Esearch3D

Esearch3D identifies active enhancers within chromatin networks by propagating transcription levels across 3D genome interaction networks to compute likelihoods of enhancer activity in intergenic regions.

Key Features:

• Network-theory inference: Applies network theory and reverse engineering of regulatory information flow to infer enhancer activity from chromatin interaction data.
• Transcription propagation: Propagates transcription levels across 3D chromatin interaction networks to score regulatory influence of genomic regions.
• Enhancer-promoter coupling: Models connections between enhancers and gene promoters mediated by the three-dimensional folding of chromatin.
• Cell type specificity: Targets identification of enhancers regulating cell type-specific genes via enhancer–promoter interactions.
• Intergenic region scoring: Calculates likelihoods of enhancer activity specifically in intergenic regions.
• Annotation enrichment: Predicted high-activity regions are enriched for enhancer-associated histone marks, bidirectional CAGE-seq, STARR-seq, P300 binding, RNA polymerase II, and expression quantitative trait loci (eQTLs).

Scientific Applications:

• Active enhancer discovery: Identification of active enhancers in specific cell types using 3D chromatin interaction and transcription data.
• Regulatory network characterization: Elucidation of enhancer–promoter regulatory networks that underlie gene expression control.
• Integration with functional genomics: Validation and interpretation of predictions using histone marks, CAGE-seq, STARR-seq, P300, RNA polymerase II, and eQTL datasets.
• Genomics and epigenetics studies: Investigation of how chromatin architecture contributes to transcriptional regulation and cell type-specific gene expression.

Methodology:

Reverse engineers regulatory information flow and propagates transcription levels across 3D genome networks to compute likelihood scores of enhancer activity in intergenic regions.