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Metaviromic identification of discriminative genomic features in SARS-CoV-2 using machine learning.
Park, Jonathan J; Chen, Sidi.
  • Park JJ; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
  • Chen S; System Biology Institute, Yale University, West Haven, CT, USA.
Patterns (N Y) ; 3(2): 100407, 2022 Feb 11.
Article in English | MEDLINE | ID: covidwho-1521457
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 immunologic 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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Systematic review/Meta Analysis Topics: Vaccines Language: English Journal: Patterns (N Y) Year: 2022 Document Type: Article Affiliation country: J.patter.2021.100407

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study / Systematic review/Meta Analysis Topics: Vaccines Language: English Journal: Patterns (N Y) Year: 2022 Document Type: Article Affiliation country: J.patter.2021.100407