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Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.
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| name | matchms |
| description | Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing. |
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.
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:
For detailed importing/exporting documentation, consult references/importing_exporting.md.
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:
Matchms provides 40+ filters. For the complete filter reference, consult references/filtering.md.
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:
For detailed similarity function documentation, consult references/similarity.md.
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]
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)
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)
For typical mass spectrometry analysis workflows, including:
Consult references/workflows.md for detailed examples.
uv pip install matchms
For molecular structure processing (SMILES, InChI):
uv pip install matchms[chemistry]
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 examplesLoad these references as needed for detailed information about specific matchms capabilities.
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