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matchms

Analyze and harmonize mass spectrometry data for reproducible MS workflows.

18|1|Updated Dec 27, 2025
Search Tags:#bioinformatics#open-source#data-processing#spectral-analysis#mass-spectrometry#reproducible-workflows#metadata-harmonization
One-click install
npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill matchms-logauaengstrom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: matchms
Source: https://github.com/LogauaEngstrom/claude-scientific-skills/tree/main/scientific-skills/matchms
Command: npx skills add https://github.com/LogauaEngstrom/claude-scientific-skills --skill matchms-logauaengstrom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Mass spectrometry data analysis often suffers from fragmented tooling and non-reproducible workflows. This Skill provides an integrated framework to import, harmonize metadata, filter spectra, calculate multiple spectral similarity metrics, and build reusable pipelines for metabolomics and related MS workflows.

Core Features & Use Cases

  • Import/export support for common MS formats (MGF, MSP, mzML, JSON) and Universal Spectrum Identifiers (USI).
  • Metadata harmonization, peak filtering, and quality control to produce consistent, analysis-ready spectra.
  • Spectrum similarity scoring (CosineGreedy, CosineHungarian, ModifiedCosine, NeutralLossesCosine) and multi-metric workflows for robust identification.
  • Pipeline orchestration via SpectrumProcessor to build reproducible analyses and large-scale comparisons.
  • Case examples: library matching, multi-metric scoring, precursor-filtered searches, and large-scale library comparisons.

Quick Start

Install matchms and run a simple workflow to load spectra, apply default_filters, normalize_intensities, and compute CosineGreedy similarity.

Frequently Asked Questions about matchms

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

FAQPage Schema
How do I calculate spectral similarity for mass spectrometry data?▼

Spectral similarity for mass spectrometry data is calculated using scoring functions like CosineGreedy, CosineHungarian, ModifiedCosine, and NeutralLossesCosine to enable robust compound identification.

Can I import and export MS spectra in MGF, MSP, mzML, or JSON formats?▼

Yes, you can import and export MS spectra across MGF, MSP, mzML, and JSON formats, as well as retrieve spectra using Universal Spectrum Identifiers (USI) for standardized data access.

What's the best way to standardize metadata for reproducible mass spectrometry workflows?▼

Standardizing metadata for reproducible mass spectrometry workflows involves harmonizing spectral metadata, applying peak filtering, and running quality control to produce consistent, analysis-ready data.

How do I build a reusable pipeline for metabolomics spectral analysis?▼

Build a reusable metabolomics spectral analysis pipeline by using SpectrumProcessor to orchestrate data loading, apply default filters, normalize intensities, and automate large-scale library comparisons.

Does this mass spectrometry data processing approach support multi-metric scoring?▼

Yes, multi-metric scoring is supported by combining similarity functions like ModifiedCosine and NeutralLossesCosine, allowing multi-metric workflows for robust spectral identification and precursor-filtered searches.

When do I need metadata harmonization for mass spectrometry data?▼

Metadata harmonization for mass spectrometry data is needed when integrating fragmented tooling outputs across metabolomics and proteomics workflows to ensure consistent, reproducible spectral analysis pipelines.

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