Lib Matchms

• 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

uv pip install matchms

For molecular structure processing (SMILES, InChI):

uv 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.