| name | tooluniverse-metabolomics-pathway |
| description | Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses. |
Metabolomics Pathway Analysis
Identify metabolites, map to metabolic pathways, find disease associations, and connect to enzymes/genes.
Domain Reasoning
Metabolite-to-pathway mapping requires correct, database-specific identifiers. HMDB IDs link to KEGG/Reactome but must be converted via BridgeDb; PubChem CIDs need explicit cross-referencing. Always verify metabolite identity first: the same common name can refer to structurally distinct isomers, and PubChem names frequently differ from CTD/KEGG names.
LOOK UP DON'T GUESS
- Pathway membership: call
MetaCyc_get_compound, KEGG_get_compound, or ReactomeContent_search
- Cross-database IDs: use
BridgeDb_xrefs
- Enzyme-metabolite relationships: use
CTD_get_chemical_gene_interactions or KEGG_get_compound
- Disease associations: query
Metabolite_get_diseases or CTD_get_chemical_diseases
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Workflow
Phase 0: Identify & Resolve → Phase 1: Characterize → Phase 2: Pathway Map →
Phase 3: Enzyme/Gene Linkage → Phase 4: Disease Associations → Phase 5: Cross-DB Enrichment → Report
Phase 0: Metabolite Identification & Resolution
