INSnet: a method for detecting insertions based on deep learning network.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Many studies have shown that structural variations (SVs) strongly impact human disease. As a common type of SV, insertions are usually associated with genetic diseases. Therefore, accurately detecting insertions is of great significance. Although many methods for detecting insertions have been proposed, these methods often generate some errors and miss some variants. Hence, accurately detecting insertions remains a challenging task.

Authors

  • Runtian Gao
    School of Software, Henan Polytechnic University, Jiaozuo, 454003, China.
  • 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.
  • Haixia Zhai
    College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454003, China.