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Tool Reference: Multi-Omics Disease Characterization import https://github.com/mims-harvard/ToolUniverse/blob/e2520a96/skills/tooluniverse-multiomic-disease-characterization/tool-reference.md e2520a96 2026-06-26 prompt accepted upstream false

Tool Reference: Multi-Omics Disease Characterization

Detailed tool parameters, inputs/outputs, and per-phase workflows.

Phase 0: Disease Disambiguation Tools

OpenTargets_get_disease_id_description_by_name (primary):

  • Input: diseaseName (string)
  • Output: {data: {search: {hits: [{id, name, description}]}}}
  • CRITICAL: Disease IDs use underscore format (e.g., MONDO_0004975), NOT colon format

OSL_get_efo_id_by_disease_name (secondary):

  • Input: disease (string)
  • Output: {efo_id, name}

OpenTargets_get_disease_description_by_efoId:

  • Input: efoId (string, e.g., MONDO_0004975)
  • Output: {data: {disease: {id, name, description, dbXRefs}}}

OpenTargets_get_disease_synonyms_by_efoId:

  • Input: efoId (string)
  • Output: {data: {disease: {id, name, synonyms: [{relation, terms}]}}}

OpenTargets_get_disease_therapeutic_areas_by_efoId:

  • Input: efoId (string)
  • Output: {data: {disease: {id, name, therapeuticAreas: [{id, name}]}}}

OpenTargets_get_disease_ancestors_parents_by_efoId:

  • Input: efoId (string)
  • Output: {data: {disease: {id, name, ancestors: [{id, name}]}}}

OpenTargets_get_disease_descendants_children_by_efoId:

  • Input: efoId (string)
  • Output: {data: {disease: {id, name, descendants: [{id, name}]}}}

OpenTargets_map_any_disease_id_to_all_other_ids:

  • Input: inputId (string, e.g., OMIM:104300, UMLS:C0002395)
  • Output: {data: {disease: {id, name, dbXRefs: [str], ...}}}

Phase 0 Workflow

  1. Search by disease name to get primary ID (OpenTargets)
  2. Get full description and cross-references
  3. Get synonyms for search term expansion
  4. Get therapeutic areas for context
  5. Get disease hierarchy (parents/children)
  6. If user provided OMIM/other ID, map to MONDO/EFO first

Collision-Aware Search

  • Check if user's input matches any hit exactly
  • If ambiguous, present top 3-5 options and ask user to select
  • Prefer the most specific disease (not parent categories)
  • For cancer, prefer the specific tumor type over generic "cancer"

Key Disease IDs to Track

After disambiguation, store for downstream queries:

  • efo_id - Primary ID for OpenTargets (e.g., MONDO_0004975)
  • disease_name - Canonical name
  • synonyms - For literature search expansion
  • therapeutic_areas - For context
  • dbXRefs - Cross-references (OMIM, UMLS, DOID, etc.)

Phase 1: Genomics Layer Tools

OpenTargets_get_associated_targets_by_disease_efoId (primary):

  • Input: efoId (string)
  • Output: {data: {disease: {id, name, associatedTargets: {count, rows: [{target: {id, approvedSymbol}, score}]}}}}
  • NOTE: Returns top 25 by default. Note the total count

OpenTargets_get_evidence_by_datasource:

  • Input: efoId (string), ensemblId (string), optional datasourceIds (array), size (int, default 50)
  • Output: {data: {disease: {evidences: {count, rows: [{...}]}}}}
  • Key datasourceIds for genomics:
    • ['ot_genetics_portal'] - GWAS/genetics
    • ['gene2phenotype', 'genomics_england', 'orphanet'] - Rare variants
    • ['eva'] - ClinVar variants

gwas_search_associations (GWAS Catalog):

  • Input: disease_trait (string), size (int, default 20)
  • Output: {data: [{association_id, p_value, or_per_copy_num, or_value, beta, risk_frequency, efo_traits}], metadata: {pagination: {totalElements}}}
  • NOTE: Use disease name (e.g., "Alzheimer"), not ID

gwas_get_studies_for_trait:

  • Input: disease_trait (string), size (int)
  • NOTE: May return empty if trait name does not match exactly. Try synonyms

gwas_get_variants_for_trait:

  • Input: disease_trait (string), size (int)

GWAS_search_associations_by_gene:

  • Input: gene_name (string)

OpenTargets_search_gwas_studies_by_disease:

  • Input: diseaseIds (array of strings), enableIndirect (bool, default true), size (int, default 10)
  • Output: {data: {studies: {count, rows: [{id, studyType, traitFromSource, publicationFirstAuthor, publicationDate, pubmedId, nSamples, nCases, nControls}]}}}

ClinVar_search_variants:

  • Input: condition (string) or gene (string), optional max_results (int)

Phase 1 Workflow

  1. Get associated genes from OpenTargets (overall scores)
  2. For top 10-15 genes, get genetic evidence via OpenTargets_get_evidence_by_datasource
  3. Search GWAS Catalog for associations
  4. Search OpenTargets GWAS studies
  5. Search ClinVar for rare variants
  6. For top GWAS genes, check GWAS_search_associations_by_gene

Gene Tracking

Maintain a dictionary of genes found in genomics layer:

genomics_genes = {
    'PSEN1': {'score': 0.87, 'evidence': 'genetic', 'ensembl_id': 'ENSG00000080815', 'layer': 'genomics'},
    'APP': {'score': 0.82, 'evidence': 'genetic', 'ensembl_id': 'ENSG00000142192', 'layer': 'genomics'},
}

Phase 2: Transcriptomics Layer Tools

ExpressionAtlas_search_differential:

  • Input: optional gene (string), condition (string), species (string, default 'homo sapiens')

ExpressionAtlas_search_experiments:

  • Input: optional gene (string), condition (string), species (string)

expression_atlas_disease_target_score:

  • Input: efoId (string), pageSize (int, required)

europepmc_disease_target_score:

  • Input: efoId (string), pageSize (int, required)

HPA_get_rna_expression_by_source (Human Protein Atlas):

  • Input: gene_name (string), source_type (string: 'tissue', 'blood', 'brain'), source_name (string)
  • Output: {status, data: {gene_name, source_type, source_name, expression_value, expression_level, expression_unit}}
  • NOTE: ALL 3 params required. source_type options: 'tissue', 'blood', 'brain', 'cell_line', 'single_cell'

HPA_get_rna_expression_in_specific_tissues:

  • Input: gene_name (string), tissues (array of strings)

HPA_get_cancer_prognostics_by_gene:

  • Input: gene_name (string) - for cancer context

HPA_get_subcellular_location:

  • Input: gene_name (string)

HPA_search_genes_by_query:

  • Input: query (string)

Phase 2 Workflow

  1. Search Expression Atlas for differential expression studies
  2. Get expression-based disease scores
  3. Get literature-based disease scores (EuropePMC)
  4. For top 10-15 genes from genomics layer, check tissue expression via HPA
  5. Check disease-relevant tissue expression patterns
  6. For cancer: check prognostic biomarkers

Phase 3: Proteomics & Interaction Layer Tools

STRING_get_interaction_partners (primary PPI):

  • Input: protein_ids (array of strings), species (int, default 9606), confidence_score (float, default 0.4), limit (int, default 20)
  • Output: {status: 'success', data: [{stringId_A, stringId_B, preferredName_A, preferredName_B, ncbiTaxonId, score, nscore, fscore, pscore, ascore, escore, dscore, tscore}]}
  • NOTE: protein_ids is an array, NOT string. Gene symbols like ['APOE'] work

STRING_get_network:

  • Input: protein_ids (array), species (int), confidence_score (float)

STRING_functional_enrichment:

  • Input: protein_ids (array), species (int)

STRING_ppi_enrichment:

  • Input: protein_ids (array), species (int)

intact_get_interactions:

  • Input: identifier (string - UniProt ID or gene name)

intact_search_interactions:

  • Input: query (string), first (int, default 0), max (int, default 25)

HPA_get_protein_interactions_by_gene:

  • Input: gene_name (string)
  • Output: {gene, interactions, interactor_count, interactors: [...]}

humanbase_ppi_analysis:

  • Input: gene_list (array), tissue (string), max_node (int), interaction (string), string_mode (bool)
  • NOTE: ALL params required. interaction options: 'coexpression', 'interaction', 'coexpression_and_interaction'

Phase 3 Workflow

  1. Take top 15-20 genes from genomics + transcriptomics layers
  2. Query STRING for interaction partners of each gene
  3. Build composite PPI network using STRING_get_network
  4. Test PPI enrichment (are genes more connected than random?)
  5. Get functional enrichment from STRING
  6. For disease-relevant tissue, get tissue-specific network (HumanBase)
  7. Identify hub genes (highest degree centrality)
  8. Check IntAct for experimentally validated interactions

Hub Gene Analysis

  • Degree: Number of interaction partners
  • Betweenness: Number of shortest paths through node
  • Hub score: Genes with degree > mean + 1 SD are hubs

Phase 4: Pathway & Network Layer Tools

enrichr_gene_enrichment_analysis (primary enrichment):

  • Input: gene_list (array, min 2), libs (array of library names)
  • Output: {status: 'success', data: '{...JSON string...}'}
  • Key libraries: ['KEGG_2021_Human'], ['Reactome_2022'], ['WikiPathway_2023_Human'], ['GO_Biological_Process_2023'], ['GO_Molecular_Function_2023'], ['GO_Cellular_Component_2023']
  • NOTE: data field is a JSON string, needs parsing. libs is REQUIRED as array

ReactomeAnalysis_pathway_enrichment:

  • Input: identifiers (string - space-separated gene list), optional page_size (int, default 20), include_disease (bool), projection (bool)
  • Output: {data: {token, analysis_type, pathways_found, pathways: [{pathway_id, name, species, is_disease, is_lowest_level, entities_found, entities_total, entities_ratio, p_value, fdr, reactions_found, reactions_total}]}}

Reactome_map_uniprot_to_pathways:

  • Input: id (string - UniProt accession)

Reactome_get_pathway / Reactome_get_pathway_reactions:

  • Input: stId (string, e.g., 'R-HSA-73817')

kegg_search_pathway:

  • Input: keyword (string)

kegg_get_pathway_info:

  • Input: pathway_id (string, e.g., 'hsa04930')

WikiPathways_search:

  • Input: query (string), optional organism (string, e.g., 'Homo sapiens')

Phase 4 Workflow

  1. Collect all genes from genomics + transcriptomics layers (top 20-30)
  2. Run Enrichr enrichment for KEGG, Reactome, WikiPathways
  3. Run ReactomeAnalysis for detailed Reactome enrichment with p-values
  4. Search KEGG for disease-specific pathways
  5. Search WikiPathways for disease pathways
  6. For top Reactome pathways, get detailed reactions
  7. Identify cross-pathway connections (genes in multiple pathways)

Phase 5: Gene Ontology & Functional Annotation Tools

enrichr_gene_enrichment_analysis (GO enrichment):

  • Use with libs=['GO_Biological_Process_2023'] for BP
  • Use with libs=['GO_Molecular_Function_2023'] for MF
  • Use with libs=['GO_Cellular_Component_2023'] for CC

GO_get_annotations_for_gene:

  • Input: gene_id (string - gene symbol or UniProt ID)

GO_search_terms:

  • Input: query (string)

QuickGO_annotations_by_gene:

  • Input: gene_product_id (string, e.g., 'UniProtKB:P02649'), optional aspect ('biological_process', 'molecular_function', 'cellular_component'), taxon_id (int: 9606), limit (int: 25)

OpenTargets_get_target_gene_ontology_by_ensemblID:

  • Input: ensemblId (string)

Phase 5 Workflow

  1. Run Enrichr GO enrichment for all 3 aspects using combined gene list
  2. For top 5 genes, get detailed GO annotations from QuickGO
  3. For top genes, get OpenTargets GO terms
  4. Summarize key biological processes, molecular functions, cellular components

Phase 6: Therapeutic Landscape Tools

OpenTargets_get_associated_drugs_by_disease_efoId (primary):

  • Input: efoId (string), size (int, REQUIRED - use 100)
  • Output: {data: {disease: {knownDrugs: {count, rows: [{drug: {id, name, tradeNames, maximumClinicalTrialPhase, isApproved, hasBeenWithdrawn}, phase, mechanismOfAction, target: {id, approvedSymbol}, disease: {id, name}, urls: [{url, name}]}]}}}}

OpenTargets_get_target_tractability_by_ensemblID:

  • Input: ensemblId (string)

OpenTargets_get_associated_drugs_by_target_ensemblID:

  • Input: ensemblId (string), size (int, REQUIRED)

search_clinical_trials:

  • Input: query_term (string, REQUIRED), optional condition (string), intervention (string), pageSize (int, default 10)
  • NOTE: query_term is REQUIRED even if condition is provided

OpenTargets_get_drug_mechanisms_of_action_by_chemblId:

  • Input: chemblId (string)

Phase 6 Workflow

  1. Get all drugs for disease from OpenTargets
  2. For top disease-associated genes, check tractability
  3. For top genes with no approved drugs, identify repurposing candidates
  4. Search clinical trials for disease
  5. For top approved drugs, get mechanism of action

Drug Tracking

drug_targets = {
    'PSEN1': {'drugs': ['Semagacestat'], 'tractability': 'small_molecule', 'clinical_phase': 3},
    'ACHE': {'drugs': ['Donepezil', 'Galantamine'], 'tractability': 'small_molecule', 'clinical_phase': 4},
}

Tool Parameter Quick Reference

Tool Key Parameters Notes
OpenTargets_get_disease_id_description_by_name diseaseName Primary disambiguation
OSL_get_efo_id_by_disease_name disease Secondary disambiguation
OpenTargets_get_associated_targets_by_disease_efoId efoId Returns top 25 genes
OpenTargets_get_evidence_by_datasource efoId, ensemblId, datasourceIds[], size Per-gene evidence
OpenTargets_search_gwas_studies_by_disease diseaseIds[], size GWAS studies
gwas_search_associations disease_trait, size GWAS Catalog
ClinVar_search_variants condition or gene, max_results Rare variants
ExpressionAtlas_search_differential condition, species DEGs
expression_atlas_disease_target_score efoId, pageSize (REQUIRED) Expression scores
europepmc_disease_target_score efoId, pageSize (REQUIRED) Literature scores
HPA_get_rna_expression_by_source gene_name, source_type, source_name (ALL REQUIRED) Tissue expression
STRING_get_interaction_partners protein_ids[], species (9606), limit PPI partners
STRING_get_network protein_ids[], species PPI network
STRING_functional_enrichment protein_ids[], species Functional enrichment
STRING_ppi_enrichment protein_ids[], species Network significance
intact_search_interactions query, max Experimental PPIs
humanbase_ppi_analysis gene_list[], tissue, max_node, interaction, string_mode (ALL REQ) Tissue PPI
enrichr_gene_enrichment_analysis gene_list[], libs[] (BOTH REQUIRED) Pathway/GO enrichment
ReactomeAnalysis_pathway_enrichment identifiers (space-sep string) Reactome enrichment
Reactome_map_uniprot_to_pathways id (UniProt accession) Protein-pathway mapping
kegg_search_pathway keyword KEGG pathway search
WikiPathways_search query, organism WikiPathways search
GO_get_annotations_for_gene gene_id GO annotations
QuickGO_annotations_by_gene gene_product_id (e.g., 'UniProtKB:P02649') Detailed GO
OpenTargets_get_associated_drugs_by_disease_efoId efoId, size (REQUIRED) Disease drugs
OpenTargets_get_target_tractability_by_ensemblID ensemblId Druggability
search_clinical_trials query_term (REQUIRED), condition, pageSize Clinical trials
PubMed_search_articles query, limit Literature
ensembl_lookup_gene gene_id, species ('homo_sapiens' REQUIRED) Gene lookup
MyGene_query_genes query, species, fields, size Gene info
OpenTargets_get_similar_entities_by_disease_efoId efoId, threshold, size (ALL REQUIRED) Similar diseases
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