{"enrichment":{"faq":[{"a":"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.","q":"What transcription factors regulate TP53?"},{"a":"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.","q":"Which genes does CREB1 target?"},{"a":"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.","q":"How do I distinguish direct binding from indirect co-expression regulatory evidence?"},{"a":"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.","q":"Can tooluniverse-gene-regulatory-networks reconstruct gene regulatory networks?"},{"a":"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.","q":"What data sources does this skill use for TF binding analysis?"},{"a":"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.","q":"How does tooluniverse-gene-regulatory-networks handle tissue-specific regulation?"}],"shadow_tags":["tf-target-inference","motif-discovery","chromatin-accessibility","perturbation-analysis","expression-qtl","regulatory-scoring","network-reconstruction","evidence-grading","cell-type-specificity","variant-annotation"],"summary_rewrite":"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."},"files":[{"bytes":11385,"path":"skills/tooluniverse-gene-regulatory-networks/SKILL.md","sha256":"dd1ef084041409e6136aca8efc3a815e6ce6c55acaedffad0d578062379f3fbe","url":"https://skillfed.io/files/mims-harvard/ToolUniverse/tooluniverse-gene-regulatory-networks/4ebf480f/SKILL.md"}],"id":"mims-harvard/ToolUniverse/tooluniverse-gene-regulatory-networks","links":{"html":"https://skillfed.io/mims-harvard/ToolUniverse/tooluniverse-gene-regulatory-networks","md":"https://skillfed.io/mims-harvard/ToolUniverse/tooluniverse-gene-regulatory-networks.md","repo":"https://github.com/mims-harvard/ToolUniverse"},"meta":{"agents_supported":[],"first_seen":"2026-07-28","forks":242,"language":"Python","last_updated":"2026-07-27","license":"Apache-2.0","name":"tooluniverse-gene-regulatory-networks","publisher":"mims-harvard","stars":1595},"relations":{"similar":[{"id":"mims-harvard/ToolUniverse/tooluniverse-epigenomics-chromatin"},{"id":"mims-harvard/ToolUniverse/tooluniverse-regulatory-genomics"},{"id":"mims-harvard/ToolUniverse/tooluniverse-regulatory-variant-analysis"},{"id":"jaechang-hits/SciAgent-Skills/encode-database"},{"id":"jaechang-hits/SciAgent-Skills/jaspar-database"},{"id":"LeonChaoX/qinyan-academic-skills/jaspar-database"},{"id":"jaechang-hits/SciAgent-Skills/remap-database"},{"id":"jaechang-hits/SciAgent-Skills/regulomedb-database"},{"id":"jaechang-hits/SciAgent-Skills/homer-motif-analysis"},{"id":"google-deepmind/science-skills/jaspar_database"}]},"slug":{"owner":"mims-harvard","repo":"ToolUniverse","skill":"tooluniverse-gene-regulatory-networks"},"version":"4ebf480f"}