Modeling gene regulation from matched expression and chromatin accessibility data

We propose a statistical method to model gene regulation by paired expression and chromatin accessibility (PECA) data, which dissects the regulatory elements (RE) of the genome by modeling their interaction with general chromatin regulators (CR) and sequence-specific transcriptional regulators (TF) and consequent effects on transcriptional changes. To generate RNA-seq and ATAC-seq data on the retinoic acid-induced mESC differenation at Day 6.