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Identification of active transcriptional regulatory elements from GRO-seq data

Nature Methods volume 12, pages 433–438 (2015)Cite this article

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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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Figure 1: dREG schematic and validation.
Figure 2: Comparison of putative TREs detected using dREG, DNase I and ChromHMM.
Figure 3: Sequence-specific TFs identified using dREG transcribed TREs.
Figure 4: eQTL and GWAS SNP enrichments in the four classes of functional element.

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Acknowledgements

We thank I. Jonkers and N. Dukler for comments and helpful discussions on an early manuscript draft, and B. Gulko for critical discussions about support vector machines. This work was made possible by generous seed grants from the Cornell University Center for Vertebrate Genomics (CVG) and Center for Comparative and Population Genetics (3CPG), a US National Human Genome Research Institute grant (5R01HG007070-02) to A.S. and J.T.L., and US National Institutes of Health R01 (DK058110) to W.L.K. The content is solely the responsibility of the authors and does not necessarily represent the official views of the US National Institutes of Health.

Author information

Author notes

  1. Leighton J Core, Colin T Waters & Adam Siepel

    Present address: Present addresses: Department of Molecular and Cell Biology, Institute for Systems Genomics, University of Connecticut, Storrs, Connecticut, USA (L.J.C.); Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA (C.T.W.); Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA (A.S.).,

Authors and Affiliations

  1. Baker Institute for Animal Health, Cornell University, Ithaca, New York, USA

    Charles G Danko

  2. Department of Biomedical Sciences, Cornell University, Ithaca, New York, USA

    Charles G Danko

  3. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA

    Charles G Danko & Adam Siepel

  4. Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA

    Stephanie L Hyland

  5. Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA

    Leighton J Core, Colin T Waters, Hyung Won Lee & John T Lis

  6. Graduate Field in Computational Biology, Cornell University, Ithaca, New York, USA

    Andre L Martins

  7. Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA

    Vivian G Cheung

  8. Howard Hughes Medical Institute, Chevy Chase, Maryland, USA

    Vivian G Cheung

  9. Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    W Lee Kraus

  10. Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, Texas, USA

    W Lee Kraus

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Contributions

C.G.D. designed the dREG tool. C.G.D., A.L.M. and S.L.H. designed and implemented the software. C.G.D., S.L.H., A.L.M., L.J.C., J.T.L. and A.S. analyzed the data and interpreted the results. L.J.C., C.T.W., C.G.D., H.W.L., J.T.L., W.L.K. and V.G.C. contributed data and helped to troubleshoot experiments. C.G.D., A.S., J.T.L., L.J.C., S.L.H. and A.L.M. wrote the manuscript.

Corresponding authors

Correspondence to Charles G Danko, John T Lis or Adam Siepel.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Browser shot demonstrating the dREG technique.

Browser shot depicts raw dREG scores and dREG ‘peaks’ alongside PRO-seq, GRO-cap, DNase-I, and ENCODE ChIP-seq data for H3K27ac, H3K4me1, and H3K4me3.

Supplementary Figure 2 Illustration of the dREG feature vector and the resulting accuracy of TRE identification.

(a) The signal for +dREG TREs depicted as barcharts at decreasing window size (from top to bottom). Boxes represent consecutive, non-overlapping window sizes. The number of windows in the optimal feature vector, and the size represented by each bar, is shown at right. (b) The ROC plot shows the accuracy of dREG at distinguishing regulatory TREs given equal numbers of positive and negative examples (AUC = 0.99).

Supplementary Figure 3 Sensitivity to read depth and library quality.

(a,b) dREG sensitivity at a 10% false discovery rate at the indicated read depth or asymptotic library complexity. Dotted lines indicate a model that has been trained specifically on the indicated library. Solid lines indicate the model trained on the native K562 PRO-seq libraries. Pink and cyan denote GRO-cap sites and pairs, respectively. (c,d) SVR threshold required to achieve a 10% FDR for SVR models that have (dotted) or have not (solid) been trained specifically to the indicated parameters.

Supplementary Figure 4 dREG TREs are associated with chromatin marks characteristic of both promoters and enhancers.

The majority (>90%) of dREG TREs intersect post-translational histone modifications previously associated with either promoters or enhancers, and interpreted by ChromHMM.

Supplementary Figure 5 Chromatin marks associated with three classes of DNase I–hypersensitive sites.

DNase-I hypersensitive sites identified by either the UW and Duke assays alone, or their intersection, are associated with the indicated fraction of regulatory marks (blue), transcribed regions (red), or repeat/ heterochromatin (purple), as annotated by ChromHMM.

Supplementary Figure 6 Fraction of TREs in each class among four cell types.

Barplots represent the fraction of TREs in each of the four nested classes of TRE compared across four cell types for which all sources of data are available (K562, GM12878, CD4+ T-cells, and HeLa carcinoma cells).

Supplementary Figure 7 High-confidence DNase I peaks covered by dREG.

Fraction of DNase-I peaks (excluding CTCF-bound insulators) that intersect a dREG site (Y-axis) as a function of the PRO-seq read depth (X-axis) in K562 cells.

Supplementary Figure 8 Association of TREs in each class to independent functional marks.

(a,b) Comparison of read-densities for H3K9ac (a) and H3K4me3 (b) in each class of functional element. (c) The fraction of ENCODE peak calls for the specified mark (H3K4me1, H3K27ac, H3K9ac, and CTCF) in each of the four classes. The ‘other’ category denotes peaks for the indicated mark falling outside of the putative TREs identified by other assays. (d) Plots show the ENCODE MNase-seq signal centered on the indicated class of TRE.

Supplementary Figure 9 Enrichment of H3K27ac and PRO-seq signal intensity.

Enrichment of H3K27ac at dREG TREs that lack an H3K27ac peak call (left); and PRO-seq signal on the plus (red) and minus (blue) strand at H3K27ac peak calls without a dREG TRE prediction (right).

Supplementary Figure 10 Sequence-specific transcription factors distinguish between DNase I–hypersensitive and transcribed regulatory TREs.

(a) TFs are either associated with DNase-I hypersensitive peaks that are actively transcribed (+dREG) or open but non-transcribed (-dREG and DNase-I hypersensitive insulators), as indicated by the presence of Pol II (red rocket). ROC plots depict the accuracy with which these classes of regulatory TRE can be distinguished in three cell types based on the patterns of TF binding. (b) Logistic regression coefficients for each transcription factor correlated with transcription initiation (positive, red) or repression (negative, blue) following a 1,000 sample bootstrap.

Supplementary Figure 11 Browser shot of CTCF ChIP-seq and PRO-seq signal.

UCSC genome browser signal compares dREG, GRO-cap, CTCF, and PRO-seq in the indicated region of chr1 in K562 cells.

Supplementary Figure 12 Distance of each class to the nearest RefSeq annotated transcription start site.

Each point shows the fraction of TREs in the indicated class with a distance to the nearest RefSeq annotated transcription start site greater than the value indicated on the X-axis (i.e., 1-cumulative density function). In this plot, separate lines show the distribution for the set of all +dREG TREs (red, dotted) and for the subset which intersects chromatin marks indicative of enhancers (red, solid).

Supplementary Figure 13 PhyloP scores among the placental mammals in each class of TRE.

Violin plots denote the distribution of the maximum PhyloP score within each occurrence of the indicated class of TRE in GM12878 cells.

Supplementary Figure 14 Cell type–specific differences in TRE class.

Heatmap denotes the median frequency with which the indicated class of TRE in one cell type (‘From’ axis) intersects with the indicated TRE class in a second cell type (‘To’ axis).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Tables 1–4 and Supplementary Discussion (PDF 1779 kb)

Supplementary Software

dREG software package. The models used for running dREG are located at https://github.com/Danko-Lab/dREG. (ZIP 3517 kb)

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Danko, C., Hyland, S., Core, L. et al. Identification of active transcriptional regulatory elements from GRO-seq data. Nat Methods 12, 433–438 (2015). https://doi.org/10.1038/nmeth.3329

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