DETIRE: a hybrid deep learning model for identifying viral sequences from metagenomes

A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, archaea, and eukaryotes. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a type of major pathogens, detecting viruses from metagenomes pl...

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Bibliographic Details
Published in:Frontiers in microbiology 2023-06, Vol.14, p.1169791-1169791
Main Authors: Miao, Yan, Bian, Jilong, Dong, Guanghui, Dai, Tianhong
Format: Article
Language:English
Subjects:
codon embedding
deep learning
graph convolutional network
metagenome
Microbiology
viral identification
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Summary:A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, archaea, and eukaryotes. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a type of major pathogens, detecting viruses from metagenomes plays a crucial role in analyzing the viral component of samples and is the very first step for clinical diagnosis. However, detecting viral fragments directly from the metagenomes is still a tough issue because of the existence of a huge number of short sequences. In this study a hybrid Deep lEarning model for idenTifying vIral sequences fRom mEtagenomes (DETIRE) is proposed to solve the problem. First, the graph-based nucleotide sequence embedding strategy is utilized to enrich the expression of DNA sequences by training an embedding matrix. Then, the spatial and sequential features are extracted by trained CNN and BiLSTM networks, respectively, to enrich the features of short sequences. Finally, the two sets of features are weighted combined for the final decision. Trained by 220,000 sequences of 500 bp subsampled from the Virus and Host RefSeq genomes, DETIRE identifies more short viral sequences (
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2023.1169791