What problem does it solve?
This Skill removes the friction from untargeted metabolomics and lipidomics analysis by turning LC-MS peak tables and lipid species matrices into interpretable biological results inside an AnnData workflow. It helps analysts move from feature names and raw intensities to peak annotation, pathway enrichment, lipid class parsing, and ontology-level interpretation without stitching together separate tools. It is especially useful when you need to handle omicverse metabolomics workflows that depend on mummichog, LIPID MAPS shorthand, and LION enrichment.
Core Features & Use Cases
- Untargeted metabolomics: Parse m/z and retention time from LC-MS feature IDs, annotate peaks against KEGG candidates, and run mummichog to recover enriched pathways from per-peak p-values.
- Lipidomics analysis: Recognize LIPID MAPS-style names, annotate lipid classes and chain composition, aggregate to class-level matrices, and run LION enrichment on significant species.
- Workflow safety and validation: Use cached KEGG, ChEBI, and LION resources, choose between basic and external mummichog backends, and validate inputs such as polarity, ppm tolerance, and species-level hit lists before enrichment.
Quick Start
Ask the skill to load your metabolomics or lipidomics AnnData data, annotate the features, and run the appropriate mummichog or LION analysis for the study design you want to interpret.
