BreakNet: detecting deletions using long reads and a deep learning approach.

Journal: BMC bioinformatics
Published Date: Dec 2, 2021

Abstract

BACKGROUND: Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs.

Authors

  • Junwei Luo
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Hongyu Ding
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Jiquan Shen
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China. sjq@hpu.edu.cn.
  • Haixia Zhai
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Zhengjiang Wu
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.
  • Chaokun Yan
    School of Computer Science and Information Engineering, Henan University, Kaifeng, 475001, China.
  • Huimin Luo

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