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Virus classification for viral genomic fragments using PhaGCN2

Citation

Jiang, Jingzhe et al. (2022), Virus classification for viral genomic fragments using PhaGCN2, Dryad, Dataset, https://doi.org/10.5061/dryad.vmcvdncvw

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 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 4 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.

Funding

National Natural Science Foundation of China, Award: 31872499 and 31972847

Central Public-interest Scientific Institution Basal Research Fund, Award: 2020TD42 and 2021SD05

Guangdong Provincial Special Fund for Modern Agriculture Industry Technology Innovation Teams, Award: 2019KJ141