A flexible repertoire of transcription factor binding sites and a diversity threshold determines enhancer activity in embryonic stem cells.

Transcriptional enhancers are critical for development and phenotype evolution and are often mutated in disease contexts; however, even in well-studied cell types, the sequence code conferring enhancer activity remains unknown. To examine the enhancer regulatory code for pluripotent stem cells, we identified genomic regions with conserved binding of multiple transcription factors in mouse and human embryonic stem cells (ESCs). Examination of these regions revealed that they contain on average 12.

C3D: a tool to predict 3D genomic interactions between cis-regulatory elements.

Motivation: The 3D genome architecture influences the regulation of genes by facilitating chromatin interactions between distal cis-regulatory elements and gene promoters. We implement Cross Cell-type Correlation based on DNA accessibility (C3D), a customizable computational tool that predicts chromatin interactions using an unsupervised algorithm that utilizes correlations in chromatin measurements, such as DNaseI hypersensitivity signals.

Results: C3D accurately predicts 32.

Variational infinite heterogeneous mixture model for semi-supervised clustering of heart enhancers.

Motivation: Mammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant.