alterlab-matchms

name	alterlab-matchms
description	Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.
license	Apache-2.0
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Self-contained — runs under `uv run python` with the skill's Python package installed; no API key or account required.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

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, ModifiedCosineGreedy, 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=ModifiedCosineGreedy(tolerance=0.1))

# Get best matches. The cosine functions return a structured score
# (score + matched-peak count), so to SORT you must name the field to
# sort by — `sort=True` alone raises IndexError. The field is
# "<FunctionName>_score", e.g. "ModifiedCosineGreedy_score" / "CosineGreedy_score".
best_matches = scores.scores_by_query(query_spectra[0],
                                      name="ModifiedCosineGreedy_score",
                                      sort=True)[:10]

Available similarity functions:

• CosineGreedy/CosineHungarian: Peak-based cosine similarity with different matching algorithms
• ModifiedCosineGreedy (also ModifiedCosineHungarian): cosine similarity accounting for precursor mass differences. Note the rename — the class is no longer called ModifiedCosine.
• 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. Each step is a callable, a registered filter
# name (str), or a ("filter_name", {kwargs}) tuple (introspectable via
# processor.processing_steps). NOTE: default_filters is a composite, so pass it
# as the callable — the string "default_filters" is not a registered name.
processor = SpectrumProcessor([
    default_filters,
    "normalize_intensities",
    ("select_by_relative_intensity", {"intensity_from": 0.01}),
    ("remove_peaks_around_precursor_mz", {"mz_tolerance": 17}),
])

# A SpectrumProcessor is NOT callable. Use .process_spectrum() for one
# spectrum, or .process_spectra() for a list (returns a (spectra, report)
# tuple — unpack it, don't treat the result as the spectra list).
processed_spectra, report = processor.process_spectra(spectra)
# single spectrum: processed = processor.process_spectrum(spectrum)

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)
# fingerprint_type must be one of: "daylight", "morgan1", "morgan2", "morgan3"
# (the digit is the Morgan radius). Plain "morgan" is NOT valid.
spectrum = add_fingerprint(spectrum, fingerprint_type="morgan2", 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

uv pip install matchms

Molecular-structure processing (SMILES/InChI/fingerprints) needs rdkit, which ships in the base matchms install on current versions — there is no separate [chemistry] extra. If import rdkit fails, uv pip install rdkit explicitly.

API notes below were verified against matchms 0.33.x.

Version gotchas (verified, matchms 0.33.x)

These trip people up and the code examples here account for them:

• SpectrumProcessor instances are not callable. Use processor.process_spectrum(spectrum) for one spectrum or processor.process_spectra(spectra) for a list — the latter returns a (processed_spectra, report) tuple, not a bare list.
• scores_by_query(query, sort=True) raises IndexError for cosine-family scores. You must pass name="<FunctionName>_score" (e.g. "CosineGreedy_score") so the structured score knows which field to sort on.
• scores.scores[i, j] is a structured element, not a float — it carries both ..._score and ..._matches fields. For plain float matrices use scores.to_array("CosineGreedy_score"); there is no to_dataframe/to_list.
• add_fingerprint(fingerprint_type=...) accepts only "daylight", "morgan1", "morgan2", "morgan3" (no "morgan", no radius= argument).

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.