Identification of regulatory elements from nascent transcription using dREG

Our genomes encode a wealth of transcription initiation regions (TIRs) that can be identified by their distinctive patterns of transcription initiation. We previously introduced dREG to identify TIRs using PRO-seq data. Here we introduce an efficient new implementation of dREG that uses PRO-seq data to identify both uni- and bidirectionally transcribed TIRs with 70% improvements in accuracy, 3-4-fold higher resolution, and >100-fold increases in computational efficiency. Using a novel strategy to identify TIRs based on their statistical confidence reveals extensive overlap with orthogonal assays, yet also reveals thousands of additional weakly-transcribed TIRs that were not identified by H3K27ac ChIP-seq or DNase-I-hypersensitivity. Novel TIRs discovered by dREG were often associated with RNA polymerase III initiation or bound by transcription factors that recognize DNA concurrently with a nucleosome. We provide a web interface to dREG that can be used by the scientific community (http://dREG.DNASequence.org). We report the development of dREG, a sensitive machine learning tool to identify transcriptional regulatory elements in a sample of interest.