tooluniverse-multiomic-disease-characterization
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 perspectiv
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-multiomic-disease-characterization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Characterizes diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide a systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.
How it works
- It follows a report-first approach: create a comprehensive multi-omics report file at the start, then populate sections progressively.
- It performs disease disambiguation first to resolve standard identifiers before omics analysis.
- It conducts layer-by-layer analyses across genomics, transcriptomics, proteomics, and pathways, followed by cross-layer integration to find concordant gene signals.
- Each piece of evidence is graded by evidence type (T1 to T4) and scored within a structured Multi-Omics Confidence Score (0-100).
- It emphasizes tissue context and prioritizes druggable targets, biomarker candidates, and mechanistic hypotheses.
- It requires citing tools/databases for every statement and produces a detailed report template with sections for each omics layer, integration, and a completeness checklist.
- It uses English terms in tool calls and responds in the user’s language.
When to use it
Use when users ask about disease mechanisms across omics layers, require multi-omics characterization, seek systems biology insights, need biomarker discovery, or want druggable targets identified from a disease profile. It is not for single-gene validation, drug safety profiling, general disease overviews, variant interpretation, GWAS-specific analyses, or purely pathway-focused work (use related skills instead).
What it can touch
The skill references tools and databases in the integration workflow and cites them in the data sources section. Specific tools and their outputs are described within the report sections (e.g., GWAS associations, differential expression data, PPI networks, pathway enrichments, and therapeutic landscapes). Access to these tools is governed by the OpenTargets and related integrations listed in the workflow.
Caveats
The skill specifies a structured, citation-heavy pipeline with a fixed 0–100 confidence scoring system and a comprehensive report structure. It relies on external data sources (GWAS, expression, proteomics, pathways, clinical/drug data) being available for the disease; missing data are reflected in the scoring and sections without speculation. It requires disclosing exact tool/database citations for every statement and adheres to the defined evidence tiers (T1–T4).
# 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 cove
- When to Use This Skill
- Input Parameters
- Multi-Omics Confidence Score (0-100)
- Score Components
- Score Interpretation
- Evidence Grading System
- Report Template
- Phase 0: Disease Disambiguation (ALWAYS FIRST)
- Tools Used
- Workflow
- Collision-Aware Search
- Key Disease IDs to Track
- Phase 1: Genomics Layer
- Gene Tracking
What does the tooluniverse-multiomic-disease-characterization skill do?
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 perspectiv
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-multiomic-disease-characterization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.