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Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques
Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for trans...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643916/ https://www.ncbi.nlm.nih.gov/pubmed/36348471 http://dx.doi.org/10.1186/s13059-022-02805-9 |
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author | Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Nika, Ioanna van der Lugt, Mart J. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B. F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle A. Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika F. Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle L. Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael |
author_facet | Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Nika, Ioanna van der Lugt, Mart J. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B. F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle A. Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika F. Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle L. 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 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those 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 mutant prevalence in situations where clinical sequencing is unavailable. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02805-9. |
format | Online Article Text |
id | pubmed-9643916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96439162022-11-14 Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Nika, Ioanna van der Lugt, Mart J. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B. F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle A. Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika F. Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle L. Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael Genome Biol Method Effectively monitoring the spread of SARS-CoV-2 mutants is essential to efforts to counter the ongoing pandemic. Predicting lineage abundance from wastewater, however, is technically challenging. We show that by sequencing SARS-CoV-2 RNA in wastewater and applying algorithms initially used for transcriptome quantification, we can estimate lineage abundance in wastewater samples. We find high variability in signal among individual samples, but the overall trends match those 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 mutant prevalence in situations where clinical sequencing is unavailable. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02805-9. BioMed Central 2022-11-08 /pmc/articles/PMC9643916/ /pubmed/36348471 http://dx.doi.org/10.1186/s13059-022-02805-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Method Baaijens, Jasmijn A. Zulli, Alessandro Ott, Isabel M. Nika, Ioanna van der Lugt, Mart J. Petrone, Mary E. Alpert, Tara Fauver, Joseph R. Kalinich, Chaney C. Vogels, Chantal B. F. Breban, Mallery I. Duvallet, Claire McElroy, Kyle A. Ghaeli, Newsha Imakaev, Maxim Mckenzie-Bennett, Malaika F. Robison, Keith Plocik, Alex Schilling, Rebecca Pierson, Martha Littlefield, Rebecca Spencer, Michelle L. Simen, Birgitte B. Hanage, William P. Grubaugh, Nathan D. Peccia, Jordan Baym, Michael Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques |
title | Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques |
title_full | Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques |
title_fullStr | Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques |
title_full_unstemmed | Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques |
title_short | Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques |
title_sort | lineage abundance estimation for sars-cov-2 in wastewater using transcriptome quantification techniques |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9643916/ https://www.ncbi.nlm.nih.gov/pubmed/36348471 http://dx.doi.org/10.1186/s13059-022-02805-9 |
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