Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

Abstract Background A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented oppor...

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Bibliographic Details
Main Authors: Michael R. Garvin, Erica T. Prates, Mirko Pavicic, Piet Jones, B. Kirtley Amos, Armin Geiger, Manesh B. Shah, Jared Streich, Joao Gabriel Felipe Machado Gazolla, David Kainer, Ashley Cliff, Jonathon Romero, Nathan Keith, James B. Brown, Daniel Jacobson
Format: Article
Language:English
Published: BMC 2020-12-01
Series:Genome Biology
Subjects:
Molecular evolution
Coronavirus
SARS-CoV-2
COVID-19
Local adaptation
Adaptive mutation
Online Access:https://doi.org/10.1186/s13059-020-02191-0

Internet

https://doi.org/10.1186/s13059-020-02191-0

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