Identification of active transcriptional regulatory elements from GRO-seq data

Abstract

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.

Enhancer RNA profiling predicts transcription factor activity

“…S1, S2). As previously observed (Danko et al 2015;Azofeifa and Dowell 2017), marks of active chromatin as well as TF binding events strongly associate with Tfit-predicted sites of bidirectional transcription (Supplemental Figs. S3-S5; Supplemental Table S1).…”

“…S3A). Previous efforts to predict sites of TF binding using joint eRNA and TF-DNA motifs focused on only a small set of TFs (Danko et al 2015). We extended this analysis to include 139 TF ChIP-seq experiments and observed a wide spectrum of association between TF binding sites and eRNA presence, suggesting that eRNA presence alone is not sufficient to fully explain TF binding (Fig.…”

“…Previous work demonstrated that the presence of eRNAs correlates with active regulatory regions and, consequently, a subset of TF binding sites (Danko et al 2015). Separately, it has been noted that some binding sites are apparently "silent" with respect to transcription (Cusanovich et al 2014) or reflect artifacts of ChIP (Teytelman et al 2013;Worsley Hunt and Wasserman 2014).…”

“…When a specific TF is activated, eRNA transcription generally increases at the location of the TF binding event Hah et al 2013;Allen et al 2014;Puc et al 2015). While the functions of eRNAs are only beginning to be understood Li et al 2013;Sigova et al 2015), their presence is nonetheless an indicator of enhancer activity (Andersson et al 2014;Danko et al 2015).…”

RTFBSDB: an integrated framework for transcription factor binding site analysis

“…We created an empirical test using publicly available data from the ENCODE project to determine whether dividing sequences into multiple GC content groups improves the accuracy of TFBS predictions. We used motif match scores to classify high-confidence DNase-I hypersensitive sites (DHS), defined as the intersection of DHS discovered using Duke and UW assays (Danko et al, 2015), as bound or unbound to its cognate TF. Chromatin immunopercipitation and sequencing (ChIP-seq) peaks from 21 TFs were used as a gold-standard set.…”