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, and biomarker-and-target identification from multi-omics data.
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, and biomarker-and-target identification from multi-omics data.
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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 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)
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.
