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 progressively
- Disease disambiguation FIRST - Resolve all identifiers before omics analysis
- Layer-by-layer analysis - Systematically cover all omics layers
- Cross-layer integration - Identify genes/targets appearing in multiple layers
- Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
- Tissue context - Emphasize disease-relevant tissues/organs
- Quantitative scoring - Multi-Omics Confidence Score (0-100)
- Druggable focus - Prioritize targets with therapeutic potential
- Biomarker identification - Highlight diagnostic/prognostic markers
- Mechanistic synthesis - Generate testable hypotheses
- Source references - Every statement must cite tool/database
- Completeness checklist - Mandatory section showing analysis coverage
- English-first queries - Always use English terms in tool calls. Respond in user's language
Multi-omics disease characterization asks: what molecular layers are dysregulated? Genomic mutations → transcriptomic changes → proteomic effects → metabolomic consequences. Concordance across layers strengthens the finding. Discordance reveals regulatory complexity.
LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
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 |
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:
gwas_search_associations (use efo_id for precision, not free-text disease_trait), gwas_get_snps_for_gene, ClinVar, OpenTargets associated targets
gnomad_get_gene_constraints — gene constraint metrics (pLI, oe_lof) to interpret whether LoF variants are tolerated vs. haploinsufficient- Get top 10-15 genes with genetic evidence scores; track Ensembl IDs for downstream phases
Phase 2: Transcriptomics Layer
Identify differentially expressed genes, tissue-specific expression, and expression-based biomarkers.
GTEx_get_expression_summary — baseline expression across 54 tissues (accepts gene_symbol directly)- Tools: Expression Atlas, HPA (tissue expression), EuropePMC scores
- Check expression in disease-relevant tissues for top genes from Phase 1
Phase 3: Proteomics & Interaction Layer
Map protein-protein interactions, identify hub genes, and characterize interaction networks.
UniProt_get_function_by_accession — protein function narrative (essential for mechanistic context)- Tools:
STRING_get_network (param: identifiers, species=9606), intact_get_interactions, HumanBase
- Build PPI network from top 15-20 genes; identify hub genes by degree centrality
Phase 4: Pathway & Network Layer
Identify enriched biological pathways and cross-pathway connections.
ReactomeAnalysis_pathway_enrichment — identifiers are newline-separated (\n), NOT space-separatedenrichr_gene_enrichment_analysis — param: gene_list (array), libs (array). NOTE: data field is a JSON string that needs parsingkegg_search_pathway — pathway keyword search
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.
DGIdb_get_drug_gene_interactions — drug interactions by gene (param: genes as array). Often more comprehensive than OpenTargets for drug-gene data.- OpenTargets drugs/tractability (use EFO IDs like
EFO_0000384 for Crohn's, not MONDO — MONDO IDs may return null for drug queries)
search_clinical_trials — query_term is REQUIRED
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 colonSTRING protein_ids: must be array (['APOE']), not stringenrichr 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 REQUIREDsearch_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:
| File |
Contents |
tool-reference.md |
Full tool parameters, inputs/outputs, per-phase workflows, quick reference table |
report-template.md |
Complete report markdown template with all sections and checklists |
integration-scoring.md |
Confidence score formula (0-100), evidence grading (T1-T4), integration procedures, quality checklist |
response-formats.md |
Verified JSON response structures for key tools |
use-patterns.md |
Common use patterns, edge case handling, fallback strategies |
