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Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples.
Terbot, John W; Johri, Parul; Liphardt, Schuyler W; Soni, Vivak; Pfeifer, Susanne P; Cooper, Brandon S; Good, Jeffrey M; Jensen, Jeffrey D.
Afiliación
  • Terbot JW; University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.
  • Johri P; Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
  • Liphardt SW; Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
  • Soni V; University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.
  • Pfeifer SP; Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
  • Cooper BS; Arizona State University, School of Life Sciences, Center for Evolution & Medicine, Tempe, Arizona, United States of America.
  • Good JM; University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.
  • Jensen JD; University of Montana, Division of Biological Sciences, Missoula, Montana, United States of America.
PLoS Pathog ; 19(4): e1011265, 2023 04.
Article en En | MEDLINE | ID: mdl-37018331
Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: PLoS Pathog Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: SARS-CoV-2 / COVID-19 Tipo de estudio: Guideline Límite: Humans Idioma: En Revista: PLoS Pathog Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos