BIT: Bayesian Identification of Transcriptional Regulators.

BIT is a novel Bayesian hierarchical model capable of predicting transcriptional regulators (TRs) from the input of user-provided epigenomic regions. TRs are critical molecules in transcriptional regulation. Many diseases and cancers are linked to the dysfunction of TRs. Knowing TRs in certain biological process can help find new biomarkers or therapeutic targets. Thus, BIT formulates a novel Bayesian hierarchical model with the Pólya-gamma data augmentation strategy. Based on collected ChIP-seq datasets, BIT can identify TRs responsible for the genome-wide binding pattern within the user-provided epigenomic regions. BIT has been validated by using a simulation study and three applications.