TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.

Journal: Bioinformatics (Oxford, England)
PMID: 36063453

Abstract

MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of human diseases, we hypothesize that rare non-coding variants discovered in WGS play a regulatory role in predisposing disease risk.

Authors

  • Hai Yang
    Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, PR China.
  • Rui Chen
    College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shanxi, China.
  • Quan Wang
    Laboratory of Surgical Oncology, Peking University People's Hospital, Peking University, Beijing, China.
  • Qiang Wei
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Ying Ji
  • Xue Zhong
    Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390.
  • Bingshan Li
    Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA.

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