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. 2015 May;12(5):433-8.
doi: 10.1038/nmeth.3329. Epub 2015 Mar 23.

Identification of active transcriptional regulatory elements from GRO-seq data

Charles G Danko  1 , Stephanie L Hyland  2 , Leighton J Core  3 , Andre L Martins  4 , Colin T Waters  3 , Hyung Won Lee  3 , Vivian G Cheung  5 , W Lee Kraus  6 , John T Lis  3 , Adam Siepel  7
Affiliations

Identification of active transcriptional regulatory elements from GRO-seq data

Charles G Danko et al. Nat Methods. 2015 May.

Abstract

Modifications to the global run-on and sequencing (GRO-seq) protocol that enrich for 5'-capped RNAs can be used to reveal active transcriptional regulatory elements (TREs) with high accuracy. Here, we introduce discriminative regulatory-element detection from GRO-seq (dREG), a sensitive machine learning method that uses support vector regression to identify active TREs from GRO-seq data without requiring cap-based enrichment (https://github.com/Danko-Lab/dREG/). This approach allows TREs to be assayed together with gene expression levels and other transcriptional features in a single experiment. Predicted TREs are more enriched for several marks of transcriptional activation—including expression quantitative trait loci, disease-associated polymorphisms, acetylated histone 3 lysine 27 (H3K27ac) and transcription factor binding—than those identified by alternative functional assays. Using dREG, we surveyed TREs in eight human cell types and provide new insights into global patterns of TRE function.

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Figures

Figure 1
Figure 1
dREG schematic and validation. (a) High PRO-seq signal intensity marks TREs (highlighted with pink background) and gene bodies (yellow background). dREG is a shape detector trained to recognize the characteristic pattern of TREs in PRO-seq data (#1). After training, dREG can be used to identify TREs using a new PRO-seq data set (red peaks) (#2). Browser shot compares dREG-predicted TREs to ChromHMM-predicted promoters (red), enhancers (yellow), and insulators (green) (#3). (b) Bar charts (left) represent the genome-wide sensitivity of dREG for various classes of TRE at a 5% (line) or 10% (bar) false discovery rate in K562 (pink) and GM12878 (blue) cells. Classes of regulatory elements represent GRO-cap transcribed DHS (Transcribed DHS), transcription start sites identified by CAGE (CAGE TSS), histone acetylation associated with DHS (Acetyl DHS), GRO-cap transcribed ChromHMM promoters (Promoters), GRO-cap transcribed chromHMM enhancers (Enhancers), GRO-cap TSS inside annotated Gene Bodies (Gene Body), and GRO-cap pairs (GRO-cap Pairs). Pie charts (right) represent the fraction of sites aligning within RefSeq transcription start sites (TSS), introns, or intergenic regions in each validation set.
Figure 2
Figure 2
Comparison of putative TREs detected using dREG, DNase-I, and ChromHMM. (a) Four-way Venn diagram depicting the relationships among separate genomic assays, which support the existence of four distinct classes of regulatory element. Numbers give the rounded overall fraction of TREs that fall into the specified intersection. TREs discovered using multiple assays were classified as +dREG, −dREG, Insulator, or as modified chromatin only (MCO). (b) Comparison of read-densities for H3K27ac (left) and H3K4me1 (right) in each class of functional element. (c) Distribution of MNase-seq reads in the +dREG (red), −dREG (black), and modified chromatin only classes (MCO; blue). (d) Histogram compares the number of transcription factors found in each of the four functional classes.
Figure 3
Figure 3
Sequence-specific transcription factors identified using dREG transcribed TREs. ROC plot shows the accuracy of predicting ELF1 binding to strong matches to the ELF1 consensus binding motif (sequence logo shown) using PIQ (black; AUC= 0.92), dREG (red; AUC= 0.88), the DNA sequence motif (gray; AUC= 0.67), or a joint logistic regression model considering all three variables (blue; AUC= 0.94). Motif matches that intersect ENCODE ChIP-seq peak calls were used as the set of true binding sites.
Figure 4
Figure 4
eQTL and GWAS SNP enrichments in the four classes of functional element. (a) The density of eQTL (n= 755) per site found in +dREG (further divided into promoters and enhancers using ChromHMM), −dREG, modified chromatin only (MCO), and Insulator classes. The asterisk indicates significantly lower eQTL densities than in dREG enhancers by a Fisher’s exact test (P<2×10−5). (b) The density of GWAS SNPs that correlate with cell-type specific phenotypes (autoimmune disorders) in GM12878, a B-cell line (blue), and primary CD4+ T-cells (red).
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