CSV-Filter: a deep learning-based comprehensive structural variant filtering method for both short and long reads.

Journal: Bioinformatics (Oxford, England)
PMID: 39240375

Abstract

MOTIVATION: Structural variants (SVs) play an important role in genetic research and precision medicine. As existing SV detection methods usually contain a substantial number of false positive calls, approaches to filter the detection results are needed.

Authors

  • Zeyu Xia
    College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, P. R. China.
  • Weiming Xiang
    School of Computer and Cyber Sciences, Augusta University, Augusta GA 30912, USA. Electronic address: wxiang@augusta.edu.
  • Qingzhe Wang
    College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, P. R. China.
  • Xingze Li
    College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, P. R. China.
  • Yilin Li
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Junyu Gao
    College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, P. R. China.
  • Tao Tang
    Ocean College, Zhejiang University, #1 Zheda Road, Zhoushan, Zhejiang 316021, China.
  • Canqun Yang
    School of Computer Science, National University of Defense Technology, Changsha, 410073, China.
  • Yingbo Cui
    College of Computer Science and Technology, National University of Defense Technology, Hunan 410073, P. R. China.

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