Virus classification for viral genomic fragments using PhaGCN2

Author:

Jiang Jing-Zhe1234ORCID,Yuan Wen-Guang2ORCID,Shang Jiayu5ORCID,Shi Ying-Hui2,Yang Li-Ling4,Liu Min3,Zhu Peng3,Jin Tao6,Sun Yanni5ORCID,Yuan Li-Hong2

Affiliation:

1. Key Laboratory of South China Sea Fishery Resources Exploitation & Utilization, Ministry of Agriculture and Rural Affairs, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences , Guangzhou 510300, Guangdong, China

2. Guangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University , Guangzhou 510006, Guangdong, China

3. College of Fisheries and Life Science, Shanghai Ocean University , Shanghai 201306, China

4. Tianjin Agricultural University , Tianjin 300384, China

5. Department of Electrical Engineering, City University of Hong Kong , Hong Kong (SAR), China

6. Guangdong Magigene Biotechnology Co., Ltd , Guangzhou 510000, Guangdong, China

Abstract

Abstract Viruses are the most ubiquitous and diverse entities in the biome. Due to the rapid growth of newly identified viruses, there is an urgent need for accurate and comprehensive virus classification, particularly for novel viruses. Here, we present PhaGCN2, which can rapidly classify the taxonomy of viral sequences at the family level and supports the visualization of the associations of all families. We evaluate the performance of PhaGCN2 and compare it with the state-of-the-art virus classification tools, such as vConTACT2, CAT and VPF-Class, using the widely accepted metrics. The results show that PhaGCN2 largely improves the precision and recall of virus classification, increases the number of classifiable virus sequences in the Global Ocean Virome dataset (v2.0) by four times and classifies more than 90% of the Gut Phage Database. PhaGCN2 makes it possible to conduct high-throughput and automatic expansion of the database of the International Committee on Taxonomy of Viruses. The source code is freely available at https://github.com/KennthShang/PhaGCN2.0.

Funder

Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams

Central Public-Interest Scientific Institution Basal Research Fund

Natural Science Foundation of China

Key-Area Research and Development Program of Guangdong Province

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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