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

Pipeline Overview

The pipeline runs 9 phases sequentially. Each phase uses specific tools documented in detail in tool-reference.md.

Phase 0: Disease Disambiguation (ALWAYS FIRST)

Resolve disease to standard identifiers (MONDO/EFO) for all downstream queries.

• Primary tool: OpenTargets_get_disease_id_description_by_name
• Get description, synonyms, therapeutic areas, disease hierarchy, cross-references
• CRITICAL: Disease IDs use underscore format (e.g., MONDO_0004975), NOT colon
• If ambiguous, present top 3-5 options and ask user to select

Phase 1: Genomics Layer

Identify genetic variants, GWAS associations, and genetically implicated genes.

• Tools: OpenTargets associated targets, evidence by datasource, GWAS Catalog, ClinVar
• Get top 10-15 genes with genetic evidence scores
• Track genes with Ensembl IDs for downstream phases

Phase 2: Transcriptomics Layer

Identify differentially expressed genes, tissue-specific expression, and expression-based biomarkers.

• Tools: Expression Atlas (differential + experiments), HPA (tissue expression), EuropePMC scores
• Check expression in disease-relevant tissues for top genes from Phase 1
• For cancer: check prognostic biomarkers via HPA

Phase 3: Proteomics & Interaction Layer

Map protein-protein interactions, identify hub genes, and characterize interaction networks.

• Tools: STRING (partners, network, enrichment), IntAct, HumanBase (tissue-specific PPI)
• Build PPI network from top 15-20 genes across previous layers
• Identify hub genes by degree centrality (degree > mean + 1 SD)

Phase 4: Pathway & Network Layer

Identify enriched biological pathways and cross-pathway connections.

• Tools: Enrichr (KEGG, Reactome, WikiPathways), ReactomeAnalysis, KEGG search
• Run enrichment on combined gene list (top 20-30 genes)
• NOTE: Enrichr data field is a JSON string that needs parsing

Phase 5: Gene Ontology & Functional Annotation

Characterize biological processes, molecular functions, and cellular components.

• Tools: Enrichr (GO libraries), QuickGO, GO annotations, OpenTargets GO
• Run GO enrichment for all 3 aspects (BP, MF, CC)

Phase 6: Therapeutic Landscape

Map approved drugs, druggable targets, repurposing opportunities, and clinical trials.

• Tools: OpenTargets drugs/tractability, clinical trials search, drug mechanisms
• size parameter is REQUIRED for OpenTargets drug queries (use 100)
• query_term is REQUIRED for clinical trials search

Phase 7: Multi-Omics Integration

Integrate findings across all layers. See integration-scoring.md for full details.

• Cross-layer gene concordance: count layers per gene, score multi-layer hub genes
• Direction concordance: genetics + expression agreement
• Biomarker identification: diagnostic, prognostic, predictive
• Mechanistic hypothesis generation

Phase 8: Report Finalization

Write executive summary, calculate confidence score, verify completeness.

• See integration-scoring.md for quality checklist and scoring formula

Key Tool Parameter Notes

These are the most common parameter pitfalls:

• OpenTargets disease IDs: underscore format (MONDO_0004975), NOT colon
• STRING protein_ids: must be array (['APOE']), not string
• enrichr libs: must be array (['KEGG_2021_Human'])
• HPA_get_rna_expression_by_source: ALL 3 params required (gene_name, source_type, source_name)
• humanbase_ppi_analysis: ALL params required (gene_list, tissue, max_node, interaction, string_mode)
• expression_atlas_disease_target_score: pageSize is REQUIRED
• search_clinical_trials: query_term is REQUIRED even if condition is provided

For full tool parameters and per-phase workflows, see tool-reference.md.

Reference Files

All detailed content is in reference files in this directory: