title: Identification of active transcriptional regulatory elements from GRO-seq data.
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.
authors: 1] Baker Institute for Animal Health, Cornell University, Ithaca, New York, USA. [2] Department of Biomedical Sciences, Cornell University, Ithaca, New York, USA. [3] Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
authors: Charles G
authors: CG
authors: Danko
authors: Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, USA.
authors: Stephanie L
authors: SL
authors: Hyland
authors: Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA.
authors: Leighton J
authors: LJ
authors: Core
authors: Graduate Field in Computational Biology, Cornell University, Ithaca, New York, USA.
authors: Andre L
authors: AL
authors: Martins
authors: Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA.
authors: Colin T
authors: 0000000230960833
authors: CT
authors: Waters
authors: Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA.
authors: Hyung Won
authors: HW
authors: Lee
authors: 1] Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA. [2] Howard Hughes Medical Institute, Chevy Chase, Maryland, USA.
authors: Vivian G
authors: VG
authors: Cheung
authors: 1] 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. [2] Division of Basic Research, Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
authors: W Lee
authors: WL
authors: Kraus
authors: Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA.
authors: John T
authors: JT
authors: Lis
authors: Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York, USA.
authors: Adam
authors: A
authors: Siepel
mesh_terms: D001185:Artificial Intelligence; D002460:Cell Line; D005786:Gene Expression Regulation / Q000502:physiology*; D055106:Genome-Wide Association Study; D006657:Histones; D006801:Humans; D020014:K562 Cells; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci; D050436:Regulatory Elements, Transcriptional / Q000235:genetics / Q000502:physiology*; D012984:Software
chemical_list: D006657:Histones