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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:
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:
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 mismatchesMatchms provides 40+ filters. For the complete filter reference, consult references/filtering.md.
3. Calculating Spectral Similarities Compare spectra using various similarity metrics:
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 algorithmsModifiedCosine : Cosine similarity accounting for precursor mass differencesNeutralLossesCosine : Similarity based on neutral loss patternsFingerprintSimilarity : Molecular structure similarity using fingerprintsMetadataMatch : Compare user-defined metadata fieldsPrecursorMzMatch/ParentMassMatch : Simple mass-based filtering For detailed similarity function documentation, consult references/similarity.md.
4. Building Processing Pipelines Create reproducible, multi-step analysis workflows:
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:
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:
# 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 For molecular structure processing (SMILES, InChI):
uv pip install matchms[chemistry]
Reference Documentation Detailed reference documentation is available in the references/ directory:
filtering.md - Complete filter function reference with descriptionssimilarity.md - All similarity metrics and when to use themimporting_exporting.md - File format details and I/O operationsworkflows.md - Common analysis patterns and examples Load these references as needed for detailed information about specific matchms capabilities.
