# Matchms

Spectral similarity and compound identification for metabolomics.

- Repository: https://github.com/LeonChaoX/qinyan-academic-skills (996 stars)
- Category: Engineering
- License: Apache-2.0 license
- Last updated: 2026-07-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add LeonChaoX/qinyan-academic-skills --skill matchms -g -a claude-code -y`
Codex: `npx skills add LeonChaoX/qinyan-academic-skills --skill matchms -g -a codex -y`
Cursor: `npx skills add LeonChaoX/qinyan-academic-skills --skill matchms -g -a cursor -y`
Gemini CLI: `npx skills add LeonChaoX/qinyan-academic-skills --skill matchms -g -a gemini-cli -y`

## Description

Spectral similarity and compound identification for metabolomics. Use for comparing mass spectra, computing similarity scores (cosine, modified cosine), and identifying unknown compounds from spectral libraries. Best for metabolite identification, spectral matching, library searching. For full LC-MS/MS proteomics pipelines use pyopenms.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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:**
- 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
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)
```

The rest: https://github.com/LeonChaoX/qinyan-academic-skills/blob/HEAD/skills/06-化学信息与药物发现/matchms/SKILL.md