tooluniverse-metabolomics-pathway

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

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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

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Phase 0: Metabolite Identification & Resolution

By Name

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.

By Mass/Formula

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}.

By ID

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.

ID Cross-Referencing

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.

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Phase 1: Metabolite Characterization

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.

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Phase 2: Pathway Mapping

MetaCyc

• MetaCyc_search_pathways: query (keyword search, e.g., "glycolysis")
• MetaCyc_get_pathway: pathway_id (e.g., "GLYCOLYSIS") -- reactions, enzymes, compounds
• MetaCyc_get_compound: compound_id (e.g., "PYRUVATE") -- pathways it participates in
• MetaCyc_get_reaction: reaction_id -- substrates, products, enzymes

KEGG

• KEGG_get_gene_pathways: gene_id (e.g., "hsa:5230") -- pathways for enzyme gene
• KEGG_get_pathway_genes: pathway_id (e.g., "hsa00010") -- all genes in pathway

Reactome

• ReactomeContent_search: query, types (e.g., "Pathway"), species
• Reactome_get_pathway: id (e.g., "R-HSA-70171")
• ReactomeAnalysis_pathway_enrichment: identifiers (space-separated string, NOT array)
• Reactome_map_uniprot_to_pathways: uniprot_id

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Phase 3: Enzyme & Gene Linkage

CTD_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.

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Phase 4: Disease Associations

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.

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Phase 5: Cross-Database Enrichment

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.

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Common Mistakes to Avoid

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)

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Fallback Strategies

• Metabolite_search empty -> MetabolomicsWorkbench_search_compound_by_name or KEGG_get_compound
• MetaCyc not found -> KEGG or Reactome pathways
• CTD empty for disease -> Metabolite_get_diseases with HMDB/CID
• No KEGG compound ID -> BridgeDb_xrefs from HMDB/ChEBI
• exactmass fails -> search_by_mz with M+H adduct
• Need enzyme genes -> CTD_get_chemical_gene_interactions

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Evidence Grading

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

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Limitations

• HMDB has no open API; use Metabolite_get_info (PubChem-backed).
• MetaCyc pathways are reference (not organism-specific like KEGG).
• CTD can return very large sets for common metabolites (22K+ for acetaminophen).
• ReactomeAnalysis expects gene/protein IDs, not metabolite IDs directly.
• BridgeDb coverage depends on the metabolite being in mapping databases.