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Bacteriophages, or phages, are viruses that specifically infect bacteria, utilizing their genetic material to hijack host cellular machinery for replication. DNA bacteriophages employ single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA) genomes. These phages exhibit diverse replication strategies and host interactions, influencing their ecological roles and applications in biotechnology and medicine.ssDNA BacteriophagesssDNA phages, with their small genomes, utilize unique strategies to...
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Virus classification for viral genomic fragments using PhaGCN2.

Jing-Zhe Jiang1,2,3,4, Wen-Guang Yuan2, Jiayu Shang5

  • 1Key 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.

Briefings in Bioinformatics
|December 4, 2022
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Summary

PhaGCN2 rapidly classifies viral sequences, improving accuracy and enabling high-throughput database expansion for the International Committee on Taxonomy of Viruses. This tool significantly enhances virus identification and classification in large datasets.

Keywords:
ICTVPhaGCN2graph convolutional networksemi-supervised machine learningvirus classification

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Area of Science:

  • Virology
  • Bioinformatics
  • Computational Biology

Background:

  • Viruses are highly diverse and ubiquitous, necessitating efficient classification methods.
  • The rapid discovery of novel viruses demands accurate taxonomic assignment.
  • Existing virus classification tools face challenges with large and diverse viral datasets.

Purpose of the Study:

  • To introduce PhaGCN2, a novel computational tool for rapid and accurate viral sequence classification.
  • To enable visualization of associations between viral families.
  • To facilitate high-throughput expansion of the International Committee on Taxonomy of Viruses (ICTV) database.

Main Methods:

  • Development of PhaGCN2, a graph convolutional network-based classification tool.
  • Evaluation of PhaGCN2 performance using widely accepted metrics.
  • Comparison of PhaGCN2 against state-of-the-art tools: vConTACT2, CAT, and VPF-Class.

Main Results:

  • PhaGCN2 significantly improves precision and recall in virus classification.
  • The tool increases the number of classifiable viral sequences in the Global Ocean Virome dataset (v2.0) by fourfold.
  • PhaGCN2 successfully classifies over 90% of sequences in the Gut Phage Database.

Conclusions:

  • PhaGCN2 offers a substantial advancement in viral taxonomy classification.
  • The tool supports automated and high-throughput expansion of viral taxonomic databases.
  • PhaGCN2 is a valuable resource for virology research and database curation.