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
1from matchms.importing import load_from_mgf, load_from_mzml, load_from_msp, load_from_json
2from matchms.exporting import save_as_mgf, save_as_msp, save_as_json
3
4# Import spectra
5spectra = list(load_from_mgf("spectra.mgf"))
6spectra = list(load_from_mzml("data.mzML"))
7spectra = list(load_from_msp("library.msp"))
8
9# Export processed spectra
10save_as_mgf(spectra, "output.mgf")
11save_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
1from matchms.filtering import default_filters, normalize_intensities
2from matchms.filtering import select_by_relative_intensity, require_minimum_number_of_peaks
3
4# Apply default metadata harmonization filters
5spectrum = default_filters(spectrum)
6
7# Normalize peak intensities
8spectrum = normalize_intensities(spectrum)
9
10# Filter peaks by relative intensity
11spectrum = select_by_relative_intensity(spectrum, intensity_from=0.01, intensity_to=1.0)
12
13# Require minimum peaks
14spectrum = 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
1from matchms import calculate_scores
2from matchms.similarity import CosineGreedy, ModifiedCosine, CosineHungarian
3
4# Calculate cosine similarity (fast, greedy algorithm)
5scores = calculate_scores(references=library_spectra,
6                         queries=query_spectra,
7                         similarity_function=CosineGreedy())
8
9# Calculate modified cosine (accounts for precursor m/z differences)
10scores = calculate_scores(references=library_spectra,
11                         queries=query_spectra,
12                         similarity_function=ModifiedCosine(tolerance=0.1))
13
14# Get best matches
15best_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
1from matchms import SpectrumProcessor
2from matchms.filtering import default_filters, normalize_intensities
3from matchms.filtering import select_by_relative_intensity, remove_peaks_around_precursor_mz
4
5# Define a processing pipeline
6processor = SpectrumProcessor([
7    default_filters,
8    normalize_intensities,
9    lambda s: select_by_relative_intensity(s, intensity_from=0.01),
10    lambda s: remove_peaks_around_precursor_mz(s, mz_tolerance=17)
11])
12
13# Apply to all spectra
14processed_spectra = [processor(s) for s in spectra]

5. Working with Spectrum Objects

The core Spectrum class contains mass spectral data:

python
1from matchms import Spectrum
2import numpy as np
3
4# Create a spectrum
5mz = np.array([100.0, 150.0, 200.0, 250.0])
6intensities = np.array([0.1, 0.5, 0.9, 0.3])
7metadata = {"precursor_mz": 250.5, "ionmode": "positive"}
8
9spectrum = Spectrum(mz=mz, intensities=intensities, metadata=metadata)
10
11# Access spectrum properties
12print(spectrum.peaks.mz)           # m/z values
13print(spectrum.peaks.intensities)  # Intensity values
14print(spectrum.get("precursor_mz")) # Metadata field
15
16# Visualize spectra
17spectrum.plot()
18spectrum.plot_against(reference_spectrum)

6. Metadata Management

Standardize and harmonize spectrum metadata:

python
1# Metadata is automatically harmonized
2spectrum.set("Precursor_mz", 250.5)  # Gets harmonized to lowercase key
3print(spectrum.get("precursor_mz"))   # Returns 250.5
4
5# Derive chemical information
6from matchms.filtering import derive_inchi_from_smiles, derive_inchikey_from_inchi
7from matchms.filtering import add_fingerprint
8
9spectrum = derive_inchi_from_smiles(spectrum)
10spectrum = derive_inchikey_from_inchi(spectrum)
11spectrum = 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
1uv pip install matchms

For molecular structure processing (SMILES, InChI):

bash
1uv 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.