Tooluniverse Multi Omics Integration
This is the orchestrator for working with multiple omics layers at once. It handles the messy parts of integrating RNA-seq, proteomics, methylation, genomics, and metabolomics data: matching samples across datasets, mapping features to common gene identifiers, running cross-omics correlations (like checking if high mRNA actually translates to high protein), and doing multi-omics clustering with methods like MOFA+ or SNF. You'd reach for this when you have patient data across multiple molecular layers and need to find concordant signals, identify regulatory chains (SNP to methylation to expression), or do patient subtyping with combined evidence. The documentation is unusually honest about discordance being interesting biology rather than just noise.
- skills.sh installs
- 400
- GitHub stars
- 1.8K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-multi-omics-integration --agent claude-codeInstalls into .claude/skills of the current project.
Files
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.
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- Repository
- github.com/mims-harvard/tooluniverse
- Categories
- Automation & Workflows, Sales & Marketing
- First seen
- Jun 3, 2026
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