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tooluniverse-data-integration-analysis

Integrate statistical findings with biological annotations from UniProt, GO, Reactome, ClinVar, and OpenTargets.

1.7k|254|Updated Mar 3, 2025
Search Tags:#bioinformatics#pathway analysis#data integration#causal inference#annotation#gwas#evidence grading
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-data-integration-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-data-integration-analysis
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/plugins/tooluniverse/skills/tooluniverse-data-integration-analysis
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-data-integration-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, scipy, statsmodels, matplotlib.

What problem does it solve?

Statistical analyses like differential expression or GWAS produce lists of significant genes and variants, but p-values alone do not explain biological meaning. This Skill bridges that gap by connecting computed results to biological context from ToolUniverse databases, turning raw statistics into mechanistic interpretations.

Core Features & Use Cases

  • Multi-Database Evidence Integration: Query UniProt, GO, Reactome, KEGG, ClinVar, OpenTargets, STRING, and PubMed to annotate significant genes, variants, chemicals, and metabolites with function, pathway, and disease context.
  • Causal Reasoning Frameworks: Apply DAG construction, triangulation, Mendelian randomization logic, and mediation analysis to move from association toward causal claims.
  • Cross-Validation and Graded Reporting: Attempt falsification through replication, genetic support, dose-response, and negative controls, then produce evidence summary tables with strength grades and next-step hypotheses.
  • Use Case: After a DESeq2 analysis yields 200 significant genes, use this Skill to annotate them with pathway and disease evidence, check GWAS support, and produce a graded evidence table explaining which findings are mechanistically robust.

Quick Start

Interpret my list of significant genes from this differential expression analysis using ToolUniverse biological databases and produce an evidence-graded report.

Frequently Asked Questions about tooluniverse-data-integration-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I interpret significant genes after differential expression analysis?▼

Map the gene list to biological questions, then query pathway databases like Reactome and KEGG, literature via PubMed, and genetic evidence via OpenTargets. Synthesize results into an evidence-graded table covering mechanism, literature support, and replication.

What databases can annotate GWAS hits with biological context?▼

Use ClinVar for variant clinical significance, OpenTargets and GWAS Catalog for genetic association evidence, GTEx for eQTL data, and STRING or BioGRID for protein interaction context. Combining multiple sources strengthens interpretation.

When should I use data integration analysis instead of gene enrichment?▼

Use data integration when you already have statistical results from a prior analysis and need multi-source biological interpretation. Use dedicated gene enrichment skills for standalone enrichment without prior analysis, and variant interpretation skills for single variants.

How does Mendelian randomization support causal inference from associations?▼

Genetic variants are assigned at conception, so they avoid confounding and reverse causation. If a variant increasing exposure X also increases disease Y, this supports causation, provided instrument strength, exclusion restriction, and pleiotropy checks pass.

What are the limitations of pathway-based biological interpretation?▼

Pathway inference alone is hypothesis-grade evidence, not proof. Findings need independent replication, genetic support, and biological plausibility checks such as animal or cell model evidence before being reported as strong.

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