Identification of active transcriptional regulatory elements with GRO-seq

Transcriptional regulatory elements (TREs), including enhancers and promoters, determine the transcription levels of associated genes. We have recently shown that global run-on and sequencing (GRO-seq) with enrichment for 5'-capped RNAs reveals active TREs with high accuracy. Here, we demonstrate that active TREs can be identified by applying sensitive machine-learning methods to standard GRO-seq data. This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Our prediction method, called discriminative Regulatory Element detection from GRO-seq (dREG), summarizes GRO-seq read counts at multiple scales and uses support vector regression to identify active TREs. The predicted TREs are more strongly enriched for several marks of transcriptional activation, including eQTL, GWAS-associated SNPs, H3K27ac, and transcription factor binding than those identified by alternative functional assays. Using dREG, we survey TREs in eight human cell types and provide new insights into global patterns of TRE function. We analyzed GRO-seq or PRO-seq data from eight human cell lines. Please note that this study comprises new sample data plus reanalysis of old Sample data submitted by another user. Existing PRO-seq or GRO-seq data was combined as detailed in the GSE66031_readme.txt. See GSM1613181 and GSM1613182 Sample records for data processing information.