Introduction

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.

Publications

  1. Identification of active transcriptional regulatory elements from GRO-seq data.
    Cite this
    Danko CG, Hyland SL, Core LJ, Martins AL, Waters CT, Lee HW, Cheung VG, Kraus WL, Lis JT, Siepel A, 2015-05-01 - Nature methods

Credits

  1. Charles G Danko
    Developer

    1] Baker Institute for Animal Health, Cornell University, United States of America

  2. Stephanie L Hyland
    Developer

    Tri-Institutional Training Program in Computational Biology and Medicine, New York, United States of America

  3. Leighton J Core
    Developer

    Department of Molecular Biology and Genetics, Cornell University, United States of America

  4. André L Martins
    Developer

    Graduate Field in Computational Biology, Cornell University, United States of America

  5. Colin T Waters
    Developer

    Department of Molecular Biology and Genetics, Cornell University, United States of America

  6. Hyung Won Lee
    Developer

    Department of Molecular Biology and Genetics, Cornell University, United States of America

  7. Vivian G Cheung
    Developer

    1] Life Sciences Institute, University of Michigan, United States of America

  8. W Lee Kraus
    Developer

    1] Laboratory of Signaling and Gene Regulation, Cecil H. and Ida Green Center for Reproductive Biology Sciences, United States of America

  9. John T Lis
    Developer

    Department of Molecular Biology and Genetics, Cornell University, United States of America

  10. Adam Siepel
    Investigator

    Department of Biological Statistics and Computational Biology, Cornell University, United States of America

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Summary
AccessionBT006479
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByAdam Siepel