Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping,

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 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 separated enrichr gene enrichment analysis — param: gene list (array), libs (array). NOTE: data field is a JSON string that needs parsing kegg 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 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: 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

tooluniverse-multiomic-disease-characterization