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Metaviromic identification of genetic hotspots of coronavirus pathogenicity using machine learning

Jonathan J. Park, View ORCID ProfileSidi Chen
doi: https://doi.org/10.1101/2020.08.13.248575
Jonathan J. Park
1Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
2System Biology Institute, Yale University, West Haven, Connecticut, USA
3Center for Cancer Systems Biology, Yale University, West Haven, Connecticut, USA
4M.D.-Ph.D. Program, Yale University, West Haven, Connecticut, USA
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Sidi Chen
1Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA
2System Biology Institute, Yale University, West Haven, Connecticut, USA
3Center for Cancer Systems Biology, Yale University, West Haven, Connecticut, USA
4M.D.-Ph.D. Program, Yale University, West Haven, Connecticut, USA
5Immunobiology Program, Yale University, New Haven, Connecticut, USA
6Molecular Cell Biology, Genetics, and Development Program, Yale University, New Haven, Connecticut, USA
7Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, Connecticut, USA
8Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
9Comprehensive Cancer Center, Yale University School of Medicine, New Haven, Connecticut, USA
10Stem Cell Center, Yale University School of Medicine, New Haven, Connecticut, USA
11Liver Center, Yale University School of Medicine, New Haven, Connecticut, USA
12Center for Biomedical Data Science, Yale University School of Medicine, New Haven, Connecticut, USA
13Center for RNA Science and Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
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  • ORCID record for Sidi Chen
  • For correspondence: sidi.chen@yale.edu
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Abstract

The COVID-19 pandemic caused by SARS-CoV-2 has become a major threat across the globe. Here, we developed machine learning approaches to identify key pathogenic regions in coronavirus genomes. We trained and evaluated 7,562,625 models on 3,665 genomes including SARS-CoV-2, MERS-CoV, SARS-CoV and other coronaviruses of human and animal origins to return quantitative and biologically interpretable signatures at nucleotide and amino acid resolutions. We identified hotspots across the SARS-CoV-2 genome including previously unappreciated features in spike, RdRp and other proteins. Finally, we integrated pathogenicity genomic profiles with B cell and T cell epitope predictions for enrichment of sequence targets to help guide vaccine development. These results provide a systematic map of predicted pathogenicity in SARS-CoV-2 that incorporates sequence, structural and immunological features, providing an unbiased collection of genetic elements for functional studies. This metavirome-based framework can also be applied for rapid characterization of new coronavirus strains or emerging pathogenic viruses.

Competing Interest Statement

The authors have declared no competing interest.

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-NC-ND 4.0 International license.
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Posted August 14, 2020.
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Metaviromic identification of genetic hotspots of coronavirus pathogenicity using machine learning
Jonathan J. Park, Sidi Chen
bioRxiv 2020.08.13.248575; doi: https://doi.org/10.1101/2020.08.13.248575
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Metaviromic identification of genetic hotspots of coronavirus pathogenicity using machine learning
Jonathan J. Park, Sidi Chen
bioRxiv 2020.08.13.248575; doi: https://doi.org/10.1101/2020.08.13.248575

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