matchms

Process and analyze mass spectrometry data with spectral matching and similarity scoring.

8|Updated Nov 19, 2025

Search Tags:#metabolomics #mass spectrometry #data processing #cheminformatics #spectral analysis #compound identification

Ready to run

npx skills add https://github.com/sanand0/scientific-research --skill matchms-sanand0

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/matchms
Command: npx skills add https://github.com/sanand0/scientific-research --skill matchms-sanand0

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies and standardizes the complex process of analyzing mass spectrometry data, enabling faster and more reliable compound identification and characterization.

Core Features & Use Cases

Data Import/Export: Load and save spectra from various formats (mzML, MGF, MSP, JSON).
Spectral Processing: Filter, normalize, and clean spectral data for consistent analysis.
Similarity Scoring: Compare spectra using multiple algorithms (Cosine, Modified Cosine, Fingerprint) for identification.
Use Case: Identify unknown compounds in a metabolomics study by comparing their mass spectra against a reference library, leveraging advanced filtering and similarity metrics for accurate matches.

Quick Start

Use the matchms skill to load spectra from 'sample_data.mgf' and calculate cosine similarity against 'library.mgf'.

Frequently Asked Questions about matchms

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

FAQPage Schema

How do I identify unknown compounds in mass spectrometry data using spectral library matching?▼

You can identify unknown compounds by loading mass spectrometry data from formats like mzML, MGF, or MSP, then calculating similarity scores against a reference library using cosine, modified cosine, or fingerprint-based algorithms.

What is the best way to normalize and filter mass spectrometry peaks for metabolomics analysis?▼

Mass spectrometry peak filtering and intensity normalization are handled through dedicated spectral processing functions, which clean and standardize spectral data to ensure consistent analysis and reliable compound identification results.

Does matchms support importing and exporting mzML and MSP files for mass spectrometry analysis?▼

Yes, mass spectrometry data import and export supports multiple file formats including mzML, MGF, MSP, and JSON, enabling seamless loading and saving of spectra for metabolomics workflows.

How do I calculate cosine similarity between experimental mass spectra and a reference library?▼

Cosine similarity calculations are performed by loading experimental spectra and a reference library, then applying similarity scoring algorithms to compare spectral data and achieve accurate compound identification.

Can I use this Skill for large-scale metabolomics data processing and spectral analysis?▼

Yes, this Skill standardizes mass spectrometry data processing tasks including peak filtering, intensity normalization, and similarity calculations, making it suitable for metabolomics studies requiring consistent spectral analysis.