Modeling transcriptional regulation of model species with deep learning.

Journal: Genome research
Published Date:

Abstract

To enable large-scale analyses of transcription regulation in model species, we developed DeepArk, a set of deep learning models of the -regulatory activities for four widely studied species: , , , and DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed) and enables the regulatory annotation of understudied model species.

Authors

  • Evan M Cofer
    Department of Computer Science, Trinity University, San Antonio, TX, USA.
  • João Raimundo
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Alicja Tadych
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
  • Yuji Yamazaki
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Aaron K Wong
    Flatiron Institute, Simons Foundation, New York, NY, USA.
  • Chandra L Theesfeld
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
  • Michael S Levine
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, USA.
  • Olga G Troyanskaya
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. ogt@cs.princeton.edu.