Matchms — Анализ и обработка данных масс-спектрометрии

--- name: matchms description: Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms. license: Apache-2.0 license metadata: skill-author: K-Dense Inc. --- # Matchms ## Overview Matchms is an open-source Python library for mass spectrometry data processing and analysis. Import spectra from various formats, standardize metadata, filter peaks, calculate spectral similarities, and build reproducible analytical workflows. ## Core Capabilities ### 1. Importing and Exporting Mass Spectrometry Data Load spectra from multiple file formats and export processed data: ```python from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json from matchms.exporting import save_as_mgf, save_as_msp, save_as_json # Import spectra spectra = list(load_from_mgf("spectra.mgf")) spectra = list(load_from_mzml("data.mzML")) spectra = list(load_from_msp("library.msp")) # Export processed spectra save_as_mgf(spectra, "output.mgf") save_as_json(spectra, "output.json") ``` **Supported formats:** - mzML and mzXML (raw mass spectrometry formats) - MGF (Mascot Generic Format) - MSP (spectral library format) - JSON (GNPS-compatible) - metabolomics-USI references - Pickle (Python serialization) For detailed importing/exporting documentation, consult `references/importing_exporting.md`. ### 2. Spectrum Filtering and Processing Apply comprehensive filters to standardize metadata and refine peak data: ```python from matchms.filtering import default_filters, normalize_intensities from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks # Apply default metadata harmonization filters spectrum = default_filters(spectrum) # Normalize peak intensities spectrum = normalize_intensities(spectrum) # Filter peaks by relative intensity spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0) # Require minimum peaks spectrum = require_minimum_number_of_peaks(spectrum, n_required=5) ``` **Filter categories:** - **Metadata processing**: Harmonize compound names, derive chemical structures, standardize adducts, correct charges - **Peak filtering**: Normalize intensities, select by m/z or intensity, remove precursor peaks - **Quality control**: Require minimum peaks, validate precursor m/z, ensure metadata completeness - **Chemical annotation**: Add fingerprints, derive InChI/SMILES, repair structural mismatches Matchms provides 40+ filters. For the complete filter reference, consult `references/filtering.md`. ### 3. Calculating Spectral Similarities Compare spectra using various similarity metrics: ```python from matchms import calculate_scores from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian # Calculate cosine similarity (fast, greedy algorithm) scores = calculate_scores(references=library_spectra, queries=query_spectra, similarity_function=CosineGreedy()) # Calculate modified cosine (accounts for precursor m/z differences) scores = calculate_scores(references=library_spectra, queries=query_spectra, similarity_function=ModifiedCosine(tolerance=0.1)) # Get best matches best_matches = scores.scores_by_query(query_spectra[0], sort=True)[:10] ``` **Available similarity functions:** - **CosineGreedy/CosineHungarian**: Peak-based cosine similarity with different matching algorithms - **ModifiedCosine**: Cosine similarity accounting for precursor mass differences - **NeutralLossesCosine**: Similarity based on neutral loss patterns - **FingerprintSimilarity**: Molecular structure similarity using fingerprints - **MetadataMatch**: Compare user-defined metadata fields - **PrecursorMzMatch/ParentMassMatch**: Simple mass-based filtering For detailed similarity function documentation, consult `references/similarity.md`. ### 4. Building Processing Pipelines Create reproducible, multi-step analysis workflows: ```python from matchms import SpectrumProcessor from matchms.filtering import default_filters, normalize_intensities from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz # Define a processing pipeline processor = SpectrumProcessor([ default_filters, normalize_intensities, lambda s: select_by_relative_intensity(s, intensity_from=0.01), lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17) ]) # Apply to all spectra processed_spectra = [processor(s) for s in spectra] ``` ### 5. Working with Spectrum Objects The core `Spectrum` class contains mass spectral data: ```python from matchms import Spectrum import numpy as np # Create a spectrum mz = np.array([100.0, 150.0, 200.0, 250.0]) intensities = np.array([0.1, 0.5, 0.9, 0.3]) metadata = {"precursor_mz": 250.5, "ionmode": "positive"} spectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata) # Access spectrum properties print(spectrum.peaks.mz) # m/z values print(spectrum.peaks.intensities) # Intensity values print(spectrum.get("precursor_mz")) # Metadata field # Visualize spectra spectrum.plot() spectrum.plot_against(reference_spectrum) ``` ### 6. Metadata Management Standardize and harmonize spectrum metadata: ```python # Metadata is automatically harmonized spectrum.set("Precursor_mz", 250.5) # Gets harmonized to lowercase key print(spectrum.get("precursor_mz")) # Returns 250.5 # Derive chemical information from matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi from matchms.filtering import add_fingerprint spectrum = derive_inchi_from_smiles(spectrum) spectrum = derive_inchikey_from_inchi(spectrum) spectrum = add_fingerprint(spectrum, fingerprint_type="morgan", nbits=2048) ``` ## Common Workflows For typical mass spectrometry analysis workflows, including: - Loading and preprocessing spectral libraries - Matching unknown spectra against reference libraries - Quality filtering and data cleaning - Large-scale similarity comparisons - Network-based spectral clustering Consult `references/workflows.md` for detailed examples. ## Installation ```bash uv pip install matchms ``` For molecular structure processing (SMILES, InChI): ```bash uv pip install matchms[chemistry] ``` ## Reference Documentation Detailed reference documentation is available in the `references/` directory: - `filtering.md` - Complete filter function reference with descriptions - `similarity.md` - All similarity metrics and when to use them - `importing_exporting.md` - File format details and I/O operations - `workflows.md` - Common analysis patterns and examples Load these references as needed for detailed information about specific matchms capabilities.