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

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:**

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

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:**

Matchms provides 40+ filters. For the complete filter reference, consult `references/filtering.md`.

3. Calculating Spectral Similarities

Compare spectra using various similarity metrics:

```python

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:**

For detailed similarity function documentation, consult `references/similarity.md`.

4. Building Processing Pipelines

Create reproducible, multi-step analysis workflows:

```python

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:

```python

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:

```python

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:

Consult `references/workflows.md` for detailed examples.

Installation

```bash

uv pip install matchms

```

For molecular structure processing (SMILES, InChI):

```bash

uv pip install matchms[chemistry]

```

Reference Documentation

Detailed reference documentation is available in the `references/` directory:

Load these references as needed for detailed information about specific matchms capabilities.