Identification of active transcriptional regulatory elements from GRO-seq data

Charles G. Danko, Stephanie L. Hyland, Leighton J. Core, Andre L. Martins, Colin T. Waters, Hyung Won Lee, Vivian G. Cheung, W. Lee Kraus, John T. Lis, Adam Siepel

Research output: Contribution to journalArticlepeer-review

107 Scopus citations


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 ( 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.

Original languageEnglish (US)
Pages (from-to)433-438
Number of pages6
JournalNature methods
Issue number5
StatePublished - Apr 29 2015

ASJC Scopus subject areas

  • Biotechnology
  • Biochemistry
  • Molecular Biology
  • Cell Biology


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