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Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification
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 wastewate...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423229/ https://www.ncbi.nlm.nih.gov/pubmed/34494031 http://dx.doi.org/10.1101/2021.08.31.21262938 |
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author | Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B.F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael |
author_facet | Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B.F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael |
author_sort | Baaijens, Jasmijn A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8423229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-84232292021-09-08 Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B.F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael medRxiv Article 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. Cold Spring Harbor Laboratory 2021-09-02 /pmc/articles/PMC8423229/ /pubmed/34494031 http://dx.doi.org/10.1101/2021.08.31.21262938 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B.F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification |
title | Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification |
title_full | Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification |
title_fullStr | Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification |
title_full_unstemmed | Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification |
title_short | Variant abundance estimation for SARS-CoV-2 in wastewater using RNA-Seq quantification |
title_sort | variant abundance estimation for sars-cov-2 in wastewater using rna-seq quantification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423229/ https://www.ncbi.nlm.nih.gov/pubmed/34494031 http://dx.doi.org/10.1101/2021.08.31.21262938 |
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