From mims-harvard-tooluniverse
Performs metabolomics pathway analysis: metabolite identification, pathway mapping, disease associations, cross-database enrichment, enzyme/gene linkage via PubChem, HMDB, KEGG, Reactome.
npx claudepluginhub joshuarweaver/cascade-data-analytics --plugin mims-harvard-tooluniverseThis skill uses the workspace's default tool permissions.
Identify metabolites, map to metabolic pathways, find disease associations, and connect to enzymes/genes.
Conducts multi-round deep research on GitHub repos via API and web searches, generating markdown reports with executive summaries, timelines, metrics, and Mermaid diagrams.
Dynamically discovers and combines enabled skills into cohesive, unexpected delightful experiences like interactive HTML or themed artifacts. Activates on 'surprise me', inspiration, or boredom cues.
Generates images from structured JSON prompts via Python script execution. Supports reference images and aspect ratios for characters, scenes, products, visuals.
Identify metabolites, map to metabolic pathways, find disease associations, and connect to enzymes/genes.
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.
MetaCyc_get_compound, KEGG_get_compound, or ReactomeContent_searchBridgeDb_xrefsCTD_get_chemical_gene_interactions or KEGG_get_compoundMetabolite_get_diseases or CTD_get_chemical_diseasesWhen 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.
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
Metabolite_search: query (REQUIRED), search_type ("name"/"formula"). Returns PubChem matches with CID, name, formula, MW, SMILES.
MetabolomicsWorkbench_search_compound_by_name: name (REQUIRED). Cross-reference with RefMet.
MetabolomicsWorkbench_search_by_mz: mz (REQUIRED), adduct (e.g., "M+H"), tolerance. Uses moverz/REFMET/{mz}/{adduct}/{tolerance}.
MetabolomicsWorkbench_search_by_exact_mass: exact_mass (REQUIRED), tolerance. Uses moverz/REFMET/{mass}/M/{tolerance}.
Metabolite_get_info: compound_name, hmdb_id (e.g., "HMDB0000122"), or pubchem_cid. Returns HMDB ID, CID, InChIKey, classification.
KEGG_get_compound: compound_id (e.g., "C00031"). Returns linked pathways, enzymes, reactions.
BridgeDb_xrefs: identifier (REQUIRED), source (REQUIRED: "Ch"=HMDB, "Cs"=ChemSpider, "Ck"=KEGG, "Ce"=ChEBI), target (optional).
BridgeDb_search: query (REQUIRED), organism. Free-text metabolite search.
Metabolite_get_info: classification (super_class/class/sub_class), biological_roles, cellular_locations.
MetabolomicsWorkbench_get_refmet_info: refmet_name (REQUIRED). Standardized RefMet classification.
KEGG_get_compound: linked enzyme/reaction/pathway IDs.
MetaCyc_search_pathways: query (keyword search, e.g., "glycolysis")MetaCyc_get_pathway: pathway_id (e.g., "GLYCOLYSIS") -- reactions, enzymes, compoundsMetaCyc_get_compound: compound_id (e.g., "PYRUVATE") -- pathways it participates inMetaCyc_get_reaction: reaction_id -- substrates, products, enzymesKEGG_get_gene_pathways: gene_id (e.g., "hsa:5230") -- pathways for enzyme geneKEGG_get_pathway_genes: pathway_id (e.g., "hsa00010") -- all genes in pathwayReactomeContent_search: query, types (e.g., "Pathway"), speciesReactome_get_pathway: id (e.g., "R-HSA-70171")ReactomeAnalysis_pathway_enrichment: identifiers (space-separated string, NOT array)Reactome_map_uniprot_to_pathways: uniprot_idCTD_get_chemical_gene_interactions: input_terms (chemical name). Returns interacting genes.
KEGG_get_gene_pathways: which pathways an enzyme gene participates in.
BridgeDb_attributes: identifier, source, organism. Get attributes for identifier.
Workflow: KEGG compound -> enzyme IDs -> MetaCyc reaction -> enzyme names -> Reactome uniprot -> pathways -> MyGene for gene info.
CTD_get_chemical_diseases: input_terms (chemical name, MeSH, CAS RN). Curated associations with direct/inferred evidence.
CTD_get_gene_diseases: input_terms (gene name). For metabolite-processing genes from Phase 3.
Metabolite_get_diseases: compound_name/hmdb_id/pubchem_cid, limit (default 50). CTD-backed.
MetabolomicsWorkbench_get_study: study_id (e.g., "ST000001").
MetabolomicsWorkbench_get_compound_by_pubchem_cid: pubchem_cid.
PubMed_search_articles / EuropePMC_search_articles: literature context.
For metabolite list enrichment: (1) convert names to gene/enzyme IDs via CTD, (2) run ReactomeAnalysis_pathway_enrichment with space-separated identifiers, (3) use KEGG_get_gene_pathways per enzyme.
| Mistake | Correction |
|---|---|
| Array to ReactomeAnalysis_pathway_enrichment | Must be space-separated string |
| HMDB IDs in CTD_get_chemical_diseases | CTD uses common names or MeSH IDs |
| Not resolving names first | Always start with Metabolite_search |
| gene_id without organism prefix for KEGG | Need "hsa:5230" not "5230" |
| Expecting HMDB API | No open API; use Metabolite_get_info (PubChem-backed) |
| PubChem title to CTD when names differ | Try both PubChem name and common synonyms |
| MetabolomicsWorkbench exactmass | Use moverz/REFMET/{mass}/M/{tolerance} (exactmass broken) |
| Tier | Criteria | Sources |
|---|---|---|
| T1 | Curated disease association, direct evidence | CTD curated, OMIM |
| T2 | Multiple database pathway concordance | MetaCyc + KEGG + Reactome agreement |
| T3 | Inferred or single-database | CTD inferred, single pathway DB |
| T4 | Computational prediction or text-mining | Literature, RefMet classification |