matchms

Match MS/MS spectra against reference libraries to identify compounds.

7|Updated Apr 5, 2026
One-click install
npx skills add https://github.com/dailycafi/metabolism-skills --skill matchms-dailycafi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/dailycafi/metabolism-skills/tree/main/skills/ms-data-processing/matchms
Command: npx skills add https://github.com/dailycafi/metabolism-skills --skill matchms-dailycafi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Mass spectrometry data analysis requires accurate and scalable identification of compounds by comparing MS/MS spectra to reference libraries. Manual spectrum matching is slow and error-prone, especially for large datasets.

Core Features & Use Cases

  • Import spectra from common formats (MGF, MSP, mzML)
  • Compute multiple spectral similarity metrics and identify top matches
  • Integrate with downstream annotation workflows and library queries

Quick Start

Load a query MS/MS spectrum and run a library search to identify the top matching compound.

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 from MS/MS spectra?▼

You can identify unknown compounds by matching MS/MS spectra against reference libraries. This approach loads spectral data and computes similarity scores to return top candidate identifications for untargeted metabolomics.

What mass spectrometry file formats can I use for spectral library matching?▼

Spectral library matching supports common mass spectrometry formats including MGF, MSP, and mzML. You can load query spectra from these files to compute similarity scores against reference libraries.

How do I calculate cosine similarity for mass spectrometry data in Python?▼

You can calculate cosine similarity for mass spectrometry data using Python-based workflows. The process computes multiple spectral similarity metrics to match query MS/MS spectra against reference libraries.

Can I integrate spectral matching results into downstream annotation pipelines?▼

Yes, spectral matching results integrate with downstream annotation pipelines. The Python-based workflow ensures metadata harmonization and reproducible processing for untargeted metabolomics and library-driven annotation.

What is the best way to automate compound identification for large spectral datasets?▼

Automating compound identification for large spectral datasets requires matching MS/MS spectra against reference libraries. This replaces slow manual spectrum matching, computing similarity scores to reveal unknowns reproducibly.