| name | tooluniverse-multi-omics-integration |
| description | Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology analysis, multi-modal disease characterization, and cross-omics biomarker discovery. |
Multi-Omics Integration
Coordinate and integrate multiple omics datasets for comprehensive systems biology analysis. Orchestrates specialized ToolUniverse skills to perform cross-omics correlation, multi-omics clustering, pathway-level integration, and unified interpretation.
Domain Reasoning
Multi-omics integration asks whether different molecular layers tell a concordant story. If a gene is upregulated in RNA-seq AND its protein is elevated in proteomics, that is concordant evidence of true biological change. Discordance — high mRNA but low protein, or elevated protein without matching mRNA — may indicate post-transcriptional regulation (miRNA silencing, protein degradation, translational control) and is itself a meaningful finding worth reporting. Not every discordance is noise; some are the most interesting biology.
LOOK UP DON'T GUESS
- Expected RNA-protein correlation ranges: compute Spearman r from the actual data; the typical range (0.4-0.6) is a guide, not a guarantee.
- Pathway enrichment results: run
ReactomeAnalysis_pathway_enrichment or gseapy on the actual gene lists; never list enriched pathways from memory.
- eQTL associations: query GTEx or eQTL databases for the specific variant and tissue; do not assume regulatory relationships.
- Methylation-expression directionality at specific loci: retrieve experimental data; promoter repression is the canonical model but exceptions exist.
When to Use This Skill
- User has multiple omics datasets (RNA-seq + proteomics, methylation + expression, etc.)
- Cross-omics correlation queries (e.g., "How does methylation affect expression?")
