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Bacteriophage classification for assembled contigs using graph convolutional network

文献类型： 外文期刊

作者： Shang, Jiayu 1 ; Jiang, Jingzhe 2 ; Sun, Yanni 1 ;

作者机构： 1.City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China

2.Chinese Acad Fishery Sci, South China Sea Fisheries Res Inst, Key Lab South China Sea Fishery Resources Exploit, Minist Agr, Guangzhou, Guangdong, Peoples R China

期刊名称：BIOINFORMATICS （ 影响因子：6.937； 五年影响因子：8.47 ）

ISSN： 1367-4803

年卷期： 2021 年 37 卷

页码：

收录情况： SCI

摘要： Motivation: Bacteriophages (aka phages), which mainly infect bacteria, play key roles in the biology of microbes. As the most abundant biological entities on the planet, the number of discovered phages is only the tip of the iceberg. Recently, many new phages have been revealed using high-throughput sequencing, particularly metagenomic sequencing. Compared to the fast accumulation of phage-like sequences, there is a serious lag in taxonomic classification of phages. High diversity, abundance and limited known phages pose great challenges for taxonomic analysis. In particular, alignment-based tools have difficulty in classifying fast accumulating contigs assembled from metagenomic data. Results: In this work, we present a novel semi-supervised learning model, named PhaGCN, to conduct taxonomic classification for phage contigs. In this learning model, we construct a knowledge graph by combining the DNA sequence features learned by convolutional neural network and protein sequence similarity gained from gene-sharing network. Then we apply graph convolutional network to utilize both the labeled and unlabeled samples in training to enhance the learning ability. We tested PhaGCN on both simulated and real sequencing data. The results clearly show that our method competes favorably against available phage classification tools.

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