DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach
Author(s) -
ShuFang Vivienne Wu,
Zhencheng Fang,
Jie Tan,
Mo Li,
Chunhui Wang,
Qian Guo,
Congmin Xu,
Xiaoqing Jiang,
Huaiqiu Zhu
Publication year - 2021
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giab056
Subject(s) - virulence , temperateness , temperate climate , biology , contig , computational biology , genome , bacteriophage , genetics , gene , ecology , escherichia coli
Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactions with bacterial hosts and regulation of microbial communities. However, there is no experimental or computational approach to effectively classify their sequences in culture-independent metavirome. We present a new computational method, DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment.
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