IPEV: identification of prokaryotic and eukaryotic virus-derived sequences in virome using deep learning.

Journal: GigaScience
PMID: 38649300

Abstract

BACKGROUND: The virome obtained through virus-like particle enrichment contains a mixture of prokaryotic and eukaryotic virus-derived fragments. Accurate identification and classification of these elements are crucial to understanding their roles and functions in microbial communities. However, the rapid mutation rates of viral genomes pose challenges in developing high-performance tools for classification, potentially limiting downstream analyses.

Authors

  • Hengchuang Yin
    Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China.
  • Shufang Wu
    State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, No.5 Yiheyuan Road Haidian District, Beijing 100871, China.
  • Jie Tan
    Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Qian Guo
    State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China.
  • Mo Li
    School of Computer Science and Engineering, Key Laboratory of Big Data Management and Analytics (Liaoning), Northeastern University, China.
  • Jinyuan Guo
    Department of Biomedical Engineering, College of Future Technology, and Center for Quantitative Biology, Peking University, Beijing 100871, China.
  • Yaqi Wang
    Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Xiaoqing Jiang
    State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, Beijing, China.
  • Huaiqiu Zhu
    Department of Biomedical Engineering, College of Engineering, and Centre for Quantitative Biology, Peking University, Beijing, China.

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