Metaboanalyst Tools

Configuration File: metaboanalyst_tools.json Tool Type: Local Tools Count: 4

This page contains all tools defined in the metaboanalyst_tools.json configuration file.

Available Tools

MetaboAnalyst_biomarker_enrichment (Type: MetaboAnalystTool)

Find statistically enriched metabolite sets (biomarker panels) from a list of metabolites. Tests …

MetaboAnalyst_biomarker_enrichment tool specification

Tool Information:

  • Name: MetaboAnalyst_biomarker_enrichment

  • Type: MetaboAnalystTool

  • Description: Find statistically enriched metabolite sets (biomarker panels) from a list of metabolites. Tests input metabolites against curated metabolite set libraries derived from SMPDB and HMDB, including glycolysis, TCA cycle, urea cycle, amino acid metabolism, fatty acid pathways, purine/pyrimidine metabolism, glutathione metabolism, tryptophan metabolism, bile acid biosynthesis, ketone body metabolism, pentose phosphate pathway, sphingolipid metabolism, and more. Uses hypergeometric test with Benjamini-Hochberg FDR correction. Use for: identifying which biochemical processes or biomarker panels are represented in a metabolite list.

Parameters:

  • metabolites (array) (required) List of metabolite names to test for set enrichment. Example: [‘glucose’, ‘pyruvate’, ‘lactate’, ‘alanine’, ‘glycine’]

Example Usage:

query = {
    "name": "MetaboAnalyst_biomarker_enrichment",
    "arguments": {
        "metabolites": ["item1", "item2"]
    }
}
result = tu.run(query)

MetaboAnalyst_get_pathway_library (Type: MetaboAnalystTool)

List available KEGG metabolic pathways for a species with compound counts. Returns all pathways w…

MetaboAnalyst_get_pathway_library tool specification

Tool Information:

  • Name: MetaboAnalyst_get_pathway_library

  • Type: MetaboAnalystTool

  • Description: List available KEGG metabolic pathways for a species with compound counts. Returns all pathways with their IDs, names, category (metabolic vs non-metabolic), and number of metabolites in each pathway. Use for: browsing available pathway libraries before running enrichment analysis, or identifying pathways relevant to a metabolomics study.

Parameters:

  • organism (string) (required) KEGG organism code. Default ‘hsa’ (human). Common codes: hsa=human, mmu=mouse, rno=rat, dme=fly, sce=yeast, eco=E.coli.

Example Usage:

query = {
    "name": "MetaboAnalyst_get_pathway_library",
    "arguments": {
        "organism": "example_value"
    }
}
result = tu.run(query)

MetaboAnalyst_name_to_id (Type: MetaboAnalystTool)

Map metabolite common names to database identifiers (KEGG, HMDB, PubChem, ChEBI). Resolves metabo…

MetaboAnalyst_name_to_id tool specification

Tool Information:

  • Name: MetaboAnalyst_name_to_id

  • Type: MetaboAnalystTool

  • Description: Map metabolite common names to database identifiers (KEGG, HMDB, PubChem, ChEBI). Resolves metabolite names against the KEGG compound database and retrieves cross-references to HMDB, PubChem, and ChEBI. Returns matched name, molecular formula, and exact mass for each metabolite. Use for: converting metabolite lists to standardized IDs before pathway or enrichment analysis.

Parameters:

  • metabolites (array) (required) List of metabolite common names to map. Example: [‘glucose’, ‘pyruvate’, ‘lactate’]

Example Usage:

query = {
    "name": "MetaboAnalyst_name_to_id",
    "arguments": {
        "metabolites": ["item1", "item2"]
    }
}
result = tu.run(query)

MetaboAnalyst_pathway_enrichment (Type: MetaboAnalystTool)

Perform metabolite pathway enrichment analysis (over-representation analysis) using hypergeometri…

MetaboAnalyst_pathway_enrichment tool specification

Tool Information:

  • Name: MetaboAnalyst_pathway_enrichment

  • Type: MetaboAnalystTool

  • Description: Perform metabolite pathway enrichment analysis (over-representation analysis) using hypergeometric test. Maps metabolite names to KEGG compound IDs, then tests which KEGG metabolic pathways are statistically enriched for the input metabolites. Returns pathways ranked by p-value with fold enrichment, FDR correction, and hit metabolites. Use for: identifying dysregulated metabolic pathways from a list of differentially abundant metabolites.

Parameters:

  • metabolites (array) (required) List of metabolite names to test for pathway enrichment. Example: [‘glucose’, ‘pyruvate’, ‘lactate’, ‘alanine’, ‘glycine’]

  • organism (string) (optional) KEGG organism code. Default ‘hsa’ (human). Common: hsa=human, mmu=mouse, rno=rat, dme=fly, sce=yeast, eco=E.coli.

Example Usage:

query = {
    "name": "MetaboAnalyst_pathway_enrichment",
    "arguments": {
        "metabolites": ["item1", "item2"]
    }
}
result = tu.run(query)