claude-scientific-skills/scientific-packages/matchms/SKILL.md

name, description

name

description

matchms

Process and analyze mass spectrometry data using matchms, a Python library for spectral similarity calculations, metadata harmonization, and compound identification. Use this skill when: (1) Working with mass spectrometry data files (mzML, mzXML, MGF, MSP, JSON) - importing, exporting, or converting between formats; (2) Compound identification tasks - matching unknown spectra against reference libraries using cosine similarity, modified cosine, or neutral loss patterns; (3) Spectral data preprocessing - harmonizing metadata, normalizing intensities, filtering peaks by m/z or intensity, removing precursor peaks, or applying quality control filters; (4) Building reproducible workflows - creating standardized processing pipelines, batch processing multiple datasets, or implementing consistent analysis protocols; (5) Chemical structure analysis - deriving SMILES/InChI from spectra, adding molecular fingerprints, validating structural annotations, or comparing structural similarities; (6) Large-scale spectral comparisons - performing library-to-library comparisons, finding duplicate spectra, or clustering similar compounds; (7) Multi-metric scoring - combining spectral similarity with structural similarity or metadata matching for robust compound identification; (8) Quality control and validation - filtering low-quality spectra, validating precursor masses, ensuring metadata completeness, or generating identification reports. This skill is essential for metabolomics, proteomics, natural products research, environmental analysis, and any field requiring mass spectrometry data processing and compound identification.

Matchms

Overview

Matchms is an open-source Python library for mass spectrometry data processing and analysis. It provides tools for importing spectra from various formats, standardizing metadata, filtering peaks, calculating spectral similarities, and building reproducible analytical workflows. The library democratizes mass spectrometry informatics through accessible, standardized Python tools.

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.