tooluniverse-gene-regulatory-networks
This skill maps transcription factor regulation by combining binding motif analysis, chromatin immunoprecipitation data, and expression quantitative trait loci to answer which TFs control a gene and which genes a TF targets. It separates direct regulatory evidence (ChIP-seq binding) from indirect signals (co-expression), integrates protein interaction networks, and grounds claims in perturbation experiments rather than computational prediction alone.
Gene Regulatory Network Analysis identifies which transcription factors regulate your target gene using binding motifs, ChIP-seq data, and perturbation evidence.
AI-generated summary based on this skill's SKILL.md
Install
mims-harvard/ToolUniverse/tooluniverse-gene-regulatory-networks · repository language: Python
git clone https://github.com/mims-harvard/ToolUniverse
cp -r ToolUniverse/skills/tooluniverse-gene-regulatory-networks ~/.claude/skills/tooluniverse-gene-regulatory-networksFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What transcription factors regulate TP53?
tooluniverse-gene-regulatory-networks identifies transcription factors that regulate TP53 by integrating ChIP-seq binding data, motif analysis, and eQTL evidence. The skill distinguishes direct binding (where TFs physically contact TP53 promoter or enhancers) from indirect co-expression signals, then grounds findings in perturbation experiments to confirm functional regulation rather than relying on computational prediction alone.
Which genes does CREB1 target?
tooluniverse-gene-regulatory-networks finds target genes regulated by CREB1 by combining chromatin immunoprecipitation data, transcription factor binding motif scanning (including JASPAR), and expression quantitative trait loci. The skill maps direct binding sites near target genes and integrates protein-protein interactions among regulators to reconstruct the full regulatory pathway controlled by CREB1.
How do I distinguish direct binding from indirect co-expression regulatory evidence?
tooluniverse-gene-regulatory-networks separates direct regulation (ChIP-seq confirmed TF binding at target promoters or enhancers) from indirect signals (genes that co-express without physical TF contact). The skill uses knockout perturbation data and histone modification patterns to validate which regulatory relationships are mechanistic versus correlative, ensuring claims rest on binding evidence rather than expression correlation alone.
Can tooluniverse-gene-regulatory-networks reconstruct gene regulatory networks?
tooluniverse-gene-regulatory-networks reconstructs gene regulatory networks and transcriptional pathways by integrating ChIP-seq binding, motif disruption analysis for variants, ENCODE data, tissue-specific TF activity, and eQTL integration. It combines direct binding evidence with protein-protein interactions among regulators and perturbation outcomes to map complete regulatory circuits rather than isolated TF-gene pairs.
What data sources does this skill use for TF binding analysis?
tooluniverse-gene-regulatory-networks leverages ChIP-seq data from ENCODE, JASPAR motif scanning, GTEx eQTL lookups, histone modification patterns marking active enhancers, and regulatory element annotation. It integrates knockout perturbation evidence and protein-protein interaction networks to ground transcription factor binding predictions in experimental validation across multiple evidence layers.
How does tooluniverse-gene-regulatory-networks handle tissue-specific regulation?
tooluniverse-gene-regulatory-networks incorporates tissue-specific transcription factor activity by analyzing eQTL data across tissues, ChIP-seq experiments from relevant cell types, and histone modifications in context-specific samples. The skill reconstructs regulatory pathways that vary by tissue, ensuring that TF-target relationships reflect actual biological context rather than collapsing all evidence into a single network.
SKILL.md
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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
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skills/tooluniverse-gene-regulatory-networks/SKILL.md