tooluniverse-multiomic-disease-characterization

name

tooluniverse-multiomic-disease-characterization

description

Comprehensive multi-omics disease characterization integrating genomics, transcriptomics, proteomics, pathway, and therapeutic layers for systems-level understanding. Produces a detailed multi-omics report with quantitative confidence scoring (0-100), cross-layer gene concordance analysis, biomarker candidates, therapeutic opportunities, and mechanistic hypotheses. Uses 80+ ToolUniverse tools across 8 analysis layers. Use when users ask about disease mechanisms, multi-omics analysis, systems biology of disease, biomarker discovery, or therapeutic target identification from a disease perspective.

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:

1. Report-first approach - Create report file FIRST, then populate progressively
2. Disease disambiguation FIRST - Resolve all identifiers before omics analysis
3. Layer-by-layer analysis - Systematically cover all omics layers
4. Cross-layer integration - Identify genes/targets appearing in multiple layers
5. Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
6. Tissue context - Emphasize disease-relevant tissues/organs
7. Quantitative scoring - Multi-Omics Confidence Score (0-100)
8. Druggable focus - Prioritize targets with therapeutic potential
9. Biomarker identification - Highlight diagnostic/prognostic markers
10. Mechanistic synthesis - Generate testable hypotheses
11. Source references - Every statement must cite tool/database
12. Completeness checklist - Mandatory section showing analysis coverage
13. English-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_0004975
tissue	No	Tissue/organ of interest	brain, liver, blood
focus_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 |
|------|-------------------|-------------|----------|--------|


 Differential expression datasets:
 Top DEGs:
 Tissue-specific patterns:

: (tools used)

---




| Protein A | Protein B | Score | Source |
|-----------|-----------|-------|--------|


| Gene | Degree | Betweenness | Role | Source |
|------|--------|-------------|------|--------|


| Complex | Members | Function | Source |
|---------|---------|----------|--------|


| Gene | Interaction Score | Tissue | Source |
|------|-------------------|--------|--------|


 Total PPIs:
 Hub genes:
 Network modules:

: (tools used)

---




| Pathway | Database | P-value | Genes | Source |
|---------|----------|---------|-------|--------|


| Pathway ID | Name | Genes Involved | Source |
|------------|------|----------------|--------|


| Pathway ID | Name | Description | Source |
|------------|------|-------------|--------|


| Pathway ID | Name | Organism | Source |
|------------|------|----------|--------|


 Top enriched pathways:
 Key pathway nodes:
 Cross-pathway connections:

: (tools used)

---




| GO Term | Name | P-value | Genes | Source |
|---------|------|---------|-------|--------|


| GO Term | Name | P-value | Genes | Source |
|---------|------|---------|-------|--------|


| GO Term | Name | P-value | Genes | Source |
|---------|------|---------|-------|--------|

: (tools used)

---




| Drug | ChEMBL ID | Mechanism | Target | Phase | Source |
|------|-----------|-----------|--------|-------|--------|


| Gene | Tractability | Modality | Clinical Precedent | Source |
|------|-------------|----------|-------------------|--------|


| Drug | Original Indication | Mechanism | Target | Source |
|------|---------------------|-----------|--------|--------|


| NCT ID | Title | Phase | Status | Intervention | Source |
|--------|-------|-------|--------|--------------|--------|


 Approved drugs:
 Clinical pipeline:
 Novel targets:

: (tools used)

---




| Gene | Genomics | Transcriptomics | Proteomics | Pathways | Layers | Evidence Tier |
|------|----------|-----------------|------------|----------|--------|---------------|


| Rank | Gene | Layers Found | Key Evidence | Druggable | Source |
|------|------|-------------|--------------|-----------|--------|


| Biomarker | Type | Evidence Layers | Confidence | Source |
|-----------|------|-----------------|------------|--------|


 (Hypothesis with supporting evidence from multiple layers)
 ...


 Key disrupted processes:
 Critical pathway nodes:
 Therapeutic intervention points:
 Testable hypotheses:

---



| 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 | |
|  | |  | |

: XX/100 - [Tier]

---



| 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 | | |

---



 [ ] 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

---




| # | Tool | Parameters | Section | Items Retrieved |
|---|------|------------|---------|-----------------|


 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 name
• Output: {data: {search: {hits: [{id, name, description}]}}}
• Use: Get MONDO/EFO IDs and description
• CRITICAL: 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 name
• Output: {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

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

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 expansion
• therapeutic_areas - For context
• dbXRefs - 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 ID
• Output: {data: {disease: {id, name, associatedTargets: {count, rows: [{target: {id, approvedSymbol}, score}]}}}}
• Use: Get ALL disease-associated genes ranked by overall evidence score
• NOTE: 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 associations
• NOTE: 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

clinvar_search_variants:

• Input: condition (string) or gene (string), optional max_results (int)
• Output: List of ClinVar variants with clinical significance
• Use: Rare variant / monogenic disease evidence

Workflow

1. Get associated genes from OpenTargets (overall scores)
2. For top 10-15 genes, get genetic evidence specifically 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

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 results
• Use: 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 condition
• Use: 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 disease
• Use: 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 disease
• Use: 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

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

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 genes
• 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)
• Output: Network of interactions between input proteins
• Use: 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

STRING_ppi_enrichment:

• 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

intact_get_interactions:

• 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: [...]}

humanbase_ppi_analysis:

• Input: gene_list (array), tissue (string), max_node (int), interaction (string), string_mode (bool)
• Output: Tissue-specific PPI network
• NOTE: ALL params required. interaction options: 'coexpression', 'interaction', 'coexpression_and_interaction'. string_mode: true/false

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

Calculate network centrality metrics:

• 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

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 results
• NOTE: 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 protein
• Use: Map individual proteins to pathways

Reactome_get_pathway:

• 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

kegg_search_pathway:

• Input: keyword (string)
• Output: Array of KEGG pathway matches

kegg_get_pathway_info:

• Input: pathway_id (string, e.g., 'hsa04930')
• Output: Detailed pathway information

WikiPathways_search:

• Input: query (string), optional organism (string, e.g., 'Homo sapiens')
• Output: Matching community-curated pathways

Workflow

1. Collect all genes from genomics + transcriptomics layers (top 20-30)
2. Run Enrichr enrichment for KEGG, Reactome, WikiPathways
3. Run ReactomeAnalysis for more 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)

---

---

Extended Reference: For detailed tool tables, examples, and templates, read REFERENCE.md in this skill directory. The agent can access it via: read skills/tooluniverse-multiomic-disease-characterization/REFERENCE.md