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Multi-Omics Disease Characterization Pipeline
Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.
KEY PRINCIPLES :
Report-first approach - Create report file FIRST, then populate progressivelyDisease disambiguation FIRST - Resolve all identifiers before omics analysisLayer-by-layer analysis - Systematically cover all omics layersCross-layer integration - Identify genes/targets appearing in multiple layersEvidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)Tissue context - Emphasize disease-relevant tissues/organsQuantitative scoring - Multi-Omics Confidence Score (0-100)Druggable focus - Prioritize targets with therapeutic potentialBiomarker identification - Highlight diagnostic/prognostic markersMechanistic synthesis - Generate testable hypothesesSource references - Every statement must cite tool/databaseCompleteness checklist - Mandatory section showing analysis coverageEnglish-first queries - Always use English terms in tool calls. Respond in user's language
When to Use This Skill
Apply when users:
Ask about disease mechanisms across omics layers
Need multi-omics characterization of a disease
Want to understand disease at the systems biology level
Ask "What pathways/genes/proteins are involved in [disease]?"
Need biomarker discovery for a disease
Want to identify druggable targets from disease profiling
Ask for integrated genomics + transcriptomics + proteomics analysis
Need cross-layer concordance analysis
Ask about disease network biology / hub genes NOT for (use other skills instead):
Single gene/target validation -> Use tooluniverse-drug-target-validation
Drug safety profiling -> Use tooluniverse-adverse-event-detection
General disease overview -> Use tooluniverse-disease-research
Variant interpretation -> Use tooluniverse-variant-interpretation
GWAS-specific analysis -> Use tooluniverse-gwas-* skills
Pathway-only analysis -> Use tooluniverse-systems-biology
Input Parameters Parameter Required Description Example disease Yes Disease name, OMIM ID, EFO ID, or MONDO ID Alzheimer disease, MONDO_0004975tissue No Tissue/organ of interest brain, liver, bloodfocus_layers No Specific omics layers to emphasize genomics, transcriptomics, pathways
Multi-Omics Confidence Score (0-100)
Score Components Data Availability (0-40 points) :
Genomics data available (GWAS or rare variants): 10 points
Transcriptomics data available (DEGs or expression): 10 points
Protein data available (PPI or expression): 5 points
Pathway data available (enriched pathways): 10 points
Clinical/drug data available (approved drugs or trials): 5 points
Evidence Concordance (0-40 points) :
Multi-layer genes (appear in 3+ layers): up to 20 points (2 per gene, max 10 genes)
Consistent direction (genetics + expression concordant): 10 points
Pathway-gene concordance (genes found in enriched pathways): 10 points
Evidence Quality (0-20 points) :
Strong genetic evidence (GWAS p < 5e-8): 10 points
Clinical validation (approved drugs): 10 points
Score Interpretation Score Tier Interpretation 80-100 Excellent Comprehensive multi-omics coverage, high confidence, strong cross-layer concordance 60-79 Good Good coverage across most layers, some gaps 40-59 Moderate Moderate coverage, limited cross-layer integration 0-39 Limited Limited data, single-layer analysis dominates
Evidence Grading System Tier Symbol Criteria Examples T1 [T1] Direct human evidence, clinical proof FDA-approved drug, GWAS hit (p<5e-8), clinical trial result T2 [T2] Experimental evidence Differential expression (validated), functional screen, mouse KO T3 [T3] Computational/database evidence PPI network, pathway mapping, expression correlation T4 [T4] Annotation/prediction only GO annotation, text-mined association, predicted interaction
Report Template Create this file structure at the start: {disease_name}_multiomic_report.md
# Multi-Omics Disease Characterization: {Disease Name}
**Report Generated**: {date}
**Disease Identifiers**: (to be filled)
**Multi-Omics Confidence Score**: (to be calculated)
---
## Executive Summary
(2-3 sentence disease mechanism synthesis - fill after all layers complete)
---
## 1. Disease Definition & Context
### Disease Identifiers
| System | ID | Source |
|--------|-----|--------|
### Description
### Synonyms
### Disease Hierarchy (parents/children)
### Affected Tissues/Organs
### Therapeutic Areas
**Sources**: (tools used)
---
## 2. Genomics Layer
### 2.1 GWAS Associations
| SNP | P-value | Effect | Gene | Study | Source |
|-----|---------|--------|------|-------|--------|
### 2.2 GWAS Studies Summary
| Study ID | Trait | Sample Size | Year | Source |
|----------|-------|-------------|------|--------|
### 2.3 Associated Genes (Genetic Evidence)
| Gene | Ensembl ID | Association Score | Evidence Type | Source |
|------|------------|-------------------|---------------|--------|
### 2.4 Rare Variants (ClinVar)
| Variant | Gene | Clinical Significance | Source |
|---------|------|-----------------------|--------|
### Genomics Layer Summary
- Total GWAS hits:
- Top genes by genetic evidence:
- Genetic architecture:
**Sources**: (tools used)
---
## 3. Transcriptomics Layer
### 3.1 Differential Expression Studies
| Experiment | Condition | Up-regulated | Down-regulated | Source |
|------------|-----------|--------------|----------------|--------|
### 3.2 Expression Atlas Disease Evidence
| Gene | Score | Source |
|------|-------|--------|
### 3.3 Tissue Expression Patterns (GTEx/HPA)
| Gene | Tissue | Expression Level | Source |
|------|--------|-----------------|--------|
### 3.4 Biomarker Candidates (Expression-Based)
| Gene | Tissue Specificity | Fold Change | Evidence | Source |
|------|-------------------|-------------|----------|--------|
### Transcriptomics Layer Summary
- Differential expression datasets:
- Top DEGs:
- Tissue-specific patterns:
**Sources**: (tools used)
---
## 4. Proteomics & Interaction Layer
### 4.1 Protein-Protein Interactions (STRING)
| Protein A | Protein B | Score | Source |
|-----------|-----------|-------|--------|
### 4.2 Hub Genes (Network Centrality)
| Gene | Degree | Betweenness | Role | Source |
|------|--------|-------------|------|--------|
### 4.3 Protein Complexes (IntAct)
| Complex | Members | Function | Source |
|---------|---------|----------|--------|
### 4.4 Tissue-Specific PPI Network
| Gene | Interaction Score | Tissue | Source |
|------|-------------------|--------|--------|
### Proteomics Layer Summary
- Total PPIs:
- Hub genes:
- Network modules:
**Sources**: (tools used)
---
## 5. Pathway & Network Layer
### 5.1 Enriched Pathways (Enrichr/Reactome)
| Pathway | Database | P-value | Genes | Source |
|---------|----------|---------|-------|--------|
### 5.2 Reactome Pathway Details
| Pathway ID | Name | Genes Involved | Source |
|------------|------|----------------|--------|
### 5.3 KEGG Pathways
| Pathway ID | Name | Description | Source |
|------------|------|-------------|--------|
### 5.4 WikiPathways
| Pathway ID | Name | Organism | Source |
|------------|------|----------|--------|
### Pathway Layer Summary
- Top enriched pathways:
- Key pathway nodes:
- Cross-pathway connections:
**Sources**: (tools used)
---
## 6. Gene Ontology & Functional Annotation
### 6.1 Biological Processes
| GO Term | Name | P-value | Genes | Source |
|---------|------|---------|-------|--------|
### 6.2 Molecular Functions
| GO Term | Name | P-value | Genes | Source |
|---------|------|---------|-------|--------|
### 6.3 Cellular Components
| GO Term | Name | P-value | Genes | Source |
|---------|------|---------|-------|--------|
**Sources**: (tools used)
---
## 7. Therapeutic Landscape
### 7.1 Approved Drugs
| Drug | ChEMBL ID | Mechanism | Target | Phase | Source |
|------|-----------|-----------|--------|-------|--------|
### 7.2 Druggable Targets
| Gene | Tractability | Modality | Clinical Precedent | Source |
|------|-------------|----------|-------------------|--------|
### 7.3 Drug Repurposing Candidates
| Drug | Original Indication | Mechanism | Target | Source |
|------|---------------------|-----------|--------|--------|
### 7.4 Clinical Trials
| NCT ID | Title | Phase | Status | Intervention | Source |
|--------|-------|-------|--------|--------------|--------|
### Therapeutic Summary
- Approved drugs:
- Clinical pipeline:
- Novel targets:
**Sources**: (tools used)
---
## 8. Multi-Omics Integration
### 8.1 Cross-Layer Gene Concordance
| Gene | Genomics | Transcriptomics | Proteomics | Pathways | Layers | Evidence Tier |
|------|----------|-----------------|------------|----------|--------|---------------|
### 8.2 Multi-Omics Hub Genes (Top 20)
| Rank | Gene | Layers Found | Key Evidence | Druggable | Source |
|------|------|-------------|--------------|-----------|--------|
### 8.3 Biomarker Candidates
| Biomarker | Type | Evidence Layers | Confidence | Source |
|-----------|------|-----------------|------------|--------|
### 8.4 Mechanistic Hypotheses
1. (Hypothesis with supporting evidence from multiple layers)
2. ...
### 8.5 Systems-Level Insights
- Key disrupted processes:
- Critical pathway nodes:
- Therapeutic intervention points:
- Testable hypotheses:
---
## Multi-Omics Confidence Score
| Component | Points | Max | Details |
|-----------|--------|-----|---------|
| Genomics data | | 10 | |
| Transcriptomics data | | 10 | |
| Protein data | | 5 | |
| Pathway data | | 10 | |
| Clinical data | | 5 | |
| Multi-layer genes | | 20 | |
| Direction concordance | | 10 | |
| Pathway-gene concordance | | 10 | |
| Genetic evidence quality | | 10 | |
| Clinical validation | | 10 | |
| **TOTAL** | | **100** | |
**Score**: XX/100 - [Tier]
---
## Data Availability Checklist
| Omics Layer | Data Available | Tools Used | Findings |
|-------------|---------------|------------|----------|
| Genomics (GWAS) | Yes/No | | |
| Genomics (Rare Variants) | Yes/No | | |
| Transcriptomics (DEGs) | Yes/No | | |
| Transcriptomics (Expression) | Yes/No | | |
| Proteomics (PPI) | Yes/No | | |
| Proteomics (Expression) | Yes/No | | |
| Pathways (Enrichment) | Yes/No | | |
| Pathways (KEGG/Reactome) | Yes/No | | |
| Gene Ontology | Yes/No | | |
| Drugs/Therapeutics | Yes/No | | |
| Clinical Trials | Yes/No | | |
| Literature | Yes/No | | |
---
## Completeness Checklist
- [ ] Disease disambiguation complete (IDs resolved)
- [ ] Genomics layer analyzed (GWAS + variants)
- [ ] Transcriptomics layer analyzed (DEGs + expression)
- [ ] Proteomics layer analyzed (PPI + interactions)
- [ ] Pathway layer analyzed (enrichment + mapping)
- [ ] Gene Ontology analyzed (BP + MF + CC)
- [ ] Therapeutic landscape analyzed (drugs + targets + trials)
- [ ] Cross-layer integration complete (concordance analysis)
- [ ] Multi-Omics Confidence Score calculated
- [ ] Biomarker candidates identified
- [ ] Hub genes identified
- [ ] Mechanistic hypotheses generated
- [ ] Executive summary written
- [ ] All sections have source citations
---
## References
### Data Sources Used
| # | Tool | Parameters | Section | Items Retrieved |
|---|------|------------|---------|-----------------|
### Database Versions
- OpenTargets: (current)
- GWAS Catalog: (current)
- STRING: (current)
- Reactome: (current)
Phase 0: Disease Disambiguation (ALWAYS FIRST) Objective : Resolve disease to standard identifiers for all downstream queries.
Tools Used OpenTargets_get_disease_id_description_by_name (primary):
Input : diseaseName (string) - Disease nameOutput : {data: {search: {hits: [{id, name, description}]}}}Use : Get MONDO/EFO IDs and descriptionCRITICAL : Disease IDs from OpenTargets use underscore format (e.g., MONDO_0004975), NOT colon format OSL_get_efo_id_by_disease_name (secondary):
Input : disease (string) - Disease nameOutput : {efo_id, name}Use : Get EFO/MONDO ID OpenTargets_get_disease_description_by_efoId :
Input : efoId (string) - Disease ID (e.g., MONDO_0004975)Output : {data: {disease: {id, name, description, dbXRefs}}}Use : Get full description, cross-references (OMIM, UMLS, DOID, etc.) 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) - Any known disease ID (e.g., OMIM:104300, UMLS:C0002395)Output : {data: {disease: {id, name, dbXRefs: [str], ...}}}Use : Cross-map between OMIM, UMLS, ICD10, DOID, etc.
Workflow
Search by disease name to get primary ID (OpenTargets)
Get full description and cross-references
Get synonyms for search term expansion
Get therapeutic areas for context
Get disease hierarchy (parents/children)
If user provided OMIM/other ID, map to MONDO/EFO first
Collision-Aware Search When disease name returns multiple hits:
Check if user's input matches any hit exactly
If ambiguous, present top 3-5 options and ask user to select
Always 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 these for all downstream queries:
efo_id - Primary ID for OpenTargets queries (e.g., MONDO_0004975)disease_name - Canonical name (e.g., Alzheimer disease)synonyms - For literature search expansiontherapeutic_areas - For contextdbXRefs - Cross-references (OMIM, UMLS, DOID, etc.)
Phase 1: Genomics Layer Objective : Identify genetic variants, GWAS associations, and genetically implicated genes.
Tools Used OpenTargets_get_associated_targets_by_disease_efoId (primary):
Input : efoId (string) - Disease EFO/MONDO IDOutput : {data: {disease: {id, name, associatedTargets: {count, rows: [{target: {id, approvedSymbol}, score}]}}}}Use : Get ALL disease-associated genes ranked by overall evidence scoreNOTE : Returns top 25 by default. For comprehensive analysis, 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: [{...evidence details}]}}}}Use : Get specific evidence types. 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}}}Use : Get genome-wide significant associationsNOTE : Use disease name (e.g., "Alzheimer"), not ID. Returns paginated results gwas_get_studies_for_trait :
Input : disease_trait (string), size (int)Output : {data: [...studies], metadata: {pagination}}NOTE : May return empty if trait name does not match exactly. Try synonyms gwas_get_variants_for_trait :
Input : disease_trait (string), size (int)Output : {data: [...variants], metadata: {pagination}} GWAS_search_associations_by_gene :
Input : gene_name (string)Output : Associations for a specific gene 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, ...}]}}}Use : Get GWAS studies from OpenTargets genetics portal
Input : condition (string) or gene (string), optional max_results (int)Output : List of ClinVar variants with clinical significanceUse : Rare variant / monogenic disease evidence
Workflow
Get associated genes from OpenTargets (overall scores)
For top 10-15 genes, get genetic evidence specifically via OpenTargets_get_evidence_by_datasource
Search GWAS Catalog for associations
Search OpenTargets GWAS studies
Search ClinVar for rare variants
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 Objective : Identify differentially expressed genes, tissue-specific expression, and expression-based biomarkers.
Tools Used ExpressionAtlas_search_differential :
Input : optional gene (string), condition (string), species (string, default 'homo sapiens')Output : Differential expression studies and resultsUse : Find studies where genes are differentially expressed in disease ExpressionAtlas_search_experiments :
Input : optional gene (string), condition (string), species (string)Output : Expression experiments relevant to conditionUse : Find all Expression Atlas experiments for the disease expression_atlas_disease_target_score :
Input : efoId (string), pageSize (int, required)Output : Genes scored by expression evidence for the diseaseUse : Get expression-based disease-gene association scores europepmc_disease_target_score :
Input : efoId (string), pageSize (int, required)Output : Genes scored by literature evidence for the diseaseUse : Complement expression evidence with literature-mined associations HPA_get_rna_expression_by_source (Human Protein Atlas):
Input : gene_name (string), source_type (string: 'tissue', 'blood', 'brain'), source_name (string: e.g., 'brain', 'liver')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)Output : Expression across specified tissues HPA_get_cancer_prognostics_by_gene :
Input : gene_name (string)Output : Cancer prognostic data (if cancer context) HPA_get_subcellular_location :
Input : gene_name (string)Output : Subcellular localization data HPA_search_genes_by_query :
Input : query (string)Output : Matching genes in HPA
Workflow
Search Expression Atlas for differential expression studies
Get expression-based disease scores
Get literature-based disease scores (EuropePMC)
For top 10-15 genes from genomics layer, check tissue expression via HPA
Check disease-relevant tissue expression patterns
For cancer: check prognostic biomarkers
Gene Tracking Add transcriptomics genes to tracking:
transcriptomics_genes = {
'APOE': {'expression_score': 0.75, 'tissues': ['brain'], 'evidence': 'differential_expression', 'layer': 'transcriptomics'},
# ...
}
Phase 3: Proteomics & Interaction Layer Objective : Map protein-protein interactions, identify hub genes, and characterize interaction networks.
Tools Used STRING_get_interaction_partners (primary PPI):
Input : protein_ids (array of strings - gene names work), 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}]}Use : Get interaction partners for disease genesNOTE : protein_ids is an array, NOT string. Gene symbols like ['APOE'] work
Input : protein_ids (array), species (int), confidence_score (float)Output : Network of interactions between input proteinsUse : Build disease-specific PPI network STRING_functional_enrichment :
Input : protein_ids (array), species (int)Output : Functional enrichment results (GO, KEGG, etc.)Use : Functional characterization of disease gene set
Input : protein_ids (array), species (int)Output : Statistical test for PPI enrichment (more interactions than expected)Use : Test if disease genes form a connected module
Input : identifier (string - UniProt ID or gene name)Output : Molecular interaction data from IntAct intact_search_interactions :
Input : query (string), first (int, default 0), max (int, default 25)Output : Search results for interactions HPA_get_protein_interactions_by_gene :
Input : gene_name (string)Output : {gene, interactions, interactor_count, interactors: [...]}
Input : gene_list (array), tissue (string), max_node (int), interaction (string), string_mode (bool)Output : Tissue-specific PPI networkNOTE : ALL params required. interaction options: 'coexpression', 'interaction', 'coexpression_and_interaction'. string_mode: true/false
Workflow
Take top 15-20 genes from genomics + transcriptomics layers
Query STRING for interaction partners of each gene
Build composite PPI network using STRING_get_network
Test PPI enrichment (are genes more connected than random?)
Get functional enrichment from STRING
For disease-relevant tissue, get tissue-specific network (HumanBase)
Identify hub genes (highest degree centrality)
Check IntAct for experimentally validated interactions
Hub Gene Analysis Calculate network centrality metrics:
Degree : Number of interaction partnersBetweenness : Number of shortest paths through nodeHub score : Genes with degree > mean + 1 SD are hubs
Phase 4: Pathway & Network Layer Objective : Identify enriched biological pathways and cross-pathway connections.
Tools Used enrichr_gene_enrichment_analysis (primary enrichment):
Input : gene_list (array of gene symbols, min 2), libs (array of library names)Output : {status: 'success', data: '{...JSON string with enrichment results...}'}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. Contains connected_paths and per-library resultsNOTE : 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}]}}Use : Reactome-specific pathway enrichment with statistical testing Reactome_map_uniprot_to_pathways :
Input : id (string - UniProt accession)Output : List of Reactome pathways containing this proteinUse : Map individual proteins to pathways
Input : stId (string - Reactome stable ID, e.g., 'R-HSA-73817')Output : Pathway details Reactome_get_pathway_reactions :
Input : stId (string)Output : Reactions within pathway
Input : keyword (string)Output : Array of KEGG pathway matches
Input : pathway_id (string, e.g., 'hsa04930')Output : Detailed pathway information
Input : query (string), optional organism (string, e.g., 'Homo sapiens')Output : Matching community-curated pathways
Workflow
Collect all genes from genomics + transcriptomics layers (top 20-30)
Run Enrichr enrichment for KEGG, Reactome, WikiPathways
Run ReactomeAnalysis for more detailed Reactome enrichment with p-values
Search KEGG for disease-specific pathways
Search WikiPathways for disease pathways
For top Reactome pathways, get detailed reactions
Identify cross-pathway connections (genes in multiple pathways)
Phase 5: Gene Ontology & Functional Annotation Objective : Characterize biological processes, molecular functions, and cellular components.
Tools Used 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)Output : List of GO annotations with terms, aspects, evidence codes
Input : query (string)Output : Matching GO terms QuickGO_annotations_by_gene :
Input : gene_product_id (string - UniProt accession, e.g., 'UniProtKB:P02649'), optional aspect (string: 'biological_process', 'molecular_function', 'cellular_component'), taxon_id (int: 9606), limit (int: 25)Output : GO annotations with evidence codes OpenTargets_get_target_gene_ontology_by_ensemblID :
Input : ensemblId (string)Output : GO terms associated with target
Workflow
Run Enrichr GO enrichment for all 3 aspects using combined gene list
For top 5 genes, get detailed GO annotations from QuickGO
For top genes, get OpenTargets GO terms
Summarize key biological processes, molecular functions, cellular components
Phase 6: Therapeutic Landscape Objective : Map approved drugs, druggable targets, repurposing opportunities, and clinical trials.
Tools Used 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}]}]}}}}Use : All drugs associated with disease (approved + investigational) OpenTargets_get_target_tractability_by_ensemblID :
Input : ensemblId (string)Output : Tractability assessment (small molecule, antibody, PROTAC, etc.) OpenTargets_get_associated_drugs_by_target_ensemblID :
Input : ensemblId (string), size (int, REQUIRED)Output : Drugs targeting this gene/protein
Input : query_term (string, REQUIRED), optional condition (string), intervention (string), pageSize (int, default 10)Output : Clinical trial resultsNOTE : query_term is REQUIRED even if condition is provided OpenTargets_get_drug_mechanisms_of_action_by_chemblId :
Input : chemblId (string)Output : Mechanism of action details
Workflow
Get all drugs for disease from OpenTargets
For top disease-associated genes, check tractability
For top genes with no approved drugs, identify repurposing candidates
Search clinical trials for disease
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},
# ...
}
Phase 7: Multi-Omics Integration Objective : Integrate findings across all layers to identify cross-layer genes, calculate concordance, and generate mechanistic hypotheses.
Cross-Layer Gene Concordance Analysis This is the core integrative step. For each gene found in the analysis:
Count layers : In how many omics layers does this gene appear?
Genomics (GWAS, rare variants, genetic association)
Transcriptomics (DEGs, expression score)
Proteomics (PPI hub, protein expression)
Pathways (enriched pathway member)
Therapeutics (drug target)
Score genes : Genes appearing in 3+ layers are "multi-omics hub genes"
Direction concordance : Do genetics and expression agree?
Risk allele + upregulated = concordant gain-of-function
Risk allele + downregulated = concordant loss-of-function
Discordant = needs investigation
Biomarker Identification For each multi-omics hub gene, assess biomarker potential:
Diagnostic : Gene expression distinguishes disease vs healthyPrognostic : Expression/variant predicts outcome (cancer prognostics from HPA)Predictive : Variant/expression predicts treatment response (pharmacogenomics)Evidence level : Number of supporting omics layers
Mechanistic Hypothesis Generation From the integrated data:
Identify the most supported biological processes (GO + pathways)
Map causal chain: genetic variant -> gene expression -> protein function -> pathway disruption -> disease
Identify intervention points (druggable nodes in the causal chain)
Generate testable hypotheses
Confidence Score Calculation Calculate the Multi-Omics Confidence Score (0-100) based on:
Data availability across layers
Cross-layer concordance
Evidence quality
Clinical validation
Phase 8: Report Finalization
Executive Summary Write a 2-3 sentence synthesis covering:
Disease mechanism in systems terms
Key genes/pathways identified
Therapeutic opportunities
Final Report Quality Checklist Before presenting to user, verify:
Tool Parameter Quick Reference Tool Key Parameters Notes OpenTargets_get_disease_id_description_by_namediseaseNamePrimary disambiguation OSL_get_efo_id_by_disease_namediseaseSecondary disambiguation OpenTargets_get_associated_targets_by_disease_efoIdefoIdReturns top 25 genes OpenTargets_get_evidence_by_datasourceefoId, ensemblId, datasourceIds[], sizePer-gene evidence OpenTargets_search_gwas_studies_by_diseasediseaseIds[], sizeGWAS studies gwas_search_associationsdisease_trait, sizeGWAS Catalog clinvar_search_variantscondition or gene, max_resultsRare variants ExpressionAtlas_search_differentialcondition, speciesDEGs expression_atlas_disease_target_scoreefoId, pageSize (REQUIRED)Expression scores europepmc_disease_target_scoreefoId, pageSize (REQUIRED)Literature scores HPA_get_rna_expression_by_sourcegene_name, source_type, source_name (ALL REQUIRED)Tissue expression STRING_get_interaction_partnersprotein_ids[], species (9606), limitPPI partners STRING_get_networkprotein_ids[], speciesPPI network STRING_functional_enrichmentprotein_ids[], speciesFunctional enrichment STRING_ppi_enrichmentprotein_ids[], speciesNetwork significance intact_search_interactionsquery, maxExperimental PPIs humanbase_ppi_analysisgene_list[], tissue, max_node, interaction, string_mode (ALL REQ)Tissue PPI enrichr_gene_enrichment_analysisgene_list[], libs[] (BOTH REQUIRED)Pathway/GO enrichment ReactomeAnalysis_pathway_enrichmentidentifiers (space-sep string)Reactome enrichment Reactome_map_uniprot_to_pathwaysid (UniProt accession)Protein-pathway mapping kegg_search_pathwaykeywordKEGG pathway search WikiPathways_searchquery, organismWikiPathways search GO_get_annotations_for_genegene_idGO annotations QuickGO_annotations_by_genegene_product_id (e.g., 'UniProtKB:P02649')Detailed GO OpenTargets_get_associated_drugs_by_disease_efoIdefoId, size (REQUIRED)Disease drugs OpenTargets_get_target_tractability_by_ensemblIDensemblIdDruggability search_clinical_trialsquery_term (REQUIRED), condition, pageSizeClinical trials PubMed_search_articlesquery, limitLiterature ensembl_lookup_genegene_id, species ('homo_sapiens' REQUIRED)Gene lookup MyGene_query_genesquery, species, fields, sizeGene info OpenTargets_get_similar_entities_by_disease_efoIdefoId, threshold, size (ALL REQUIRED)Similar diseases
Response Format Notes (Verified)
OpenTargets Associated Targets {
"data": {
"disease": {
"id": "MONDO_0004975",
"name": "Alzheimer disease",
"associatedTargets": {
"count": 2456,
"rows": [
{
"target": {"id": "ENSG00000080815", "approvedSymbol": "PSEN1"},
"score": 0.87
}
]
}
}
}
}
GWAS Catalog Associations {
"data": [
{
"association_id": 216440893,
"p_value": 2e-09,
"or_per_copy_num": 0.94,
"or_value": "0.94",
"efo_traits": [{"..."}],
"risk_frequency": "NR"
}
],
"metadata": {"pagination": {"totalElements": 1061816}}
}
STRING Interactions {
"status": "success",
"data": [
{
"stringId_A": "9606.ENSP00000252486",
"stringId_B": "9606.ENSP00000466775",
"preferredName_A": "APOE",
"preferredName_B": "APOC2",
"score": 0.999
}
]
}
Reactome Enrichment {
"data": {
"token": "...",
"pathways_found": 154,
"pathways": [
{
"pathway_id": "R-HSA-1251985",
"name": "Nuclear signaling by ERBB4",
"species": "Homo sapiens",
"is_disease": false,
"is_lowest_level": true,
"entities_found": 3,
"entities_total": 47,
"entities_ratio": 0.00291,
"p_value": 4.0e-06,
"fdr": 0.00068,
"reactions_found": 3,
"reactions_total": 34
}
]
}
}
HPA RNA Expression {
"status": "success",
"data": {
"gene_name": "APOE",
"source_type": "tissue",
"source_name": "brain",
"expression_value": "2714.9",
"expression_level": "very high",
"expression_unit": "nTPM"
}
}
Enrichr Results {
"status": "success",
"data": "{\"connected_paths\": {\"Path: ...\": \"Total Weight: ...\"}}"
}
NOTE : The data field is a JSON string that needs parsing.
Common Use Patterns
1. Comprehensive Disease Profiling User: "Characterize Alzheimer's disease across omics layers"
-> Run all 8 phases
-> Produce full multi-omics report
2. Therapeutic Target Discovery User: "What are druggable targets for rheumatoid arthritis?"
-> Emphasize Phase 1 (genomics), Phase 6 (therapeutics), Phase 7 (integration)
-> Focus on tractability and clinical precedent
3. Biomarker Identification User: "Find diagnostic biomarkers for pancreatic cancer"
-> Emphasize Phase 2 (transcriptomics), Phase 3 (proteomics), Phase 7 (biomarkers)
-> Focus on tissue-specific expression and diagnostic potential
4. Mechanism Elucidation User: "What pathways are dysregulated in Crohn's disease?"
-> Emphasize Phase 4 (pathways), Phase 5 (GO), Phase 7 (mechanistic hypotheses)
-> Focus on pathway enrichment and cross-pathway connections
5. Drug Repurposing User: "What existing drugs could be repurposed for ALS?"
-> Emphasize Phase 1 (genetics), Phase 6 (therapeutic landscape), Phase 7 (repurposing)
-> Focus on drugs targeting disease-associated genes
6. Systems Biology User: "What are the hub genes and key pathways in type 2 diabetes?"
-> Emphasize Phase 3 (PPI network), Phase 4 (pathways), Phase 7 (network analysis)
-> Focus on hub genes and network modules
Edge Case Handling
Rare Diseases (limited data)
Genomics layer may dominate (single gene)
Limited GWAS data (monogenic)
Focus on ClinVar variants, pathway consequences
Confidence score will be lower (less cross-layer data)
Common Diseases (overwhelming data)
Thousands of GWAS associations
Prioritize by effect size and significance
Focus on top 20-30 genes for downstream analysis
Use strict significance thresholds (p < 5e-8)
Cancer
Include somatic mutations (if CIViC/cBioPortal available)
Check cancer prognostics via HPA
Include tumor-specific expression patterns
Clinical trial landscape may be extensive
Monogenic Diseases
Single gene dominates
ClinVar/OMIM evidence is primary
Pathway analysis reveals downstream effects
Therapeutic landscape may be limited (gene therapy, enzyme replacement)
Polygenic Diseases
Many weak genetic signals
GWAS provides the gene list
Pathway enrichment reveals convergent biology
Network analysis identifies hub genes
Tissue Ambiguity
Diseases affecting multiple tissues
Query HPA for all relevant tissues
Compare tissue-specific expression patterns
Use tissue context from disease ontology
Fallback Strategies
If disease name not found
Try synonyms
Try broader disease category
Try OMIM/UMLS ID mapping
Report disambiguation failure and ask user
If no GWAS data
Check ClinVar for rare variants
Use OpenTargets genetic evidence
Note in report as "Limited genetic data"
Adjust confidence score accordingly
If no expression data
Try different disease name/synonym
Check HPA for individual gene expression
Use OpenTargets expression evidence
Note as "Limited transcriptomics data"
If no pathway enrichment
Reduce gene list stringency
Try different pathway databases
Map individual genes to pathways via Reactome
Note as "No significant pathway enrichment"
If no drugs found
Check if disease is rare/orphan
Look for drugs targeting individual genes
Check clinical trials for investigational therapies
Note as "No approved drugs - novel therapeutic opportunity"
