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Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification
Jasmijn A. Baaijens; Alessandro Zulli; Isabel M. Ott; Mary E. Petrone; Tara Alpert; Joseph R. Fauver; Chaney C. Kalinich; Chantal B.F. Vogels; Mallery I. Breban; Claire Duvallet; Kyle McElroy; Newsha Ghaeli; Maxim Imakaev; Malaika Mckenzie-Bennett; Keith Robison; Alex Plocik; Rebecca Schilling; Martha Pierson; Rebecca Littlefield; Michelle Spencer; Birgitte B. Simen; - Yale SARS-CoV-2 Genomic Surveillance Initiative; William P. Hanage; Nathan D. Grubaugh; Jordan Peccia; Michael Baym.
Afiliação
  • Jasmijn A. Baaijens; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
  • Alessandro Zulli; Department of Chemical and Environmental Engineering, Yale University, New Haven, CT, USA
  • Isabel M. Ott; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Mary E. Petrone; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Tara Alpert; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Joseph R. Fauver; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Chaney C. Kalinich; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Chantal B.F. Vogels; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Mallery I. Breban; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA
  • Claire Duvallet; Biobot Analytics, Inc., Cambridge, MA, USA
  • Kyle McElroy; Biobot Analytics, Inc., Cambridge, MA, USA
  • Newsha Ghaeli; Biobot Analytics, Inc., Cambridge, MA, USA
  • Maxim Imakaev; Biobot Analytics, Inc., Cambridge, MA, USA
  • Malaika Mckenzie-Bennett; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Keith Robison; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Alex Plocik; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Rebecca Schilling; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Martha Pierson; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Rebecca Littlefield; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Michelle Spencer; Ginkgo Bioworks, Inc., Boston, MA, USA
  • Birgitte B. Simen; Ginkgo Bioworks, Inc., Boston, MA, USA
  • - Yale SARS-CoV-2 Genomic Surveillance Initiative; Yale University, New Haven, CT, USA
  • William P. Hanage; Center for Communicable Disease Dynamics and Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
  • Nathan D. Grubaugh; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA and Department of Ecology and Evolutionary Biology, Yale Uni
  • Jordan Peccia; Department of Chemical and Environmental Engineering, Yale University, New Haven, CT, USA
  • Michael Baym; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21262938
ABSTRACT
Effectively monitoring the spread of SARS-CoV-2 variants is essential to efforts to counter the ongoing pandemic. Wastewater monitoring of SARS-CoV-2 RNA has proven an effective and efficient technique to approximate COVID-19 case rates in the population. Predicting variant abundances from wastewater, however, is technically challenging. Here we show that by sequencing SARS-CoV-2 RNA in wastewater and applying computational techniques initially used for RNA-Seq quantification, we can estimate the abundance of variants in wastewater samples. We show by sequencing samples from wastewater and clinical isolates in Connecticut U.S.A. between January and April 2021 that the temporal dynamics of variant strains broadly correspond. We further show that this technique can be used with other wastewater sequencing techniques by expanding to samples taken across the United States in a similar timeframe. We find high variability in signal among individual samples, and limited ability to detect the presence of variants with clinical frequencies <10%; nevertheless, the overall trends match what we observed from sequencing clinical samples. Thus, while clinical sequencing remains a more sensitive technique for population surveillance, wastewater sequencing can be used to monitor trends in variant prevalence in situations where clinical sequencing is unavailable or impractical.
Licença
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Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo diagnóstico / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: medRxiv Tipo de estudo: Estudo diagnóstico / Estudo observacional / Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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