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tooluniverse-metabolomics-analysis
Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with other omics layers.
plugins/tooluniverse/skills/tooluniverse-metabolomics-analysis/SKILL.md
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# 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.
## LOOK UP DON'T GUESS
- Metabolite identities: use `Metabolite_search` and `Metabolite_get_info` to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone.
- Pathway memberships: query KEGG, MetaCyc, or Reactome tools; do not list pathways from memory.
- Disease associations: retrieve from CTD via `Metabolite_get_diseases`; do not infer clinical relevance without database evidence.
- CV thresholds and QC criteria: apply the values defined in this workflow (CV < 30%, blank ratio > 3x); do not override with guesses.