gtex-database

Access histone ChIP-seq data, chromatin accessibility maps, and regulatory element annotations from ENCODE and Roadmap Epigenomics to investigate chromatin state by tissue, transcription factor binding patterns, and cis-regulatory landscapes. Interpret histone marks, eQTL associations, and variant regulatory impact through multi-layer evidence synthesis.

This skill maps the causal chain from a genetic variant to its disease mechanism by querying regulatory context, target genes, molecular pathways, and phenotypic consequences across multiple evidence layers. It combines data from 7+ databases to construct evidence-graded mechanistic narratives for GWAS hits, eQTL variants, and non-coding regulatory elements.

This skill systematically evaluates non-coding variants through GWAS associations, tissue-specific eQTL effects, and regulatory context. It integrates ENCODE chromatin marks, RegulomeDB scores, and transcription factor binding data to build evidence-graded functional impact predictions, distinguishing regulatory mechanisms from coding pathogenicity.

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.

This skill applies Bayesian fine-mapping methods to pinpoint causal variants within GWAS-associated regions, moving beyond the lead SNP to compute posterior probabilities for each variant in a credible set. It integrates locus-to-gene predictions and functional annotations to link variants to their target genes, helping researchers distinguish true causal effects from linkage disequilibrium artifacts.

This skill systematically maps gene regulation by integrating transcription factor motifs (JASPAR), experimental ChIP-seq and chromatin data (ENCODE), regulatory variant scoring (RegulomeDB), and sequence-based deep-learning models to predict regulatory activity. Query what controls a gene, whether a variant hits a regulatory element, or how non-coding changes affect expression and accessibility.