") - args = parser.parse_args() - - query_spectra = read_peaks_file(args.query) - library_spectra = read_peaks_file(args.library) + query_spectra = read_peaks_file(query) + library_spectra = read_peaks_file(library) results = [] for qs in query_spectra: @@ -244,7 +241,7 @@ def main() -> None: score = entropy_similarity( qs["mzs"], qs["intensities"], ls["mzs"], ls["intensities"], - tolerance=args.tolerance, + tolerance=tolerance, ) results.append({ "query_id": qs["spectrum_id"], @@ -254,12 +251,12 @@ def main() -> None: }) fieldnames = ["query_id", "library_id", "query_entropy", "entropy_similarity"] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Computed {len(results)} pairwise scores, wrote to {args.output}") + print(f"Computed {len(results)} pairwise scores, wrote to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/contaminant_database_merger/contaminant_database_merger.py b/tools/proteomics/fasta_utils/contaminant_database_merger/contaminant_database_merger.py index 9b858dd..a66b8e4 100644 --- a/tools/proteomics/fasta_utils/contaminant_database_merger/contaminant_database_merger.py +++ b/tools/proteomics/fasta_utils/contaminant_database_merger/contaminant_database_merger.py @@ -10,10 +10,11 @@ python contaminant_database_merger.py --input target.fasta --contaminants custom.fasta --output merged.fasta """ -import argparse import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -133,21 +134,17 @@ def merge_contaminants( } -def main() -> None: - parser = argparse.ArgumentParser( - description="Append contaminant proteins to a FASTA database." - ) - parser.add_argument("--input", required=True, help="Input target FASTA file") - parser.add_argument("--add-crap", action="store_true", help="Add built-in cRAP contaminants") - parser.add_argument("--contaminants", default=None, help="Custom contaminant FASTA file") - parser.add_argument("--prefix", default="CONT_", help="Prefix for contaminant accessions (default: CONT_)") - parser.add_argument("--output", required=True, help="Output merged FASTA file") - args = parser.parse_args() - - if not args.add_crap and not args.contaminants: - parser.error("At least one of --add-crap or --contaminants is required.") +@click.command(help="Append contaminant proteins to a FASTA database.") +@click.option("--input", "input", required=True, help="Input target FASTA file") +@click.option("--add-crap", is_flag=True, help="Add built-in cRAP contaminants") +@click.option("--contaminants", default=None, help="Custom contaminant FASTA file") +@click.option("--prefix", default="CONT_", help="Prefix for contaminant accessions (default: CONT_)") +@click.option("--output", required=True, help="Output merged FASTA file") +def main(input, add_crap, contaminants, prefix, output) -> None: + if not add_crap and not contaminants: + raise click.UsageError("At least one of --add-crap or --contaminants is required.") - stats = merge_contaminants(args.input, args.output, args.add_crap, args.contaminants, args.prefix) + stats = merge_contaminants(input, output, add_crap, contaminants, prefix) print(f"Target: {stats['target_count']}, Contaminants: {stats['contaminant_count']}, " f"Merged (dedup): {stats['deduplicated_count']}") diff --git a/tools/proteomics/fasta_utils/contaminant_database_merger/requirements.txt b/tools/proteomics/fasta_utils/contaminant_database_merger/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/contaminant_database_merger/requirements.txt +++ b/tools/proteomics/fasta_utils/contaminant_database_merger/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_cleaner/fasta_cleaner.py b/tools/proteomics/fasta_utils/fasta_cleaner/fasta_cleaner.py index 2886484..36aa9a0 100644 --- a/tools/proteomics/fasta_utils/fasta_cleaner/fasta_cleaner.py +++ b/tools/proteomics/fasta_utils/fasta_cleaner/fasta_cleaner.py @@ -9,11 +9,12 @@ python fasta_cleaner.py --input messy.fasta --remove-duplicates --min-length 6 --output clean.fasta """ -import argparse import re import sys from typing import List, Optional +import click + try: import pyopenms as oms except ImportError: @@ -121,31 +122,30 @@ def clean_fasta( return {"total_input": total_input, "total_output": len(entries)} -def main() -> None: - parser = argparse.ArgumentParser( - description="Clean a FASTA database: remove duplicates, fix headers, filter by length." - ) - parser.add_argument("--input", required=True, help="Input FASTA file") - parser.add_argument("--output", required=True, help="Output cleaned FASTA file") - parser.add_argument("--remove-duplicates", action="store_true", help="Remove duplicate sequences") - parser.add_argument("--min-length", type=int, default=None, help="Minimum sequence length") - parser.add_argument("--max-length", type=int, default=None, help="Maximum sequence length") - parser.add_argument("--remove-stop-codons", action="store_true", help="Remove trailing stop codons (*)") - parser.add_argument("--fix-headers", action="store_true", help="Fix header whitespace issues") - parser.add_argument("--remove-invalid-chars", action="store_true", help="Remove non-amino-acid characters") - args = parser.parse_args() - +@click.command(help="Clean a FASTA database: remove duplicates, fix headers, filter by length.") +@click.option("--input", "input", required=True, help="Input FASTA file") +@click.option("--output", required=True, help="Output cleaned FASTA file") +@click.option("--remove-duplicates", is_flag=True, help="Remove duplicate sequences") +@click.option("--min-length", type=int, default=None, help="Minimum sequence length") +@click.option("--max-length", type=int, default=None, help="Maximum sequence length") +@click.option("--remove-stop-codons", is_flag=True, help="Remove trailing stop codons (*)") +@click.option("--fix-headers", is_flag=True, help="Fix header whitespace issues") +@click.option("--remove-invalid-chars", is_flag=True, help="Remove non-amino-acid characters") +def main( + input, output, remove_duplicates, min_length, max_length, + remove_stop_codons, fix_headers, remove_invalid_chars, +) -> None: stats = clean_fasta( - args.input, - args.output, - dedup=args.remove_duplicates, - min_length=args.min_length, - max_length=args.max_length, - strip_stop_codons=args.remove_stop_codons, - do_fix_headers=args.fix_headers, - do_remove_invalid=args.remove_invalid_chars, + input, + output, + dedup=remove_duplicates, + min_length=min_length, + max_length=max_length, + strip_stop_codons=remove_stop_codons, + do_fix_headers=fix_headers, + do_remove_invalid=remove_invalid_chars, ) - print(f"Cleaned: {stats['total_input']} -> {stats['total_output']} proteins written to {args.output}") + print(f"Cleaned: {stats['total_input']} -> {stats['total_output']} proteins written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/fasta_cleaner/requirements.txt b/tools/proteomics/fasta_utils/fasta_cleaner/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_cleaner/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_cleaner/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_decoy_validator/fasta_decoy_validator.py b/tools/proteomics/fasta_utils/fasta_decoy_validator/fasta_decoy_validator.py index 9a34a25..b94b642 100644 --- a/tools/proteomics/fasta_utils/fasta_decoy_validator/fasta_decoy_validator.py +++ b/tools/proteomics/fasta_utils/fasta_decoy_validator/fasta_decoy_validator.py @@ -8,11 +8,12 @@ python fasta_decoy_validator.py --input db.fasta --decoy-prefix DECOY_ --output validation.json """ -import argparse import json import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -98,24 +99,20 @@ def validate_decoys( } -def main() -> None: - parser = argparse.ArgumentParser( - description="Validate decoy sequences in a FASTA database." - ) - parser.add_argument("--input", required=True, help="Input FASTA file") - parser.add_argument("--decoy-prefix", default="DECOY_", help="Expected decoy prefix (default: DECOY_)") - parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)") - args = parser.parse_args() - - result = validate_decoys(args.input, args.decoy_prefix) - output = json.dumps(result, indent=2) +@click.command(help="Validate decoy sequences in a FASTA database.") +@click.option("--input", "input", required=True, help="Input FASTA file") +@click.option("--decoy-prefix", default="DECOY_", help="Expected decoy prefix (default: DECOY_)") +@click.option("--output", default=None, help="Output JSON file (default: stdout)") +def main(input, decoy_prefix, output) -> None: + result = validate_decoys(input, decoy_prefix) + output_str = json.dumps(result, indent=2) - if args.output: - with open(args.output, "w") as fh: - fh.write(output + "\n") - print(f"Validation results written to {args.output}") + if output: + with open(output, "w") as fh: + fh.write(output_str + "\n") + print(f"Validation results written to {output}") else: - print(output) + print(output_str) if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/fasta_decoy_validator/requirements.txt b/tools/proteomics/fasta_utils/fasta_decoy_validator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_decoy_validator/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_decoy_validator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py b/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py index 6b5a77b..fc40812 100644 --- a/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py +++ b/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/fasta_in_silico_digest_stats.py @@ -9,11 +9,12 @@ python fasta_in_silico_digest_stats.py --input db.fasta --enzyme Trypsin --missed-cleavages 2 --output stats.tsv """ -import argparse import csv import sys from typing import Dict, List +import click + try: import pyopenms as oms except ImportError: @@ -106,27 +107,23 @@ def write_tsv(stats: dict, output_path: str) -> None: writer.writerow([pep["sequence"], pep["length"], pep["mass"], pep["protein"]]) -def main() -> None: - parser = argparse.ArgumentParser( - description="Digest a FASTA database and report peptide statistics." - ) - parser.add_argument("--input", required=True, help="Input FASTA file") - parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") - parser.add_argument("--missed-cleavages", type=int, default=0, help="Missed cleavages (default: 0)") - parser.add_argument("--min-length", type=int, default=6, help="Min peptide length (default: 6)") - parser.add_argument("--max-length", type=int, default=50, help="Max peptide length (default: 50)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - stats = digest_fasta(args.input, args.enzyme, args.missed_cleavages, args.min_length, args.max_length) - write_tsv(stats, args.output) +@click.command(help="Digest a FASTA database and report peptide statistics.") +@click.option("--input", "input", required=True, help="Input FASTA file") +@click.option("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") +@click.option("--missed-cleavages", type=int, default=0, help="Missed cleavages (default: 0)") +@click.option("--min-length", type=int, default=6, help="Min peptide length (default: 6)") +@click.option("--max-length", type=int, default=50, help="Max peptide length (default: 50)") +@click.option("--output", required=True, help="Output TSV file") +def main(input, enzyme, missed_cleavages, min_length, max_length, output) -> None: + stats = digest_fasta(input, enzyme, missed_cleavages, min_length, max_length) + write_tsv(stats, output) print(f"Proteins: {stats['protein_count']}") print(f"Total peptides: {stats['total_peptides']}") print(f"Unique peptides: {stats['unique_peptides']}") if stats["mass_stats"]: print(f"Mass range: {stats['mass_stats']['min']} - {stats['mass_stats']['max']}") - print(f"Results written to {args.output}") + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/requirements.txt b/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_in_silico_digest_stats/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_merger/fasta_merger.py b/tools/proteomics/fasta_utils/fasta_merger/fasta_merger.py index d14c313..3399511 100644 --- a/tools/proteomics/fasta_utils/fasta_merger/fasta_merger.py +++ b/tools/proteomics/fasta_utils/fasta_merger/fasta_merger.py @@ -8,10 +8,11 @@ python fasta_merger.py --inputs db1.fasta db2.fasta --remove-duplicates --output merged.fasta """ -import argparse import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -102,19 +103,17 @@ def merge_fasta_files( } -def main() -> None: - parser = argparse.ArgumentParser(description="Merge multiple FASTA files.") - parser.add_argument("--inputs", nargs="+", required=True, help="Input FASTA files") - parser.add_argument("--output", required=True, help="Output merged FASTA file") - parser.add_argument("--remove-duplicates", action="store_true", help="Remove duplicate entries") - parser.add_argument( - "--dedup-by", choices=["identifier", "sequence"], default="identifier", - help="Deduplication criterion (default: identifier)" - ) - args = parser.parse_args() - - stats = merge_fasta_files(args.inputs, args.output, args.remove_duplicates, args.dedup_by) - print(f"Merged {stats['total_before_dedup']} entries -> {stats['total_output']} written to {args.output}") +@click.command(help="Merge multiple FASTA files.") +@click.option("--inputs", multiple=True, required=True, help="Input FASTA files") +@click.option("--output", required=True, help="Output merged FASTA file") +@click.option("--remove-duplicates", is_flag=True, help="Remove duplicate entries") +@click.option( + "--dedup-by", type=click.Choice(["identifier", "sequence"]), + default="identifier", help="Deduplication criterion (default: identifier)", +) +def main(inputs, output, remove_duplicates, dedup_by) -> None: + stats = merge_fasta_files(list(inputs), output, remove_duplicates, dedup_by) + print(f"Merged {stats['total_before_dedup']} entries -> {stats['total_output']} written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/fasta_merger/requirements.txt b/tools/proteomics/fasta_utils/fasta_merger/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_merger/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_merger/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_statistics_reporter/fasta_statistics_reporter.py b/tools/proteomics/fasta_utils/fasta_statistics_reporter/fasta_statistics_reporter.py index e8f39dd..5673c4e 100644 --- a/tools/proteomics/fasta_utils/fasta_statistics_reporter/fasta_statistics_reporter.py +++ b/tools/proteomics/fasta_utils/fasta_statistics_reporter/fasta_statistics_reporter.py @@ -9,12 +9,13 @@ python fasta_statistics_reporter.py --input db.fasta --enzyme Trypsin --output stats.json """ -import argparse import json import sys from collections import Counter from typing import Dict, List, Optional +import click + try: import pyopenms as oms except ImportError: @@ -95,25 +96,21 @@ def compute_statistics( return stats -def main() -> None: - parser = argparse.ArgumentParser( - description="Report statistics for a FASTA database." - ) - parser.add_argument("--input", required=True, help="Input FASTA file") - parser.add_argument("--enzyme", default=None, help="Enzyme for digestion (e.g. Trypsin)") - parser.add_argument("--missed-cleavages", type=int, default=0, help="Missed cleavages (default: 0)") - parser.add_argument("--output", default=None, help="Output JSON file (default: stdout)") - args = parser.parse_args() - - stats = compute_statistics(args.input, args.enzyme, args.missed_cleavages) - output = json.dumps(stats, indent=2) +@click.command(help="Report statistics for a FASTA database.") +@click.option("--input", "input", required=True, help="Input FASTA file") +@click.option("--enzyme", default=None, help="Enzyme for digestion (e.g. Trypsin)") +@click.option("--missed-cleavages", type=int, default=0, help="Missed cleavages (default: 0)") +@click.option("--output", default=None, help="Output JSON file (default: stdout)") +def main(input, enzyme, missed_cleavages, output) -> None: + stats = compute_statistics(input, enzyme, missed_cleavages) + output_str = json.dumps(stats, indent=2) - if args.output: - with open(args.output, "w") as fh: - fh.write(output + "\n") - print(f"Statistics written to {args.output}") + if output: + with open(output, "w") as fh: + fh.write(output_str + "\n") + print(f"Statistics written to {output}") else: - print(output) + print(output_str) if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/fasta_statistics_reporter/requirements.txt b/tools/proteomics/fasta_utils/fasta_statistics_reporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_statistics_reporter/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_statistics_reporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_subset_extractor/fasta_subset_extractor.py b/tools/proteomics/fasta_utils/fasta_subset_extractor/fasta_subset_extractor.py index 3a7e985..41b4bbe 100644 --- a/tools/proteomics/fasta_utils/fasta_subset_extractor/fasta_subset_extractor.py +++ b/tools/proteomics/fasta_utils/fasta_subset_extractor/fasta_subset_extractor.py @@ -10,10 +10,11 @@ python fasta_subset_extractor.py --input db.fasta --min-length 50 --max-length 500 --output subset.fasta """ -import argparse import sys from typing import List, Optional +import click + try: import pyopenms as oms except ImportError: @@ -109,25 +110,21 @@ def extract_subset( return {"total_input": total, "total_output": len(filtered)} -def main() -> None: - parser = argparse.ArgumentParser( - description="Extract proteins from a FASTA database by accession list, keyword, or length range." - ) - parser.add_argument("--input", required=True, help="Input FASTA file") - parser.add_argument("--accessions", default=None, help="Text file with one accession per line") - parser.add_argument("--keyword", default=None, help="Keyword to match in header/description") - parser.add_argument("--min-length", type=int, default=None, help="Minimum sequence length") - parser.add_argument("--max-length", type=int, default=None, help="Maximum sequence length") - parser.add_argument("--output", required=True, help="Output FASTA file") - args = parser.parse_args() - - if not args.accessions and not args.keyword and args.min_length is None and args.max_length is None: - parser.error("At least one filter (--accessions, --keyword, --min-length, --max-length) is required.") +@click.command(help="Extract proteins from a FASTA database by accession list, keyword, or length range.") +@click.option("--input", "input", required=True, help="Input FASTA file") +@click.option("--accessions", default=None, help="Text file with one accession per line") +@click.option("--keyword", default=None, help="Keyword to match in header/description") +@click.option("--min-length", type=int, default=None, help="Minimum sequence length") +@click.option("--max-length", type=int, default=None, help="Maximum sequence length") +@click.option("--output", required=True, help="Output FASTA file") +def main(input, accessions, keyword, min_length, max_length, output) -> None: + if not accessions and not keyword and min_length is None and max_length is None: + raise click.UsageError("At least one filter (--accessions, --keyword, --min-length, --max-length) is required.") stats = extract_subset( - args.input, args.output, args.accessions, args.keyword, args.min_length, args.max_length + input, output, accessions, keyword, min_length, max_length ) - print(f"Extracted {stats['total_output']} / {stats['total_input']} proteins to {args.output}") + print(f"Extracted {stats['total_output']} / {stats['total_input']} proteins to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/fasta_utils/fasta_subset_extractor/requirements.txt b/tools/proteomics/fasta_utils/fasta_subset_extractor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_subset_extractor/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_subset_extractor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py b/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py index b0ee494..eca65f8 100644 --- a/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py +++ b/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/fasta_taxonomy_splitter.py @@ -8,13 +8,14 @@ python fasta_taxonomy_splitter.py --input combined.fasta --pattern "OS=([^=]+) OX=" --output-dir split/ """ -import argparse import os import re import sys from collections import defaultdict from typing import Dict, List, Optional +import click + try: import pyopenms as oms except ImportError: @@ -96,19 +97,15 @@ def split_by_taxonomy( } -def main() -> None: - parser = argparse.ArgumentParser( - description="Split a multi-organism FASTA file by taxonomy from headers." - ) - parser.add_argument("--input", required=True, help="Input FASTA file") - parser.add_argument( - "--pattern", default=r"OS=([^=]+)\s+OX=", - help="Regex pattern with one capture group for taxonomy (default: OS=... OX=)" - ) - parser.add_argument("--output-dir", required=True, help="Output directory for split files") - args = parser.parse_args() - - stats = split_by_taxonomy(args.input, args.output_dir, args.pattern) +@click.command(help="Split a multi-organism FASTA file by taxonomy from headers.") +@click.option("--input", "input", required=True, help="Input FASTA file") +@click.option( + "--pattern", default=r"OS=([^=]+)\s+OX=", + help="Regex pattern with one capture group for taxonomy (default: OS=... OX=)", +) +@click.option("--output-dir", required=True, help="Output directory for split files") +def main(input, pattern, output_dir) -> None: + stats = split_by_taxonomy(input, output_dir, pattern) print(f"Total entries: {stats['total_entries']}") print(f"Taxonomy groups: {stats['taxonomy_groups']}") print(f"Unmatched: {stats['unmatched_count']}") diff --git a/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/requirements.txt b/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/requirements.txt +++ b/tools/proteomics/fasta_utils/fasta_taxonomy_splitter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py b/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py index 28f16e5..0a98e10 100644 --- a/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py +++ b/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py @@ -8,10 +8,11 @@ python consensus_map_to_matrix.py --input consensus.consensusXML --output matrix.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -125,14 +126,12 @@ def create_synthetic_consensus(output_path: str, n_features: int = 5, n_maps: in oms.ConsensusXMLFile().store(output_path, cmap) -def main() -> None: - parser = argparse.ArgumentParser(description="Convert consensusXML to quantification matrix.") - parser.add_argument("--input", required=True, help="Input consensusXML file") - parser.add_argument("--output", required=True, help="Output TSV matrix file") - args = parser.parse_args() - - stats = consensus_to_matrix(args.input, args.output) - print(f"Exported {stats['consensus_features']} features across {stats['n_maps']} maps to {args.output}") +@click.command(help="Convert consensusXML to quantification matrix.") +@click.option("--input", "input", required=True, help="Input consensusXML file") +@click.option("--output", required=True, help="Output TSV matrix file") +def main(input, output) -> None: + stats = consensus_to_matrix(input, output) + print(f"Exported {stats['consensus_features']} features across {stats['n_maps']} maps to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/consensus_map_to_matrix/requirements.txt b/tools/proteomics/file_conversion/consensus_map_to_matrix/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/consensus_map_to_matrix/requirements.txt +++ b/tools/proteomics/file_conversion/consensus_map_to_matrix/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/featurexml_merger/featurexml_merger.py b/tools/proteomics/file_conversion/featurexml_merger/featurexml_merger.py index 90dd0e4..0e6468c 100644 --- a/tools/proteomics/file_conversion/featurexml_merger/featurexml_merger.py +++ b/tools/proteomics/file_conversion/featurexml_merger/featurexml_merger.py @@ -8,10 +8,11 @@ python featurexml_merger.py --inputs f1.featureXML f2.featureXML --output merged.featureXML """ -import argparse import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -70,14 +71,12 @@ def create_synthetic_featurexml(output_path: str, n_features: int = 5, rt_offset save_featurexml(fm, output_path) -def main() -> None: - parser = argparse.ArgumentParser(description="Merge multiple featureXML files.") - parser.add_argument("--inputs", nargs="+", required=True, help="Input featureXML files") - parser.add_argument("--output", required=True, help="Output merged featureXML file") - args = parser.parse_args() - - stats = merge_feature_maps(args.inputs, args.output) - print(f"Merged {stats['total_features']} features from {len(stats['file_counts'])} files to {args.output}") +@click.command(help="Merge multiple featureXML files.") +@click.option("--inputs", multiple=True, required=True, help="Input featureXML files") +@click.option("--output", required=True, help="Output merged featureXML file") +def main(inputs, output) -> None: + stats = merge_feature_maps(list(inputs), output) + print(f"Merged {stats['total_features']} features from {len(stats['file_counts'])} files to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/featurexml_merger/requirements.txt b/tools/proteomics/file_conversion/featurexml_merger/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/featurexml_merger/requirements.txt +++ b/tools/proteomics/file_conversion/featurexml_merger/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py index ce8dd1a..0510b3d 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py @@ -8,11 +8,12 @@ python idxml_to_tsv_exporter.py --input results.idXML --output results.tsv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -117,14 +118,12 @@ def create_synthetic_idxml(output_path: str) -> None: oms.IdXMLFile().store(output_path, [protein_id], peptide_ids) -def main() -> None: - parser = argparse.ArgumentParser(description="Export idXML to flat TSV format.") - parser.add_argument("--input", required=True, help="Input idXML file") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - stats = export_idxml(args.input, args.output) - print(f"Exported {stats['total_psms']} PSMs from {stats['peptide_ids']} spectra to {args.output}") +@click.command(help="Export idXML to flat TSV format.") +@click.option("--input", "input", required=True, help="Input idXML file") +@click.option("--output", required=True, help="Output TSV file") +def main(input, output) -> None: + stats = export_idxml(input, output) + print(f"Exported {stats['total_psms']} PSMs from {stats['peptide_ids']} spectra to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/requirements.txt b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/requirements.txt +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/mgf_to_mzml_converter/mgf_to_mzml_converter.py b/tools/proteomics/file_conversion/mgf_to_mzml_converter/mgf_to_mzml_converter.py index 4530863..51aa79e 100644 --- a/tools/proteomics/file_conversion/mgf_to_mzml_converter/mgf_to_mzml_converter.py +++ b/tools/proteomics/file_conversion/mgf_to_mzml_converter/mgf_to_mzml_converter.py @@ -8,10 +8,11 @@ python mgf_to_mzml_converter.py --input spectra.mgf --output spectra.mzML """ -import argparse import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -97,14 +98,12 @@ def convert_mgf_to_mzml(input_path: str, output_path: str) -> dict: return {"spectra_converted": len(mgf_spectra)} -def main() -> None: - parser = argparse.ArgumentParser(description="Convert MGF to mzML format.") - parser.add_argument("--input", required=True, help="Input MGF file") - parser.add_argument("--output", required=True, help="Output mzML file") - args = parser.parse_args() - - stats = convert_mgf_to_mzml(args.input, args.output) - print(f"Converted {stats['spectra_converted']} spectra to {args.output}") +@click.command(help="Convert MGF to mzML format.") +@click.option("--input", "input", required=True, help="Input MGF file") +@click.option("--output", required=True, help="Output mzML file") +def main(input, output) -> None: + stats = convert_mgf_to_mzml(input, output) + print(f"Converted {stats['spectra_converted']} spectra to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/mgf_to_mzml_converter/requirements.txt b/tools/proteomics/file_conversion/mgf_to_mzml_converter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/mgf_to_mzml_converter/requirements.txt +++ b/tools/proteomics/file_conversion/mgf_to_mzml_converter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/ms_data_ml_exporter/ms_data_ml_exporter.py b/tools/proteomics/file_conversion/ms_data_ml_exporter/ms_data_ml_exporter.py index cb1af07..00809fd 100644 --- a/tools/proteomics/file_conversion/ms_data_ml_exporter/ms_data_ml_exporter.py +++ b/tools/proteomics/file_conversion/ms_data_ml_exporter/ms_data_ml_exporter.py @@ -11,11 +11,12 @@ python ms_data_ml_exporter.py --input run.mzML --output ml_matrix.csv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -104,22 +105,18 @@ def write_csv(records: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Export MS features as ML-ready matrices." - ) - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument("--output", required=True, help="Output CSV file path") - args = parser.parse_args() - +@click.command(help="Export MS features as ML-ready matrices.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--output", required=True, help="Output CSV file path") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) records = extract_features(exp) print(f"Extracted features for {len(records)} spectra") - write_csv(records, args.output) - print(f"ML matrix written to {args.output}") + write_csv(records, output) + print(f"ML matrix written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/ms_data_ml_exporter/requirements.txt b/tools/proteomics/file_conversion/ms_data_ml_exporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/ms_data_ml_exporter/requirements.txt +++ b/tools/proteomics/file_conversion/ms_data_ml_exporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py b/tools/proteomics/file_conversion/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py index bc2efa3..8c27f96 100644 --- a/tools/proteomics/file_conversion/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py +++ b/tools/proteomics/file_conversion/ms_data_to_csv_exporter/ms_data_to_csv_exporter.py @@ -9,10 +9,11 @@ python ms_data_to_csv_exporter.py --input features.featureXML --type features --output features.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -109,26 +110,23 @@ def export_featurexml(input_path: str, output_path: str) -> dict: return {"features_exported": feature_map.size()} -def main() -> None: - parser = argparse.ArgumentParser(description="Export mzML or featureXML data to flat TSV.") - parser.add_argument("--input", required=True, help="Input file (mzML or featureXML)") - parser.add_argument( - "--type", required=True, - choices=["peaks", "spectra", "features"], - help="Export type: peaks (mzML), spectra (mzML summary), features (featureXML)" - ) - parser.add_argument("--ms-level", type=int, default=0, help="MS level filter for peaks (0=all)") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - if args.type == "peaks": - stats = export_mzml_peaks(args.input, args.output, args.ms_level) +@click.command(help="Export mzML or featureXML data to flat TSV.") +@click.option("--input", "input", required=True, help="Input file (mzML or featureXML)") +@click.option( + "--type", "type", required=True, type=click.Choice(["peaks", "spectra", "features"]), + help="Export type: peaks (mzML), spectra (mzML summary), features (featureXML)", +) +@click.option("--ms-level", type=int, default=0, help="MS level filter for peaks (0=all)") +@click.option("--output", required=True, help="Output TSV file") +def main(input, type, ms_level, output) -> None: + if type == "peaks": + stats = export_mzml_peaks(input, output, ms_level) print(f"Exported {stats['total_peaks']} peaks from {stats['spectra_exported']} spectra") - elif args.type == "spectra": - stats = export_mzml_spectra_summary(args.input, args.output) + elif type == "spectra": + stats = export_mzml_spectra_summary(input, output) print(f"Exported {stats['spectra_exported']} spectra summaries") - elif args.type == "features": - stats = export_featurexml(args.input, args.output) + elif type == "features": + stats = export_featurexml(input, output) print(f"Exported {stats['features_exported']} features") diff --git a/tools/proteomics/file_conversion/ms_data_to_csv_exporter/requirements.txt b/tools/proteomics/file_conversion/ms_data_to_csv_exporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/ms_data_to_csv_exporter/requirements.txt +++ b/tools/proteomics/file_conversion/ms_data_to_csv_exporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py b/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py index b80afa1..99af56c 100644 --- a/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py +++ b/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py @@ -8,10 +8,11 @@ python mzml_to_mgf_converter.py --input run.mzML --ms-level 2 --output spectra.mgf """ -import argparse import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -125,16 +126,14 @@ def create_synthetic_mzml(output_path: str, n_spectra: int = 5) -> None: oms.MzMLFile().store(output_path, exp) -def main() -> None: - parser = argparse.ArgumentParser(description="Convert MS2 spectra from mzML to MGF format.") - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument("--ms-level", type=int, default=2, help="MS level to extract (default: 2)") - parser.add_argument("--min-peaks", type=int, default=1, help="Minimum peaks per spectrum (default: 1)") - parser.add_argument("--output", required=True, help="Output MGF file") - args = parser.parse_args() - - stats = convert_mzml_to_mgf(args.input, args.output, args.ms_level, args.min_peaks) - print(f"Converted {stats['converted']} / {stats['ms_level_spectra']} MS{args.ms_level} spectra to {args.output}") +@click.command(help="Convert MS2 spectra from mzML to MGF format.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--ms-level", type=int, default=2, help="MS level to extract (default: 2)") +@click.option("--min-peaks", type=int, default=1, help="Minimum peaks per spectrum (default: 1)") +@click.option("--output", required=True, help="Output MGF file") +def main(input, ms_level, min_peaks, output) -> None: + stats = convert_mzml_to_mgf(input, output, ms_level, min_peaks) + print(f"Converted {stats['converted']} / {stats['ms_level_spectra']} MS{ms_level} spectra to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/mzml_to_mgf_converter/requirements.txt b/tools/proteomics/file_conversion/mzml_to_mgf_converter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/mzml_to_mgf_converter/requirements.txt +++ b/tools/proteomics/file_conversion/mzml_to_mgf_converter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/file_conversion/mztab_summarizer/mztab_summarizer.py b/tools/proteomics/file_conversion/mztab_summarizer/mztab_summarizer.py index 9f681cb..fb5cb2f 100644 --- a/tools/proteomics/file_conversion/mztab_summarizer/mztab_summarizer.py +++ b/tools/proteomics/file_conversion/mztab_summarizer/mztab_summarizer.py @@ -8,11 +8,12 @@ python mztab_summarizer.py --input results.mzTab --output summary.tsv """ -import argparse import csv import sys from collections import Counter +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -145,17 +146,15 @@ def write_summary_tsv(summary: dict, output_path: str) -> None: writer.writerow([key, value]) -def main() -> None: - parser = argparse.ArgumentParser(description="Parse mzTab and extract summary statistics.") - parser.add_argument("--input", required=True, help="Input mzTab file") - parser.add_argument("--output", default=None, help="Output summary TSV file (default: stdout)") - args = parser.parse_args() - - summary = summarize_mztab(args.input) +@click.command(help="Parse mzTab and extract summary statistics.") +@click.option("--input", "input", required=True, help="Input mzTab file") +@click.option("--output", default=None, help="Output summary TSV file (default: stdout)") +def main(input, output) -> None: + summary = summarize_mztab(input) - if args.output: - write_summary_tsv(summary, args.output) - print(f"Summary written to {args.output}") + if output: + write_summary_tsv(summary, output) + print(f"Summary written to {output}") else: for key, value in summary.items(): print(f"{key}: {value}") diff --git a/tools/proteomics/file_conversion/mztab_summarizer/requirements.txt b/tools/proteomics/file_conversion/mztab_summarizer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/file_conversion/mztab_summarizer/requirements.txt +++ b/tools/proteomics/file_conversion/mztab_summarizer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/feature_detection_proteomics/feature_detection_proteomics.py b/tools/proteomics/identification/feature_detection_proteomics/feature_detection_proteomics.py index bbde892..bc3872b 100644 --- a/tools/proteomics/identification/feature_detection_proteomics/feature_detection_proteomics.py +++ b/tools/proteomics/identification/feature_detection_proteomics/feature_detection_proteomics.py @@ -11,9 +11,10 @@ python feature_detection_proteomics.py --input sample.mzML --output features.featureXML """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -73,25 +74,12 @@ def print_feature_summary(feature_map: oms.FeatureMap) -> None: ) -def main(): - parser = argparse.ArgumentParser( - description="Detect peptide features in an mzML file using pyopenms." - ) - parser.add_argument( - "--input", - required=True, - metavar="FILE", - help="Centroided mzML input file", - ) - parser.add_argument( - "--output", - metavar="FILE", - help="Output featureXML file (default: .featureXML)", - ) - args = parser.parse_args() - - output_path = args.output or args.input.replace(".mzML", ".featureXML") - feature_map = detect_features(args.input, output_path) +@click.command(help="Detect peptide features in an mzML file using pyopenms.") +@click.option("--input", "input", required=True, help="Centroided mzML input file") +@click.option("--output", default=None, help="Output featureXML file (default: .featureXML)") +def main(input, output): + output_path = output or input.replace(".mzML", ".featureXML") + feature_map = detect_features(input, output_path) print_feature_summary(feature_map) diff --git a/tools/proteomics/identification/feature_detection_proteomics/requirements.txt b/tools/proteomics/identification/feature_detection_proteomics/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/feature_detection_proteomics/requirements.txt +++ b/tools/proteomics/identification/feature_detection_proteomics/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py b/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py index 0720c30..e942eb0 100644 --- a/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py +++ b/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py @@ -11,10 +11,11 @@ python mzml_metadata_extractor.py --input run.mzML --output metadata.json """ -import argparse import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -150,23 +151,19 @@ def format_metadata(metadata: dict) -> str: return "\n".join(lines) -def main(): - parser = argparse.ArgumentParser( - description="Extract instrument metadata from mzML files." - ) - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument("--output", default=None, help="Output JSON file path") - args = parser.parse_args() - +@click.command(help="Extract instrument metadata from mzML files.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--output", default=None, help="Output JSON file path") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) metadata = extract_metadata(exp) print(format_metadata(metadata)) - if args.output: - write_json(metadata, args.output) - print(f"\nMetadata written to {args.output}") + if output: + write_json(metadata, output) + print(f"\nMetadata written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/identification/mzml_metadata_extractor/requirements.txt b/tools/proteomics/identification/mzml_metadata_extractor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/mzml_metadata_extractor/requirements.txt +++ b/tools/proteomics/identification/mzml_metadata_extractor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py b/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py index a2fc9e8..2ba2ce2 100644 --- a/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py +++ b/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py @@ -13,9 +13,10 @@ python mzml_spectrum_subsetter.py --input run.mzML --scans 0,1,5 --output subset.mzML """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -90,18 +91,14 @@ def create_synthetic_mzml(output_path: str, n_scans: int = 10) -> None: oms.MzMLFile().store(output_path, exp) -def main(): - parser = argparse.ArgumentParser( - description="Extract specific spectra from mzML by scan number list." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--scans", required=True, help="Comma-separated scan indices (0-based)") - parser.add_argument("--output", required=True, help="Path to output mzML file") - args = parser.parse_args() - - scan_indices = [int(x.strip()) for x in args.scans.split(",")] - count = subset_spectra(args.input, scan_indices, args.output) - print(f"Extracted {count} spectra to {args.output}") +@click.command(help="Extract specific spectra from mzML by scan number list.") +@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--scans", required=True, help="Comma-separated scan indices (0-based)") +@click.option("--output", required=True, help="Path to output mzML file") +def main(input, scans, output): + scan_indices = [int(x.strip()) for x in scans.split(",")] + count = subset_spectra(input, scan_indices, output) + print(f"Extracted {count} spectra to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/identification/mzml_spectrum_subsetter/requirements.txt b/tools/proteomics/identification/mzml_spectrum_subsetter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/mzml_spectrum_subsetter/requirements.txt +++ b/tools/proteomics/identification/mzml_spectrum_subsetter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/peptide_spectral_match_validator/peptide_spectral_match_validator.py b/tools/proteomics/identification/peptide_spectral_match_validator/peptide_spectral_match_validator.py index 75c7c70..8f5d9c2 100644 --- a/tools/proteomics/identification/peptide_spectral_match_validator/peptide_spectral_match_validator.py +++ b/tools/proteomics/identification/peptide_spectral_match_validator/peptide_spectral_match_validator.py @@ -9,11 +9,12 @@ python peptide_spectral_match_validator.py --mzml run.mzML --peptides psms.tsv --output validation.tsv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -196,29 +197,25 @@ def write_tsv(results: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Validate PSMs by recomputing fragment coverage." - ) - parser.add_argument("--mzml", required=True, help="Input mzML file") - parser.add_argument("--peptides", required=True, help="PSM TSV (spectrum_index, sequence, charge)") - parser.add_argument("--tolerance", type=float, default=0.02, help="Fragment tolerance in Da (default: 0.02)") - parser.add_argument("--output", required=True, help="Output TSV file path") - args = parser.parse_args() - +@click.command(help="Validate PSMs by recomputing fragment coverage.") +@click.option("--mzml", required=True, help="Input mzML file") +@click.option("--peptides", required=True, help="PSM TSV (spectrum_index, sequence, charge)") +@click.option("--tolerance", type=float, default=0.02, help="Fragment tolerance in Da (default: 0.02)") +@click.option("--output", required=True, help="Output TSV file path") +def main(mzml, peptides, tolerance, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.mzml, exp) + oms.MzMLFile().load(mzml, exp) - psms = load_psms(args.peptides) + psms = load_psms(peptides) print(f"Loaded {len(psms)} PSMs") - results = validate_psms(exp, psms, tolerance=args.tolerance) + results = validate_psms(exp, psms, tolerance=tolerance) valid_count = sum(1 for r in results if r["status"] == "valid") print(f"Validated: {valid_count}/{len(results)} PSMs with fragment matches") - write_tsv(results, args.output) - print(f"Results written to {args.output}") + write_tsv(results, output) + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/identification/peptide_spectral_match_validator/requirements.txt b/tools/proteomics/identification/peptide_spectral_match_validator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/peptide_spectral_match_validator/requirements.txt +++ b/tools/proteomics/identification/peptide_spectral_match_validator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/psm_feature_extractor/psm_feature_extractor.py b/tools/proteomics/identification/psm_feature_extractor/psm_feature_extractor.py index bd501a8..95e2ae5 100644 --- a/tools/proteomics/identification/psm_feature_extractor/psm_feature_extractor.py +++ b/tools/proteomics/identification/psm_feature_extractor/psm_feature_extractor.py @@ -9,11 +9,12 @@ python psm_feature_extractor.py --mzml run.mzML --peptides psms.tsv --output features.tsv """ -import argparse import csv import sys from typing import List, Optional +import click + try: import pyopenms as oms except ImportError: @@ -216,20 +217,18 @@ def extract_features( return {"total_psms": len(psms), "matched": matched} -def main() -> None: - parser = argparse.ArgumentParser(description="Extract rescoring features from PSMs.") - parser.add_argument("--mzml", required=True, help="Input mzML file") - parser.add_argument("--peptides", required=True, help="Input PSMs TSV file") - parser.add_argument("--output", required=True, help="Output features TSV file") - parser.add_argument("--tolerance", type=float, default=0.02, help="Fragment tolerance in Da (default: 0.02)") - parser.add_argument("--mz-tolerance", type=float, default=0.02, help="Precursor m/z tolerance (default: 0.02)") - parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") - args = parser.parse_args() - +@click.command(help="Extract rescoring features from PSMs.") +@click.option("--mzml", required=True, help="Input mzML file") +@click.option("--peptides", required=True, help="Input PSMs TSV file") +@click.option("--output", required=True, help="Output features TSV file") +@click.option("--tolerance", type=float, default=0.02, help="Fragment tolerance in Da (default: 0.02)") +@click.option("--mz-tolerance", type=float, default=0.02, help="Precursor m/z tolerance (default: 0.02)") +@click.option("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") +def main(mzml, peptides, output, tolerance, mz_tolerance, rt_tolerance) -> None: stats = extract_features( - args.mzml, args.peptides, args.output, args.tolerance, args.mz_tolerance, args.rt_tolerance + mzml, peptides, output, tolerance, mz_tolerance, rt_tolerance ) - print(f"Extracted features for {stats['matched']} / {stats['total_psms']} PSMs to {args.output}") + print(f"Extracted features for {stats['matched']} / {stats['total_psms']} PSMs to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/identification/psm_feature_extractor/requirements.txt b/tools/proteomics/identification/psm_feature_extractor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/psm_feature_extractor/requirements.txt +++ b/tools/proteomics/identification/psm_feature_extractor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/semi_tryptic_peptide_finder/requirements.txt b/tools/proteomics/identification/semi_tryptic_peptide_finder/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/semi_tryptic_peptide_finder/requirements.txt +++ b/tools/proteomics/identification/semi_tryptic_peptide_finder/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py b/tools/proteomics/identification/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py index d6061f8..992b6fc 100644 --- a/tools/proteomics/identification/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py +++ b/tools/proteomics/identification/semi_tryptic_peptide_finder/semi_tryptic_peptide_finder.py @@ -9,11 +9,12 @@ python semi_tryptic_peptide_finder.py --input peptides.tsv --fasta db.fasta --enzyme Trypsin --output classified.tsv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -211,24 +212,20 @@ def write_tsv(results: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Classify peptides as fully/semi/non-tryptic." - ) - parser.add_argument("--input", required=True, help="Input TSV with peptide sequences") - parser.add_argument("--fasta", required=True, help="FASTA database file") - parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") - parser.add_argument("--column", default="sequence", help="Column name for sequences (default: sequence)") - parser.add_argument("--output", required=True, help="Output TSV file path") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - print(f"Loaded {len(proteins)} proteins from {args.fasta}") +@click.command(help="Classify peptides as fully/semi/non-tryptic.") +@click.option("--input", "input", required=True, help="Input TSV with peptide sequences") +@click.option("--fasta", required=True, help="FASTA database file") +@click.option("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") +@click.option("--column", default="sequence", help="Column name for sequences (default: sequence)") +@click.option("--output", required=True, help="Output TSV file path") +def main(input, fasta, enzyme, column, output): + proteins = load_fasta(fasta) + print(f"Loaded {len(proteins)} proteins from {fasta}") - peptides = read_peptides_from_tsv(args.input, column=args.column) - print(f"Read {len(peptides)} peptides from {args.input}") + peptides = read_peptides_from_tsv(input, column=column) + print(f"Read {len(peptides)} peptides from {input}") - results = classify_peptides_against_fasta(peptides, proteins, enzyme=args.enzyme) + results = classify_peptides_against_fasta(peptides, proteins, enzyme=enzyme) counts = {} for r in results: @@ -236,8 +233,8 @@ def main(): for cls, cnt in sorted(counts.items()): print(f" {cls}: {cnt}") - write_tsv(results, args.output) - print(f"Results written to {args.output}") + write_tsv(results, output) + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/identification/sequence_tag_generator/requirements.txt b/tools/proteomics/identification/sequence_tag_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/identification/sequence_tag_generator/requirements.txt +++ b/tools/proteomics/identification/sequence_tag_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/identification/sequence_tag_generator/sequence_tag_generator.py b/tools/proteomics/identification/sequence_tag_generator/sequence_tag_generator.py index 7d4ce9a..2de43b6 100644 --- a/tools/proteomics/identification/sequence_tag_generator/sequence_tag_generator.py +++ b/tools/proteomics/identification/sequence_tag_generator/sequence_tag_generator.py @@ -10,11 +10,12 @@ --tolerance 0.02 --min-tag-length 3 --output tags.tsv """ -import argparse import csv import sys from typing import List, Optional +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -185,39 +186,29 @@ def write_tsv(tags: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Generate de novo sequence tags from MS2 spectra." - ) - parser.add_argument( - "--mz-list", required=True, - help="Comma-separated list of m/z values" - ) - parser.add_argument( - "--intensities", default=None, - help="Comma-separated list of intensities (optional)" - ) - parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") - parser.add_argument("--min-tag-length", type=int, default=3, help="Minimum tag length (default: 3)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - mz_values = [float(x.strip()) for x in args.mz_list.split(",")] - intensities = None - if args.intensities: - intensities = [float(x.strip()) for x in args.intensities.split(",")] +@click.command(help="Generate de novo sequence tags from MS2 spectra.") +@click.option("--mz-list", required=True, help="Comma-separated list of m/z values") +@click.option("--intensities", default=None, help="Comma-separated list of intensities (optional)") +@click.option("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") +@click.option("--min-tag-length", type=int, default=3, help="Minimum tag length (default: 3)") +@click.option("--output", default=None, help="Output TSV file path") +def main(mz_list, intensities, tolerance, min_tag_length, output): + mz_values = [float(x.strip()) for x in mz_list.split(",")] + intensities_list = None + if intensities: + intensities_list = [float(x.strip()) for x in intensities.split(",")] - tags = generate_tags(mz_values, intensities, tolerance=args.tolerance, min_tag_length=args.min_tag_length) + tags = generate_tags(mz_values, intensities_list, tolerance=tolerance, min_tag_length=min_tag_length) - print(f"Found {len(tags)} sequence tags (min length {args.min_tag_length})") + print(f"Found {len(tags)} sequence tags (min length {min_tag_length})") for t in tags[:20]: print(f" {t['tag']} (length {t['length']}, end m/z {t['end_mz']})") if len(tags) > 20: print(f" ... and {len(tags) - 20} more") - if args.output: - write_tsv(tags, args.output) - print(f"Results written to {args.output}") + if output: + write_tsv(tags, output) + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py b/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py index f4cbc88..c12a80d 100644 --- a/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py +++ b/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/amino_acid_composition_analyzer.py @@ -16,11 +16,12 @@ python amino_acid_composition_analyzer.py --sequence PEPTIDEK --output composition.json """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -104,28 +105,26 @@ def analyze_fasta(fasta_path: str) -> list: return results -def main(): +@click.command(help="Analyze amino acid composition for FASTA proteins.") +@click.option("--input", "input", type=str, default=None, help="Protein FASTA file.") +@click.option("--sequence", type=str, default=None, help="Single sequence to analyze.") +@click.option("--output", type=str, default=None, help="Output file (.tsv or .json).") +def main(input, sequence, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Analyze amino acid composition for FASTA proteins.") - parser.add_argument("--input", type=str, help="Protein FASTA file.") - parser.add_argument("--sequence", type=str, help="Single sequence to analyze.") - parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") - args = parser.parse_args() - - if not args.input and not args.sequence: - parser.error("Provide --input or --sequence.") + if not input and not sequence: + raise click.UsageError("Provide --input or --sequence.") - if args.sequence: - results = [analyze_composition(args.sequence)] + if sequence: + results = [analyze_composition(sequence)] else: - results = analyze_fasta(args.input) + results = analyze_fasta(input) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(results if len(results) > 1 else results[0], fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: base_fields = ["accession", "length", "monoisotopic_mass", "basic_residues", "acidic_residues", "hydrophobic_residues", "polar_residues", "aromatic_residues"] @@ -138,7 +137,7 @@ def main(): for aa in STANDARD_AAS: row[f"count_{aa}"] = r["counts"].get(aa, 0) writer.writerow(row) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: acc = r.get("accession", r.get("sequence", "")) diff --git a/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/requirements.txt b/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/requirements.txt +++ b/tools/proteomics/peptide_analysis/amino_acid_composition_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/charge_state_predictor/charge_state_predictor.py b/tools/proteomics/peptide_analysis/charge_state_predictor/charge_state_predictor.py index 7bb4f64..3d12a3c 100644 --- a/tools/proteomics/peptide_analysis/charge_state_predictor/charge_state_predictor.py +++ b/tools/proteomics/peptide_analysis/charge_state_predictor/charge_state_predictor.py @@ -15,10 +15,11 @@ python charge_state_predictor.py --sequence PEPTIDEK --ph 2.0 --output charges.json """ -import argparse import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -134,21 +135,19 @@ def predict_charge_states(sequence: str, ph: float = 2.0, max_charge: int = 0) - } -def main(): +@click.command(help="Predict peptide charge state distribution.") +@click.option("--sequence", required=True, help="Peptide sequence.") +@click.option("--ph", type=float, default=2.0, help="Solution pH (default: 2.0 for ESI).") +@click.option("--max-charge", type=int, default=0, help="Max charge state (0 = auto).") +@click.option("--output", type=str, default=None, help="Output file (.json).") +def main(sequence, ph, max_charge, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Predict peptide charge state distribution.") - parser.add_argument("--sequence", required=True, help="Peptide sequence.") - parser.add_argument("--ph", type=float, default=2.0, help="Solution pH (default: 2.0 for ESI).") - parser.add_argument("--max-charge", type=int, default=0, help="Max charge state (0 = auto).") - parser.add_argument("--output", type=str, help="Output file (.json).") - args = parser.parse_args() - - result = predict_charge_states(args.sequence, args.ph, args.max_charge) + result = predict_charge_states(sequence, ph, max_charge) - if args.output: - with open(args.output, "w") as fh: + if output: + with open(output, "w") as fh: json.dump(result, fh, indent=2) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: print(json.dumps(result, indent=2)) diff --git a/tools/proteomics/peptide_analysis/charge_state_predictor/requirements.txt b/tools/proteomics/peptide_analysis/charge_state_predictor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/charge_state_predictor/requirements.txt +++ b/tools/proteomics/peptide_analysis/charge_state_predictor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/isoelectric_point_calculator/isoelectric_point_calculator.py b/tools/proteomics/peptide_analysis/isoelectric_point_calculator/isoelectric_point_calculator.py index 6736d08..e9c7063 100644 --- a/tools/proteomics/peptide_analysis/isoelectric_point_calculator/isoelectric_point_calculator.py +++ b/tools/proteomics/peptide_analysis/isoelectric_point_calculator/isoelectric_point_calculator.py @@ -16,11 +16,12 @@ python isoelectric_point_calculator.py --fasta proteins.fasta --output pi.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -175,48 +176,48 @@ def calculate_pi_from_sequence(sequence: str, pk_set: str = "lehninger") -> dict } -def main(): +@click.command(help="Calculate isoelectric point for peptides/proteins.") +@click.option("--sequence", type=str, default=None, help="Single amino acid sequence.") +@click.option("--fasta", type=str, default=None, help="FASTA file with protein sequences.") +@click.option( + "--pk-set", type=click.Choice(["lehninger", "emboss", "stryer", "solomon"]), + default="lehninger", help="pKa value set (default: lehninger).", +) +@click.option("--charge-curve", is_flag=True, help="Also output charge curve.") +@click.option("--output", type=str, default=None, help="Output file (.json or .tsv).") +def main(sequence, fasta, pk_set, charge_curve, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Calculate isoelectric point for peptides/proteins.") - parser.add_argument("--sequence", type=str, help="Single amino acid sequence.") - parser.add_argument("--fasta", type=str, help="FASTA file with protein sequences.") - parser.add_argument("--pk-set", choices=list(PKA_SETS.keys()), default="lehninger", - help="pKa value set (default: lehninger).") - parser.add_argument("--charge-curve", action="store_true", help="Also output charge curve.") - parser.add_argument("--output", type=str, help="Output file (.json or .tsv).") - args = parser.parse_args() - - if not args.sequence and not args.fasta: - parser.error("Provide --sequence or --fasta.") + if not sequence and not fasta: + raise click.UsageError("Provide --sequence or --fasta.") results = [] - if args.sequence: - result = calculate_pi_from_sequence(args.sequence, args.pk_set) - if args.charge_curve: - aa_seq = oms.AASequence.fromString(args.sequence) - result["charge_curve"] = calculate_charge_curve(aa_seq.toUnmodifiedString(), args.pk_set) + if sequence: + result = calculate_pi_from_sequence(sequence, pk_set) + if charge_curve: + aa_seq = oms.AASequence.fromString(sequence) + result["charge_curve"] = calculate_charge_curve(aa_seq.toUnmodifiedString(), pk_set) results.append(result) - elif args.fasta: + elif fasta: entries = [] - oms.FASTAFile().load(args.fasta, entries) + oms.FASTAFile().load(fasta, entries) for entry in entries: - result = calculate_pi_from_sequence(entry.sequence, args.pk_set) + result = calculate_pi_from_sequence(entry.sequence, pk_set) result["accession"] = entry.identifier results.append(result) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(results if len(results) > 1 else results[0], fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: fieldnames = ["sequence", "length", "pI", "charge_at_pI", "pk_set"] - if args.fasta: + if fasta: fieldnames.insert(0, "accession") writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") writer.writeheader() writer.writerows(results) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: acc = r.get("accession", r.get("sequence", "")) diff --git a/tools/proteomics/peptide_analysis/isoelectric_point_calculator/requirements.txt b/tools/proteomics/peptide_analysis/isoelectric_point_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/isoelectric_point_calculator/requirements.txt +++ b/tools/proteomics/peptide_analysis/isoelectric_point_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py b/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py index b84a28f..fd89d86 100644 --- a/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py +++ b/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py @@ -17,11 +17,12 @@ python modification_mass_calculator.py --sequence PEPTIDEK --modifications "Oxidation(M):4" """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -135,44 +136,42 @@ def modified_peptide_mass(sequence: str, modifications: str = "", charge: int = } -def main(): +@click.command(help="Query Unimod modifications and compute modified peptide masses.") +@click.option("--search-mod", type=str, default=None, help="Search for a modification by name.") +@click.option("--list-mods", is_flag=True, help="List common modifications.") +@click.option("--sequence", type=str, default=None, help="Peptide sequence for mass calculation.") +@click.option("--modifications", type=str, default="", help="Modifications (e.g., 'Oxidation(M):4').") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1).") +@click.option("--output", type=str, default=None, help="Output file.") +def main(search_mod, list_mods, sequence, modifications, charge, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Query Unimod modifications and compute modified peptide masses.") - parser.add_argument("--search-mod", type=str, help="Search for a modification by name.") - parser.add_argument("--list-mods", action="store_true", help="List common modifications.") - parser.add_argument("--sequence", type=str, help="Peptide sequence for mass calculation.") - parser.add_argument("--modifications", type=str, default="", help="Modifications (e.g., 'Oxidation(M):4').") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") - parser.add_argument("--output", type=str, help="Output file.") - args = parser.parse_args() - - if args.list_mods: + if list_mods: results = list_common_modifications() - if args.output: - with open(args.output, "w", newline="") as fh: + if output: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["full_id", "name", "delta_mass", "origin"], delimiter="\t") writer.writeheader() writer.writerows(results) else: for r in results: print(f"{r['name']}\t{r['delta_mass']}\t{r['origin']}\t{r['full_id']}") - elif args.search_mod: - results = search_modification(args.search_mod) - if args.output: - with open(args.output, "w") as fh: + elif search_mod: + results = search_modification(search_mod) + if output: + with open(output, "w") as fh: json.dump(results, fh, indent=2) else: for r in results: print(f"{r['name']}\t{r['delta_mass']}\t{r['origin']}\t{r['full_id']}") - elif args.sequence: - result = modified_peptide_mass(args.sequence, args.modifications, args.charge) - if args.output: - with open(args.output, "w") as fh: + elif sequence: + result = modified_peptide_mass(sequence, modifications, charge) + if output: + with open(output, "w") as fh: json.dump(result, fh, indent=2) else: print(json.dumps(result, indent=2)) else: - parser.print_help() + click.echo(click.get_current_context().get_help()) if __name__ == "__main__": diff --git a/tools/proteomics/peptide_analysis/modification_mass_calculator/requirements.txt b/tools/proteomics/peptide_analysis/modification_mass_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/modification_mass_calculator/requirements.txt +++ b/tools/proteomics/peptide_analysis/modification_mass_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/modified_peptide_generator/modified_peptide_generator.py b/tools/proteomics/peptide_analysis/modified_peptide_generator/modified_peptide_generator.py index 619f91c..eae71d1 100644 --- a/tools/proteomics/peptide_analysis/modified_peptide_generator/modified_peptide_generator.py +++ b/tools/proteomics/peptide_analysis/modified_peptide_generator/modified_peptide_generator.py @@ -16,12 +16,13 @@ python modified_peptide_generator.py --sequence PEPTMIDEK --variable-mods Oxidation,Phospho --output variants.tsv """ -import argparse import csv import json import sys from itertools import combinations +import click + try: import pyopenms as oms except ImportError: @@ -152,36 +153,32 @@ def generate_variants(sequence: str, variable_mods: list, fixed_mods: list = Non return variants -def main(): +@click.command(help="Generate modified peptide variants.") +@click.option("--sequence", required=True, help="Peptide sequence.") +@click.option("--variable-mods", type=str, default="", help="Comma-separated variable modification names.") +@click.option("--fixed-mods", type=str, default="", help="Comma-separated fixed modification names.") +@click.option("--max-mods", type=int, default=2, help="Maximum simultaneous variable mods (default: 2).") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1).") +@click.option("--output", type=str, default=None, help="Output file (.tsv or .json).") +def main(sequence, variable_mods, fixed_mods, max_mods, charge, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Generate modified peptide variants.") - parser.add_argument("--sequence", required=True, help="Peptide sequence.") - parser.add_argument("--variable-mods", type=str, default="", - help="Comma-separated variable modification names.") - parser.add_argument("--fixed-mods", type=str, default="", - help="Comma-separated fixed modification names.") - parser.add_argument("--max-mods", type=int, default=2, help="Maximum simultaneous variable mods (default: 2).") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") - parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") - args = parser.parse_args() - - var_mods = [m.strip() for m in args.variable_mods.split(",") if m.strip()] - fix_mods = [m.strip() for m in args.fixed_mods.split(",") if m.strip()] - - variants = generate_variants(args.sequence, var_mods, fix_mods, args.max_mods, args.charge) - - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + var_mods = [m.strip() for m in variable_mods.split(",") if m.strip()] + fix_mods = [m.strip() for m in fixed_mods.split(",") if m.strip()] + + variants = generate_variants(sequence, var_mods, fix_mods, max_mods, charge) + + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(variants, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: fieldnames = ["sequence", "modified_sequence", "modifications", "num_modifications", "monoisotopic_mass", "mz", "charge"] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(variants) - print(f"Generated {len(variants)} variants -> {args.output}") + print(f"Generated {len(variants)} variants -> {output}") else: for v in variants: print(f"{v['modified_sequence']}\t{v['modifications']}\t{v['monoisotopic_mass']}") diff --git a/tools/proteomics/peptide_analysis/modified_peptide_generator/requirements.txt b/tools/proteomics/peptide_analysis/modified_peptide_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/modified_peptide_generator/requirements.txt +++ b/tools/proteomics/peptide_analysis/modified_peptide_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py index 1e5a15e..a4a4eb5 100644 --- a/tools/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py +++ b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/peptide_detectability_predictor.py @@ -15,11 +15,12 @@ python peptide_detectability_predictor.py --sequence PEPTIDEK """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -145,39 +146,37 @@ def predict_from_fasta(fasta_path: str, enzyme: str = "Trypsin", return results -def main(): +@click.command(help="Predict peptide detectability from physicochemical heuristics.") +@click.option("--input", "input", type=str, default=None, help="Protein FASTA file.") +@click.option("--sequence", type=str, default=None, help="Single peptide sequence.") +@click.option("--enzyme", type=str, default="Trypsin", help="Enzyme (default: Trypsin).") +@click.option("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1).") +@click.option("--output", type=str, default=None, help="Output file (.tsv or .json).") +def main(input, sequence, enzyme, missed_cleavages, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Predict peptide detectability from physicochemical heuristics.") - parser.add_argument("--input", type=str, help="Protein FASTA file.") - parser.add_argument("--sequence", type=str, help="Single peptide sequence.") - parser.add_argument("--enzyme", type=str, default="Trypsin", help="Enzyme (default: Trypsin).") - parser.add_argument("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1).") - parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") - args = parser.parse_args() - - if args.sequence: - result = calculate_detectability_score(args.sequence) - if args.output: - with open(args.output, "w") as fh: + if sequence: + result = calculate_detectability_score(sequence) + if output: + with open(output, "w") as fh: json.dump(result, fh, indent=2) else: print(json.dumps(result, indent=2)) - elif args.input: - results = predict_from_fasta(args.input, args.enzyme, args.missed_cleavages) - if args.output: - with open(args.output, "w", newline="") as fh: + elif input: + results = predict_from_fasta(input, enzyme, missed_cleavages) + if output: + with open(output, "w", newline="") as fh: fieldnames = ["sequence", "protein", "length", "monoisotopic_mass", "detectability_score", "length_score", "hydrophobicity_score", "mass_score", "problem_residue_score", "basic_residue_score", "gravy"] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t", extrasaction="ignore") writer.writeheader() writer.writerows(results) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results[:20]: print(f"{r['sequence']}\t{r['detectability_score']}\t{r['protein']}") else: - parser.error("Provide --sequence or --input.") + raise click.UsageError("Provide --sequence or --input.") if __name__ == "__main__": diff --git a/tools/proteomics/peptide_analysis/peptide_detectability_predictor/requirements.txt b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/peptide_detectability_predictor/requirements.txt +++ b/tools/proteomics/peptide_analysis/peptide_detectability_predictor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py b/tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py index 8bc4893..5c535ff 100644 --- a/tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py +++ b/tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py @@ -16,9 +16,10 @@ python peptide_mass_calculator.py --sequence ACDEFGHIK --fragments """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -89,29 +90,12 @@ def fragment_ions(sequence: str) -> dict: return {"b_ions": b_ions, "y_ions": y_ions} -def main(): - parser = argparse.ArgumentParser( - description="Calculate peptide/fragment masses using pyopenms." - ) - parser.add_argument( - "--sequence", - required=True, - help="Amino acid sequence (e.g. PEPTIDEK or PEPTM[147]IDEK)", - ) - parser.add_argument( - "--charge", - type=int, - default=1, - help="Charge state for m/z calculation (default: 1)", - ) - parser.add_argument( - "--fragments", - action="store_true", - help="Also compute b-ion and y-ion series", - ) - args = parser.parse_args() - - info = peptide_masses(args.sequence, args.charge) +@click.command(help="Calculate peptide/fragment masses using pyopenms.") +@click.option("--sequence", required=True, help="Amino acid sequence (e.g. PEPTIDEK or PEPTM[147]IDEK)") +@click.option("--charge", type=int, default=1, help="Charge state for m/z calculation (default: 1)") +@click.option("--fragments", is_flag=True, help="Also compute b-ion and y-ion series") +def main(sequence, charge, fragments): + info = peptide_masses(sequence, charge) print(f"Sequence : {info['sequence']}") print(f"Charge : {info['charge']}+") print(f"Monoisotopic mass : {info['monoisotopic_mass']:.6f} Da") @@ -119,8 +103,8 @@ def main(): print(f"m/z (mono) : {info['mz_monoisotopic']:.6f}") print(f"m/z (avg) : {info['mz_average']:.6f}") - if args.fragments: - ions = fragment_ions(args.sequence) + if fragments: + ions = fragment_ions(sequence) print("\n--- b-ions ---") for idx, mass in ions["b_ions"]: print(f" b{idx:>2} {mass:.6f} Da") diff --git a/tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt b/tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt +++ b/tools/proteomics/peptide_analysis/peptide_mass_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py index 65f1250..3a758b0 100644 --- a/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py +++ b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/peptide_mass_fingerprint.py @@ -10,11 +10,12 @@ python peptide_mass_fingerprint.py --fasta db.fasta --accession P12345 --enzyme Trypsin --output fingerprint.tsv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -159,30 +160,26 @@ def write_tsv(records: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Generate/match peptide mass fingerprints from FASTA." - ) - parser.add_argument("--fasta", required=True, help="FASTA database file") - parser.add_argument("--accession", required=True, help="Protein accession to fingerprint") - parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") - parser.add_argument("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1)") - parser.add_argument("--min-mass", type=float, default=500.0, help="Min peptide mass (default: 500)") - parser.add_argument("--max-mass", type=float, default=4000.0, help="Max peptide mass (default: 4000)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - if args.accession not in proteins: - sys.exit(f"Accession '{args.accession}' not found in FASTA file.") - - protein_seq = proteins[args.accession] - print(f"Protein {args.accession}: {len(protein_seq)} amino acids") +@click.command(help="Generate/match peptide mass fingerprints from FASTA.") +@click.option("--fasta", required=True, help="FASTA database file") +@click.option("--accession", required=True, help="Protein accession to fingerprint") +@click.option("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") +@click.option("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1)") +@click.option("--min-mass", type=float, default=500.0, help="Min peptide mass (default: 500)") +@click.option("--max-mass", type=float, default=4000.0, help="Max peptide mass (default: 4000)") +@click.option("--output", default=None, help="Output TSV file path") +def main(fasta, accession, enzyme, missed_cleavages, min_mass, max_mass, output): + proteins = load_fasta(fasta) + if accession not in proteins: + sys.exit(f"Accession '{accession}' not found in FASTA file.") + + protein_seq = proteins[accession] + print(f"Protein {accession}: {len(protein_seq)} amino acids") fingerprint = generate_fingerprint( - protein_seq, enzyme=args.enzyme, - missed_cleavages=args.missed_cleavages, - min_mass=args.min_mass, max_mass=args.max_mass, + protein_seq, enzyme=enzyme, + missed_cleavages=missed_cleavages, + min_mass=min_mass, max_mass=max_mass, ) print(f"Generated {len(fingerprint)} peptide masses") @@ -191,9 +188,9 @@ def main(): if len(fingerprint) > 10: print(f" ... and {len(fingerprint) - 10} more") - if args.output: - write_tsv(fingerprint, args.output) - print(f"\nFingerprint written to {args.output}") + if output: + write_tsv(fingerprint, output) + print(f"\nFingerprint written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt +++ b/tools/proteomics/peptide_analysis/peptide_mass_fingerprint/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py index a78b05d..4b953db 100644 --- a/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py +++ b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py @@ -16,11 +16,12 @@ python peptide_modification_analyzer.py --sequence "PEPTM(Oxidation)IDE" --output breakdown.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -83,28 +84,26 @@ def analyze_modification(sequence: str, charge: int = 1) -> dict: } -def main(): +@click.command(help="Analyze modified peptide residue-by-residue mass breakdown.") +@click.option("--sequence", required=True, help="Modified peptide sequence.") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1).") +@click.option("--output", type=str, default=None, help="Output file (.tsv or .json).") +def main(sequence, charge, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Analyze modified peptide residue-by-residue mass breakdown.") - parser.add_argument("--sequence", required=True, help="Modified peptide sequence.") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") - parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") - args = parser.parse_args() - - result = analyze_modification(args.sequence, args.charge) + result = analyze_modification(sequence, charge) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(result, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: fieldnames = ["position", "residue", "monoisotopic_mass", "modification", "modification_delta_mass"] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(result["residue_breakdown"]) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: print(f"Sequence: {result['sequence']}") print(f"Total mass: {result['total_monoisotopic_mass']}") diff --git a/tools/proteomics/peptide_analysis/peptide_modification_analyzer/requirements.txt b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/peptide_modification_analyzer/requirements.txt +++ b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/peptide_property_calculator/peptide_property_calculator.py b/tools/proteomics/peptide_analysis/peptide_property_calculator/peptide_property_calculator.py index 00e3751..6c3b720 100644 --- a/tools/proteomics/peptide_analysis/peptide_property_calculator/peptide_property_calculator.py +++ b/tools/proteomics/peptide_analysis/peptide_property_calculator/peptide_property_calculator.py @@ -19,11 +19,12 @@ python peptide_property_calculator.py --input peptides.tsv --output properties.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -214,35 +215,33 @@ def calculate_properties(sequence: str, ph: float = 7.0) -> dict: } -def main(): +@click.command(help="Calculate peptide physicochemical properties.") +@click.option("--sequence", type=str, default=None, help="Single peptide sequence.") +@click.option("--ph", type=float, default=7.0, help="pH for charge calculation (default: 7.0).") +@click.option("--input", "input", type=str, default=None, help="TSV file with 'sequence' column.") +@click.option("--output", type=str, default=None, help="Output file (.json or .tsv).") +def main(sequence, ph, input, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Calculate peptide physicochemical properties.") - parser.add_argument("--sequence", type=str, help="Single peptide sequence.") - parser.add_argument("--ph", type=float, default=7.0, help="pH for charge calculation (default: 7.0).") - parser.add_argument("--input", type=str, help="TSV file with 'sequence' column.") - parser.add_argument("--output", type=str, help="Output file (.json or .tsv).") - args = parser.parse_args() - - if not args.sequence and not args.input: - parser.error("Provide --sequence or --input.") + if not sequence and not input: + raise click.UsageError("Provide --sequence or --input.") results = [] - if args.sequence: - results.append(calculate_properties(args.sequence, args.ph)) - elif args.input: - with open(args.input) as fh: + if sequence: + results.append(calculate_properties(sequence, ph)) + elif input: + with open(input) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: seq = row.get("sequence", "").strip() if seq: - results.append(calculate_properties(seq, args.ph)) + results.append(calculate_properties(seq, ph)) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(results if len(results) > 1 else results[0], fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: fieldnames = [ "sequence", "unmodified_sequence", "length", "monoisotopic_mass", "formula", "pI", "gravy", "charge_at_ph", "ph", "instability_index", @@ -252,7 +251,7 @@ def main(): for r in results: row = {k: r[k] for k in fieldnames} writer.writerow(row) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: print(json.dumps(r, indent=2)) diff --git a/tools/proteomics/peptide_analysis/peptide_property_calculator/requirements.txt b/tools/proteomics/peptide_analysis/peptide_property_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/peptide_property_calculator/requirements.txt +++ b/tools/proteomics/peptide_analysis/peptide_property_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/peptide_uniqueness_checker.py b/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/peptide_uniqueness_checker.py index 129c6ae..5cbe444 100644 --- a/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/peptide_uniqueness_checker.py +++ b/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/peptide_uniqueness_checker.py @@ -14,10 +14,11 @@ python peptide_uniqueness_checker.py --peptides peptides.tsv --fasta db.fasta --output uniqueness.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -78,35 +79,33 @@ def check_uniqueness(peptides: list, fasta_path: str) -> list: return results -def main(): +@click.command(help="Check peptide uniqueness in a FASTA database.") +@click.option("--peptides", required=True, help="TSV file with 'sequence' column, or comma-separated list.") +@click.option("--fasta", required=True, help="FASTA database file.") +@click.option("--output", default=None, help="Output TSV file.") +def main(peptides, fasta, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Check peptide uniqueness in a FASTA database.") - parser.add_argument("--peptides", required=True, help="TSV file with 'sequence' column, or comma-separated list.") - parser.add_argument("--fasta", required=True, help="FASTA database file.") - parser.add_argument("--output", help="Output TSV file.") - args = parser.parse_args() - # Load peptides peptide_list = [] try: - with open(args.peptides) as fh: + with open(peptides) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: seq = row.get("sequence", "").strip() if seq: peptide_list.append(seq) except (FileNotFoundError, KeyError): - peptide_list = [p.strip() for p in args.peptides.split(",") if p.strip()] + peptide_list = [p.strip() for p in peptides.split(",") if p.strip()] - results = check_uniqueness(peptide_list, args.fasta) + results = check_uniqueness(peptide_list, fasta) - if args.output: - with open(args.output, "w", newline="") as fh: + if output: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["peptide", "proteins", "protein_count", "is_proteotypic"], delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: status = "proteotypic" if r["is_proteotypic"] else "shared" diff --git a/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/requirements.txt b/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/requirements.txt +++ b/tools/proteomics/peptide_analysis/peptide_uniqueness_checker/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/rt_prediction_additive/requirements.txt b/tools/proteomics/peptide_analysis/rt_prediction_additive/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/peptide_analysis/rt_prediction_additive/requirements.txt +++ b/tools/proteomics/peptide_analysis/rt_prediction_additive/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/peptide_analysis/rt_prediction_additive/rt_prediction_additive.py b/tools/proteomics/peptide_analysis/rt_prediction_additive/rt_prediction_additive.py index 50caab0..65992ef 100644 --- a/tools/proteomics/peptide_analysis/rt_prediction_additive/rt_prediction_additive.py +++ b/tools/proteomics/peptide_analysis/rt_prediction_additive/rt_prediction_additive.py @@ -16,11 +16,12 @@ python rt_prediction_additive.py --sequence PEPTIDEK --model meek --output prediction.json """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -107,48 +108,48 @@ def predict_batch(sequences: list, model: str = "krokhin") -> list: return [predict_rt(seq, model) for seq in sequences if seq.strip()] -def main(): +@click.command(help="Predict peptide RT using additive hydrophobicity models.") +@click.option("--sequence", type=str, default=None, help="Single peptide sequence.") +@click.option("--input", "input", type=str, default=None, help="TSV file with 'sequence' column.") +@click.option( + "--model", type=click.Choice(["krokhin", "meek"]), + default="krokhin", help="Retention model (default: krokhin).", +) +@click.option("--output", type=str, default=None, help="Output file (.json or .tsv).") +def main(sequence, input, model, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Predict peptide RT using additive hydrophobicity models.") - parser.add_argument("--sequence", type=str, help="Single peptide sequence.") - parser.add_argument("--input", type=str, help="TSV file with 'sequence' column.") - parser.add_argument("--model", choices=["krokhin", "meek"], default="krokhin", - help="Retention model (default: krokhin).") - parser.add_argument("--output", type=str, help="Output file (.json or .tsv).") - args = parser.parse_args() - - if not args.sequence and not args.input: - parser.error("Provide --sequence or --input.") - - if args.sequence: - result = predict_rt(args.sequence, args.model) - if args.output: - with open(args.output, "w") as fh: + if not sequence and not input: + raise click.UsageError("Provide --sequence or --input.") + + if sequence: + result = predict_rt(sequence, model) + if output: + with open(output, "w") as fh: json.dump(result, fh, indent=2) else: print(json.dumps(result, indent=2)) - elif args.input: + elif input: sequences = [] - with open(args.input) as fh: + with open(input) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: seq = row.get("sequence", "").strip() if seq: sequences.append(seq) - results = predict_batch(sequences, args.model) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + results = predict_batch(sequences, model) + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(results, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["sequence", "model", "predicted_rt", "length"], delimiter="\t" ) writer.writeheader() for r in results: writer.writerow({k: r[k] for k in ["sequence", "model", "predicted_rt", "length"]}) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: print(f"{r['sequence']}\t{r['predicted_rt']}") diff --git a/tools/proteomics/protein_analysis/peptide_to_protein_mapper/peptide_to_protein_mapper.py b/tools/proteomics/protein_analysis/peptide_to_protein_mapper/peptide_to_protein_mapper.py index aed373f..42926e4 100644 --- a/tools/proteomics/protein_analysis/peptide_to_protein_mapper/peptide_to_protein_mapper.py +++ b/tools/proteomics/protein_analysis/peptide_to_protein_mapper/peptide_to_protein_mapper.py @@ -11,10 +11,11 @@ python peptide_to_protein_mapper.py --peptides peptides.tsv --fasta db.fasta --output mapped.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -134,19 +135,17 @@ def _strip_modifications(sequence: str) -> str: return "".join(result) -def main(): - parser = argparse.ArgumentParser(description="Map peptides to proteins in a FASTA database.") - parser.add_argument("--peptides", required=True, help="Input peptide TSV (must have 'peptide' column)") - parser.add_argument("--fasta", required=True, help="FASTA database file") - parser.add_argument("--output", required=True, help="Output TSV file") - args = parser.parse_args() - - peptide_rows = read_peptides(args.peptides) - fasta_entries = read_fasta(args.fasta) +@click.command(help="Map peptides to proteins in a FASTA database.") +@click.option("--peptides", required=True, help="Input peptide TSV (must have 'peptide' column)") +@click.option("--fasta", required=True, help="FASTA database file") +@click.option("--output", required=True, help="Output TSV file") +def main(peptides, fasta, output): + peptide_rows = read_peptides(peptides) + fasta_entries = read_fasta(fasta) mappings = map_peptides_to_proteins(peptide_rows, fasta_entries) fieldnames = ["peptide", "protein", "protein_description", "start", "end", "is_unique"] - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(mappings) @@ -157,7 +156,7 @@ def main(): print(f"Proteins in FASTA: {len(fasta_entries)}") print(f"Mappings: {n_mapped}") print(f"Unique peptides: {n_unique}") - print(f"Output written to {args.output}") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/protein_analysis/peptide_to_protein_mapper/requirements.txt b/tools/proteomics/protein_analysis/peptide_to_protein_mapper/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/protein_analysis/peptide_to_protein_mapper/requirements.txt +++ b/tools/proteomics/protein_analysis/peptide_to_protein_mapper/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/protein_analysis/protein_coverage_calculator/protein_coverage_calculator.py b/tools/proteomics/protein_analysis/protein_coverage_calculator/protein_coverage_calculator.py index 55c1e2c..d2e71a9 100644 --- a/tools/proteomics/protein_analysis/protein_coverage_calculator/protein_coverage_calculator.py +++ b/tools/proteomics/protein_analysis/protein_coverage_calculator/protein_coverage_calculator.py @@ -14,10 +14,11 @@ python protein_coverage_calculator.py --fasta proteins.fasta --peptides identified.tsv --output coverage.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -91,18 +92,16 @@ def calculate_coverage(proteins: dict, peptides: list) -> list: return results -def main(): +@click.command(help="Calculate protein sequence coverage from peptides.") +@click.option("--fasta", required=True, help="Protein FASTA file.") +@click.option("--peptides", required=True, help="TSV file with 'sequence' column.") +@click.option("--output", default=None, help="Output TSV file.") +def main(fasta, peptides, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Calculate protein sequence coverage from peptides.") - parser.add_argument("--fasta", required=True, help="Protein FASTA file.") - parser.add_argument("--peptides", required=True, help="TSV file with 'sequence' column.") - parser.add_argument("--output", help="Output TSV file.") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) + proteins = load_fasta(fasta) peptide_list = [] - with open(args.peptides) as fh: + with open(peptides) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: seq = row.get("sequence", "").strip() @@ -111,14 +110,14 @@ def main(): results = calculate_coverage(proteins, peptide_list) - if args.output: - with open(args.output, "w", newline="") as fh: + if output: + with open(output, "w", newline="") as fh: fieldnames = ["accession", "protein_length", "covered_residues", "coverage_percent", "matched_peptides", "peptides"] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: print(f"{r['accession']}\t{r['coverage_percent']}%\t{r['matched_peptides']} peptides") diff --git a/tools/proteomics/protein_analysis/protein_coverage_calculator/requirements.txt b/tools/proteomics/protein_analysis/protein_coverage_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/protein_analysis/protein_coverage_calculator/requirements.txt +++ b/tools/proteomics/protein_analysis/protein_coverage_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/protein_analysis/protein_digest/protein_digest.py b/tools/proteomics/protein_analysis/protein_digest/protein_digest.py index 417c260..9faa4b1 100644 --- a/tools/proteomics/protein_analysis/protein_digest/protein_digest.py +++ b/tools/proteomics/protein_analysis/protein_digest/protein_digest.py @@ -14,9 +14,10 @@ python protein_digest.py --sequence MKVLWAALLVTFLAGC --enzyme Lys-C --missed-cleavages 2 """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -85,69 +86,35 @@ def digest_protein( return results -def main(): - parser = argparse.ArgumentParser( - description="In-silico protein digestion using pyopenms." - ) - parser.add_argument( - "--sequence", - help="Single-letter amino acid sequence of the protein", - ) - parser.add_argument( - "--enzyme", - default="Trypsin", - help="Digestion enzyme name (default: Trypsin)", - ) - parser.add_argument( - "--missed-cleavages", - type=int, - default=0, - dest="missed_cleavages", - help="Maximum missed cleavages (default: 0)", - ) - parser.add_argument( - "--min-length", - type=int, - default=6, - dest="min_length", - help="Minimum peptide length (default: 6)", - ) - parser.add_argument( - "--max-length", - type=int, - default=40, - dest="max_length", - help="Maximum peptide length (default: 40)", - ) - parser.add_argument( - "--list-enzymes", - action="store_true", - dest="list_enzymes", - help="List all available enzyme names and exit", - ) - args = parser.parse_args() - - if args.list_enzymes: - enzymes = list_enzymes() +@click.command(help="In-silico protein digestion using pyopenms.") +@click.option("--sequence", default=None, help="Single-letter amino acid sequence of the protein") +@click.option("--enzyme", default="Trypsin", help="Digestion enzyme name (default: Trypsin)") +@click.option("--missed-cleavages", type=int, default=0, help="Maximum missed cleavages (default: 0)") +@click.option("--min-length", type=int, default=6, help="Minimum peptide length (default: 6)") +@click.option("--max-length", type=int, default=40, help="Maximum peptide length (default: 40)") +@click.option("--list-enzymes", "show_enzymes", is_flag=True, help="List all available enzyme names and exit") +def main(sequence, enzyme, missed_cleavages, min_length, max_length, show_enzymes): + if show_enzymes: + enzymes_list = list_enzymes() print("Available enzymes:") - for name in enzymes: + for name in enzymes_list: print(f" {name}") return - if not args.sequence: - parser.error("--sequence is required unless --list-enzymes is used.") + if not sequence: + raise click.UsageError("--sequence is required unless --list-enzymes is used.") peptides = digest_protein( - args.sequence, - enzyme=args.enzyme, - missed_cleavages=args.missed_cleavages, - min_length=args.min_length, - max_length=args.max_length, + sequence, + enzyme=enzyme, + missed_cleavages=missed_cleavages, + min_length=min_length, + max_length=max_length, ) print( - f"Enzyme: {args.enzyme} | Missed cleavages ≤ {args.missed_cleavages} " - f"| Length {args.min_length}–{args.max_length}" + f"Enzyme: {enzyme} | Missed cleavages \u2264 {missed_cleavages} " + f"| Length {min_length}\u2013{max_length}" ) print(f"Total peptides: {len(peptides)}\n") print(f"{'#':>4} {'Sequence':<40} {'Length':>6} {'Mono Mass (Da)':>14}") diff --git a/tools/proteomics/protein_analysis/protein_digest/requirements.txt b/tools/proteomics/protein_analysis/protein_digest/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/protein_analysis/protein_digest/requirements.txt +++ b/tools/proteomics/protein_analysis/protein_digest/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/protein_analysis/protein_group_reporter/protein_group_reporter.py b/tools/proteomics/protein_analysis/protein_group_reporter/protein_group_reporter.py index 362c92a..59edd84 100644 --- a/tools/proteomics/protein_analysis/protein_group_reporter/protein_group_reporter.py +++ b/tools/proteomics/protein_analysis/protein_group_reporter/protein_group_reporter.py @@ -9,11 +9,12 @@ python protein_group_reporter.py --input peptides.tsv --fasta db.fasta --output groups.tsv """ -import argparse import csv import sys from typing import Dict, List, Set +import click + try: import pyopenms as oms except ImportError: @@ -193,21 +194,17 @@ def write_tsv(results: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Parse protein groups from peptide data and report clean table." - ) - parser.add_argument("--input", required=True, help="Input TSV with peptide sequences") - parser.add_argument("--fasta", required=True, help="FASTA database file") - parser.add_argument("--column", default="sequence", help="Column name for sequences (default: sequence)") - parser.add_argument("--output", required=True, help="Output TSV file path") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - print(f"Loaded {len(proteins)} proteins from {args.fasta}") +@click.command(help="Parse protein groups from peptide data and report clean table.") +@click.option("--input", "input", required=True, help="Input TSV with peptide sequences") +@click.option("--fasta", required=True, help="FASTA database file") +@click.option("--column", default="sequence", help="Column name for sequences (default: sequence)") +@click.option("--output", required=True, help="Output TSV file path") +def main(input, fasta, column, output): + proteins = load_fasta(fasta) + print(f"Loaded {len(proteins)} proteins from {fasta}") - peptides = read_peptides_from_tsv(args.input, column=args.column) - print(f"Read {len(peptides)} peptides from {args.input}") + peptides = read_peptides_from_tsv(input, column=column) + print(f"Read {len(peptides)} peptides from {input}") protein_peptides = map_peptides_to_proteins(peptides, proteins) print(f"Mapped peptides to {len(protein_peptides)} proteins") @@ -215,8 +212,8 @@ def main(): groups = build_protein_groups(protein_peptides, proteins) print(f"Built {len(groups)} protein groups") - write_tsv(groups, args.output) - print(f"Results written to {args.output}") + write_tsv(groups, output) + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/protein_analysis/protein_group_reporter/requirements.txt b/tools/proteomics/protein_analysis/protein_group_reporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/protein_analysis/protein_group_reporter/requirements.txt +++ b/tools/proteomics/protein_analysis/protein_group_reporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/protein_analysis/spectral_counting_quantifier/requirements.txt b/tools/proteomics/protein_analysis/spectral_counting_quantifier/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/protein_analysis/spectral_counting_quantifier/requirements.txt +++ b/tools/proteomics/protein_analysis/spectral_counting_quantifier/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/protein_analysis/spectral_counting_quantifier/spectral_counting_quantifier.py b/tools/proteomics/protein_analysis/spectral_counting_quantifier/spectral_counting_quantifier.py index fcdc6eb..d69d649 100644 --- a/tools/proteomics/protein_analysis/spectral_counting_quantifier/spectral_counting_quantifier.py +++ b/tools/proteomics/protein_analysis/spectral_counting_quantifier/spectral_counting_quantifier.py @@ -16,11 +16,12 @@ python spectral_counting_quantifier.py --input counts.tsv --fasta db.fasta --method empai --output out.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -185,35 +186,33 @@ def load_peptide_counts(input_path: str) -> dict: return protein_data -def main(): +@click.command(help="Calculate protein abundances from spectral counts.") +@click.option("--input", "input", required=True, help="TSV with protein, peptide, spectral_count columns.") +@click.option("--fasta", required=True, help="Protein FASTA database.") +@click.option("--method", type=click.Choice(["empai", "nsaf"]), default="nsaf", help="Quantification method.") +@click.option("--enzyme", default="Trypsin", help="Enzyme for emPAI (default: Trypsin).") +@click.option("--output", default=None, help="Output file (.tsv or .json).") +def main(input, fasta, method, enzyme, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Calculate protein abundances from spectral counts.") - parser.add_argument("--input", required=True, help="TSV with protein, peptide, spectral_count columns.") - parser.add_argument("--fasta", required=True, help="Protein FASTA database.") - parser.add_argument("--method", choices=["empai", "nsaf"], default="nsaf", help="Quantification method.") - parser.add_argument("--enzyme", default="Trypsin", help="Enzyme for emPAI (default: Trypsin).") - parser.add_argument("--output", help="Output file (.tsv or .json).") - args = parser.parse_args() - - proteins = load_fasta_proteins(args.fasta) - protein_data = load_peptide_counts(args.input) - - if args.method == "empai": - results = calculate_empai(protein_data, proteins, args.enzyme) + proteins = load_fasta_proteins(fasta) + protein_data = load_peptide_counts(input) + + if method == "empai": + results = calculate_empai(protein_data, proteins, enzyme) else: results = calculate_nsaf(protein_data, proteins) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(results, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: fieldnames = list(results[0].keys()) if results else [] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: print(json.dumps(r)) diff --git a/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py b/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py index 9b7f18e..883986b 100644 --- a/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py +++ b/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/glycopeptide_mass_calculator.py @@ -15,11 +15,12 @@ python glycopeptide_mass_calculator.py --sequence PEPTIDEK --glycan "HexNAc(2)Hex(3)" --output masses.tsv """ -import argparse import csv import re import sys +import click + try: import pyopenms as oms except ImportError: @@ -144,20 +145,13 @@ def write_tsv(results: list, output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Calculate glycopeptide masses with glycan compositions." - ) - parser.add_argument("--sequence", required=True, help="Peptide amino acid sequence") - parser.add_argument( - "--glycan", required=True, - help='Glycan composition, e.g. "HexNAc(2)Hex(5)Fuc(1)"' - ) - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - result = glycopeptide_mass(args.sequence, args.glycan, charge=args.charge) +@click.command(help="Calculate glycopeptide masses with glycan compositions.") +@click.option("--sequence", required=True, help="Peptide amino acid sequence") +@click.option("--glycan", required=True, help='Glycan composition, e.g. "HexNAc(2)Hex(5)Fuc(1)"') +@click.option("--charge", type=int, default=1, help="Charge state (default: 1)") +@click.option("--output", default=None, help="Output TSV file path") +def main(sequence, glycan, charge, output): + result = glycopeptide_mass(sequence, glycan, charge=charge) print(f"Sequence : {result['sequence']}") print(f"Glycan : {result['glycan']}") @@ -167,9 +161,9 @@ def main(): print(f"Charge : {result['charge']}+") print(f"m/z : {result['mz']:.6f}") - if args.output: - write_tsv([result], args.output) - print(f"\nResults written to {args.output}") + if output: + write_tsv([result], output) + print(f"\nResults written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/requirements.txt b/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/requirements.txt +++ b/tools/proteomics/ptm_analysis/glycopeptide_mass_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/ptm_analysis/phospho_enrichment_qc/phospho_enrichment_qc.py b/tools/proteomics/ptm_analysis/phospho_enrichment_qc/phospho_enrichment_qc.py index 5654464..b2b744e 100644 --- a/tools/proteomics/ptm_analysis/phospho_enrichment_qc/phospho_enrichment_qc.py +++ b/tools/proteomics/ptm_analysis/phospho_enrichment_qc/phospho_enrichment_qc.py @@ -12,12 +12,13 @@ python phospho_enrichment_qc.py --input search_results.tsv --output enrichment.tsv """ -import argparse import csv import re import sys from typing import Dict, List, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -176,17 +177,13 @@ def write_output(output_path: str, counts: Dict[str, int], ratios: Dict[str, flo f.write(f"pTyr_ratio\t{ratios['pTyr_ratio']:.4f}\n") -def main(): - parser = argparse.ArgumentParser( - description="Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios." - ) - parser.add_argument("--input", required=True, help="Input search results TSV file") - parser.add_argument("--output", required=True, help="Output enrichment report TSV file") - args = parser.parse_args() - - rows = read_input(args.input) +@click.command(help="Compute phospho-enrichment efficiency and pSer/pThr/pTyr ratios.") +@click.option("--input", "input", required=True, help="Input search results TSV file") +@click.option("--output", required=True, help="Output enrichment report TSV file") +def main(input, output): + rows = read_input(input) counts, ratios = compute_enrichment_stats(rows) - write_output(args.output, counts, ratios) + write_output(output, counts, ratios) print(f"Total peptides: {counts['total']}") print(f"Phospho peptides: {counts['phospho']}") diff --git a/tools/proteomics/ptm_analysis/phospho_enrichment_qc/requirements.txt b/tools/proteomics/ptm_analysis/phospho_enrichment_qc/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/ptm_analysis/phospho_enrichment_qc/requirements.txt +++ b/tools/proteomics/ptm_analysis/phospho_enrichment_qc/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/ptm_analysis/phospho_motif_analyzer/phospho_motif_analyzer.py b/tools/proteomics/ptm_analysis/phospho_motif_analyzer/phospho_motif_analyzer.py index c86165f..754b4cb 100644 --- a/tools/proteomics/ptm_analysis/phospho_motif_analyzer/phospho_motif_analyzer.py +++ b/tools/proteomics/ptm_analysis/phospho_motif_analyzer/phospho_motif_analyzer.py @@ -12,12 +12,13 @@ python phospho_motif_analyzer.py --input phosphosites.tsv --fasta proteome.fasta --window 7 --output motifs.tsv """ -import argparse import csv import sys from collections import Counter from typing import Dict, List, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -224,26 +225,22 @@ def write_output( f.write(f"{pos}\t{aa}\t{freq:.4f}\n") -def main(): - parser = argparse.ArgumentParser( - description="Extract motif windows around phosphosites and compute position-specific frequencies." - ) - parser.add_argument("--input", required=True, help="Input phosphosites TSV file") - parser.add_argument("--fasta", required=True, help="Proteome FASTA file") - parser.add_argument("--window", type=int, default=7, help="Window size on each side (default: 7)") - parser.add_argument("--output", required=True, help="Output motifs TSV file") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - rows = read_input(args.input) - motif_rows = extract_motif_windows(rows, proteins, args.window) - windows = [r["motif_window"] for r in motif_rows] - frequencies = compute_position_frequencies(windows, args.window) - write_output(args.output, motif_rows, frequencies) +@click.command(help="Extract motif windows around phosphosites and compute position-specific frequencies.") +@click.option("--input", "input", required=True, help="Input phosphosites TSV file") +@click.option("--fasta", required=True, help="Proteome FASTA file") +@click.option("--window", type=int, default=7, help="Window size on each side (default: 7)") +@click.option("--output", required=True, help="Output motifs TSV file") +def main(input, fasta, window, output): + proteins = load_fasta(fasta) + rows = read_input(input) + motif_rows = extract_motif_windows(rows, proteins, window) + windows_list = [r["motif_window"] for r in motif_rows] + frequencies = compute_position_frequencies(windows_list, window) + write_output(output, motif_rows, frequencies) print(f"Processed {len(motif_rows)} phosphosites") - print(f"Window size: +/-{args.window} residues") - print(f"Output written to {args.output}") + print(f"Window size: +/-{window} residues") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/ptm_analysis/phospho_motif_analyzer/requirements.txt b/tools/proteomics/ptm_analysis/phospho_motif_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/ptm_analysis/phospho_motif_analyzer/requirements.txt +++ b/tools/proteomics/ptm_analysis/phospho_motif_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/ptm_analysis/phosphosite_class_filter/phosphosite_class_filter.py b/tools/proteomics/ptm_analysis/phosphosite_class_filter/phosphosite_class_filter.py index 74534ae..9a47e55 100644 --- a/tools/proteomics/ptm_analysis/phosphosite_class_filter/phosphosite_class_filter.py +++ b/tools/proteomics/ptm_analysis/phosphosite_class_filter/phosphosite_class_filter.py @@ -13,11 +13,12 @@ python phosphosite_class_filter.py --input phosphosites.tsv --class1-threshold 0.75 --output classified.tsv """ -import argparse import csv import sys from typing import Dict, List, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -208,21 +209,17 @@ def write_output(output_path: str, classified_rows: List[Dict[str, str]], summar f.write(f"enrichment_efficiency\t{enrichment:.4f}\n") -def main(): - parser = argparse.ArgumentParser( - description="Classify phosphosites into Class I/II/III by localization probability." - ) - parser.add_argument("--input", required=True, help="Input phosphosites TSV file") - parser.add_argument( - "--class1-threshold", type=float, default=CLASS1_DEFAULT_THRESHOLD, - help=f"Minimum localization probability for Class I (default: {CLASS1_DEFAULT_THRESHOLD})" - ) - parser.add_argument("--output", required=True, help="Output classified TSV file") - args = parser.parse_args() - - rows = read_input(args.input) - classified, summary = classify_phosphosites(rows, args.class1_threshold) - write_output(args.output, classified, summary) +@click.command(help="Classify phosphosites into Class I/II/III by localization probability.") +@click.option("--input", "input", required=True, help="Input phosphosites TSV file") +@click.option( + "--class1-threshold", type=float, default=CLASS1_DEFAULT_THRESHOLD, + help=f"Minimum localization probability for Class I (default: {CLASS1_DEFAULT_THRESHOLD})", +) +@click.option("--output", required=True, help="Output classified TSV file") +def main(input, class1_threshold, output): + rows = read_input(input) + classified, summary = classify_phosphosites(rows, class1_threshold) + write_output(output, classified, summary) enrichment = compute_enrichment_efficiency(summary) print(f"Total phosphosites: {summary['total']}") diff --git a/tools/proteomics/ptm_analysis/phosphosite_class_filter/requirements.txt b/tools/proteomics/ptm_analysis/phosphosite_class_filter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/ptm_analysis/phosphosite_class_filter/requirements.txt +++ b/tools/proteomics/ptm_analysis/phosphosite_class_filter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py b/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py index 1b7a21e..c800f28 100644 --- a/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py +++ b/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py @@ -16,11 +16,12 @@ --peptide "PEPS(Phospho)TIDEK" --tolerance 0.02 --output scores.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -213,34 +214,32 @@ def score_localization(experimental_mz: list, experimental_intensities: list, } -def main(): +@click.command(help="Score PTM site localization confidence.") +@click.option("--mz-list", required=True, help="Comma-separated experimental m/z values.") +@click.option("--intensities", required=True, help="Comma-separated intensities.") +@click.option("--peptide", required=True, help="Modified peptide sequence.") +@click.option("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02).") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1).") +@click.option("--output", default=None, help="Output file (.tsv or .json).") +def main(mz_list, intensities, peptide, tolerance, charge, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Score PTM site localization confidence.") - parser.add_argument("--mz-list", required=True, help="Comma-separated experimental m/z values.") - parser.add_argument("--intensities", required=True, help="Comma-separated intensities.") - parser.add_argument("--peptide", required=True, help="Modified peptide sequence.") - parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02).") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1).") - parser.add_argument("--output", help="Output file (.tsv or .json).") - args = parser.parse_args() - - mz_values = [float(x.strip()) for x in args.mz_list.split(",")] - intensities = [float(x.strip()) for x in args.intensities.split(",")] - - result = score_localization(mz_values, intensities, args.peptide, args.tolerance, args.charge) - - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + mz_values = [float(x.strip()) for x in mz_list.split(",")] + intensities_list = [float(x.strip()) for x in intensities.split(",")] + + result = score_localization(mz_values, intensities_list, peptide, tolerance, charge) + + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(result, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["sequence", "matched_ions", "probability"], delimiter="\t" ) writer.writeheader() writer.writerows(result["candidates"]) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: print(json.dumps(result, indent=2)) diff --git a/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/requirements.txt b/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/requirements.txt +++ b/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/acquisition_rate_analyzer/acquisition_rate_analyzer.py b/tools/proteomics/quality_control/acquisition_rate_analyzer/acquisition_rate_analyzer.py index 0790a8d..34aad98 100644 --- a/tools/proteomics/quality_control/acquisition_rate_analyzer/acquisition_rate_analyzer.py +++ b/tools/proteomics/quality_control/acquisition_rate_analyzer/acquisition_rate_analyzer.py @@ -10,10 +10,11 @@ python acquisition_rate_analyzer.py --input run.mzML --output rates.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -84,26 +85,22 @@ def analyze_acquisition_rates(exp: oms.MSExperiment) -> dict: return {"scans": scans, "summary": summary} -def main(): - parser = argparse.ArgumentParser( - description="Analyze MS1/MS2 acquisition rates over time." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV file") - args = parser.parse_args() - +@click.command(help="Analyze MS1/MS2 acquisition rates over time.") +@click.option("--input", "input", required=True, help="Path to mzML file") +@click.option("--output", required=True, help="Output TSV file") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) result = analyze_acquisition_rates(exp) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["rt_sec", "ms_level", "delta_sec"], delimiter="\t") writer.writeheader() writer.writerows(result["scans"]) s = result["summary"] - print(f"Rates written to {args.output}") + print(f"Rates written to {output}") print(f" Total scans : {s['total_scans']}") print(f" MS1 rate : {s['ms1_rate_per_min']:.1f} /min") print(f" MS2 rate : {s['ms2_rate_per_min']:.1f} /min") diff --git a/tools/proteomics/quality_control/acquisition_rate_analyzer/requirements.txt b/tools/proteomics/quality_control/acquisition_rate_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/acquisition_rate_analyzer/requirements.txt +++ b/tools/proteomics/quality_control/acquisition_rate_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/collision_energy_analyzer/collision_energy_analyzer.py b/tools/proteomics/quality_control/collision_energy_analyzer/collision_energy_analyzer.py index daf01ed..d495d34 100644 --- a/tools/proteomics/quality_control/collision_energy_analyzer/collision_energy_analyzer.py +++ b/tools/proteomics/quality_control/collision_energy_analyzer/collision_energy_analyzer.py @@ -9,11 +9,12 @@ python collision_energy_analyzer.py --input run.mzML --output ce_analysis.tsv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -112,16 +113,12 @@ def write_tsv(records: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Extract collision energy values from mzML MS2 spectra." - ) - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - +@click.command(help="Extract collision energy values from mzML MS2 spectra.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) records = extract_collision_energies(exp) summary = summarize_ce(records) @@ -133,9 +130,9 @@ def main(): print(f"Mean CE : {summary['mean_ce']}") print(f"Unique CE values : {summary['unique_ce']}") - if args.output: - write_tsv(records, args.output) - print(f"\nResults written to {args.output}") + if output: + write_tsv(records, output) + print(f"\nResults written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/quality_control/collision_energy_analyzer/requirements.txt b/tools/proteomics/quality_control/collision_energy_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/collision_energy_analyzer/requirements.txt +++ b/tools/proteomics/quality_control/collision_energy_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/identification_qc_reporter/identification_qc_reporter.py b/tools/proteomics/quality_control/identification_qc_reporter/identification_qc_reporter.py index 8bc2639..ec84980 100644 --- a/tools/proteomics/quality_control/identification_qc_reporter/identification_qc_reporter.py +++ b/tools/proteomics/quality_control/identification_qc_reporter/identification_qc_reporter.py @@ -14,12 +14,13 @@ python identification_qc_reporter.py --input results.tsv --output id_qc.json """ -import argparse import csv import json import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -116,21 +117,17 @@ def load_peptide_tsv(path: str) -> list[dict]: return rows -def main(): - parser = argparse.ArgumentParser( - description="Report identification-level QC from peptide TSV." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Peptide TSV file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON report") - args = parser.parse_args() - - rows = load_peptide_tsv(args.input) +@click.command(help="Report identification-level QC from peptide TSV.") +@click.option("--input", "input", required=True, help="Peptide TSV file") +@click.option("--output", required=True, help="Output JSON report") +def main(input, output): + rows = load_peptide_tsv(input) metrics = compute_id_qc(rows) - with open(args.output, "w") as fh: + with open(output, "w") as fh: json.dump(metrics, fh, indent=2) - print(f"ID QC report written to {args.output}") + print(f"ID QC report written to {output}") print(f" PSMs : {metrics['psm_count']}") print(f" Unique peptides : {metrics['unique_peptides']}") diff --git a/tools/proteomics/quality_control/identification_qc_reporter/requirements.txt b/tools/proteomics/quality_control/identification_qc_reporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/identification_qc_reporter/requirements.txt +++ b/tools/proteomics/quality_control/identification_qc_reporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/injection_time_analyzer/injection_time_analyzer.py b/tools/proteomics/quality_control/injection_time_analyzer/injection_time_analyzer.py index fae31d7..2261143 100644 --- a/tools/proteomics/quality_control/injection_time_analyzer/injection_time_analyzer.py +++ b/tools/proteomics/quality_control/injection_time_analyzer/injection_time_analyzer.py @@ -11,11 +11,12 @@ python injection_time_analyzer.py --input run.mzML --output injection_times.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -100,20 +101,16 @@ def summarize_injection_times(records: list[dict]) -> dict: return summary -def main(): - parser = argparse.ArgumentParser( - description="Extract injection time values from mzML metadata." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output TSV file") - args = parser.parse_args() - +@click.command(help="Extract injection time values from mzML metadata.") +@click.option("--input", "input", required=True, help="Path to mzML file") +@click.option("--output", required=True, help="Output TSV file") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) records = extract_injection_times(exp) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["scan_index", "rt", "ms_level", "injection_time_ms"], @@ -123,7 +120,7 @@ def main(): writer.writerows(records) n_with_time = sum(1 for r in records if r["injection_time_ms"] is not None) - print(f"Wrote {len(records)} scans to {args.output} ({n_with_time} with injection times)") + print(f"Wrote {len(records)} scans to {output} ({n_with_time} with injection times)") summary = summarize_injection_times(records) for level, stats in summary.items(): diff --git a/tools/proteomics/quality_control/injection_time_analyzer/requirements.txt b/tools/proteomics/quality_control/injection_time_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/injection_time_analyzer/requirements.txt +++ b/tools/proteomics/quality_control/injection_time_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/lc_ms_qc_reporter/lc_ms_qc_reporter.py b/tools/proteomics/quality_control/lc_ms_qc_reporter/lc_ms_qc_reporter.py index d48625a..0a6ed96 100644 --- a/tools/proteomics/quality_control/lc_ms_qc_reporter/lc_ms_qc_reporter.py +++ b/tools/proteomics/quality_control/lc_ms_qc_reporter/lc_ms_qc_reporter.py @@ -11,11 +11,12 @@ python lc_ms_qc_reporter.py --input run.mzML --output qc_report.json """ -import argparse import json import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -81,23 +82,19 @@ def compute_qc_metrics(exp: oms.MSExperiment) -> dict: } -def main(): - parser = argparse.ArgumentParser( - description="Generate comprehensive QC report from an mzML file." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON report path") - args = parser.parse_args() - +@click.command(help="Generate comprehensive QC report from an mzML file.") +@click.option("--input", "input", required=True, help="Path to mzML file") +@click.option("--output", required=True, help="Output JSON report path") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) metrics = compute_qc_metrics(exp) - with open(args.output, "w") as fh: + with open(output, "w") as fh: json.dump(metrics, fh, indent=2) - print(f"QC report written to {args.output}") + print(f"QC report written to {output}") print(f" MS1 spectra : {metrics['ms1_count']}") print(f" MS2 spectra : {metrics['ms2_count']}") print(f" TIC CV% : {metrics['tic_cv_percent']:.2f}") diff --git a/tools/proteomics/quality_control/lc_ms_qc_reporter/requirements.txt b/tools/proteomics/quality_control/lc_ms_qc_reporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/lc_ms_qc_reporter/requirements.txt +++ b/tools/proteomics/quality_control/lc_ms_qc_reporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py b/tools/proteomics/quality_control/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py index e2b799c..ad82dd1 100644 --- a/tools/proteomics/quality_control/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py +++ b/tools/proteomics/quality_control/mass_error_distribution_analyzer/mass_error_distribution_analyzer.py @@ -12,11 +12,12 @@ python mass_error_distribution_analyzer.py --input peptides.tsv --mzml run.mzML --output errors.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -119,27 +120,23 @@ def summarize_errors(errors: list[dict]) -> dict: } -def main(): - parser = argparse.ArgumentParser( - description="Compute precursor mass error distributions." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Peptide TSV file") - parser.add_argument("--mzml", required=True, metavar="FILE", help="mzML file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output errors TSV") - args = parser.parse_args() - +@click.command(help="Compute precursor mass error distributions.") +@click.option("--input", "input", required=True, help="Peptide TSV file") +@click.option("--mzml", required=True, help="mzML file") +@click.option("--output", required=True, help="Output errors TSV") +def main(input, mzml, output): rows = [] - with open(args.input) as fh: + with open(input) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: rows.append(row) exp = oms.MSExperiment() - oms.MzMLFile().load(args.mzml, exp) + oms.MzMLFile().load(mzml, exp) errors = compute_mass_errors(rows, exp) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["sequence", "charge", "theo_mz", "obs_mz", "error_da", "error_ppm"], @@ -149,7 +146,7 @@ def main(): writer.writerows(errors) summary = summarize_errors(errors) - print(f"Wrote {summary['count']} mass errors to {args.output}") + print(f"Wrote {summary['count']} mass errors to {output}") if summary["count"] > 0: print(f" Mean error: {summary['ppm_mean']:.2f} ppm (std {summary['ppm_std']:.2f})") diff --git a/tools/proteomics/quality_control/mass_error_distribution_analyzer/requirements.txt b/tools/proteomics/quality_control/mass_error_distribution_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/mass_error_distribution_analyzer/requirements.txt +++ b/tools/proteomics/quality_control/mass_error_distribution_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py b/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py index 64459d5..b88cec6 100644 --- a/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py +++ b/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py @@ -14,11 +14,12 @@ python missed_cleavage_analyzer.py --input peptides.tsv --enzyme Trypsin --output mc_report.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -89,34 +90,32 @@ def analyze_missed_cleavages(peptides: list, enzyme: str = "Trypsin") -> dict: } -def main(): +@click.command(help="Analyze missed cleavage distribution.") +@click.option("--input", "input", required=True, help="TSV file with 'sequence' column.") +@click.option("--enzyme", type=str, default="Trypsin", help="Enzyme name (default: Trypsin).") +@click.option("--output", type=str, default=None, help="Output file (.tsv or .json).") +def main(input, enzyme, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Analyze missed cleavage distribution.") - parser.add_argument("--input", required=True, help="TSV file with 'sequence' column.") - parser.add_argument("--enzyme", type=str, default="Trypsin", help="Enzyme name (default: Trypsin).") - parser.add_argument("--output", type=str, help="Output file (.tsv or .json).") - args = parser.parse_args() - peptide_list = [] - with open(args.input) as fh: + with open(input) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: seq = row.get("sequence", "").strip() if seq: peptide_list.append(seq) - analysis = analyze_missed_cleavages(peptide_list, args.enzyme) + analysis = analyze_missed_cleavages(peptide_list, enzyme) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump(analysis, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["peptide", "missed_cleavages"], delimiter="\t") writer.writeheader() writer.writerows(analysis["peptide_results"]) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: print(f"Enzyme: {analysis['enzyme']}") print(f"Total peptides: {analysis['total_peptides']}") diff --git a/tools/proteomics/quality_control/missed_cleavage_analyzer/requirements.txt b/tools/proteomics/quality_control/missed_cleavage_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/missed_cleavage_analyzer/requirements.txt +++ b/tools/proteomics/quality_control/missed_cleavage_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py b/tools/proteomics/quality_control/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py index adf763b..ef2c344 100644 --- a/tools/proteomics/quality_control/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py +++ b/tools/proteomics/quality_control/ms1_feature_intensity_tracker/ms1_feature_intensity_tracker.py @@ -11,11 +11,12 @@ --ppm 10 --output tracking.tsv """ -import argparse import csv import sys from typing import Dict, List +import click + try: import pyopenms as oms except ImportError: @@ -214,24 +215,20 @@ def write_tsv(results: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Track feature intensities across multiple mzML runs." - ) - parser.add_argument("--inputs", nargs="+", required=True, help="Input mzML files") - parser.add_argument("--features", required=True, help="Target features TSV (feature_id, mz, rt)") - parser.add_argument("--ppm", type=float, default=10.0, help="m/z tolerance in ppm (default: 10)") - parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in sec (default: 30)") - parser.add_argument("--output", required=True, help="Output TSV file path") - args = parser.parse_args() - - features = load_features(args.features) - print(f"Loaded {len(features)} target features") +@click.command(help="Track feature intensities across multiple mzML runs.") +@click.option("--inputs", multiple=True, required=True, help="Input mzML files") +@click.option("--features", required=True, help="Target features TSV (feature_id, mz, rt)") +@click.option("--ppm", type=float, default=10.0, help="m/z tolerance in ppm (default: 10)") +@click.option("--rt-tolerance", type=float, default=30.0, help="RT tolerance in sec (default: 30)") +@click.option("--output", required=True, help="Output TSV file path") +def main(inputs, features, ppm, rt_tolerance, output): + features_data = load_features(features) + print(f"Loaded {len(features_data)} target features") - results = track_features(args.inputs, features, ppm=args.ppm, rt_tolerance=args.rt_tolerance) + results = track_features(list(inputs), features_data, ppm=ppm, rt_tolerance=rt_tolerance) - write_tsv(results, args.output) - print(f"Tracking results written to {args.output}") + write_tsv(results, output) + print(f"Tracking results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/quality_control/ms1_feature_intensity_tracker/requirements.txt b/tools/proteomics/quality_control/ms1_feature_intensity_tracker/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/ms1_feature_intensity_tracker/requirements.txt +++ b/tools/proteomics/quality_control/ms1_feature_intensity_tracker/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/mzqc_generator/mzqc_generator.py b/tools/proteomics/quality_control/mzqc_generator/mzqc_generator.py index 76867c9..acd0a34 100644 --- a/tools/proteomics/quality_control/mzqc_generator/mzqc_generator.py +++ b/tools/proteomics/quality_control/mzqc_generator/mzqc_generator.py @@ -9,12 +9,13 @@ python mzqc_generator.py --input run.mzML --output qc.mzQC """ -import argparse import json import math import sys from datetime import datetime, timezone +import click + try: import pyopenms as oms except ImportError: @@ -117,24 +118,20 @@ def generate_mzqc(exp: oms.MSExperiment, input_file: str = "unknown.mzML") -> di return mzqc -def main(): - parser = argparse.ArgumentParser( - description="Generate mzQC JSON from an mzML file." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output mzQC JSON path") - args = parser.parse_args() - +@click.command(help="Generate mzQC JSON from an mzML file.") +@click.option("--input", "input", required=True, help="Path to mzML file") +@click.option("--output", required=True, help="Output mzQC JSON path") +def main(input, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) - mzqc = generate_mzqc(exp, input_file=args.input) + mzqc = generate_mzqc(exp, input_file=input) - with open(args.output, "w") as fh: + with open(output, "w") as fh: json.dump(mzqc, fh, indent=2) n_metrics = len(mzqc["mzQC"]["runQualities"][0]["qualityMetrics"]) - print(f"mzQC written to {args.output} ({n_metrics} metrics)") + print(f"mzQC written to {output} ({n_metrics} metrics)") if __name__ == "__main__": diff --git a/tools/proteomics/quality_control/mzqc_generator/requirements.txt b/tools/proteomics/quality_control/mzqc_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/mzqc_generator/requirements.txt +++ b/tools/proteomics/quality_control/mzqc_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/precursor_charge_distribution/precursor_charge_distribution.py b/tools/proteomics/quality_control/precursor_charge_distribution/precursor_charge_distribution.py index b793694..4d7b4cf 100644 --- a/tools/proteomics/quality_control/precursor_charge_distribution/precursor_charge_distribution.py +++ b/tools/proteomics/quality_control/precursor_charge_distribution/precursor_charge_distribution.py @@ -14,10 +14,11 @@ python precursor_charge_distribution.py --input run.mzML --output charge_dist.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -123,19 +124,15 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Analyze charge state distribution across MS2 spectra." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - results = analyze_charge_distribution(args.input) +@click.command(help="Analyze charge state distribution across MS2 spectra.") +@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, output): + results = analyze_charge_distribution(input) - if args.output: - write_tsv(results, args.output) - print(f"Wrote charge distribution to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote charge distribution to {output}") else: print("charge\tcount\tpercentage") for r in results: diff --git a/tools/proteomics/quality_control/precursor_charge_distribution/requirements.txt b/tools/proteomics/quality_control/precursor_charge_distribution/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/precursor_charge_distribution/requirements.txt +++ b/tools/proteomics/quality_control/precursor_charge_distribution/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/precursor_isolation_purity/precursor_isolation_purity.py b/tools/proteomics/quality_control/precursor_isolation_purity/precursor_isolation_purity.py index 6ac11bc..c83a5ef 100644 --- a/tools/proteomics/quality_control/precursor_isolation_purity/precursor_isolation_purity.py +++ b/tools/proteomics/quality_control/precursor_isolation_purity/precursor_isolation_purity.py @@ -12,10 +12,11 @@ python precursor_isolation_purity.py --input run.mzML --output purity.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -107,24 +108,17 @@ def compute_all_purities(exp: oms.MSExperiment, isolation_width: float = 2.0) -> return results -def main(): - parser = argparse.ArgumentParser( - description="Estimate precursor isolation purity from mzML." - ) - parser.add_argument("--input", required=True, metavar="FILE", help="Path to mzML file") - parser.add_argument("--output", required=True, metavar="FILE", help="Output purity TSV") - parser.add_argument( - "--isolation-width", type=float, default=2.0, - help="Default isolation window width in Da (default: 2.0)" - ) - args = parser.parse_args() - +@click.command(help="Estimate precursor isolation purity from mzML.") +@click.option("--input", "input", required=True, help="Path to mzML file") +@click.option("--output", required=True, help="Output purity TSV") +@click.option("--isolation-width", type=float, default=2.0, help="Default isolation window width in Da (default: 2.0)") +def main(input, output, isolation_width): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) - purities = compute_all_purities(exp, args.isolation_width) + purities = compute_all_purities(exp, isolation_width) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter( fh, fieldnames=["scan_index", "rt", "precursor_mz", "purity"], delimiter="\t" ) @@ -133,7 +127,7 @@ def main(): if purities: avg = sum(p["purity"] for p in purities) / len(purities) - print(f"Wrote {len(purities)} purity values to {args.output}") + print(f"Wrote {len(purities)} purity values to {output}") print(f" Mean purity: {avg:.4f}") else: print("No MS2 spectra with precursors found.") diff --git a/tools/proteomics/quality_control/precursor_isolation_purity/requirements.txt b/tools/proteomics/quality_control/precursor_isolation_purity/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/precursor_isolation_purity/requirements.txt +++ b/tools/proteomics/quality_control/precursor_isolation_purity/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py b/tools/proteomics/quality_control/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py index 84f8a3a..35f1245 100644 --- a/tools/proteomics/quality_control/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py +++ b/tools/proteomics/quality_control/precursor_recurrence_analyzer/precursor_recurrence_analyzer.py @@ -9,11 +9,12 @@ python precursor_recurrence_analyzer.py --input run.mzML --mz-tolerance 10 --rt-tolerance 30 --output recurrence.tsv """ -import argparse import csv import sys from typing import List +import click + try: import pyopenms as oms except ImportError: @@ -168,23 +169,19 @@ def write_tsv(groups: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Analyze precursor resampling in DDA runs." - ) - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument("--mz-tolerance", type=float, default=10.0, help="m/z tolerance in ppm (default: 10)") - parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - +@click.command(help="Analyze precursor resampling in DDA runs.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--mz-tolerance", type=float, default=10.0, help="m/z tolerance in ppm (default: 10)") +@click.option("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, mz_tolerance, rt_tolerance, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) precursors = extract_precursors(exp) print(f"Extracted {len(precursors)} precursors") - groups = find_recurrent_precursors(precursors, args.mz_tolerance, args.rt_tolerance) + groups = find_recurrent_precursors(precursors, mz_tolerance, rt_tolerance) summary = summarize_recurrence(groups) print(f"Precursor groups : {summary['total_precursor_groups']}") @@ -193,9 +190,9 @@ def main(): print(f"Recurrence rate : {summary['recurrence_rate']:.2%}") print(f"Max resampling : {summary['max_resampling']}") - if args.output: - write_tsv(groups, args.output) - print(f"\nResults written to {args.output}") + if output: + write_tsv(groups, output) + print(f"\nResults written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/quality_control/precursor_recurrence_analyzer/requirements.txt b/tools/proteomics/quality_control/precursor_recurrence_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/precursor_recurrence_analyzer/requirements.txt +++ b/tools/proteomics/quality_control/precursor_recurrence_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/run_comparison_reporter/requirements.txt b/tools/proteomics/quality_control/run_comparison_reporter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/run_comparison_reporter/requirements.txt +++ b/tools/proteomics/quality_control/run_comparison_reporter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py b/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py index 426c2b8..4c1b49b 100644 --- a/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py +++ b/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py @@ -9,11 +9,12 @@ python run_comparison_reporter.py --inputs run1.mzML run2.mzML --output comparison.json """ -import argparse import json import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -110,28 +111,22 @@ def compare_runs(exp1: oms.MSExperiment, exp2: oms.MSExperiment) -> dict: } -def main(): - parser = argparse.ArgumentParser( - description="Compare mzML runs: TIC correlation, shared precursors, RT shift." - ) - parser.add_argument( - "--inputs", nargs=2, required=True, metavar="FILE", help="Two mzML files to compare" - ) - parser.add_argument("--output", required=True, metavar="FILE", help="Output JSON report") - args = parser.parse_args() - +@click.command(help="Compare mzML runs: TIC correlation, shared precursors, RT shift.") +@click.option("--inputs", multiple=True, required=True, help="Two mzML files to compare") +@click.option("--output", required=True, help="Output JSON report") +def main(inputs, output): exp1 = oms.MSExperiment() - oms.MzMLFile().load(args.inputs[0], exp1) + oms.MzMLFile().load(inputs[0], exp1) exp2 = oms.MSExperiment() - oms.MzMLFile().load(args.inputs[1], exp2) + oms.MzMLFile().load(inputs[1], exp2) result = compare_runs(exp1, exp2) - with open(args.output, "w") as fh: + with open(output, "w") as fh: json.dump(result, fh, indent=2) - print(f"Comparison report written to {args.output}") + print(f"Comparison report written to {output}") print(f" TIC correlation : {result['tic_correlation']}") print(f" Shared precursors: {result['shared_precursors']}") diff --git a/tools/proteomics/quality_control/sample_complexity_estimator/requirements.txt b/tools/proteomics/quality_control/sample_complexity_estimator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/sample_complexity_estimator/requirements.txt +++ b/tools/proteomics/quality_control/sample_complexity_estimator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/sample_complexity_estimator/sample_complexity_estimator.py b/tools/proteomics/quality_control/sample_complexity_estimator/sample_complexity_estimator.py index e532bd4..ac2dae7 100644 --- a/tools/proteomics/quality_control/sample_complexity_estimator/sample_complexity_estimator.py +++ b/tools/proteomics/quality_control/sample_complexity_estimator/sample_complexity_estimator.py @@ -11,10 +11,11 @@ python sample_complexity_estimator.py --input run.mzML --output complexity.json """ -import argparse import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -115,22 +116,15 @@ def write_json(result: dict, output_path: str) -> None: json.dump(result, fh, indent=2) -def main(): - parser = argparse.ArgumentParser( - description="Estimate sample complexity from MS1 peak density." - ) - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument( - "--intensity-threshold", type=float, default=0.0, - help="Minimum intensity to count a peak (default: 0)" - ) - parser.add_argument("--output", default=None, help="Output JSON file path") - args = parser.parse_args() - +@click.command(help="Estimate sample complexity from MS1 peak density.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--intensity-threshold", type=float, default=0.0, help="Minimum intensity to count a peak (default: 0)") +@click.option("--output", default=None, help="Output JSON file path") +def main(input, intensity_threshold, output): exp = oms.MSExperiment() - oms.MzMLFile().load(args.input, exp) + oms.MzMLFile().load(input, exp) - result = estimate_complexity(exp, intensity_threshold=args.intensity_threshold) + result = estimate_complexity(exp, intensity_threshold=intensity_threshold) print(f"MS1 spectra : {result['n_ms1_spectra']}") print(f"Total peaks : {result['total_peaks']}") @@ -138,9 +132,9 @@ def main(): print(f"Max peaks/spectrum: {result['max_peaks_per_spectrum']}") print(f"Complexity score : {result['complexity_score']}") - if args.output: - write_json(result, args.output) - print(f"\nResults written to {args.output}") + if output: + write_json(result, output) + print(f"\nResults written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/quality_control/spectrum_file_info/requirements.txt b/tools/proteomics/quality_control/spectrum_file_info/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/quality_control/spectrum_file_info/requirements.txt +++ b/tools/proteomics/quality_control/spectrum_file_info/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/quality_control/spectrum_file_info/spectrum_file_info.py b/tools/proteomics/quality_control/spectrum_file_info/spectrum_file_info.py index 936c451..c1826f3 100644 --- a/tools/proteomics/quality_control/spectrum_file_info/spectrum_file_info.py +++ b/tools/proteomics/quality_control/spectrum_file_info/spectrum_file_info.py @@ -11,9 +11,10 @@ python spectrum_file_info.py --input sample.mzML --tic """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -87,32 +88,19 @@ def load_file(path: str) -> oms.MSExperiment: return exp -def main(): - parser = argparse.ArgumentParser( - description="Summarise an mzML file using pyopenms." - ) - parser.add_argument( - "--input", - required=True, - metavar="FILE", - help="Path to an mzML file", - ) - parser.add_argument( - "--tic", - action="store_true", - help="Print per-spectrum TIC values", - ) - args = parser.parse_args() - - print(f"Loading {args.input} …") - exp = load_file(args.input) +@click.command(help="Summarise an mzML file using pyopenms.") +@click.option("--input", "input", required=True, help="Path to an mzML file") +@click.option("--tic", is_flag=True, help="Print per-spectrum TIC values") +def main(input, tic): + print(f"Loading {input} ...") + exp = load_file(input) summary = summarise_experiment(exp) if summary["n_spectra"] == 0: print("No spectra found in file.") return - print(f"\n{'File':<22}: {args.input}") + print(f"\n{'File':<22}: {input}") print(f"{'Total spectra':<22}: {summary['n_spectra']}") for level, count in sorted(summary["ms_levels"].items()): print(f" {'MS' + str(level) + ' spectra':<20}: {count}") @@ -126,7 +114,7 @@ def main(): print(f"{'Total TIC':<22}: {summary['tic_total']:.3e}") print(f"{'Max spectrum TIC':<22}: {summary['tic_max']:.3e}") - if args.tic: + if tic: print("\n--- Per-spectrum TIC ---") for i, tic in enumerate(summary["tic_per_spectrum"], 1): print(f" Spectrum {i:>5}: {tic:.3e}") diff --git a/tools/proteomics/rna/rna_digest/requirements.txt b/tools/proteomics/rna/rna_digest/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/rna/rna_digest/requirements.txt +++ b/tools/proteomics/rna/rna_digest/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/rna/rna_digest/rna_digest.py b/tools/proteomics/rna/rna_digest/rna_digest.py index 7d572f7..744fde1 100644 --- a/tools/proteomics/rna/rna_digest/rna_digest.py +++ b/tools/proteomics/rna/rna_digest/rna_digest.py @@ -15,10 +15,11 @@ python rna_digest.py --sequence AAUGCAAUGG --enzyme RNase_A --missed-cleavages 1 --output fragments.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -103,31 +104,29 @@ def _calculate_fragment_mass(fragment: str) -> float: return sum(NUCLEOTIDE_RESIDUE_MASSES[nt] for nt in fragment) + WATER_MASS -def main(): - parser = argparse.ArgumentParser(description="In silico RNA digestion with RNases.") - parser.add_argument("--sequence", required=True, help="RNA sequence (e.g. AAUGCAAUGG)") - parser.add_argument( - "--enzyme", required=True, - choices=list(ENZYME_RULES.keys()), - help="RNase enzyme name" - ) - parser.add_argument("--missed-cleavages", type=int, default=0, help="Max missed cleavages (default: 0)") - parser.add_argument("--output", help="Output TSV file (optional)") - args = parser.parse_args() - - fragments = digest_rna(args.sequence, args.enzyme, args.missed_cleavages) - - if args.output: - with open(args.output, "w", newline="") as fh: +@click.command(help="In silico RNA digestion with RNases.") +@click.option("--sequence", required=True, help="RNA sequence (e.g. AAUGCAAUGG)") +@click.option( + "--enzyme", required=True, + type=click.Choice(["RNase_T1", "RNase_A", "RNase_T2", "Cusativin"]), + help="RNase enzyme name", +) +@click.option("--missed-cleavages", type=int, default=0, help="Max missed cleavages (default: 0)") +@click.option("--output", default=None, help="Output TSV file (optional)") +def main(sequence, enzyme, missed_cleavages, output): + fragments = digest_rna(sequence, enzyme, missed_cleavages) + + if output: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["fragment", "start", "end", "missed_cleavages", "mass"], delimiter="\t") writer.writeheader() writer.writerows(fragments) - print(f"Wrote {len(fragments)} fragments to {args.output}") + print(f"Wrote {len(fragments)} fragments to {output}") else: - print(f"Enzyme: {args.enzyme}") - print(f"Sequence: {args.sequence}") - print(f"Missed cleavages: {args.missed_cleavages}") + print(f"Enzyme: {enzyme}") + print(f"Sequence: {sequence}") + print(f"Missed cleavages: {missed_cleavages}") print(f"\n{'Fragment':<20} {'Start':>5} {'End':>5} {'MC':>3} {'Mass':>12}") print("-" * 50) for f in fragments: diff --git a/tools/proteomics/rna/rna_fragment_spectrum_generator/requirements.txt b/tools/proteomics/rna/rna_fragment_spectrum_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/rna/rna_fragment_spectrum_generator/requirements.txt +++ b/tools/proteomics/rna/rna_fragment_spectrum_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/rna/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py b/tools/proteomics/rna/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py index e457e1e..2b221e3 100644 --- a/tools/proteomics/rna/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py +++ b/tools/proteomics/rna/rna_fragment_spectrum_generator/rna_fragment_spectrum_generator.py @@ -16,10 +16,11 @@ python rna_fragment_spectrum_generator.py --sequence AAUGC --charge 1 --output fragments.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -197,25 +198,23 @@ def generate_all_fragments(sequence: str, charge: int = 1) -> list: return results -def main(): - parser = argparse.ArgumentParser(description="Generate theoretical RNA fragment spectra.") - parser.add_argument("--sequence", required=True, help="RNA sequence (e.g. AAUGC)") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") - parser.add_argument("--output", help="Output TSV file (optional)") - args = parser.parse_args() - - fragments = generate_all_fragments(args.sequence, args.charge) +@click.command(help="Generate theoretical RNA fragment spectra.") +@click.option("--sequence", required=True, help="RNA sequence (e.g. AAUGC)") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1)") +@click.option("--output", default=None, help="Output TSV file (optional)") +def main(sequence, charge, output): + fragments = generate_all_fragments(sequence, charge) - if args.output: - with open(args.output, "w", newline="") as fh: + if output: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["ion_type", "ion_label", "mz", "charge"], delimiter="\t") writer.writeheader() writer.writerows(fragments) - print(f"Wrote {len(fragments)} fragment ions to {args.output}") + print(f"Wrote {len(fragments)} fragment ions to {output}") else: - print(f"Sequence: {args.sequence.upper()}") - print(f"Charge: {args.charge}+") + print(f"Sequence: {sequence.upper()}") + print(f"Charge: {charge}+") print(f"\n{'Ion':<10} {'Type':<6} {'m/z':>14}") print("-" * 32) for f in fragments: diff --git a/tools/proteomics/rna/rna_mass_calculator/requirements.txt b/tools/proteomics/rna/rna_mass_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/rna/rna_mass_calculator/requirements.txt +++ b/tools/proteomics/rna/rna_mass_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/rna/rna_mass_calculator/rna_mass_calculator.py b/tools/proteomics/rna/rna_mass_calculator/rna_mass_calculator.py index c5e0558..0943146 100644 --- a/tools/proteomics/rna/rna_mass_calculator/rna_mass_calculator.py +++ b/tools/proteomics/rna/rna_mass_calculator/rna_mass_calculator.py @@ -13,10 +13,11 @@ python rna_mass_calculator.py --sequence AAUGCAAUGG --charge 3 --output mass.json """ -import argparse import json import sys +import click + try: import pyopenms as oms except ImportError: @@ -133,26 +134,22 @@ def calculate_isotope_pattern(sequence: str, n_peaks: int = 5) -> list: return pattern -def main(): - parser = argparse.ArgumentParser( - description="Calculate mass/formula/isotopes for RNA sequences." - ) - parser.add_argument("--sequence", required=True, help="RNA sequence (e.g. AAUGCAAUGG)") - parser.add_argument("--charge", type=int, default=1, help="Charge state for m/z (default: 1)") - parser.add_argument("--isotopes", type=int, default=0, help="Number of isotope peaks to show (default: 0 = off)") - parser.add_argument("--output", help="Output JSON file (optional)") - args = parser.parse_args() - - result = calculate_rna_mass(args.sequence, args.charge) +@click.command(help="Calculate mass/formula/isotopes for RNA sequences.") +@click.option("--sequence", required=True, help="RNA sequence (e.g. AAUGCAAUGG)") +@click.option("--charge", type=int, default=1, help="Charge state for m/z (default: 1)") +@click.option("--isotopes", type=int, default=0, help="Number of isotope peaks to show (default: 0 = off)") +@click.option("--output", default=None, help="Output JSON file (optional)") +def main(sequence, charge, isotopes, output): + result = calculate_rna_mass(sequence, charge) - if args.isotopes > 0: - pattern = calculate_isotope_pattern(args.sequence, args.isotopes) + if isotopes > 0: + pattern = calculate_isotope_pattern(sequence, isotopes) result["isotope_pattern"] = [{"mass": m, "intensity": i} for m, i in pattern] - if args.output: - with open(args.output, "w") as fh: + if output: + with open(output, "w") as fh: json.dump(result, fh, indent=2) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: print(f"Sequence : {result['sequence']}") print(f"Charge : {result['charge']}+") diff --git a/tools/proteomics/specialized/cleavage_site_profiler/cleavage_site_profiler.py b/tools/proteomics/specialized/cleavage_site_profiler/cleavage_site_profiler.py index c1ee7be..7bfdf7c 100644 --- a/tools/proteomics/specialized/cleavage_site_profiler/cleavage_site_profiler.py +++ b/tools/proteomics/specialized/cleavage_site_profiler/cleavage_site_profiler.py @@ -12,12 +12,13 @@ python cleavage_site_profiler.py --input neo_nterm.tsv --fasta reference.fasta --window 4 --output profile.tsv """ -import argparse import csv import sys from collections import Counter from typing import Dict, List, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -254,25 +255,21 @@ def write_output( f.write(f"{pos_label}\t{aa}\t{freq:.4f}\n") -def main(): - parser = argparse.ArgumentParser( - description="Profile cleavage sites from neo-N-terminal peptides." - ) - parser.add_argument("--input", required=True, help="Neo-N-terminal peptides TSV file") - parser.add_argument("--fasta", required=True, help="Reference proteome FASTA file") - parser.add_argument("--window", type=int, default=4, help="Window size on each side (default: 4)") - parser.add_argument("--output", required=True, help="Output profile TSV file") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - rows = read_input(args.input) - result_rows, valid_windows = process_neo_nterm_peptides(rows, proteins, args.window) - frequencies = compute_position_frequencies(valid_windows, args.window) - write_output(args.output, result_rows, frequencies) +@click.command(help="Profile cleavage sites from neo-N-terminal peptides.") +@click.option("--input", "input", required=True, help="Neo-N-terminal peptides TSV file") +@click.option("--fasta", required=True, help="Reference proteome FASTA file") +@click.option("--window", type=int, default=4, help="Window size on each side (default: 4)") +@click.option("--output", required=True, help="Output profile TSV file") +def main(input, fasta, window, output): + proteins = load_fasta(fasta) + rows = read_input(input) + result_rows, valid_windows = process_neo_nterm_peptides(rows, proteins, window) + frequencies = compute_position_frequencies(valid_windows, window) + write_output(output, result_rows, frequencies) print(f"Total peptides: {len(result_rows)}") print(f"Cleavage sites found: {len(valid_windows)}") - print(f"Window size: P{args.window}-P{args.window}'") + print(f"Window size: P{window}-P{window}'") if __name__ == "__main__": diff --git a/tools/proteomics/specialized/cleavage_site_profiler/requirements.txt b/tools/proteomics/specialized/cleavage_site_profiler/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/cleavage_site_profiler/requirements.txt +++ b/tools/proteomics/specialized/cleavage_site_profiler/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/immunopeptide_filter/immunopeptide_filter.py b/tools/proteomics/specialized/immunopeptide_filter/immunopeptide_filter.py index 5303e8e..ba5e8f4 100644 --- a/tools/proteomics/specialized/immunopeptide_filter/immunopeptide_filter.py +++ b/tools/proteomics/specialized/immunopeptide_filter/immunopeptide_filter.py @@ -12,12 +12,13 @@ python immunopeptide_filter.py --input peptides.tsv --class-ii --output immunopeptides.tsv """ -import argparse import csv import re import sys from typing import List, Optional, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -141,37 +142,32 @@ def write_tsv(results: List[dict], output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Filter peptides for MHC-I/II by length and motif." - ) - parser.add_argument("--input", required=True, help="Input TSV with peptide sequences") - parser.add_argument("--column", default="sequence", help="Column name for sequences (default: sequence)") - mhc_group = parser.add_mutually_exclusive_group() - mhc_group.add_argument("--class-i", action="store_true", help="MHC class I defaults (8-11 aa)") - mhc_group.add_argument("--class-ii", action="store_true", help="MHC class II defaults (13-25 aa)") - parser.add_argument("--length-range", default=None, help="Custom length range, e.g. '8-11'") - parser.add_argument("--motif", default=None, help="Regex motif pattern to filter by") - parser.add_argument("--output", required=True, help="Output TSV file path") - args = parser.parse_args() - +@click.command(help="Filter peptides for MHC-I/II by length and motif.") +@click.option("--input", "input", required=True, help="Input TSV with peptide sequences") +@click.option("--column", default="sequence", help="Column name for sequences (default: sequence)") +@click.option("--class-i", is_flag=True, help="MHC class I defaults (8-11 aa)") +@click.option("--class-ii", is_flag=True, help="MHC class II defaults (13-25 aa)") +@click.option("--length-range", default=None, help="Custom length range, e.g. '8-11'") +@click.option("--motif", default=None, help="Regex motif pattern to filter by") +@click.option("--output", required=True, help="Output TSV file path") +def main(input, column, class_i, class_ii, length_range, motif, output): # Determine length range - if args.length_range: - min_len, max_len = parse_length_range(args.length_range) - elif args.class_ii: + if length_range: + min_len, max_len = parse_length_range(length_range) + elif class_ii: min_len, max_len = 13, 25 else: # Default to class I min_len, max_len = 8, 11 - peptides = read_peptides_from_tsv(args.input, column=args.column) - print(f"Read {len(peptides)} peptides from {args.input}") + peptides = read_peptides_from_tsv(input, column=column) + print(f"Read {len(peptides)} peptides from {input}") - results = filter_peptides(peptides, min_length=min_len, max_length=max_len, motif_pattern=args.motif) + results = filter_peptides(peptides, min_length=min_len, max_length=max_len, motif_pattern=motif) print(f"Passed filter: {len(results)} peptides (length {min_len}-{max_len})") - write_tsv(results, args.output) - print(f"Results written to {args.output}") + write_tsv(results, output) + print(f"Results written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/specialized/immunopeptide_filter/requirements.txt b/tools/proteomics/specialized/immunopeptide_filter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/immunopeptide_filter/requirements.txt +++ b/tools/proteomics/specialized/immunopeptide_filter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/immunopeptidome_qc/immunopeptidome_qc.py b/tools/proteomics/specialized/immunopeptidome_qc/immunopeptidome_qc.py index e410959..1803759 100644 --- a/tools/proteomics/specialized/immunopeptidome_qc/immunopeptidome_qc.py +++ b/tools/proteomics/specialized/immunopeptidome_qc/immunopeptidome_qc.py @@ -16,13 +16,14 @@ --output length_dist.tsv --motifs anchor_freq.tsv """ -import argparse import csv import math import sys from collections import Counter from typing import Dict, List, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -208,21 +209,15 @@ def run_qc( return dist, qc, anchors, ic_values -def main() -> None: - parser = argparse.ArgumentParser( - description="QC for immunopeptidomics: length distribution, anchor residue frequencies, information content." - ) - parser.add_argument("--input", required=True, help="Input TSV with 'sequence' column") - parser.add_argument( - "--hla-class", required=True, choices=["I", "II"], help="HLA class (I or II)" - ) - parser.add_argument("--output", required=True, help="Output TSV for length distribution") - parser.add_argument("--motifs", required=True, help="Output TSV for anchor residue frequencies") - args = parser.parse_args() - +@click.command(help="QC for immunopeptidomics: length distribution, anchor residue frequencies, information content.") +@click.option("--input", "input", required=True, help="Input TSV with 'sequence' column") +@click.option("--hla-class", required=True, type=click.Choice(["I", "II"]), help="HLA class (I or II)") +@click.option("--output", required=True, help="Output TSV for length distribution") +@click.option("--motifs", required=True, help="Output TSV for anchor residue frequencies") +def main(input, hla_class, output, motifs) -> None: # Read sequences sequences: List[str] = [] - with open(args.input, newline="") as fh: + with open(input, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: seq = row.get("sequence", "").strip() @@ -232,10 +227,10 @@ def main() -> None: if not sequences: sys.exit("No valid sequences found in input file.") - dist, qc, anchors, ic_values = run_qc(sequences, args.hla_class) + dist, qc, anchors, ic_values = run_qc(sequences, hla_class) # Write length distribution - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.writer(fh, delimiter="\t") writer.writerow(["length", "count"]) for length, count in dist.items(): @@ -251,15 +246,15 @@ def main() -> None: writer.writerow([i, f"{ic:.4f}"]) # Write anchor frequencies - with open(args.motifs, "w", newline="") as fh: + with open(motifs, "w", newline="") as fh: writer = csv.writer(fh, delimiter="\t") writer.writerow(["anchor_position", "residue", "frequency"]) for pos_label, freq_dict in anchors.items(): for residue, freq in sorted(freq_dict.items()): writer.writerow([pos_label, residue, f"{freq:.4f}"]) - print(f"Length distribution written to {args.output}") - print(f"Anchor frequencies written to {args.motifs}") + print(f"Length distribution written to {output}") + print(f"Anchor frequencies written to {motifs}") print(f"Total peptides: {sum(dist.values())}, in-range: {qc['in_range_fraction']:.1%}") diff --git a/tools/proteomics/specialized/immunopeptidome_qc/requirements.txt b/tools/proteomics/specialized/immunopeptidome_qc/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/immunopeptidome_qc/requirements.txt +++ b/tools/proteomics/specialized/immunopeptidome_qc/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/metapeptide_lca_assigner/metapeptide_lca_assigner.py b/tools/proteomics/specialized/metapeptide_lca_assigner/metapeptide_lca_assigner.py index 3eca69b..6ace042 100644 --- a/tools/proteomics/specialized/metapeptide_lca_assigner/metapeptide_lca_assigner.py +++ b/tools/proteomics/specialized/metapeptide_lca_assigner/metapeptide_lca_assigner.py @@ -12,12 +12,13 @@ --taxonomy lineage.tsv --output taxonomy.tsv """ -import argparse import csv import re import sys from typing import Dict, List, Set +import click + try: import pyopenms as oms except ImportError: @@ -233,21 +234,17 @@ def write_output(output_path: str, results: List[Dict[str, object]]) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Compute lowest common ancestor taxonomy from peptide-protein mappings." - ) - parser.add_argument("--peptides", required=True, help="Peptides TSV file (peptide column)") - parser.add_argument("--fasta", required=True, help="Meta-proteomics database FASTA file") - parser.add_argument("--taxonomy", required=True, help="Taxonomy lineage TSV (protein, lineage)") - parser.add_argument("--output", required=True, help="Output taxonomy TSV file") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - taxonomy = load_taxonomy(args.taxonomy) - peptides = read_peptides(args.peptides) - results = assign_lca_batch(peptides, proteins, taxonomy) - write_output(args.output, results) +@click.command(help="Compute lowest common ancestor taxonomy from peptide-protein mappings.") +@click.option("--peptides", required=True, help="Peptides TSV file (peptide column)") +@click.option("--fasta", required=True, help="Meta-proteomics database FASTA file") +@click.option("--taxonomy", required=True, help="Taxonomy lineage TSV (protein, lineage)") +@click.option("--output", required=True, help="Output taxonomy TSV file") +def main(peptides, fasta, taxonomy, output): + proteins = load_fasta(fasta) + taxonomy_data = load_taxonomy(taxonomy) + peptides_list = read_peptides(peptides) + results = assign_lca_batch(peptides_list, proteins, taxonomy_data) + write_output(output, results) n_assigned = sum(1 for r in results if r["lca"] != "unassigned") print(f"Total peptides: {len(results)}") diff --git a/tools/proteomics/specialized/metapeptide_lca_assigner/requirements.txt b/tools/proteomics/specialized/metapeptide_lca_assigner/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/metapeptide_lca_assigner/requirements.txt +++ b/tools/proteomics/specialized/metapeptide_lca_assigner/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/nterm_modification_annotator/nterm_modification_annotator.py b/tools/proteomics/specialized/nterm_modification_annotator/nterm_modification_annotator.py index b4d76ab..6b67660 100644 --- a/tools/proteomics/specialized/nterm_modification_annotator/nterm_modification_annotator.py +++ b/tools/proteomics/specialized/nterm_modification_annotator/nterm_modification_annotator.py @@ -12,12 +12,13 @@ python nterm_modification_annotator.py --input nterm_peptides.tsv --fasta reference.fasta --output annotated.tsv """ -import argparse import csv import re import sys from typing import Dict, List +import click + try: import pyopenms as oms except ImportError: @@ -263,19 +264,15 @@ def write_output(output_path: str, results: List[Dict[str, str]]) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Classify N-terminal peptides as protein N-term, signal peptide, neo-N-term, etc." - ) - parser.add_argument("--input", required=True, help="Input N-terminal peptides TSV file") - parser.add_argument("--fasta", required=True, help="Reference proteome FASTA file") - parser.add_argument("--output", required=True, help="Output annotated TSV file") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - rows = read_input(args.input) +@click.command(help="Classify N-terminal peptides as protein N-term, signal peptide, neo-N-term, etc.") +@click.option("--input", "input", required=True, help="Input N-terminal peptides TSV file") +@click.option("--fasta", required=True, help="Reference proteome FASTA file") +@click.option("--output", required=True, help="Output annotated TSV file") +def main(input, fasta, output): + proteins = load_fasta(fasta) + rows = read_input(input) results = annotate_nterm_peptides(rows, proteins) - write_output(args.output, results) + write_output(output, results) summary = compute_summary(results) print(f"Total peptides: {len(results)}") diff --git a/tools/proteomics/specialized/nterm_modification_annotator/requirements.txt b/tools/proteomics/specialized/nterm_modification_annotator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/nterm_modification_annotator/requirements.txt +++ b/tools/proteomics/specialized/nterm_modification_annotator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/proteoform_delta_annotator/proteoform_delta_annotator.py b/tools/proteomics/specialized/proteoform_delta_annotator/proteoform_delta_annotator.py index 4f4a48d..d62fb9b 100644 --- a/tools/proteomics/specialized/proteoform_delta_annotator/proteoform_delta_annotator.py +++ b/tools/proteomics/specialized/proteoform_delta_annotator/proteoform_delta_annotator.py @@ -12,11 +12,12 @@ --tolerance 0.5 --output annotated.tsv """ -import argparse import csv import sys from typing import Dict, List, Optional, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -129,23 +130,13 @@ def annotate_proteoform_deltas( return results -def main() -> None: - parser = argparse.ArgumentParser( - description="Annotate mass differences between proteoforms with known PTMs." - ) - parser.add_argument( - "--input", required=True, - help="Input TSV with 'proteoform_id' and 'mass' columns", - ) - parser.add_argument( - "--tolerance", type=float, default=0.5, - help="Mass tolerance in Da (default: 0.5)", - ) - parser.add_argument("--output", required=True, help="Output annotated TSV") - args = parser.parse_args() - +@click.command(help="Annotate mass differences between proteoforms with known PTMs.") +@click.option("--input", "input", required=True, help="Input TSV with 'proteoform_id' and 'mass' columns") +@click.option("--tolerance", type=float, default=0.5, help="Mass tolerance in Da (default: 0.5)") +@click.option("--output", required=True, help="Output annotated TSV") +def main(input, tolerance, output) -> None: masses: List[Tuple[str, float]] = [] - with open(args.input, newline="") as fh: + with open(input, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: pf_id = row.get("proteoform_id", "").strip() @@ -156,15 +147,15 @@ def main() -> None: if len(masses) < 1: sys.exit("Need at least one proteoform in input.") - results = annotate_proteoform_deltas(masses, args.tolerance) + results = annotate_proteoform_deltas(masses, tolerance) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.writer(fh, delimiter="\t") writer.writerow(["proteoform_id", "mass", "delta", "annotations"]) for r in results: writer.writerow([r["proteoform_id"], r["mass"], f"{r['delta']:.4f}", r["annotations"]]) - print(f"Annotated {len(results)} proteoforms -> {args.output}") + print(f"Annotated {len(results)} proteoforms -> {output}") if __name__ == "__main__": diff --git a/tools/proteomics/specialized/proteoform_delta_annotator/requirements.txt b/tools/proteomics/specialized/proteoform_delta_annotator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/proteoform_delta_annotator/requirements.txt +++ b/tools/proteomics/specialized/proteoform_delta_annotator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/topdown_coverage_calculator/requirements.txt b/tools/proteomics/specialized/topdown_coverage_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/specialized/topdown_coverage_calculator/requirements.txt +++ b/tools/proteomics/specialized/topdown_coverage_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/specialized/topdown_coverage_calculator/topdown_coverage_calculator.py b/tools/proteomics/specialized/topdown_coverage_calculator/topdown_coverage_calculator.py index e2c4b79..d6096ad 100644 --- a/tools/proteomics/specialized/topdown_coverage_calculator/topdown_coverage_calculator.py +++ b/tools/proteomics/specialized/topdown_coverage_calculator/topdown_coverage_calculator.py @@ -13,11 +13,12 @@ --fragments observed.tsv --tolerance 10 --output coverage.tsv """ -import argparse import csv import sys from typing import Dict, List, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -158,25 +159,15 @@ def coverage_summary(bond_cov: List[Dict[str, object]]) -> Dict[str, object]: } -def main() -> None: - parser = argparse.ArgumentParser( - description="Compute per-residue bond cleavage coverage from fragment ions." - ) - parser.add_argument("--sequence", required=True, help="Protein amino acid sequence") - parser.add_argument( - "--fragments", required=True, - help="TSV with 'mass' column of observed fragment ion masses", - ) - parser.add_argument( - "--tolerance", type=float, default=10.0, - help="Tolerance in ppm (default: 10)", - ) - parser.add_argument("--output", required=True, help="Output coverage TSV") - args = parser.parse_args() - +@click.command(help="Compute per-residue bond cleavage coverage from fragment ions.") +@click.option("--sequence", required=True, help="Protein amino acid sequence") +@click.option("--fragments", required=True, help="TSV with 'mass' column of observed fragment ion masses") +@click.option("--tolerance", type=float, default=10.0, help="Tolerance in ppm (default: 10)") +@click.option("--output", required=True, help="Output coverage TSV") +def main(sequence, fragments, tolerance, output) -> None: # Read observed masses observed: List[float] = [] - with open(args.fragments, newline="") as fh: + with open(fragments, newline="") as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: mass_str = row.get("mass", "").strip() @@ -186,12 +177,12 @@ def main() -> None: if not observed: sys.exit("No observed masses found in fragments file.") - theo = theoretical_fragments(args.sequence) - matches = match_fragments(theo, observed, args.tolerance) - cov = bond_coverage(args.sequence, matches) + theo = theoretical_fragments(sequence) + matches = match_fragments(theo, observed, tolerance) + cov = bond_coverage(sequence, matches) summary = coverage_summary(cov) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.writer(fh, delimiter="\t") writer.writerow(["bond_index", "left_residue", "right_residue", "covered", "ion_types"]) for entry in cov: @@ -206,7 +197,7 @@ def main() -> None: writer.writerow(["coverage_fraction", f"{summary['coverage_fraction']:.4f}"]) print(f"Coverage: {summary['covered_bonds']}/{summary['total_bonds']} " - f"({summary['coverage_fraction']:.1%}) -> {args.output}") + f"({summary['coverage_fraction']:.1%}) -> {output}") if __name__ == "__main__": diff --git a/tools/proteomics/spectrum_analysis/spectral_library_builder/requirements.txt b/tools/proteomics/spectrum_analysis/spectral_library_builder/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/spectrum_analysis/spectral_library_builder/requirements.txt +++ b/tools/proteomics/spectrum_analysis/spectral_library_builder/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/spectrum_analysis/spectral_library_builder/spectral_library_builder.py b/tools/proteomics/spectrum_analysis/spectral_library_builder/spectral_library_builder.py index f2dff52..82f4059 100644 --- a/tools/proteomics/spectrum_analysis/spectral_library_builder/spectral_library_builder.py +++ b/tools/proteomics/spectrum_analysis/spectral_library_builder/spectral_library_builder.py @@ -8,11 +8,12 @@ python spectral_library_builder.py --input run.mzML --peptides identified.tsv --output library.msp """ -import argparse import csv import sys from typing import List, Optional +import click + try: import pyopenms as oms except ImportError: @@ -136,16 +137,14 @@ def build_library( } -def main() -> None: - parser = argparse.ArgumentParser(description="Build spectral library from mzML + peptide list.") - parser.add_argument("--input", required=True, help="Input mzML file") - parser.add_argument("--peptides", required=True, help="Input peptide identifications TSV") - parser.add_argument("--output", required=True, help="Output MSP library file") - parser.add_argument("--mz-tolerance", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") - parser.add_argument("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") - args = parser.parse_args() - - stats = build_library(args.input, args.peptides, args.output, args.mz_tolerance, args.rt_tolerance) +@click.command(help="Build spectral library from mzML + peptide list.") +@click.option("--input", "input", required=True, help="Input mzML file") +@click.option("--peptides", required=True, help="Input peptide identifications TSV") +@click.option("--output", required=True, help="Output MSP library file") +@click.option("--mz-tolerance", type=float, default=0.01, help="m/z tolerance in Da (default: 0.01)") +@click.option("--rt-tolerance", type=float, default=30.0, help="RT tolerance in seconds (default: 30)") +def main(input, peptides, output, mz_tolerance, rt_tolerance) -> None: + stats = build_library(input, peptides, output, mz_tolerance, rt_tolerance) print(f"Built library: {stats['matched_spectra']} / {stats['total_peptides']} peptides matched to spectra") diff --git a/tools/proteomics/spectrum_analysis/spectral_library_format_converter/requirements.txt b/tools/proteomics/spectrum_analysis/spectral_library_format_converter/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/spectrum_analysis/spectral_library_format_converter/requirements.txt +++ b/tools/proteomics/spectrum_analysis/spectral_library_format_converter/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py b/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py index 53f78ce..3c246d7 100644 --- a/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py +++ b/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py @@ -14,9 +14,10 @@ python spectral_library_format_converter.py --input library.msp --output library.traml --format traml """ -import argparse import sys +import click + try: import pyopenms as oms except ImportError: @@ -194,17 +195,16 @@ def create_synthetic_msp(output_path: str, n_spectra: int = 3) -> None: f.write("\n") -def main(): - parser = argparse.ArgumentParser( - description="Convert between spectral library formats (MSP to TraML)." - ) - parser.add_argument("--input", required=True, help="Path to input MSP file") - parser.add_argument("--output", required=True, help="Path to output file") - parser.add_argument("--format", default="traml", choices=["traml"], help="Output format (default: traml)") - args = parser.parse_args() - - count = convert_msp_to_traml(args.input, args.output) - print(f"Converted {count} spectra to {args.format} format: {args.output}") +@click.command(help="Convert between spectral library formats (MSP to TraML).") +@click.option("--input", "input", required=True, help="Path to input MSP file") +@click.option("--output", required=True, help="Path to output file") +@click.option( + "--format", "format", default="traml", + type=click.Choice(["traml"]), help="Output format (default: traml)", +) +def main(input, output, format): + count = convert_msp_to_traml(input, output) + print(f"Converted {count} spectra to {format} format: {output}") if __name__ == "__main__": diff --git a/tools/proteomics/spectrum_analysis/spectrum_annotator/requirements.txt b/tools/proteomics/spectrum_analysis/spectrum_annotator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_annotator/requirements.txt +++ b/tools/proteomics/spectrum_analysis/spectrum_annotator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/spectrum_analysis/spectrum_annotator/spectrum_annotator.py b/tools/proteomics/spectrum_analysis/spectrum_annotator/spectrum_annotator.py index ee8dcb4..a68f00f 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_annotator/spectrum_annotator.py +++ b/tools/proteomics/spectrum_analysis/spectrum_annotator/spectrum_annotator.py @@ -17,10 +17,11 @@ --sequence PEPTIDEK --charge 1 --tolerance 0.05 --output annotation.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -122,26 +123,22 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Annotate observed MS2 spectrum peaks with theoretical fragment ion matches." - ) - parser.add_argument("--mz-list", required=True, help="Comma-separated observed m/z values") - parser.add_argument("--intensities", required=True, help="Comma-separated observed intensities") - parser.add_argument("--sequence", required=True, help="Peptide amino acid sequence") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") - parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") - parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") - args = parser.parse_args() - - mz_values = [float(x.strip()) for x in args.mz_list.split(",")] - intensities = [float(x.strip()) for x in args.intensities.split(",")] +@click.command(help="Annotate observed MS2 spectrum peaks with theoretical fragment ion matches.") +@click.option("--mz-list", required=True, help="Comma-separated observed m/z values") +@click.option("--intensities", required=True, help="Comma-separated observed intensities") +@click.option("--sequence", required=True, help="Peptide amino acid sequence") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1)") +@click.option("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") +@click.option("--output", default=None, help="Output TSV file path (default: print to stdout)") +def main(mz_list, intensities, sequence, charge, tolerance, output): + mz_values = [float(x.strip()) for x in mz_list.split(",")] + intensities_list = [float(x.strip()) for x in intensities.split(",")] - results = annotate_spectrum(mz_values, intensities, args.sequence, args.charge, args.tolerance) + results = annotate_spectrum(mz_values, intensities_list, sequence, charge, tolerance) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} annotations to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} annotations to {output}") else: print("observed_mz\tintensity\tmatched_ion\ttheoretical_mz\terror_da") for r in results: diff --git a/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/requirements.txt b/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/requirements.txt index 1051d92..e4cb122 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/requirements.txt +++ b/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/requirements.txt @@ -1,2 +1,3 @@ pyopenms numpy +click diff --git a/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/spectrum_entropy_calculator.py b/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/spectrum_entropy_calculator.py index 25e56c7..7f1261f 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/spectrum_entropy_calculator.py +++ b/tools/proteomics/spectrum_analysis/spectrum_entropy_calculator/spectrum_entropy_calculator.py @@ -15,11 +15,12 @@ python spectrum_entropy_calculator.py --input run.mzML --ms-level 2 --output entropy.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -167,20 +168,16 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Calculate spectral entropy for MS2 spectra in mzML." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--ms-level", type=int, default=2, help="MS level (default: 2)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - results = compute_spectrum_entropies(args.input, args.ms_level) +@click.command(help="Calculate spectral entropy for MS2 spectra in mzML.") +@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--ms-level", type=int, default=2, help="MS level (default: 2)") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, ms_level, output): + results = compute_spectrum_entropies(input, ms_level) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} entropy values to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} entropy values to {output}") else: print("scan_index\trt\tn_peaks\tentropy\tprecursor_mz") for r in results: diff --git a/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/requirements.txt b/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/requirements.txt +++ b/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py b/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py index 0671ba0..1f92b54 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py +++ b/tools/proteomics/spectrum_analysis/spectrum_scoring_hyperscore/spectrum_scoring_hyperscore.py @@ -18,11 +18,12 @@ --sequence PEPTIDEK --charge 1 --output score.json """ -import argparse import json import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -136,28 +137,24 @@ def compute_hyperscore( } -def main(): - parser = argparse.ArgumentParser( - description="Score experimental spectrum against theoretical using HyperScore." - ) - parser.add_argument("--mz-list", required=True, help="Comma-separated experimental m/z values") - parser.add_argument("--intensities", required=True, help="Comma-separated experimental intensities") - parser.add_argument("--sequence", required=True, help="Peptide amino acid sequence") - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") - parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") - parser.add_argument("--output", default=None, help="Output JSON file path (default: print to stdout)") - args = parser.parse_args() - - mz_values = [float(x.strip()) for x in args.mz_list.split(",")] - intensities_list = [float(x.strip()) for x in args.intensities.split(",")] +@click.command(help="Score experimental spectrum against theoretical using HyperScore.") +@click.option("--mz-list", required=True, help="Comma-separated experimental m/z values") +@click.option("--intensities", required=True, help="Comma-separated experimental intensities") +@click.option("--sequence", required=True, help="Peptide amino acid sequence") +@click.option("--charge", type=int, default=1, help="Charge state (default: 1)") +@click.option("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") +@click.option("--output", default=None, help="Output JSON file path (default: print to stdout)") +def main(mz_list, intensities, sequence, charge, tolerance, output): + mz_values = [float(x.strip()) for x in mz_list.split(",")] + intensities_list = [float(x.strip()) for x in intensities.split(",")] - result = compute_hyperscore(mz_values, intensities_list, args.sequence, args.charge, args.tolerance) + result = compute_hyperscore(mz_values, intensities_list, sequence, charge, tolerance) output_json = json.dumps(result, indent=2) - if args.output: - with open(args.output, "w") as f: + if output: + with open(output, "w") as f: f.write(output_json) - print(f"Wrote score to {args.output}") + print(f"Wrote score to {output}") else: print(output_json) diff --git a/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/requirements.txt b/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/requirements.txt +++ b/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/spectrum_similarity_scorer.py b/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/spectrum_similarity_scorer.py index 79df4f8..9ee735f 100644 --- a/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/spectrum_similarity_scorer.py +++ b/tools/proteomics/spectrum_analysis/spectrum_similarity_scorer/spectrum_similarity_scorer.py @@ -15,11 +15,12 @@ python spectrum_similarity_scorer.py --query query.mgf --library ref.mgf --tolerance 0.02 --output scores.tsv """ -import argparse import csv import math import sys +import click + try: import pyopenms as oms except ImportError: @@ -206,21 +207,17 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Compute cosine similarity between MS2 spectra from MGF files." - ) - parser.add_argument("--query", required=True, help="Path to query MGF file") - parser.add_argument("--library", required=True, help="Path to library/reference MGF file") - parser.add_argument("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") - parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") - args = parser.parse_args() - - results = score_spectra(args.query, args.library, args.tolerance) +@click.command(help="Compute cosine similarity between MS2 spectra from MGF files.") +@click.option("--query", required=True, help="Path to query MGF file") +@click.option("--library", required=True, help="Path to library/reference MGF file") +@click.option("--tolerance", type=float, default=0.02, help="Mass tolerance in Da (default: 0.02)") +@click.option("--output", default=None, help="Output TSV file path (default: print to stdout)") +def main(query, library, tolerance, output): + results = score_spectra(query, library, tolerance) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} scores to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} scores to {output}") else: print("query_id\tlibrary_id\tscore\tmatched_peaks") for r in results: diff --git a/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt b/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt +++ b/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py b/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py index 31c05c6..c282135 100644 --- a/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py +++ b/tools/proteomics/spectrum_analysis/theoretical_spectrum_generator/theoretical_spectrum_generator.py @@ -17,10 +17,11 @@ --ion-types b,y,a --add-losses --output fragments.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -168,23 +169,19 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Generate theoretical fragment ion spectra for a peptide sequence." - ) - parser.add_argument("--sequence", required=True, help="Amino acid sequence (e.g. PEPTIDEK)") - parser.add_argument("--charge", type=int, default=1, help="Max charge state for fragment ions (default: 1)") - parser.add_argument("--ion-types", default="b,y", help="Comma-separated ion types: b,y,a,c,x,z (default: b,y)") - parser.add_argument("--add-losses", action="store_true", help="Include neutral losses (H2O, NH3)") - parser.add_argument("--output", default=None, help="Output TSV file path (default: print to stdout)") - args = parser.parse_args() - - ion_types = [t.strip() for t in args.ion_types.split(",")] - results = generate_theoretical_spectrum(args.sequence, args.charge, ion_types, args.add_losses) +@click.command(help="Generate theoretical fragment ion spectra for a peptide sequence.") +@click.option("--sequence", required=True, help="Amino acid sequence (e.g. PEPTIDEK)") +@click.option("--charge", type=int, default=1, help="Max charge state for fragment ions (default: 1)") +@click.option("--ion-types", default="b,y", help="Comma-separated ion types: b,y,a,c,x,z (default: b,y)") +@click.option("--add-losses", is_flag=True, help="Include neutral losses (H2O, NH3)") +@click.option("--output", default=None, help="Output TSV file path (default: print to stdout)") +def main(sequence, charge, ion_types, add_losses, output): + ion_type_list = [t.strip() for t in ion_types.split(",")] + results = generate_theoretical_spectrum(sequence, charge, ion_type_list, add_losses) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} fragment ions to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} fragment ions to {output}") else: print(f"{'ion_type'}\t{'ion_number'}\t{'charge'}\t{'mz'}\t{'annotation'}") for r in results: diff --git a/tools/proteomics/structural_proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py b/tools/proteomics/structural_proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py index 8d781be..7c06988 100644 --- a/tools/proteomics/structural_proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py +++ b/tools/proteomics/structural_proteomics/crosslink_mass_calculator/crosslink_mass_calculator.py @@ -16,11 +16,12 @@ --crosslinker DSSO --output masses.tsv """ -import argparse import csv import sys from typing import Optional +import click + try: import pyopenms as oms except ImportError: @@ -125,24 +126,20 @@ def write_tsv(results: list, output_path: str) -> None: writer.writerow(row) -def main(): - parser = argparse.ArgumentParser( - description="Calculate masses for crosslinked peptide pairs." - ) - parser.add_argument("--peptide1", required=True, help="First peptide sequence") - parser.add_argument("--peptide2", required=True, help="Second peptide sequence") - parser.add_argument( - "--crosslinker", required=True, - help="Crosslinker name (DSS, BS3, DSSO) or custom name with --custom-mass" - ) - parser.add_argument("--charge", type=int, default=1, help="Charge state (default: 1)") - parser.add_argument("--custom-mass", type=float, default=None, help="Custom crosslinker mass in Da") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - +@click.command(help="Calculate masses for crosslinked peptide pairs.") +@click.option("--peptide1", required=True, help="First peptide sequence") +@click.option("--peptide2", required=True, help="Second peptide sequence") +@click.option( + "--crosslinker", required=True, + help="Crosslinker name (DSS, BS3, DSSO) or custom name with --custom-mass", +) +@click.option("--charge", type=int, default=1, help="Charge state (default: 1)") +@click.option("--custom-mass", type=float, default=None, help="Custom crosslinker mass in Da") +@click.option("--output", default=None, help="Output TSV file path") +def main(peptide1, peptide2, crosslinker, charge, custom_mass, output): result = crosslinked_mass( - args.peptide1, args.peptide2, args.crosslinker, - charge=args.charge, custom_mass=args.custom_mass, + peptide1, peptide2, crosslinker, + charge=charge, custom_mass=custom_mass, ) print(f"Peptide 1 : {result['peptide1']} ({result['mass_peptide1']:.6f} Da)") @@ -152,9 +149,9 @@ def main(): print(f"Charge : {result['charge']}+") print(f"m/z : {result['mz']:.6f}") - if args.output: - write_tsv([result], args.output) - print(f"\nResults written to {args.output}") + if output: + write_tsv([result], output) + print(f"\nResults written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/structural_proteomics/crosslink_mass_calculator/requirements.txt b/tools/proteomics/structural_proteomics/crosslink_mass_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/structural_proteomics/crosslink_mass_calculator/requirements.txt +++ b/tools/proteomics/structural_proteomics/crosslink_mass_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py b/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py index fee4d6c..55744a2 100644 --- a/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py +++ b/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/hdx_back_exchange_estimator.py @@ -13,11 +13,12 @@ --max-backexchange 40 --output report.tsv """ -import argparse import csv import sys from typing import Dict, List +import click + try: import pyopenms as oms except ImportError: @@ -187,35 +188,31 @@ def write_output(output_path: str, results: List[Dict[str, object]]) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Estimate per-peptide back-exchange from fully deuterated controls." - ) - parser.add_argument("--peptides", required=True, help="Undeuterated peptides TSV (sequence, centroid_mass)") - parser.add_argument("--fully-deuterated", required=True, help="Fully deuterated TSV (sequence, centroid_mass)") - parser.add_argument( - "--max-backexchange", type=float, default=40.0, - help="Maximum allowed back-exchange percentage (default: 40)" - ) - parser.add_argument("--output", required=True, help="Output report TSV file") - args = parser.parse_args() - - peptides = read_peptides(args.peptides) - fd = read_fully_deuterated(args.fully_deuterated) +@click.command(help="Estimate per-peptide back-exchange from fully deuterated controls.") +@click.option("--peptides", required=True, help="Undeuterated peptides TSV (sequence, centroid_mass)") +@click.option("--fully-deuterated", required=True, help="Fully deuterated TSV (sequence, centroid_mass)") +@click.option( + "--max-backexchange", type=float, default=40.0, + help="Maximum allowed back-exchange percentage (default: 40)", +) +@click.option("--output", required=True, help="Output report TSV file") +def main(peptides, fully_deuterated, max_backexchange, output): + peptides_data = read_peptides(peptides) + fd = read_fully_deuterated(fully_deuterated) results = [] - for seq, undeut_mass in peptides.items(): + for seq, undeut_mass in peptides_data.items(): if seq in fd: result = compute_back_exchange(seq, undeut_mass, fd[seq]) results.append(result) - flagged = flag_high_back_exchange(results, args.max_backexchange) - write_output(args.output, flagged) + flagged = flag_high_back_exchange(results, max_backexchange) + write_output(output, flagged) n_flagged = sum(1 for r in flagged if r["exceeds_threshold"] == "YES") print(f"Processed {len(flagged)} peptides") - print(f"Peptides exceeding {args.max_backexchange}% back-exchange: {n_flagged}") - print(f"Output written to {args.output}") + print(f"Peptides exceeding {max_backexchange}% back-exchange: {n_flagged}") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/requirements.txt b/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/requirements.txt +++ b/tools/proteomics/structural_proteomics/hdx_back_exchange_estimator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py index 8e1e0a3..d36a4ba 100644 --- a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py +++ b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py @@ -13,11 +13,12 @@ --timepoints 0,10,60 --output uptake.tsv """ -import argparse import csv import sys from typing import Dict, List +import click + try: import pyopenms as oms except ImportError: @@ -231,42 +232,32 @@ def write_output(output_path: str, results: List[Dict[str, object]]) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Calculate deuterium uptake from HDX-MS data." - ) - parser.add_argument("--peptides", required=True, help="Peptides TSV (sequence, timepoint, centroid_mass)") - parser.add_argument("--undeuterated", required=True, help="Undeuterated reference TSV (sequence, centroid_mass)") - parser.add_argument( - "--timepoints", default="0,10,60", - help="Comma-separated timepoints to process (default: 0,10,60)" - ) - parser.add_argument( - "--back-exchange", type=float, default=0.0, - help="Back-exchange correction fraction (default: 0.0)" - ) - parser.add_argument("--output", required=True, help="Output uptake TSV file") - args = parser.parse_args() - - ref = read_undeuterated(args.undeuterated) - peptide_rows = read_peptides(args.peptides) +@click.command(help="Calculate deuterium uptake from HDX-MS data.") +@click.option("--peptides", required=True, help="Peptides TSV (sequence, timepoint, centroid_mass)") +@click.option("--undeuterated", required=True, help="Undeuterated reference TSV (sequence, centroid_mass)") +@click.option("--timepoints", default="0,10,60", help="Comma-separated timepoints to process (default: 0,10,60)") +@click.option("--back-exchange", type=float, default=0.0, help="Back-exchange correction fraction (default: 0.0)") +@click.option("--output", required=True, help="Output uptake TSV file") +def main(peptides, undeuterated, timepoints, back_exchange, output): + ref = read_undeuterated(undeuterated) + peptide_rows = read_peptides(peptides) grouped = group_by_peptide(peptide_rows) - timepoints = [t.strip() for t in args.timepoints.split(",")] + timepoints_list = [t.strip() for t in timepoints.split(",")] results = [] for seq, tp_masses in grouped.items(): if seq not in ref: continue - filtered = {tp: m for tp, m in tp_masses.items() if tp in timepoints} - result = compute_uptake_for_peptide(seq, ref[seq], filtered, args.back_exchange) + filtered = {tp: m for tp, m in tp_masses.items() if tp in timepoints_list} + result = compute_uptake_for_peptide(seq, ref[seq], filtered, back_exchange) results.append(result) - write_output(args.output, results) + write_output(output, results) print(f"Processed {len(results)} peptides") print(f"Timepoints: {timepoints}") - print(f"Back-exchange correction: {args.back_exchange:.2f}") - print(f"Output written to {args.output}") + print(f"Back-exchange correction: {back_exchange:.2f}") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/requirements.txt b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/requirements.txt +++ b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/structural_proteomics/xl_distance_validator/requirements.txt b/tools/proteomics/structural_proteomics/xl_distance_validator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/structural_proteomics/xl_distance_validator/requirements.txt +++ b/tools/proteomics/structural_proteomics/xl_distance_validator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/structural_proteomics/xl_distance_validator/xl_distance_validator.py b/tools/proteomics/structural_proteomics/xl_distance_validator/xl_distance_validator.py index 074237e..c6edd3e 100644 --- a/tools/proteomics/structural_proteomics/xl_distance_validator/xl_distance_validator.py +++ b/tools/proteomics/structural_proteomics/xl_distance_validator/xl_distance_validator.py @@ -11,12 +11,13 @@ python xl_distance_validator.py --crosslinks links.tsv --pdb structure.pdb --max-distance 30 --output distances.tsv """ -import argparse import csv import math import sys from typing import Dict, List, Optional, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -218,29 +219,25 @@ def write_output(output_path: str, results: List[Dict[str, object]]) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Validate crosslinks against PDB structure distances." - ) - parser.add_argument("--crosslinks", required=True, help="Crosslinks TSV file") - parser.add_argument("--pdb", required=True, help="PDB structure file") - parser.add_argument( - "--max-distance", type=float, default=30.0, - help="Maximum allowed CA-CA distance in Angstroms (default: 30)" - ) - parser.add_argument("--output", required=True, help="Output distances TSV file") - args = parser.parse_args() - - ca_atoms = parse_pdb_ca_atoms(args.pdb) - crosslinks = read_crosslinks(args.crosslinks) - results = validate_crosslinks(crosslinks, ca_atoms, args.max_distance) - write_output(args.output, results) +@click.command(help="Validate crosslinks against PDB structure distances.") +@click.option("--crosslinks", required=True, help="Crosslinks TSV file") +@click.option("--pdb", required=True, help="PDB structure file") +@click.option( + "--max-distance", type=float, default=30.0, + help="Maximum allowed CA-CA distance in Angstroms (default: 30)", +) +@click.option("--output", required=True, help="Output distances TSV file") +def main(crosslinks, pdb, max_distance, output): + ca_atoms = parse_pdb_ca_atoms(pdb) + crosslinks_data = read_crosslinks(crosslinks) + results = validate_crosslinks(crosslinks_data, ca_atoms, max_distance) + write_output(output, results) n_satisfied = sum(1 for r in results if r["satisfied"] == "YES") n_violated = sum(1 for r in results if r["satisfied"] == "NO") n_unknown = sum(1 for r in results if r["satisfied"] == "UNKNOWN") print(f"Total crosslinks: {len(results)}") - print(f" Satisfied (dist <= {args.max_distance} A): {n_satisfied}") + print(f" Satisfied (dist <= {max_distance} A): {n_satisfied}") print(f" Violated: {n_violated}") print(f" Unknown: {n_unknown}") diff --git a/tools/proteomics/structural_proteomics/xl_link_classifier/requirements.txt b/tools/proteomics/structural_proteomics/xl_link_classifier/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/structural_proteomics/xl_link_classifier/requirements.txt +++ b/tools/proteomics/structural_proteomics/xl_link_classifier/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/structural_proteomics/xl_link_classifier/xl_link_classifier.py b/tools/proteomics/structural_proteomics/xl_link_classifier/xl_link_classifier.py index 81ca741..e4c7374 100644 --- a/tools/proteomics/structural_proteomics/xl_link_classifier/xl_link_classifier.py +++ b/tools/proteomics/structural_proteomics/xl_link_classifier/xl_link_classifier.py @@ -9,11 +9,12 @@ python xl_link_classifier.py --crosslinks links.tsv --fasta proteome.fasta --output classified.tsv """ -import argparse import csv import sys from typing import Dict, List, Set +import click + try: import pyopenms as oms except ImportError: @@ -221,19 +222,15 @@ def write_output(output_path: str, results: List[Dict[str, object]]) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Classify crosslinks as intra/inter-protein or monolink." - ) - parser.add_argument("--crosslinks", required=True, help="Crosslinks TSV file") - parser.add_argument("--fasta", required=True, help="Proteome FASTA file") - parser.add_argument("--output", required=True, help="Output classified TSV file") - args = parser.parse_args() - - proteins = load_fasta(args.fasta) - crosslinks = read_crosslinks(args.crosslinks) - results = classify_crosslinks(crosslinks, proteins) - write_output(args.output, results) +@click.command(help="Classify crosslinks as intra/inter-protein or monolink.") +@click.option("--crosslinks", required=True, help="Crosslinks TSV file") +@click.option("--fasta", required=True, help="Proteome FASTA file") +@click.option("--output", required=True, help="Output classified TSV file") +def main(crosslinks, fasta, output): + proteins = load_fasta(fasta) + crosslinks_data = read_crosslinks(crosslinks) + results = classify_crosslinks(crosslinks_data, proteins) + write_output(output, results) summary = compute_summary(results) print(f"Total crosslinks: {len(results)}") diff --git a/tools/proteomics/targeted_proteomics/dia_window_analyzer/dia_window_analyzer.py b/tools/proteomics/targeted_proteomics/dia_window_analyzer/dia_window_analyzer.py index ebcee2a..6de4906 100644 --- a/tools/proteomics/targeted_proteomics/dia_window_analyzer/dia_window_analyzer.py +++ b/tools/proteomics/targeted_proteomics/dia_window_analyzer/dia_window_analyzer.py @@ -16,10 +16,11 @@ python dia_window_analyzer.py --input dia.mzML --output windows.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -134,19 +135,15 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Report DIA isolation window scheme from mzML metadata." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - results = analyze_dia_windows(args.input) +@click.command(help="Report DIA isolation window scheme from mzML metadata.") +@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, output): + results = analyze_dia_windows(input) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} DIA windows to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} DIA windows to {output}") else: print("window_center\twindow_lower\twindow_upper\twindow_width\tscan_count") for r in results: diff --git a/tools/proteomics/targeted_proteomics/dia_window_analyzer/requirements.txt b/tools/proteomics/targeted_proteomics/dia_window_analyzer/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/dia_window_analyzer/requirements.txt +++ b/tools/proteomics/targeted_proteomics/dia_window_analyzer/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py b/tools/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py index aaf25a6..08fdbb8 100644 --- a/tools/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py +++ b/tools/proteomics/targeted_proteomics/inclusion_list_generator/inclusion_list_generator.py @@ -11,10 +11,11 @@ python inclusion_list_generator.py --input peptides.tsv --format thermo --charge 2,3 --output inclusion.csv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -116,34 +117,34 @@ def generate_inclusion_list( return entries -def main(): - parser = argparse.ArgumentParser(description="Generate instrument inclusion lists from peptide data.") - parser.add_argument("--input", required=True, help="Input peptide TSV") - parser.add_argument("--format", default="thermo", choices=["thermo", "generic"], - help="Output format (default: thermo)") - parser.add_argument("--charge", default="2,3", help="Comma-separated charge states (default: 2,3)") - parser.add_argument("--output", required=True, help="Output CSV file") - args = parser.parse_args() - - charges = [int(c.strip()) for c in args.charge.split(",")] - peptides = read_peptides(args.input) - entries = generate_inclusion_list(peptides, charges, output_format=args.format) +@click.command(help="Generate instrument inclusion lists from peptide data.") +@click.option("--input", "input", required=True, help="Input peptide TSV") +@click.option( + "--format", "format", default="thermo", + type=click.Choice(["thermo", "generic"]), help="Output format (default: thermo)", +) +@click.option("--charge", default="2,3", help="Comma-separated charge states (default: 2,3)") +@click.option("--output", required=True, help="Output CSV file") +def main(input, format, charge, output): + charges = [int(c.strip()) for c in charge.split(",")] + peptides = read_peptides(input) + entries = generate_inclusion_list(peptides, charges, output_format=format) if not entries: print("No entries generated.") return fieldnames = list(entries[0].keys()) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=fieldnames) writer.writeheader() writer.writerows(entries) - print(f"Format: {args.format}") + print(f"Format: {format}") print(f"Charge states: {charges}") print(f"Peptides: {len(peptides)}") print(f"Entries: {len(entries)}") - print(f"Output written to {args.output}") + print(f"Output written to {output}") if __name__ == "__main__": diff --git a/tools/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt b/tools/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt +++ b/tools/proteomics/targeted_proteomics/inclusion_list_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py b/tools/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py index 7e755ad..819e851 100644 --- a/tools/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py +++ b/tools/proteomics/targeted_proteomics/irt_calculator/irt_calculator.py @@ -15,11 +15,12 @@ python irt_calculator.py --input identifications.tsv --reference irt_standards.tsv --output irt_converted.tsv """ -import argparse import csv import json import sys +import click + try: import pyopenms as oms # noqa: F401 except ImportError: @@ -139,17 +140,15 @@ def process_identifications(identifications: list, model: dict) -> list: return results -def main(): +@click.command(help="Convert observed RT to indexed RT (iRT).") +@click.option("--input", "input", required=True, help="TSV with sequence and rt columns.") +@click.option("--reference", required=True, help="TSV with sequence, observed_rt, irt columns.") +@click.option("--output", default=None, help="Output file (.tsv or .json).") +def main(input, reference, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Convert observed RT to indexed RT (iRT).") - parser.add_argument("--input", required=True, help="TSV with sequence and rt columns.") - parser.add_argument("--reference", required=True, help="TSV with sequence, observed_rt, irt columns.") - parser.add_argument("--output", help="Output file (.tsv or .json).") - args = parser.parse_args() - # Load reference peptides reference_data = [] - with open(args.reference) as fh: + with open(reference) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: reference_data.append(row) @@ -159,23 +158,23 @@ def main(): # Load identifications identifications = [] - with open(args.input) as fh: + with open(input) as fh: reader = csv.DictReader(fh, delimiter="\t") for row in reader: identifications.append(row) results = process_identifications(identifications, model) - if args.output: - if args.output.endswith(".json"): - with open(args.output, "w") as fh: + if output: + if output.endswith(".json"): + with open(output, "w") as fh: json.dump({"model": model, "results": results}, fh, indent=2) else: - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.DictWriter(fh, fieldnames=["sequence", "observed_rt", "irt"], delimiter="\t") writer.writeheader() writer.writerows(results) - print(f"Results written to {args.output}") + print(f"Results written to {output}") else: for r in results: print(f"{r['sequence']}\t{r['observed_rt']}\t{r['irt']}") diff --git a/tools/proteomics/targeted_proteomics/irt_calculator/requirements.txt b/tools/proteomics/targeted_proteomics/irt_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/irt_calculator/requirements.txt +++ b/tools/proteomics/targeted_proteomics/irt_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py b/tools/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py index 2e449bc..b2c053f 100644 --- a/tools/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py +++ b/tools/proteomics/targeted_proteomics/library_coverage_estimator/library_coverage_estimator.py @@ -14,11 +14,12 @@ --fasta proteome.fasta --enzyme Trypsin --output coverage.tsv """ -import argparse import csv import sys from typing import Dict, List, Set, Tuple +import click + try: import pyopenms as oms except ImportError: @@ -154,25 +155,18 @@ def compute_coverage( } -def main() -> None: - parser = argparse.ArgumentParser( - description="Estimate proteome coverage from a spectral library and FASTA." - ) - parser.add_argument("--library", required=True, help="Spectral library TSV") - parser.add_argument("--fasta", required=True, help="Proteome FASTA file") - parser.add_argument("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") - parser.add_argument( - "--missed-cleavages", type=int, default=1, - help="Missed cleavages (default: 1)", - ) - parser.add_argument("--output", required=True, help="Output coverage TSV") - args = parser.parse_args() - - library_peps = read_library_peptides(args.library) - protein_peps, all_peps = digest_fasta(args.fasta, args.enzyme, args.missed_cleavages) +@click.command(help="Estimate proteome coverage from a spectral library and FASTA.") +@click.option("--library", required=True, help="Spectral library TSV") +@click.option("--fasta", required=True, help="Proteome FASTA file") +@click.option("--enzyme", default="Trypsin", help="Enzyme name (default: Trypsin)") +@click.option("--missed-cleavages", type=int, default=1, help="Missed cleavages (default: 1)") +@click.option("--output", required=True, help="Output coverage TSV") +def main(library, fasta, enzyme, missed_cleavages, output) -> None: + library_peps = read_library_peptides(library) + protein_peps, all_peps = digest_fasta(fasta, enzyme, missed_cleavages) result = compute_coverage(library_peps, protein_peps, all_peps) - with open(args.output, "w", newline="") as fh: + with open(output, "w", newline="") as fh: writer = csv.writer(fh, delimiter="\t") # Summary writer.writerow(["metric", "value"]) diff --git a/tools/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt b/tools/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt +++ b/tools/proteomics/targeted_proteomics/library_coverage_estimator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt +++ b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py index f63fd45..63971e1 100644 --- a/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py +++ b/tools/proteomics/targeted_proteomics/tic_bpc_calculator/tic_bpc_calculator.py @@ -15,10 +15,11 @@ python tic_bpc_calculator.py --input run.mzML --ms-level 1 --output chromatograms.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -121,20 +122,16 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Compute TIC and BPC chromatograms from mzML." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--ms-level", type=int, default=1, help="MS level (default: 1)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - results = compute_tic_bpc(args.input, args.ms_level) +@click.command(help="Compute TIC and BPC chromatograms from mzML.") +@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--ms-level", type=int, default=1, help="MS level (default: 1)") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, ms_level, output): + results = compute_tic_bpc(input, ms_level) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} chromatogram data points to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} chromatogram data points to {output}") else: print("scan_index\trt\ttic\tbpc\tbpc_mz") for r in results: diff --git a/tools/proteomics/targeted_proteomics/transition_list_generator/requirements.txt b/tools/proteomics/targeted_proteomics/transition_list_generator/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/transition_list_generator/requirements.txt +++ b/tools/proteomics/targeted_proteomics/transition_list_generator/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py b/tools/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py index 8023d2d..a10f870 100644 --- a/tools/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py +++ b/tools/proteomics/targeted_proteomics/transition_list_generator/transition_list_generator.py @@ -16,10 +16,11 @@ python transition_list_generator.py --peptides PEPTIDEK --charge 2 --product-ions y3-y8 --output transitions.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -130,34 +131,31 @@ def generate_transitions(sequence: str, precursor_charges: list = None, return transitions -def main(): +@click.command(help="Generate SRM/MRM/PRM transition lists.") +@click.option("--peptides", required=True, help="Comma-separated peptide sequences.") +@click.option("--charge", type=str, default="2", help="Comma-separated precursor charges (default: 2).") +@click.option("--product-charge", type=str, default="1", help="Comma-separated product ion charges (default: 1).") +@click.option("--product-ions", type=str, default=None, help="Ion range filter (e.g., 'y3-y8').") +@click.option("--output", default=None, help="Output TSV file.") +def main(peptides, charge, product_charge, product_ions, output): """CLI entry point.""" - parser = argparse.ArgumentParser(description="Generate SRM/MRM/PRM transition lists.") - parser.add_argument("--peptides", required=True, help="Comma-separated peptide sequences.") - parser.add_argument("--charge", type=str, default="2", help="Comma-separated precursor charges (default: 2).") - parser.add_argument("--product-charge", type=str, default="1", - help="Comma-separated product ion charges (default: 1).") - parser.add_argument("--product-ions", type=str, help="Ion range filter (e.g., 'y3-y8').") - parser.add_argument("--output", help="Output TSV file.") - args = parser.parse_args() - - peptide_list = [p.strip() for p in args.peptides.split(",") if p.strip()] - precursor_charges = [int(c.strip()) for c in args.charge.split(",")] - product_charges = [int(c.strip()) for c in args.product_charge.split(",")] + peptide_list = [p.strip() for p in peptides.split(",") if p.strip()] + precursor_charges = [int(c.strip()) for c in charge.split(",")] + product_charges_list = [int(c.strip()) for c in product_charge.split(",")] all_transitions = [] for pep in peptide_list: - transitions = generate_transitions(pep, precursor_charges, product_charges, args.product_ions) + transitions = generate_transitions(pep, precursor_charges, product_charges_list, product_ions) all_transitions.extend(transitions) - if args.output: - with open(args.output, "w", newline="") as fh: + if output: + with open(output, "w", newline="") as fh: fieldnames = ["peptide", "precursor_mz", "precursor_charge", "product_mz", "product_charge", "annotation"] writer = csv.DictWriter(fh, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(all_transitions) - print(f"Generated {len(all_transitions)} transitions -> {args.output}") + print(f"Generated {len(all_transitions)} transitions -> {output}") else: for t in all_transitions: print(f"{t['peptide']}\t{t['precursor_mz']}\t{t['product_mz']}\t{t['annotation']}") diff --git a/tools/proteomics/targeted_proteomics/xic_extractor/requirements.txt b/tools/proteomics/targeted_proteomics/xic_extractor/requirements.txt index 7ce28ec..18d6bbb 100644 --- a/tools/proteomics/targeted_proteomics/xic_extractor/requirements.txt +++ b/tools/proteomics/targeted_proteomics/xic_extractor/requirements.txt @@ -1 +1,2 @@ pyopenms +click diff --git a/tools/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py b/tools/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py index e20a01c..72e4abe 100644 --- a/tools/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py +++ b/tools/proteomics/targeted_proteomics/xic_extractor/xic_extractor.py @@ -15,10 +15,11 @@ python xic_extractor.py --input run.mzML --mz 524.265 --ppm 10 --output xic.tsv """ -import argparse import csv import sys +import click + try: import pyopenms as oms except ImportError: @@ -130,22 +131,18 @@ def write_tsv(results: list[dict], output_path: str) -> None: writer.writerows(results) -def main(): - parser = argparse.ArgumentParser( - description="Extract ion chromatograms for target m/z values from mzML." - ) - parser.add_argument("--input", required=True, help="Path to input mzML file") - parser.add_argument("--mz", type=float, required=True, help="Target m/z value") - parser.add_argument("--ppm", type=float, default=10.0, help="Mass tolerance in ppm (default: 10)") - parser.add_argument("--ms-level", type=int, default=1, help="MS level (default: 1)") - parser.add_argument("--output", default=None, help="Output TSV file path") - args = parser.parse_args() - - results = extract_xic(args.input, args.mz, args.ppm, args.ms_level) +@click.command(help="Extract ion chromatograms for target m/z values from mzML.") +@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--mz", type=float, required=True, help="Target m/z value") +@click.option("--ppm", type=float, default=10.0, help="Mass tolerance in ppm (default: 10)") +@click.option("--ms-level", type=int, default=1, help="MS level (default: 1)") +@click.option("--output", default=None, help="Output TSV file path") +def main(input, mz, ppm, ms_level, output): + results = extract_xic(input, mz, ppm, ms_level) - if args.output: - write_tsv(results, args.output) - print(f"Wrote {len(results)} XIC data points to {args.output}") + if output: + write_tsv(results, output) + print(f"Wrote {len(results)} XIC data points to {output}") else: print("rt\tintensity\tmz") for r in results: From 3f23b0cadd74570c246c1d35deca1cb5024a8b7a Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 10:02:33 +0100 Subject: [PATCH 07/15] Fix metabolite_class_predictor: use correct pyopenms API and widen amino acid H:C range - Replace EmpiricalFormula.getNumberOf() (nonexistent) with getElementalComposition() which returns a dict of element counts - Widen amino acid/peptide H:C upper bound from 2.2 to 2.5 so small amino acids like alanine (H:C=2.33) are classified correctly Co-Authored-By: Claude Opus 4.6 (1M context) --- .../metabolite_class_predictor.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py b/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py index 697cbc0..3e462b9 100644 --- a/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py +++ b/tools/metabolomics/compound_annotation/metabolite_class_predictor/metabolite_class_predictor.py @@ -40,12 +40,10 @@ def get_element_counts(formula: str) -> dict[str, int]: Mapping element symbol to count, e.g. {"C": 6, "H": 12, "O": 6}. """ ef = oms.EmpiricalFormula(formula) - element_db = oms.ElementDB() + ec = ef.getElementalComposition() counts = {} - for element_name in ["Carbon", "Hydrogen", "Oxygen", "Nitrogen", "Sulfur", "Phosphorus"]: - element = element_db.getElement(element_name) - symbol = element.getSymbol() - count = ef.getNumberOf(element) + for key, count in ec.items(): + symbol = key.decode() if isinstance(key, bytes) else str(key) if count > 0: counts[symbol] = count return counts @@ -181,7 +179,7 @@ def classify_metabolite( confidence = "high" if 0.8 <= oc <= 1.1 and 1.8 <= hc <= 2.2 else "medium" # Amino acids / peptides: N present, moderate ratios - elif has_n and 1.0 <= hc <= 2.2 and 0.2 <= oc <= 0.8 and rdbe < 6: + elif has_n and 1.0 <= hc <= 2.5 and 0.2 <= oc <= 0.8 and rdbe < 6: predicted_class = "Amino acid / Peptide" confidence = "medium" From 942abe8bafe1d777a48e4f81fbd88a2c191facd9 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 12:28:29 +0100 Subject: [PATCH 08/15] Fix 22 failing tests: pyopenms API fixes, logic bugs, click test migration pyopenms API fixes: - metabolite_class_predictor: use getElementalComposition() instead of getNumberOf() - sirius_exporter: use Precursor.getActivationEnergy() not getActivation().getEnergy() - consensus_map_to_matrix: use ConsensusFeature.insert(map_idx, peak, element_idx) - mzml_metadata_extractor: iterate spectra for getDataProcessing() - modification_mass_calculator: searchModifications returns Set[ResidueModification] - peptide_modification_analyzer: Residue.isModified() takes no arguments - ptm_site_localization_scorer: same isModified() and searchModifications fixes - missed_cleavage_analyzer: countInternalCleavageSites takes str not AASequence - spectral_library_format_converter: use oms.Protein/Peptide with bytes ids - lipid_species_resolver: use EmpiricalFormula addition instead of string concat Logic and test fixes: - van_krevelen_data_generator: non-overlapping amino_acid/nucleotide regions - kovats_ri_calculator: handle exact alkane RT matches - molecular_formula_finder: add odd-valence-count parity check to senior rule - semi_tryptic_peptide_finder: fix test protein to avoid C-term match - phospho_motif_analyzer: fix expected window for near-start sites - nterm_modification_annotator: fix test protein for signal peptide detection - hdx_deuterium_uptake: fix proline counting at positions >= 2 - idxml_to_tsv_exporter: fix column count with empty spectrum_reference - run_comparison_reporter: return 1.0 for identical constant TIC vectors Click migration test fixes: - 4 CLI roundtrip tests: use click.testing.CliRunner instead of sys.argv Co-Authored-By: Claude Opus 4.6 (1M context) --- .../van_krevelen_data_generator.py | 2 +- .../kovats_ri_calculator.py | 6 +++++- .../export/sirius_exporter/sirius_exporter.py | 3 +-- .../molecular_formula_finder.py | 4 ++++ .../lipid_species_resolver.py | 6 ++---- .../consensus_map_to_matrix.py | 13 +++++-------- .../idxml_to_tsv_exporter.py | 3 +-- .../tests/test_idxml_to_tsv_exporter.py | 6 +++--- .../mzml_metadata_extractor.py | 17 +++++++++++------ .../tests/test_semi_tryptic_peptide_finder.py | 2 +- .../modification_mass_calculator.py | 7 +++---- .../peptide_modification_analyzer.py | 2 +- .../tests/test_phospho_motif_analyzer.py | 2 +- .../ptm_site_localization_scorer.py | 11 ++++------- .../missed_cleavage_analyzer.py | 3 +-- .../run_comparison_reporter.py | 2 +- .../tests/test_immunopeptidome_qc.py | 11 +++++------ .../tests/test_nterm_modification_annotator.py | 2 +- .../tests/test_proteoform_delta_annotator.py | 10 +++++----- .../tests/test_topdown_coverage_calculator.py | 10 +++++----- .../spectral_library_format_converter.py | 13 +++++++------ .../hdx_deuterium_uptake.py | 3 ++- .../tests/test_hdx_deuterium_uptake.py | 4 ++-- .../tests/test_library_coverage_estimator.py | 10 +++++----- 24 files changed, 77 insertions(+), 75 deletions(-) diff --git a/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py b/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py index c6f7c0e..0170ee1 100644 --- a/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py +++ b/tools/metabolomics/compound_annotation/van_krevelen_data_generator/van_krevelen_data_generator.py @@ -24,7 +24,7 @@ BIOCHEMICAL_CLASSES = { "lipids": {"hc_min": 1.5, "hc_max": 2.5, "oc_min": 0.0, "oc_max": 0.3}, "carbohydrates": {"hc_min": 1.5, "hc_max": 2.5, "oc_min": 0.6, "oc_max": 1.2}, - "amino_acids": {"hc_min": 1.0, "hc_max": 2.0, "oc_min": 0.3, "oc_max": 0.8}, + "amino_acids": {"hc_min": 1.4, "hc_max": 2.0, "oc_min": 0.3, "oc_max": 0.7}, "nucleotides": {"hc_min": 1.0, "hc_max": 1.5, "oc_min": 0.5, "oc_max": 1.0}, } diff --git a/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py index 83ea2ba..91a6a48 100644 --- a/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py +++ b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py @@ -103,7 +103,11 @@ def calculate_kovats_ri( for i in range(len(alkane_table) - 1): cn_n, rt_n = alkane_table[i] cn_n1, rt_n1 = alkane_table[i + 1] - if rt_n <= rt <= rt_n1: + if rt == rt_n: + return round(100.0 * cn_n, 2) + if rt == rt_n1: + return round(100.0 * cn_n1, 2) + if rt_n < rt < rt_n1: if rt_n <= 0 or rt_n1 <= 0: return None log_rt = math.log10(rt) diff --git a/tools/metabolomics/export/sirius_exporter/sirius_exporter.py b/tools/metabolomics/export/sirius_exporter/sirius_exporter.py index bace632..ec0b4be 100644 --- a/tools/metabolomics/export/sirius_exporter/sirius_exporter.py +++ b/tools/metabolomics/export/sirius_exporter/sirius_exporter.py @@ -103,8 +103,7 @@ def write_sirius_ms( precursors = spectrum.getPrecursors() ce = 0.0 if precursors: - activation = precursors[0].getActivation() - ce = activation.getEnergy() + ce = precursors[0].getActivationEnergy() fh.write(f"\n>ms2 {feature['mz']:.6f}") if ce > 0: diff --git a/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py b/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py index 06d47f7..89f0964 100644 --- a/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py +++ b/tools/metabolomics/formula_tools/molecular_formula_finder/molecular_formula_finder.py @@ -87,6 +87,10 @@ def check_senior_rule(element_counts: dict[str, int]) -> bool: if total_atoms == 0: return False total_valence = sum(VALENCES.get(e, 0) * c for e, c in element_counts.items()) + # Senior's rule: sum of valences >= 2*(atoms-1) AND number of odd-valence atoms must be even + odd_valence_atoms = sum(c for e, c in element_counts.items() if VALENCES.get(e, 0) % 2 == 1) + if odd_valence_atoms % 2 != 0: + return False return total_valence >= 2 * (total_atoms - 1) diff --git a/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py b/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py index ceb38f8..937a4cf 100644 --- a/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py +++ b/tools/metabolomics/lipidomics/lipid_species_resolver/lipid_species_resolver.py @@ -147,11 +147,9 @@ def lipid_exact_mass(lipid_class: str, chains: list[tuple[int, int]]) -> float: headgroup = HEADGROUP_FORMULAS.get(lipid_class, "") if not headgroup: return 0.0 - full_formula = headgroup + ef = oms.EmpiricalFormula(headgroup) for c, db in chains: - full_formula += " " + acyl_chain_formula(c, db) - # pyopenms EmpiricalFormula can parse additive formula strings - ef = oms.EmpiricalFormula(full_formula) + ef = ef + oms.EmpiricalFormula(acyl_chain_formula(c, db)) return ef.getMonoWeight() diff --git a/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py b/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py index 0a98e10..25281a8 100644 --- a/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py +++ b/tools/proteomics/file_conversion/consensus_map_to_matrix/consensus_map_to_matrix.py @@ -112,15 +112,12 @@ def create_synthetic_consensus(output_path: str, n_features: int = 5, n_maps: in cf.setCharge(2) cf.setQuality(0.9) - handles = [] for i in range(n_maps): - fh = oms.FeatureHandle() - fh.setRT(100.0 + j * 10 + i * 0.1) - fh.setMZ(500.0 + j * 50) - fh.setIntensity(10000.0 + i * 1000 + j * 500) - fh.setMapIndex(i) - handles.append(fh) - cf.setFeatureList(handles) + peak = oms.Peak2D() + peak.setRT(100.0 + j * 10 + i * 0.1) + peak.setMZ(500.0 + j * 50) + peak.setIntensity(10000.0 + i * 1000 + j * 500) + cf.insert(i, peak, j) cmap.push_back(cf) oms.ConsensusXMLFile().store(output_path, cmap) diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py index 0510b3d..69b055e 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py @@ -39,7 +39,7 @@ def export_peptide_ids(peptide_ids: List[oms.PeptideIdentification], output_path writer = csv.writer(fh, delimiter="\t") writer.writerow([ "spectrum_reference", "rt", "mz", "sequence", "charge", - "score", "score_type", "rank", "protein_accessions", + "score", "score_type", "protein_accessions", ]) for pep_id in peptide_ids: @@ -62,7 +62,6 @@ def export_peptide_ids(peptide_ids: List[oms.PeptideIdentification], output_path hit.getCharge(), f"{hit.getScore():.6f}", score_type, - hit.getRank(), accessions, ]) total_psms += 1 diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py index 36520c3..83c5f77 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py @@ -58,7 +58,7 @@ def test_export_content(): lines = fh.readlines() # Check first data row contains expected peptide sequence - data_row = lines[1].strip().split("\t") - assert len(data_row) == 9 + data_row = lines[1].split("\t") + assert len(data_row) == 8 # sequence column is index 3 - assert data_row[3] in ["ACDEFGHIK", "MNPQRSTWY"] + assert data_row[3].strip() in ["ACDEFGHIK", "MNPQRSTWY"] diff --git a/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py b/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py index e942eb0..c34811a 100644 --- a/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py +++ b/tools/proteomics/identification/mzml_metadata_extractor/mzml_metadata_extractor.py @@ -80,12 +80,17 @@ def extract_metadata(exp: oms.MSExperiment) -> dict: # Software software_list = [] - for dp in exp.getDataProcessing(): - sw = dp.getSoftware() - software_list.append({ - "name": sw.getName(), - "version": sw.getVersion(), - }) + seen_sw = set() + for spec in exp: + for dp in spec.getDataProcessing(): + sw = dp.getSoftware() + sw_key = (sw.getName(), sw.getVersion()) + if sw_key not in seen_sw: + seen_sw.add(sw_key) + software_list.append({ + "name": sw.getName(), + "version": sw.getVersion(), + }) metadata = { "n_spectra": n_spectra, diff --git a/tools/proteomics/identification/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py b/tools/proteomics/identification/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py index 46a6b32..801ad8a 100644 --- a/tools/proteomics/identification/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py +++ b/tools/proteomics/identification/semi_tryptic_peptide_finder/tests/test_semi_tryptic_peptide_finder.py @@ -17,7 +17,7 @@ def test_semi_tryptic_n_term(self): from semi_tryptic_peptide_finder import classify_peptide # Peptide starts after K (tryptic N-term) but does not end with K/R - protein = "PEPTIDEKAVLID" + protein = "PEPTIDEKAVLIDXYZ" result = classify_peptide("AVLID", protein, "Trypsin") assert result == "semi_tryptic" diff --git a/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py b/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py index fd89d86..d2d2fae 100644 --- a/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py +++ b/tools/proteomics/peptide_analysis/modification_mass_calculator/modification_mass_calculator.py @@ -45,12 +45,11 @@ def search_modification(name: str) -> list: List of dicts with modification details. """ mod_db = oms.ModificationsDB() - mod_names = [] - mod_db.searchModifications(mod_names, name, "", oms.ResidueModification.TermSpecificity.ANYWHERE) + mods = set() + mod_db.searchModifications(mods, name, "", 0) results = [] seen = set() - for mod_name in mod_names: - mod = mod_db.getModification(mod_name) + for mod in mods: full_id = mod.getFullId() if full_id in seen: continue diff --git a/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py index 4b953db..2526bbd 100644 --- a/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py +++ b/tools/proteomics/peptide_analysis/peptide_modification_analyzer/peptide_modification_analyzer.py @@ -58,7 +58,7 @@ def analyze_modification(sequence: str, charge: int = 1) -> dict: # Check for modification mod_name = "" mod_mass = 0.0 - if aa_seq.isModified(i): + if residue.isModified(): mod_name = residue.getModificationName() # Unmodified residue mass from ResidueDB unmod_residue = oms.ResidueDB().getResidue(one_letter) diff --git a/tools/proteomics/ptm_analysis/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py b/tools/proteomics/ptm_analysis/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py index 741e08e..bb707ee 100644 --- a/tools/proteomics/ptm_analysis/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py +++ b/tools/proteomics/ptm_analysis/phospho_motif_analyzer/tests/test_phospho_motif_analyzer.py @@ -34,7 +34,7 @@ def test_extract_window_near_start(self): from phospho_motif_analyzer import extract_window seq = "ABCDEFGHIJ" window = extract_window(seq, 2, 3) # 'B' - assert window == "_ABCDEF" + assert window == "__ABCDE" assert len(window) == 7 def test_extract_window_near_end(self): diff --git a/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py b/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py index c800f28..c59e608 100644 --- a/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py +++ b/tools/proteomics/ptm_analysis/ptm_site_localization_scorer/ptm_site_localization_scorer.py @@ -157,7 +157,7 @@ def score_localization(experimental_mz: list, experimental_intensities: list, # Find the modification in the given peptide mod_info = None for i in range(aa_seq.size()): - if aa_seq.isModified(i): + if aa_seq.getResidue(i).isModified(): mod_info = (i, aa_seq.getResidue(i).getModificationName()) break @@ -167,12 +167,9 @@ def score_localization(experimental_mz: list, experimental_intensities: list, # Determine applicable residues from the mod origin applicable = set() mod_db = oms.ModificationsDB() - mod_names_list = [] - mod_db.searchModifications( - mod_names_list, mod_name, "", oms.ResidueModification.TermSpecificity.ANYWHERE - ) - for mn in mod_names_list: - mod_obj = mod_db.getModification(mn) + mods_set = set() + mod_db.searchModifications(mods_set, mod_name, "", 0) + for mod_obj in mods_set: origin = mod_obj.getOrigin() if origin: applicable.add(origin) diff --git a/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py b/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py index b88cec6..b204f99 100644 --- a/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py +++ b/tools/proteomics/quality_control/missed_cleavage_analyzer/missed_cleavage_analyzer.py @@ -41,12 +41,11 @@ def count_missed_cleavages(sequence: str, enzyme: str = "Trypsin") -> int: int Number of missed cleavages. """ - aa_seq = oms.AASequence.fromString(sequence) digest = oms.ProteaseDigestion() digest.setEnzyme(enzyme) # Count internal cleavage sites (K/R for trypsin, not at the end) - count = digest.missedCleavages(aa_seq) + count = digest.countInternalCleavageSites(sequence) return count diff --git a/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py b/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py index 4c1b49b..b4578f8 100644 --- a/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py +++ b/tools/proteomics/quality_control/run_comparison_reporter/run_comparison_reporter.py @@ -69,7 +69,7 @@ def pearson_correlation(xs: list[float], ys: list[float]) -> float: sx = math.sqrt(sum((x - mx) ** 2 for x in xs)) sy = math.sqrt(sum((y - my) ** 2 for y in ys)) if sx == 0 or sy == 0: - return 0.0 + return 1.0 if sx == 0 and sy == 0 else 0.0 return cov / (sx * sy) diff --git a/tools/proteomics/specialized/immunopeptidome_qc/tests/test_immunopeptidome_qc.py b/tools/proteomics/specialized/immunopeptidome_qc/tests/test_immunopeptidome_qc.py index 85eb7ec..a2b30e0 100644 --- a/tools/proteomics/specialized/immunopeptidome_qc/tests/test_immunopeptidome_qc.py +++ b/tools/proteomics/specialized/immunopeptidome_qc/tests/test_immunopeptidome_qc.py @@ -88,8 +88,7 @@ def test_run_qc(): @requires_pyopenms def test_cli_roundtrip(tmp_path): - import sys - + from click.testing import CliRunner from immunopeptidome_qc import main input_file = tmp_path / "input.tsv" @@ -102,14 +101,14 @@ def test_cli_roundtrip(tmp_path): for seq in ["AAGIGILTV", "GILGFVFTL", "PEPTIDEK", "AAFGIILPK"]: writer.writerow([seq]) - sys.argv = [ - "immunopeptidome_qc.py", + runner = CliRunner() + result = runner.invoke(main, [ "--input", str(input_file), "--hla-class", "I", "--output", str(output_file), "--motifs", str(motifs_file), - ] - main() + ]) + assert result.exit_code == 0, f"CLI failed: {result.output}\n{result.exception}" assert output_file.exists() assert motifs_file.exists() diff --git a/tools/proteomics/specialized/nterm_modification_annotator/tests/test_nterm_modification_annotator.py b/tools/proteomics/specialized/nterm_modification_annotator/tests/test_nterm_modification_annotator.py index 3fd7915..fc9389b 100644 --- a/tools/proteomics/specialized/nterm_modification_annotator/tests/test_nterm_modification_annotator.py +++ b/tools/proteomics/specialized/nterm_modification_annotator/tests/test_nterm_modification_annotator.py @@ -74,7 +74,7 @@ def test_classify_signal_peptide_known(self): def test_classify_signal_peptide_candidate(self): from nterm_modification_annotator import classify_nterm_type # Position 20, preceded by 'A' (small residue) - protein_seq = "M" + "K" * 19 + "ADEFGHIJKLMNOP" + protein_seq = "M" + "K" * 18 + "A" + "DEFGHIJKLMNOP" result = classify_nterm_type(20, protein_seq) assert result == "signal_peptide_candidate" diff --git a/tools/proteomics/specialized/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py b/tools/proteomics/specialized/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py index d3e282f..4988d36 100644 --- a/tools/proteomics/specialized/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py +++ b/tools/proteomics/specialized/proteoform_delta_annotator/tests/test_proteoform_delta_annotator.py @@ -1,7 +1,6 @@ """Tests for proteoform_delta_annotator.""" import csv -import sys from conftest import requires_pyopenms @@ -54,6 +53,7 @@ def test_annotate_proteoform_deltas(): @requires_pyopenms def test_cli_roundtrip(tmp_path): + from click.testing import CliRunner from proteoform_delta_annotator import main input_file = tmp_path / "input.tsv" @@ -65,13 +65,13 @@ def test_cli_roundtrip(tmp_path): writer.writerow(["P1_unmod", "10000.0"]) writer.writerow(["P1_phospho", "10079.966"]) - sys.argv = [ - "proteoform_delta_annotator.py", + runner = CliRunner() + result = runner.invoke(main, [ "--input", str(input_file), "--tolerance", "0.5", "--output", str(output_file), - ] - main() + ]) + assert result.exit_code == 0, f"CLI failed: {result.output}\n{result.exception}" assert output_file.exists() with open(output_file) as fh: diff --git a/tools/proteomics/specialized/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py b/tools/proteomics/specialized/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py index 8681ef4..13c87e0 100644 --- a/tools/proteomics/specialized/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py +++ b/tools/proteomics/specialized/topdown_coverage_calculator/tests/test_topdown_coverage_calculator.py @@ -1,7 +1,6 @@ """Tests for topdown_coverage_calculator.""" import csv -import sys from conftest import requires_pyopenms @@ -66,6 +65,7 @@ def test_coverage_summary(): @requires_pyopenms def test_cli_roundtrip(tmp_path): + from click.testing import CliRunner from topdown_coverage_calculator import main, theoretical_fragments seq = "PEPTIDE" @@ -80,13 +80,13 @@ def test_cli_roundtrip(tmp_path): for _, mass in frags["b"][:3]: writer.writerow([f"{mass:.6f}"]) - sys.argv = [ - "topdown_coverage_calculator.py", + runner = CliRunner() + result = runner.invoke(main, [ "--sequence", seq, "--fragments", str(frag_file), "--tolerance", "10", "--output", str(output_file), - ] - main() + ]) + assert result.exit_code == 0, f"CLI failed: {result.output}\n{result.exception}" assert output_file.exists() diff --git a/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py b/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py index 3c246d7..38cfa1b 100644 --- a/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py +++ b/tools/proteomics/spectrum_analysis/spectral_library_format_converter/spectral_library_format_converter.py @@ -106,19 +106,20 @@ def msp_to_targeted_experiment(spectra: list[dict]) -> oms.TargetedExperiment: transitions = [] for spec_idx, spec in enumerate(spectra): - peptide_id = f"peptide_{spec_idx}" - protein_id = f"protein_{spec_idx}" + peptide_id = f"peptide_{spec_idx}".encode() + protein_id = f"protein_{spec_idx}".encode() # Create protein - protein = oms.TargetedExperiment.Protein() + protein = oms.Protein() protein.id = protein_id proteins.append(protein) # Create peptide - peptide = oms.TargetedExperiment.Peptide() + peptide = oms.Peptide() peptide.id = peptide_id peptide.protein_refs = [protein_id] - peptide.sequence = spec["name"].split("/")[0] if "/" in spec["name"] else spec["name"] + seq_str = spec["name"].split("/")[0] if "/" in spec["name"] else spec["name"] + peptide.sequence = seq_str.encode() if isinstance(seq_str, str) else seq_str if spec["charge"] > 0: peptide.setChargeState(spec["charge"]) peptides.append(peptide) @@ -130,7 +131,7 @@ def msp_to_targeted_experiment(spectra: list[dict]) -> oms.TargetedExperiment: for peak_idx, (mz, intensity) in enumerate(spec["peaks"]): transition = oms.ReactionMonitoringTransition() - transition.setNativeID(f"transition_{spec_idx}_{peak_idx}") + transition.setNativeID(f"transition_{spec_idx}_{peak_idx}".encode()) transition.setPeptideRef(peptide_id) transition.setPrecursorMZ(precursor_mz) transition.setProductMZ(mz) diff --git a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py index d36a4ba..3b18748 100644 --- a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py +++ b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/hdx_deuterium_uptake.py @@ -46,7 +46,8 @@ def count_exchangeable_amides(sequence: str) -> int: """ aa = oms.AASequence.fromString(sequence) n = aa.size() - proline_count = sum(1 for i in range(n) if aa.getResidue(i).getOneLetterCode() == "P") + # Only count prolines at positions >= 2 (first two residues are already excluded by -2) + proline_count = sum(1 for i in range(2, n) if aa.getResidue(i).getOneLetterCode() == "P") exchangeable = n - proline_count - 2 return max(0, exchangeable) diff --git a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py index 90d959d..627283d 100644 --- a/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py +++ b/tools/proteomics/structural_proteomics/hdx_deuterium_uptake/tests/test_hdx_deuterium_uptake.py @@ -15,8 +15,8 @@ def test_count_exchangeable_amides(self): def test_count_exchangeable_amides_with_proline(self): from hdx_deuterium_uptake import count_exchangeable_amides - # PPPPAAAA: 8 residues, 4 prolines, exchangeable = 8 - 4 - 2 = 2 - assert count_exchangeable_amides("PPPPAAAA") == 2 + # PPPPAAAA: 8 residues, 2 prolines at pos>=2, exchangeable = 8 - 2 - 2 = 4 + assert count_exchangeable_amides("PPPPAAAA") == 4 def test_count_exchangeable_amides_short(self): from hdx_deuterium_uptake import count_exchangeable_amides diff --git a/tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py b/tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py index 22ed8fa..298fe11 100644 --- a/tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py +++ b/tools/proteomics/targeted_proteomics/library_coverage_estimator/tests/test_library_coverage_estimator.py @@ -1,7 +1,6 @@ """Tests for library_coverage_estimator.""" import csv -import sys from conftest import requires_pyopenms @@ -61,6 +60,7 @@ def test_compute_coverage(): @requires_pyopenms def test_cli_roundtrip(tmp_path): import pyopenms as oms + from click.testing import CliRunner from library_coverage_estimator import main # Create FASTA @@ -84,12 +84,12 @@ def test_cli_roundtrip(tmp_path): writer.writerow([p]) output_file = tmp_path / "coverage.tsv" - sys.argv = [ - "library_coverage_estimator.py", + runner = CliRunner() + result = runner.invoke(main, [ "--library", str(lib_file), "--fasta", str(fasta_file), "--enzyme", "Trypsin", "--output", str(output_file), - ] - main() + ]) + assert result.exit_code == 0, f"CLI failed: {result.output}\n{result.exception}" assert output_file.exists() From ea0fcb449f08e35abbd4f39290ffa27c28d7c1b8 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 13:01:37 +0100 Subject: [PATCH 09/15] Fix idxml_to_tsv_exporter: add required document_id param to IdXMLFile.store The pyopenms IdXMLFile.store() requires a document_id string as the 4th argument. Pass empty string for synthetic test data. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../idxml_to_tsv_exporter/idxml_to_tsv_exporter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py index 69b055e..7d135ff 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py @@ -114,7 +114,7 @@ def create_synthetic_idxml(output_path: str) -> None: pep_id.setHits([pep_hit]) peptide_ids.append(pep_id) - oms.IdXMLFile().store(output_path, [protein_id], peptide_ids) + oms.IdXMLFile().store(output_path, [protein_id], peptide_ids, "") @click.command(help="Export idXML to flat TSV format.") From e4803e1f94290e504a9324f7c6f3c8627e1bbae3 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 13:39:03 +0100 Subject: [PATCH 10/15] Fix idxml_to_tsv_exporter for pyopenms 3.5.0 compatibility pyopenms 3.5.0 requires PeptideIdentificationList instead of plain Python lists for IdXMLFile.load() and store(). Add _make_peptide_id_list() helper that uses PeptideIdentificationList when available, falling back to plain list for older versions. Co-Authored-By: Claude Opus 4.6 (1M context) --- .../idxml_to_tsv_exporter.py | 20 ++++++++++++++----- .../tests/test_idxml_to_tsv_exporter.py | 6 ++---- 2 files changed, 17 insertions(+), 9 deletions(-) diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py index 7d135ff..0e440da 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/idxml_to_tsv_exporter.py @@ -20,12 +20,19 @@ sys.exit("pyopenms is required. Install it with: pip install pyopenms") +def _make_peptide_id_list(): + """Create a peptide ID container compatible with the installed pyopenms version.""" + if hasattr(oms, "PeptideIdentificationList"): + return oms.PeptideIdentificationList() + return [] + + def load_idxml(input_path: str) -> tuple: """Load an idXML file and return (protein_ids, peptide_ids).""" protein_ids = [] - peptide_ids = [] + peptide_ids = _make_peptide_id_list() oms.IdXMLFile().load(input_path, protein_ids, peptide_ids) - return protein_ids, peptide_ids + return protein_ids, list(peptide_ids) def export_peptide_ids(peptide_ids: List[oms.PeptideIdentification], output_path: str) -> dict: @@ -92,7 +99,7 @@ def create_synthetic_idxml(output_path: str) -> None: prot_hit.setScore(100.0) protein_id.setHits([prot_hit]) - peptide_ids = [] + peptide_ids = _make_peptide_id_list() sequences = ["ACDEFGHIK", "MNPQRSTWY", "ACDEFGHIK"] for i, seq in enumerate(sequences): pep_id = oms.PeptideIdentification() @@ -112,9 +119,12 @@ def create_synthetic_idxml(output_path: str) -> None: pep_hit.setPeptideEvidences([ev]) pep_id.setHits([pep_hit]) - peptide_ids.append(pep_id) + if hasattr(peptide_ids, "push_back"): + peptide_ids.push_back(pep_id) + else: + peptide_ids.append(pep_id) - oms.IdXMLFile().store(output_path, [protein_id], peptide_ids, "") + oms.IdXMLFile().store(output_path, [protein_id], peptide_ids) @click.command(help="Export idXML to flat TSV format.") diff --git a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py index 83c5f77..70092a4 100644 --- a/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py +++ b/tools/proteomics/file_conversion/idxml_to_tsv_exporter/tests/test_idxml_to_tsv_exporter.py @@ -8,16 +8,14 @@ @requires_pyopenms def test_create_synthetic_idxml(): - import pyopenms as oms from idxml_to_tsv_exporter import create_synthetic_idxml with tempfile.TemporaryDirectory() as tmp: idxml_path = os.path.join(tmp, "test.idXML") create_synthetic_idxml(idxml_path) - protein_ids = [] - peptide_ids = [] - oms.IdXMLFile().load(idxml_path, protein_ids, peptide_ids) + from idxml_to_tsv_exporter import load_idxml + protein_ids, peptide_ids = load_idxml(idxml_path) assert len(protein_ids) == 1 assert len(peptide_ids) == 3 From c9a148358080143799420e7be8d248c5730fddcf Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 15:29:29 +0100 Subject: [PATCH 11/15] minor changes --- .../mzml_to_mgf_converter.py | 110 +++++++++++++--- .../tests/test_mzml_to_mgf_converter.py | 121 +++++++++++++++++- 2 files changed, 207 insertions(+), 24 deletions(-) diff --git a/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py b/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py index 99af56c..658d900 100644 --- a/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py +++ b/tools/proteomics/file_conversion/mzml_to_mgf_converter/mzml_to_mgf_converter.py @@ -1,15 +1,20 @@ """ mzML to MGF Converter ===================== -Convert MS2 spectra from mzML format to MGF (Mascot Generic Format). +Convert MS2 spectra from mzML format to MGF (Mascot Generic Format) +with optional filtering by charge state, retention time, precursor m/z, +and minimum peak count. Usage ----- - python mzml_to_mgf_converter.py --input run.mzML --ms-level 2 --output spectra.mgf + python mzml_to_mgf_converter.py --input run.mzML --output spectra.mgf + python mzml_to_mgf_converter.py --input run.mzML --output spectra.mgf --charge 2 3 + python mzml_to_mgf_converter.py --input run.mzML --output spectra.mgf --rt-min 600 --rt-max 1800 + python mzml_to_mgf_converter.py --input run.mzML --output spectra.mgf --mz-min 400 --mz-max 1200 """ import sys -from typing import List +from typing import List, Optional import click @@ -33,19 +38,55 @@ def get_spectra_by_level(exp: oms.MSExperiment, ms_level: int = 2) -> List[oms.M return [s for s in exp if s.getMSLevel() == ms_level] +def passes_filters( + spectrum: oms.MSSpectrum, + min_peaks: int = 1, + charges: Optional[tuple] = None, + rt_min: Optional[float] = None, + rt_max: Optional[float] = None, + mz_min: Optional[float] = None, + mz_max: Optional[float] = None, + min_intensity: Optional[float] = None, +) -> bool: + """Check whether a spectrum passes all active filters.""" + mz_array, intensity_array = spectrum.get_peaks() + if len(mz_array) < min_peaks: + return False + + if rt_min is not None and spectrum.getRT() < rt_min: + return False + if rt_max is not None and spectrum.getRT() > rt_max: + return False + + precursors = spectrum.getPrecursors() + if precursors: + prec = precursors[0] + if charges and prec.getCharge() not in charges: + return False + if mz_min is not None and prec.getMZ() < mz_min: + return False + if mz_max is not None and prec.getMZ() > mz_max: + return False + elif charges or mz_min is not None or mz_max is not None: + return False + + if min_intensity is not None: + if len(intensity_array) == 0 or max(intensity_array) < min_intensity: + return False + + return True + + def spectrum_to_mgf_block(spectrum: oms.MSSpectrum, index: int) -> str: """Convert a single spectrum to an MGF block string.""" lines = ["BEGIN IONS"] - # Title native_id = spectrum.getNativeID() if spectrum.getNativeID() else f"index={index}" lines.append(f"TITLE={native_id}") - # Retention time rt = spectrum.getRT() lines.append(f"RTINSECONDS={rt:.4f}") - # Precursor info precursors = spectrum.getPrecursors() if precursors: prec = precursors[0] @@ -55,7 +96,6 @@ def spectrum_to_mgf_block(spectrum: oms.MSSpectrum, index: int) -> str: if charge > 0: lines.append(f"CHARGE={charge}+") - # Peaks mz_array, intensity_array = spectrum.get_peaks() for mz_val, intensity_val in zip(mz_array, intensity_array): lines.append(f"{mz_val:.6f} {intensity_val:.4f}") @@ -70,8 +110,14 @@ def convert_mzml_to_mgf( output_path: str, ms_level: int = 2, min_peaks: int = 1, + charges: Optional[tuple] = None, + rt_min: Optional[float] = None, + rt_max: Optional[float] = None, + mz_min: Optional[float] = None, + mz_max: Optional[float] = None, + min_intensity: Optional[float] = None, ) -> dict: - """Convert mzML to MGF format. + """Convert mzML to MGF format with optional filtering. Returns statistics about the conversion. """ @@ -79,10 +125,14 @@ def convert_mzml_to_mgf( spectra = get_spectra_by_level(exp, ms_level) converted = 0 + filtered_out = 0 with open(output_path, "w") as fh: for i, spectrum in enumerate(spectra): - mz_array, _ = spectrum.get_peaks() - if len(mz_array) < min_peaks: + if not passes_filters( + spectrum, min_peaks, charges, rt_min, rt_max, mz_min, mz_max, + min_intensity, + ): + filtered_out += 1 continue block = spectrum_to_mgf_block(spectrum, i) fh.write(block + "\n") @@ -92,21 +142,27 @@ def convert_mzml_to_mgf( "total_spectra": exp.size(), "ms_level_spectra": len(spectra), "converted": converted, + "filtered_out": filtered_out, } def create_synthetic_mzml(output_path: str, n_spectra: int = 5) -> None: - """Create a synthetic mzML file with MS2 spectra for testing.""" + """Create a synthetic mzML file with MS2 spectra for testing. + + Generates spectra with: + - RT: 10.0, 10.5, 11.0, ... (0.5s apart) + - precursor m/z: 500, 550, 600, ... + - charge: alternating 2, 3 + - base peak intensity: 10000, 9000, 8000, ... + """ exp = oms.MSExperiment() - # Add an MS1 spectrum ms1 = oms.MSSpectrum() ms1.setMSLevel(1) ms1.setRT(10.0) ms1.set_peaks(([100.0, 200.0, 300.0], [1000.0, 2000.0, 1500.0])) exp.addSpectrum(ms1) - # Add MS2 spectra for i in range(n_spectra): ms2 = oms.MSSpectrum() ms2.setMSLevel(2) @@ -115,7 +171,7 @@ def create_synthetic_mzml(output_path: str, n_spectra: int = 5) -> None: prec = oms.Precursor() prec.setMZ(500.0 + i * 50.0) - prec.setCharge(2) + prec.setCharge(2 if i % 2 == 0 else 3) ms2.setPrecursors([prec]) mzs = [100.0 + j * 50.0 for j in range(10)] @@ -126,14 +182,32 @@ def create_synthetic_mzml(output_path: str, n_spectra: int = 5) -> None: oms.MzMLFile().store(output_path, exp) -@click.command(help="Convert MS2 spectra from mzML to MGF format.") +@click.command(help="Convert MS2 spectra from mzML to MGF format with optional filtering.") @click.option("--input", "input", required=True, help="Input mzML file") @click.option("--ms-level", type=int, default=2, help="MS level to extract (default: 2)") @click.option("--min-peaks", type=int, default=1, help="Minimum peaks per spectrum (default: 1)") +@click.option("--charge", multiple=True, type=int, help="Keep only these charge states (repeatable)") +@click.option("--rt-min", type=float, default=None, help="Minimum retention time in seconds") +@click.option("--rt-max", type=float, default=None, help="Maximum retention time in seconds") +@click.option("--mz-min", type=float, default=None, help="Minimum precursor m/z") +@click.option("--mz-max", type=float, default=None, help="Maximum precursor m/z") +@click.option( + "--min-intensity", type=float, default=None, + help="Minimum base peak intensity to keep a spectrum", +) @click.option("--output", required=True, help="Output MGF file") -def main(input, ms_level, min_peaks, output) -> None: - stats = convert_mzml_to_mgf(input, output, ms_level, min_peaks) - print(f"Converted {stats['converted']} / {stats['ms_level_spectra']} MS{ms_level} spectra to {output}") +def main(input, ms_level, min_peaks, charge, rt_min, rt_max, mz_min, mz_max, + min_intensity, output) -> None: + charges = tuple(charge) if charge else None + stats = convert_mzml_to_mgf( + input, output, ms_level, min_peaks, charges, rt_min, rt_max, + mz_min, mz_max, min_intensity, + ) + print( + f"Converted {stats['converted']} / {stats['ms_level_spectra']} " + f"MS{ms_level} spectra to {output} " + f"({stats['filtered_out']} filtered out)" + ) if __name__ == "__main__": diff --git a/tools/proteomics/file_conversion/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py b/tools/proteomics/file_conversion/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py index a87d56f..8902e5e 100644 --- a/tools/proteomics/file_conversion/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py +++ b/tools/proteomics/file_conversion/mzml_to_mgf_converter/tests/test_mzml_to_mgf_converter.py @@ -33,11 +33,11 @@ def test_convert_mzml_to_mgf(): assert stats["converted"] == 3 assert stats["ms_level_spectra"] == 3 + assert stats["filtered_out"] == 0 with open(mgf_path) as fh: content = fh.read() assert content.count("BEGIN IONS") == 3 - assert content.count("END IONS") == 3 assert "PEPMASS=" in content assert "CHARGE=" in content @@ -56,12 +56,9 @@ def test_mgf_content_format(): with open(mgf_path) as fh: lines = fh.readlines() - # Check MGF format structure assert lines[0].strip() == "BEGIN IONS" - has_title = any(line.startswith("TITLE=") for line in lines) - has_pepmass = any(line.startswith("PEPMASS=") for line in lines) - assert has_title - assert has_pepmass + assert any(line.startswith("TITLE=") for line in lines) + assert any(line.startswith("PEPMASS=") for line in lines) @requires_pyopenms @@ -75,3 +72,115 @@ def test_min_peaks_filter(): create_synthetic_mzml(mzml_path, n_spectra=3) stats = convert_mzml_to_mgf(mzml_path, mgf_path, min_peaks=100) assert stats["converted"] == 0 + assert stats["filtered_out"] == 3 + + +@requires_pyopenms +def test_charge_filter(): + """Synthetic spectra alternate charge 2, 3. Filter for charge 2 only.""" + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=6) + stats = convert_mzml_to_mgf(mzml_path, mgf_path, charges=(2,)) + # Spectra 0, 2, 4 have charge 2; spectra 1, 3, 5 have charge 3 + assert stats["converted"] == 3 + assert stats["filtered_out"] == 3 + + with open(mgf_path) as fh: + content = fh.read() + assert "CHARGE=2+" in content + assert "CHARGE=3+" not in content + + +@requires_pyopenms +def test_charge_filter_multiple(): + """Filter for both charge 2 and 3 — should keep all.""" + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=4) + stats = convert_mzml_to_mgf(mzml_path, mgf_path, charges=(2, 3)) + assert stats["converted"] == 4 + assert stats["filtered_out"] == 0 + + +@requires_pyopenms +def test_rt_range_filter(): + """Synthetic spectra at RT 10.0, 10.5, 11.0, 11.5, 12.0. Filter RT 10.5-11.5.""" + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=5) + stats = convert_mzml_to_mgf( + mzml_path, mgf_path, rt_min=10.5, rt_max=11.5, + ) + # RT 10.5, 11.0, 11.5 pass; RT 10.0, 12.0 filtered out + assert stats["converted"] == 3 + assert stats["filtered_out"] == 2 + + +@requires_pyopenms +def test_mz_range_filter(): + """Synthetic precursor m/z: 500, 550, 600, 650, 700. Filter 550-650.""" + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=5) + stats = convert_mzml_to_mgf( + mzml_path, mgf_path, mz_min=550.0, mz_max=650.0, + ) + # m/z 550, 600, 650 pass; 500, 700 filtered out + assert stats["converted"] == 3 + assert stats["filtered_out"] == 2 + + +@requires_pyopenms +def test_min_intensity_filter(): + """All synthetic spectra have base peak intensity 10000. Filter > 10000 removes all.""" + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + create_synthetic_mzml(mzml_path, n_spectra=3) + stats = convert_mzml_to_mgf(mzml_path, mgf_path, min_intensity=10001.0) + assert stats["converted"] == 0 + + stats = convert_mzml_to_mgf(mzml_path, mgf_path, min_intensity=5000.0) + assert stats["converted"] == 3 + + +@requires_pyopenms +def test_combined_filters(): + """Apply charge + RT + m/z filters together.""" + from mzml_to_mgf_converter import convert_mzml_to_mgf, create_synthetic_mzml + + with tempfile.TemporaryDirectory() as tmp: + mzml_path = os.path.join(tmp, "test.mzML") + mgf_path = os.path.join(tmp, "test.mgf") + + # 6 spectra: RT 10.0-12.5, m/z 500-750, charge alternating 2/3 + create_synthetic_mzml(mzml_path, n_spectra=6) + stats = convert_mzml_to_mgf( + mzml_path, mgf_path, + charges=(2,), rt_min=10.0, rt_max=11.5, mz_min=500.0, mz_max=700.0, + ) + # charge 2: indices 0, 2, 4 (RT 10.0/11.0/12.0, m/z 500/600/700) + # RT filter: removes index 4 (RT 12.0) + # m/z filter: all 500, 600, 700 pass (700 is at boundary, passes <=) + # Result: indices 0, 2 pass + assert stats["converted"] == 2 From 3cbf5d3f2de56ec212d2342c29a3d485f0764a46 Mon Sep 17 00:00:00 2001 From: Timo Sachsenberg Date: Wed, 25 Mar 2026 15:48:41 +0100 Subject: [PATCH 12/15] update markdown --- .claude/skills/contribute-script.md | 31 +++++++++++++++++------------ .claude/skills/validate-script.md | 2 +- 2 files changed, 19 insertions(+), 14 deletions(-) diff --git a/.claude/skills/contribute-script.md b/.claude/skills/contribute-script.md index 1a69ce3..7e2b0be 100644 --- a/.claude/skills/contribute-script.md +++ b/.claude/skills/contribute-script.md @@ -23,20 +23,24 @@ Ask the user: Ask: Is this a **proteomics** or **metabolomics** tool? If neither fits, discuss whether a new domain directory is needed. -### 3. Pick a name +### 3. Pick a topic + +Choose the topic directory under the selected domain from the options documented in `AGENTS.md`. Confirm the topic with the user before scaffolding files. + +### 4. Pick a name Choose a descriptive snake_case name for the tool (e.g. `peptide_mass_calculator`, `isotope_pattern_matcher`). Confirm with the user. -### 4. Create a feature branch +### 5. Create a feature branch ```bash git checkout -b add/ ``` -### 5. Scaffold the directory +### 6. Scaffold the directory ```bash -mkdir -p tools///tests +mkdir -p tools////tests ``` Create these files: @@ -44,6 +48,7 @@ Create these files: **`requirements.txt`:** ``` pyopenms +click ``` Add any additional dependencies the script needs (one per line, no version pins). @@ -66,9 +71,9 @@ except ImportError: requires_pyopenms = pytest.mark.skipif(not HAS_PYOPENMS, reason="pyopenms not installed") ``` -### 6. Write the script +### 7. Write the script -Create `tools///.py` following these patterns: +Create `tools////.py` following these patterns: - Module-level docstring with description, supported features, and CLI usage examples - pyopenms import guard: @@ -83,9 +88,9 @@ Create `tools///.py` following these patterns: - `main()` function with click CLI - `if __name__ == "__main__": main()` guard -### 7. Write tests +### 8. Write tests -Create `tools///tests/test_.py`: +Create `tools////tests/test_.py`: - Import `requires_pyopenms` from conftest - Decorate test classes with `@requires_pyopenms` @@ -93,17 +98,17 @@ Create `tools///tests/test_.py`: - For file-I/O scripts: generate synthetic data using pyopenms objects in test fixtures, write to `tempfile.TemporaryDirectory()` - Cover: basic functionality, edge cases, key parameters -### 8. Write README +### 9. Write README -Create `tools///README.md` with a brief description and CLI usage examples. +Create `tools////README.md` with a brief description and CLI usage examples. -### 9. Validate +### 10. Validate Invoke the `validate-script` skill on the new script directory. Both ruff and pytest must pass. -### 10. Commit +### 11. Commit ```bash -git add tools/// +git add tools//// git commit -m "Add : " ``` diff --git a/.claude/skills/validate-script.md b/.claude/skills/validate-script.md index ba4a92d..562b26c 100644 --- a/.claude/skills/validate-script.md +++ b/.claude/skills/validate-script.md @@ -9,7 +9,7 @@ Validate any script in the agentomics repo by running ruff and pytest in a fresh ## Steps (follow exactly — rigid skill) -1. **Identify the script directory.** If the user provided a path, use it. Otherwise, ask which script to validate. The path should be `tools///`. +1. **Identify the script directory.** If the user provided a path, use it. Otherwise, ask which script to validate. The path should be `tools////`. 2. **Verify the directory structure.** Confirm it contains: - `.py` From 704408082f51aa44fbe98bfe78d4d4bf43166a53 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 15:52:49 +0100 Subject: [PATCH 13/15] Address Copilot PR #3 review: fix paths, API safety, docs, and CLI UX - Fix isotope_pattern_matcher path in CLAUDE.md example - Guard .decode() on getElementalComposition keys for cross-version safety - Add per-tool requirements.txt install loop in CI workflow - Fix click multiple=True docstring examples (isotope_pattern_matcher, mass_accuracy_calculator) - Rename input -> input_path in mzml_spectrum_subsetter to avoid shadowing - Add segment to all doc paths (skills, design spec) - Replace __import__() with direct import in test - Use math.isclose() for float comparison in kovats_ri_calculator Co-Authored-By: Claude Opus 4.6 (1M context) --- .github/workflows/validate.yml | 6 ++++++ CLAUDE.md | 2 +- .../specs/2026-03-24-ai-contributor-skills-design.md | 8 ++++---- .../tests/test_mass_difference_network_builder.py | 4 ++-- .../export/kovats_ri_calculator/kovats_ri_calculator.py | 4 ++-- .../formula_validator_golden_rules.py | 2 +- .../mass_accuracy_calculator/mass_accuracy_calculator.py | 2 +- .../formula_tools/rdbe_calculator/rdbe_calculator.py | 2 +- .../isotope_pattern_matcher/isotope_pattern_matcher.py | 2 +- .../mzml_spectrum_subsetter/mzml_spectrum_subsetter.py | 6 +++--- 10 files changed, 22 insertions(+), 16 deletions(-) diff --git a/.github/workflows/validate.yml b/.github/workflows/validate.yml index b587532..a209594 100644 --- a/.github/workflows/validate.yml +++ b/.github/workflows/validate.yml @@ -49,6 +49,12 @@ jobs: run: | python -m venv /tmp/validate_venv /tmp/validate_venv/bin/python -m pip install pyopenms numpy scipy click pytest ruff + DIRS='${{ needs.detect-changes.outputs.matrix }}' + echo "$DIRS" | jq -r '.[]' | while read -r dir; do + if [ -f "${dir}requirements.txt" ]; then + /tmp/validate_venv/bin/python -m pip install -r "${dir}requirements.txt" + fi + done - name: Lint changed tools run: | diff --git a/CLAUDE.md b/CLAUDE.md index eacc6f0..2a94c39 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -26,7 +26,7 @@ for d in tools/*/*/*/; do PYTHONPATH="$d" python -m pytest "$d/tests/" -v; done # Run a script directly python tools/proteomics/peptide_analysis/peptide_mass_calculator/peptide_mass_calculator.py --sequence PEPTIDEK --charge 2 -python tools/metabolomics/formula_tools/isotope_pattern_matcher/isotope_pattern_matcher.py --formula C6H12O6 +python tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py --formula C6H12O6 ``` ## Architecture diff --git a/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md b/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md index 202e35e..c95c3e2 100644 --- a/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md +++ b/docs/superpowers/specs/2026-03-24-ai-contributor-skills-design.md @@ -6,10 +6,10 @@ Define the skills, contributor docs, and CI pipeline that enable AI agents to co ## Per-Script Directory Structure -Every script is a self-contained package under `tools///`: +Every script is a self-contained package under `tools////`: ``` -tools/proteomics/peptide_mass_calculator/ +tools/proteomics/peptide_analysis/peptide_mass_calculator/ ├── peptide_mass_calculator.py ├── requirements.txt ├── README.md @@ -84,7 +84,7 @@ Guides an AI agent through creating a new script end-to-end. Rigid — follow ex 1. **Ask what the tool does** — what pyopenms functionality does it wrap, what gap does it fill 2. **Determine domain** — proteomics or metabolomics (or prompt if a new domain is needed) -3. **Scaffold directory** — create `tools///` with `requirements.txt`, empty `README.md`, empty test file +3. **Scaffold directory** — create `tools////` with `requirements.txt`, empty `README.md`, empty test file 4. **Write the script** — following established patterns: - pyopenms try/except import with user-friendly error message - `PROTON = 1.007276` constant where mass-to-charge calculations are needed @@ -117,7 +117,7 @@ Platform-agnostic contributor guide at repo root for any AI agent (Copilot, Curs 1. **Project purpose** — agentic-only pyopenms tools for proteomics/metabolomics that don't yet exist in OpenMS 2. **Contribution requirements:** - - Self-contained directory under `tools///` + - Self-contained directory under `tools////` - Must include: script `.py`, `requirements.txt`, `README.md`, `tests/` with pytest tests - Must use latest pyopenms (no version pinning) - Must pass ruff + pytest in an isolated venv diff --git a/tools/metabolomics/drug_metabolism/mass_difference_network_builder/tests/test_mass_difference_network_builder.py b/tools/metabolomics/drug_metabolism/mass_difference_network_builder/tests/test_mass_difference_network_builder.py index 8bd90d7..bc44928 100644 --- a/tools/metabolomics/drug_metabolism/mass_difference_network_builder/tests/test_mass_difference_network_builder.py +++ b/tools/metabolomics/drug_metabolism/mass_difference_network_builder/tests/test_mass_difference_network_builder.py @@ -52,7 +52,7 @@ def test_default_reactions(self): assert "Dehydration" in names def test_load_reactions_none(self): - from mass_difference_network_builder import load_reactions + from mass_difference_network_builder import DEFAULT_REACTIONS, load_reactions reactions = load_reactions(None) - assert reactions == __import__("mass_difference_network_builder").DEFAULT_REACTIONS + assert reactions == DEFAULT_REACTIONS diff --git a/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py index 91a6a48..a7b6006 100644 --- a/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py +++ b/tools/metabolomics/export/kovats_ri_calculator/kovats_ri_calculator.py @@ -103,9 +103,9 @@ def calculate_kovats_ri( for i in range(len(alkane_table) - 1): cn_n, rt_n = alkane_table[i] cn_n1, rt_n1 = alkane_table[i + 1] - if rt == rt_n: + if math.isclose(rt, rt_n, rel_tol=1e-9, abs_tol=1e-12): return round(100.0 * cn_n, 2) - if rt == rt_n1: + if math.isclose(rt, rt_n1, rel_tol=1e-9, abs_tol=1e-12): return round(100.0 * cn_n1, 2) if rt_n < rt < rt_n1: if rt_n <= 0 or rt_n1 <= 0: diff --git a/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py b/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py index b43c3c3..d803258 100644 --- a/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py +++ b/tools/metabolomics/formula_tools/formula_validator_golden_rules/formula_validator_golden_rules.py @@ -34,7 +34,7 @@ def get_element_counts(formula: str) -> dict: """ ef = oms.EmpiricalFormula(formula) composition = ef.getElementalComposition() - return {k.decode(): v for k, v in composition.items()} + return {k.decode() if isinstance(k, (bytes, bytearray)) else str(k): v for k, v in composition.items()} def compute_rdbe(counts: dict) -> float: diff --git a/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py b/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py index ddba34b..584f30e 100644 --- a/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py +++ b/tools/metabolomics/formula_tools/mass_accuracy_calculator/mass_accuracy_calculator.py @@ -18,7 +18,7 @@ # Multiple observed values at charge 2 python mass_accuracy_calculator.py --sequence ACDEFGHIK --charge 2 \\ - --observed 554.2478 554.2480 554.2482 + --observed 554.2478 --observed 554.2480 --observed 554.2482 """ import sys diff --git a/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py b/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py index 076679c..d32f5f9 100644 --- a/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py +++ b/tools/metabolomics/formula_tools/rdbe_calculator/rdbe_calculator.py @@ -34,7 +34,7 @@ def get_element_counts(formula: str) -> dict: """ ef = oms.EmpiricalFormula(formula) composition = ef.getElementalComposition() - return {k.decode(): v for k, v in composition.items()} + return {k.decode() if isinstance(k, (bytes, bytearray)) else str(k): v for k, v in composition.items()} def calculate_rdbe(formula: str) -> float: diff --git a/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py b/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py index 24d080c..e974619 100644 --- a/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py +++ b/tools/metabolomics/spectral_analysis/isotope_pattern_matcher/isotope_pattern_matcher.py @@ -12,7 +12,7 @@ # Compare against observed peaks (m/z intensity pairs on stdin or --peaks) python isotope_pattern_matcher.py --formula C6H12O6 \\ - --peaks 181.0709,100.0 182.0742,6.7 183.0775,0.4 + --peaks 181.0709,100.0 --peaks 182.0742,6.7 --peaks 183.0775,0.4 """ import math diff --git a/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py b/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py index 2ba2ce2..a648dc4 100644 --- a/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py +++ b/tools/proteomics/identification/mzml_spectrum_subsetter/mzml_spectrum_subsetter.py @@ -92,12 +92,12 @@ def create_synthetic_mzml(output_path: str, n_scans: int = 10) -> None: @click.command(help="Extract specific spectra from mzML by scan number list.") -@click.option("--input", "input", required=True, help="Path to input mzML file") +@click.option("--input", "input_path", required=True, help="Path to input mzML file") @click.option("--scans", required=True, help="Comma-separated scan indices (0-based)") @click.option("--output", required=True, help="Path to output mzML file") -def main(input, scans, output): +def main(input_path, scans, output): scan_indices = [int(x.strip()) for x in scans.split(",")] - count = subset_spectra(input, scan_indices, output) + count = subset_spectra(input_path, scan_indices, output) print(f"Extracted {count} spectra to {output}") From 78bc254957de82313c158639b67d5a58a2de1112 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 15:59:35 +0100 Subject: [PATCH 14/15] Update tools/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../tests/test_charge_state_predictor.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tools/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py b/tools/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py index 9fff871..f128486 100644 --- a/tools/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py +++ b/tools/proteomics/peptide_analysis/charge_state_predictor/tests/test_charge_state_predictor.py @@ -1,8 +1,11 @@ """Tests for charge_state_predictor.""" -from conftest import requires_pyopenms +import pytest +def requires_pyopenms(obj): + pytest.importorskip("pyopenms") + return obj @requires_pyopenms class TestChargeStatePredictor: def test_basic_sites_counting(self): From 45f5369271ec6d458570b56b5ddfbc5f1df6c8b5 Mon Sep 17 00:00:00 2001 From: Yasset Perez-Riverol Date: Wed, 25 Mar 2026 15:59:51 +0100 Subject: [PATCH 15/15] Update tools/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../tests/test_modification_mass_calculator.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tools/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py b/tools/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py index e3393f2..16423a3 100644 --- a/tools/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py +++ b/tools/proteomics/peptide_analysis/modification_mass_calculator/tests/test_modification_mass_calculator.py @@ -1,9 +1,8 @@ """Tests for modification_mass_calculator.""" -from conftest import requires_pyopenms +import pytest - -@requires_pyopenms +pytest.importorskip("pyopenms") class TestModificationMassCalculator: def test_search_oxidation(self): from modification_mass_calculator import search_modification