Tooluniverse Metabolomics Analysis

Handles the full metabolomics pipeline from LC-MS or GC-MS peak tables through to pathway enrichment and multi-omics integration. You get QC filtering (CV thresholds, blank subtraction), normalization options (TIC, PQN, internal standards), standard differential analysis with FDR correction, and KEGG/Reactome pathway mapping. The workflow is opinionated about thresholds (CV < 30%, blank ratio > 3x, |log2FC| > 1.0) and pushes you to look up metabolite identities and pathway memberships rather than guess. Useful if you need to connect metabolite changes to enzyme expression or combine with transcriptomics data. The emphasis on normalization method choice and batch effect correction reflects real pain points in untargeted metabolomics where technical variation easily swamps biology.

skills.sh installs: 393
GitHub stars: 1.8K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-metabolomics-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

Metabolomics Analysis

Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.

Domain Reasoning

Metabolomics quantification depends critically on normalization. Total ion current (TIC) normalization corrects for sample-loading variation and works well for global abundance changes; internal standard normalization is more accurate for targeted analysis where specific metabolite concentrations matter. Missing values in a peak table may reflect signal below the detection limit — not true absence — and should be imputed or handled explicitly rather than treated as zero. Failing to account for batch effects across instrument runs is a frequent source of spurious differential metabolites.