matchms

Process and compare mass spectrometry data for compound identification.

1|Updated Mar 12, 2026
One-click install
npx skills add https://github.com/yf8578/clawomics --skill matchms-yf8578
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/yf8578/clawomics/tree/main/skills/matchms
Command: npx skills add https://github.com/yf8578/clawomics --skill matchms-yf8578

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matchms, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the analysis of mass spectrometry data, enabling efficient identification and comparison of chemical compounds.

Core Features & Use Cases

  • Spectral Similarity: Compare mass spectra to identify unknown compounds by matching against spectral libraries.
  • Data Processing: Import, filter, and standardize mass spectrometry data from various formats (mzML, MGF, MSP).
  • Use Case: Identify metabolites in a biological sample by comparing its mass spectrum against a curated spectral library, providing potential compound identifications with confidence scores.

Quick Start

Use the matchms skill to compare the spectrum in 'unknown.mgf' against the library in 'library.mgf'.

Frequently Asked Questions about matchms

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compare mass spectra to identify unknown compounds from an MGF file?

To identify unknown compounds, compare mass spectra by matching your MGF file against a curated spectral library using similarity calculations. This process yields potential compound identifications complete with confidence scores.

What mass spectrometry data formats can I import and standardize for spectral analysis?

For spectral analysis, you can import, filter, and standardize mass spectrometry data from mzML, MGF, and MSP formats. Standardizing these formats ensures consistent spectral comparison across different metabolomics data sources.

Which spectral similarity algorithms are available for metabolomics data processing?

Available spectral similarity algorithms for metabolomics data processing include cosine, modified cosine, and fingerprint-based methods. These algorithms calculate similarity scores to robustly match spectra against reference libraries.

Does matchms work with mzML files for proteomics and metabolomics research?

Yes, matchms works with mzML files, supporting spectral analysis for both proteomics and metabolomics research. It imports and processes mzML spectra to enable subsequent compound identification and spectral comparison.

What is the best way to filter and standardize mass spectrometry data before library matching?

The best way to filter and standardize mass spectrometry data before library matching is importing spectra from mzML, MGF, or MSP formats. Standardization aligns mass spectra, ensuring accurate similarity calculations during compound identification.