Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

Journal: BMC bioinformatics
Published Date: May 31, 2018

Abstract

BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide.

Authors

  • Yifeng Li
    Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia Vancouver, British Columbia V5Z 4H4, Canada; Information and Communications Technologies, National Research Council of Canada, Ottawa, Ontario K1A 0R6, Canada. Electronic address: yifeng.li@nrc-cnrc.gc.ca.
  • Wenqiang Shi
    Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, Department of Medical Genetics, University of British Columbia, Rm 3109, 950 West 28th Avenue, Vancouver, V5Z 4H4, Canada.
  • Wyeth W Wasserman
    Centre for Molecular Medicine and Therapeutics, Child and Family Research Institute, Department of Medical Genetics, University of British Columbia Vancouver, British Columbia V5Z 4H4, Canada. Electronic address: wyeth@cmmt.ubc.ca.

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