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alterlab-matchms

Match mass spectra against reference libraries to identify unknown metabolites.

58|9|Updated Mar 16, 2026
Search Tags:#metabolomics#data-analysis#cosine-similarity#mass-spectrometry#library-search#spectral-matching#metabolite-identification
One-click install
npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-matchms
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alterlab-matchms
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/cheminformatics/alterlab-matchms
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-matchms

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Mass spectrometry workflows for metabolomics can struggle to confidently identify unknown compounds from complex spectra. This skill provides a standardized approach to compare experimental spectra to curated libraries and rank candidate metabolites for accelerated annotation.

Core Features & Use Cases

  • Import spectra from common formats (MGF, MSP, mzML, JSON) and harmonize metadata for consistent comparison.
  • Compute multiple spectral similarity metrics (CosineGreedy, ModifiedCosine, NeutralLossesCosine) and combine results with optional structural fingerprints for robust identification.
  • Use cases include large-scale library matching, metabolite annotation, quality control, and cross-dataset validation in LC-MS/MS metabolomics.

Quick Start

Import your unknown spectra and a reference library, preprocess them with default filters, then run a cosine-based search to identify metabolites.

Frequently Asked Questions about alterlab-matchms

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

FAQPage Schema
How do I identify unknown metabolites from mass spectrometry data?▼

Spectral matching compares experimental mass spectra against reference libraries using similarity metrics. This Skill computes CosineGreedy, ModifiedCosine, and NeutralLossesCosine scores, optionally combining them with structural fingerprints to rank candidate metabolites for rapid annotation.

Can I import mzML and MGF files for spectral library search?▼

Yes, you can import MGF, MSP, mzML, and JSON formats for spectral library search. The Skill preprocesses imported spectra with default filters and harmonizes metadata to ensure consistent comparison before running cosine-based searches.

What's the best way to compare experimental spectra to a reference library?▼

The best approach is computing multiple similarity metrics simultaneously. This Skill calculates CosineGreedy, ModifiedCosine, and NeutralLossesCosine scores, and optionally integrates structural fingerprints to improve candidate ranking during large-scale library matching.

Does this metabolomics tool support cross-dataset validation and quality control?▼

Yes, cross-dataset validation and quality control are supported. The Skill applies standardized spectral matching and metadata harmonization across large-scale LC-MS/MS metabolomics datasets to enable consistent comparisons and confident metabolite annotation.

How do structural fingerprints improve metabolite identification during spectral matching?▼

Structural fingerprints improve metabolite identification by adding an orthogonal scoring dimension. The Skill optionally integrates structural fingerprints with cosine similarity metrics to enhance candidate ranking robustness and reduce false positives during library matching.

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