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 mismatches

Matchms 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 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:

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

pip install matchms

For molecular structure processing (SMILES, InChI):

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.