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Machine learning enables scalable and systematic hierarchical virus taxonomy.

Benjamin Bolduc1,2,3, Olivier Zablocki4,5, Dann Turner6

  • 1Department of Microbiology, Ohio State University, Columbus, OH, USA. bolduc.10@osu.edu.

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A new machine learning tool, vConTACT3, enhances virus taxonomy by improving scalability and accuracy for both prokaryotic and eukaryotic viruses. It accurately classifies millions of viral genomes, including previously unknown taxa.

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Area of Science:

  • Virology
  • Bioinformatics
  • Computational Biology

Background:

  • Virus ecogenomics has greatly expanded our understanding of the virosphere.
  • Current virus classification tools struggle with taxonomic resolution, scalability for large datasets, and classifying novel viral sequences.

Purpose of the Study:

  • To develop an advanced, machine learning-based tool, vConTACT3, for accurate and scalable virus taxonomy.
  • To improve the classification of both prokaryotic and eukaryotic viruses across multiple taxonomic realms.
  • To establish accurate hierarchical taxonomy from genus to order for viral sequences.

Main Methods:

  • Developed vConTACT3, a machine learning tool optimizing gene-sharing thresholds and adaptive, realm-specific cut-offs.
  • Applied vConTACT3 to large datasets of public prokaryotic and eukaryotic virus genomes.
  • Evaluated vConTACT3's performance against existing taxonomy and its predecessor, vConTACT2.

Main Results:

  • vConTACT3 demonstrates >95% agreement with official taxonomy for over 35,000 prokaryotic and 13,000 eukaryotic virus genomes.
  • The tool surpasses vConTACT2 in accuracy across most realms and offers improved speed.
  • vConTACT3 successfully classifies previously uncharacterized viral taxa and provides rapid, automated taxonomy assignments for tens of thousands of unclassified viruses.

Conclusions:

  • vConTACT3 significantly enhances virus classification accuracy and scalability, addressing limitations of previous tools.
  • The tool provides systematic taxonomic assignments for vast numbers of unclassified viral sequences.
  • Analysis using vConTACT3 suggests potential revisions to the number of supported taxonomic ranks and highlights challenging areas in virosphere classification.