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Petabase-scale sequence alignment catalyses viral discovery

Robert C. Edgar, Jeff Taylor, Tomer Altman, Pierre Barbera, Dmitry Meleshko, Victor Lin, Dan Lohr, Gherman Novakovsky, Basem Al-Shayeb, Jillian F. Banfield, Anton Korobeynikov, Rayan Chikhi, View ORCID ProfileArtem Babaian
doi: https://doi.org/10.1101/2020.08.07.241729
Robert C. Edgar
1Unaffiliated
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Jeff Taylor
1Unaffiliated
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Tomer Altman
2Altman Analytics LLC
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Pierre Barbera
3Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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Dmitry Meleshko
4Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia
5Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, USA
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Victor Lin
1Unaffiliated
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Dan Lohr
1Unaffiliated
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Gherman Novakovsky
6Department of Medical Genetics, University of British Columbia. Vancouver, BC, Canada
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Basem Al-Shayeb
7Department of Plant and Microbial Biology, University of California, Berkeley
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Jillian F. Banfield
8Department of Earth and Planetary Science, University of California, Berkeley
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Anton Korobeynikov
4Center for Algorithmic Biotechnology, St. Petersburg State University, St. Petersburg, Russia
9Department of Statistical Modelling, St. Petersburg State University, St. Petersburg, Russia
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Rayan Chikhi
10Institut Pasteur, CNRS, Paris, France
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Artem Babaian
3Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
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  • ORCID record for Artem Babaian
  • For correspondence: ababaian@bccrc.ca
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Abstract

Public sequence data represents a major opportunity for viral discovery, but its exploration has been inhibited by a lack of efficient methods for searching this corpus, which is currently at the petabase scale and growing exponentially. To address the ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 and expand the known sequence diversity of viruses, we aligned pangenomes for coronaviruses (CoV) and other viral families to 5.6 petabases of public sequencing data from 3.8 million biologically diverse samples. To implement this strategy, we developed a cloud computing architecture, Serratus, tailored for ultra-high throughput sequence alignment at the petabase scale. From this search, we identified and assembled thousands of CoV and CoV-like genomes and genome fragments ranging from known strains to putatively novel genera. We generalise this strategy to other viral families, identifying several novel deltaviruses and huge bacteriophages. To catalyse a new era of viral discovery we made millions of viral alignments and family identifications freely available to the research community. Expanding the known diversity and zoonotic reservoirs of CoV and other emerging pathogens can accelerate vaccine and therapeutic developments for the current pandemic, and help us anticipate and mitigate future ones.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/ababaian/serratus

  • ↵1 https://bitbucket.org/tomeraltman/darth/

  • ↵2 https://gitlab.pasteur.fr/rchikhi_pasteur/serratus-batch-assembly/-/blob/master/template/template.yaml

  • ↵3 https://gitlab.pasteur.fr/rchikhi_pasteur/serratus-batch-assembly/-/blob/master/stats/bgc_parse_and_extract.py

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted August 10, 2020.
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Petabase-scale sequence alignment catalyses viral discovery
Robert C. Edgar, Jeff Taylor, Tomer Altman, Pierre Barbera, Dmitry Meleshko, Victor Lin, Dan Lohr, Gherman Novakovsky, Basem Al-Shayeb, Jillian F. Banfield, Anton Korobeynikov, Rayan Chikhi, Artem Babaian
bioRxiv 2020.08.07.241729; doi: https://doi.org/10.1101/2020.08.07.241729
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Petabase-scale sequence alignment catalyses viral discovery
Robert C. Edgar, Jeff Taylor, Tomer Altman, Pierre Barbera, Dmitry Meleshko, Victor Lin, Dan Lohr, Gherman Novakovsky, Basem Al-Shayeb, Jillian F. Banfield, Anton Korobeynikov, Rayan Chikhi, Artem Babaian
bioRxiv 2020.08.07.241729; doi: https://doi.org/10.1101/2020.08.07.241729

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