Gene sharing networks to automate genome-based prokaryotic viral taxonomy

Jang, Ho Bin; Bolduc, Benjamin; Zablocki, Olivier; Kuhn, Jens H.; Roux, Simon; Adriaenssens, Evelien M.; Brister, J. Rodney; Kropinski, Andrew M.; Krupovic, Mart; Turner, Dann; Sullivan, Matthew B.

doi:10.1101/533240

Title: Gene sharing networks to automate genome-based prokaryotic viral taxonomy

Journal Article · Tue Jan 29 00:00:00 EST 2019 · Nature Biotechnology

DOI:https://doi.org/10.1101/533240· OSTI ID:1569046

Jang, Ho Bin ^[1]; Bolduc, Benjamin ^[1];

^[1]; Kuhn, Jens H. ^[2];

^[3];

^[4]; Brister, J. Rodney ^[5]; Kropinski, Andrew M. ^[6];

^[7];

^[8];

^[1]

The Ohio State Univ., Columbus, OH (United States)
National Institutes of Health, Fort Detrick, Frederick, MD (United States)
USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
Univ. of Liverpool, Liverpool (United Kingdom)
National Inst. of Health (NIH), Bethesda, MD (United States)
Univ. of Guelph, Guelph, ON (Canada)
Inst. Pasteur, Paris (France)
Univ. of the West of England, Bristol (United Kingdom)

Viruses of bacteria and archaea impact natural, engineered and human ecosystems, but their study is hampered by the lack of a universal or scalable taxonomic framework. Furthermore we introduce vConTACT v2.0, a network-based application to establish prokaryotic virus taxonomy that scales to thousands of uncultivated virus genomes, and integrates distance-based hierarchical clustering and confidence scores for all taxonomic predictions. Performance tests demonstrated significant improvements over the original tool and near-identical (96%) correspondence to current International Committee on Taxonomy of Viruses (ICTV) viral taxonomy where genus-level assignments are available. Beyond these “known viruses”, vConTACT v2.0 suggested automatic genus assignments for 1,364 previously unclassified reference viruses, with perfectly scoring assignments submitted as new taxonomic proposals to ICTV. Scaling experiments with 15,280 global ocean large viral genome fragments demonstrated that the reference network was rapidly scalable and robust to adding large-scale viral metagenomic datasets. Together these efforts provide a critically-needed, systematically classified reference network and an accurate, scalable, and automatable taxonomic analysis tool.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1569046

Journal Information:: Nature Biotechnology, Vol. 37, Issue 6; ISSN 1087-0156

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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