tooluniverse-gene-regulatory-networks

name	tooluniverse-gene-regulatory-networks
description	Gene regulatory network analysis — TF-target inference (JASPAR motifs, ChIP-seq), motif scanning, eQTL integration, perturbation evidence (knockout/overexpression). Use for 'which TF regulates gene X', 'which genes does TF Y target', regulatory pathway reconstruction. Distinguishes direct (binding) vs indirect (co-expression) regulatory evidence.

Gene Regulatory Network Analysis

GRN inference starts with: which TF regulates which gene? Direct evidence (ChIP-seq binding) is stronger than indirect (co-expression correlation). A TF binding near a gene doesn't prove regulation — check if expression changes when the TF is perturbed. JASPAR provides binding motifs but motif presence in a promoter is only computational evidence (T3); ENCODE ChIP-seq data that places the TF at the locus in the relevant cell type is stronger (T1). eQTLs from GTEx show which variants affect expression but don't identify the upstream regulator — combine with TF motif disruption analysis for mechanistic insight.

LOOK UP DON'T GUESS: never assume JASPAR matrix IDs, Enrichr library names, or GTEx tissue identifiers — always search JASPAR by TF name and verify library names before calling enrichr.

When to Use

Activate this skill when the user asks about:

• Transcription factor (TF) binding sites, motifs, or target genes
• Gene regulatory networks or transcriptional regulation
• Chromatin state and histone modifications in regulatory context
• TF-target relationships and co-regulation
• eQTL effects on gene regulation
• Protein-protein interactions among regulatory factors

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.

Workflow

Phase 0: Input Disambiguation