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• W3048550170 abstract "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." @default.
• W3048550170 created "2020-08-18" @default.
• W3048550170 creator A5050630382 @default.
• W3048550170 creator A5076821347 @default.
• W3048550170 date "2020-08-14" @default.
• W3048550170 modified "2023-09-30" @default.
• W3048550170 title "Metaviromic identification of genetic hotspots of coronavirus pathogenicity using machine learning" @default.
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• W3048550170 doi "https://doi.org/10.1101/2020.08.13.248575" @default.
• W3048550170 hasPublicationYear "2020" @default.
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