Genome Biology (Nov 2022)
Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques
- Jasmijn A. Baaijens,
- Alessandro Zulli,
- Isabel M. Ott,
- Ioanna Nika,
- Mart J. van der Lugt,
- Mary E. Petrone,
- Tara Alpert,
- Joseph R. Fauver,
- Chaney C. Kalinich,
- Chantal B. F. Vogels,
- Mallery I. Breban,
- Claire Duvallet,
- Kyle A. McElroy,
- Newsha Ghaeli,
- Maxim Imakaev,
- Malaika F. Mckenzie-Bennett,
- Keith Robison,
- Alex Plocik,
- Rebecca Schilling,
- Martha Pierson,
- Rebecca Littlefield,
- Michelle L. Spencer,
- Birgitte B. Simen,
- Yale SARS-CoV-2 Genomic Surveillance Initiative,
- William P. Hanage,
- Nathan D. Grubaugh,
- Jordan Peccia,
- Michael Baym
Affiliations
- Jasmijn A. Baaijens
- Department of Biomedical Informatics, Harvard Medical School
- Alessandro Zulli
- Department of Chemical and Environmental Engineering, Yale University
- Isabel M. Ott
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Ioanna Nika
- Department of Intelligent Systems, Delft University of Technology
- Mart J. van der Lugt
- Department of Intelligent Systems, Delft University of Technology
- Mary E. Petrone
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Tara Alpert
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Joseph R. Fauver
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Chaney C. Kalinich
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Chantal B. F. Vogels
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Mallery I. Breban
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Claire Duvallet
- Biobot Analytics, Inc.
- Kyle A. McElroy
- Biobot Analytics, Inc.
- Newsha Ghaeli
- Biobot Analytics, Inc.
- Maxim Imakaev
- Biobot Analytics, Inc.
- Malaika F. Mckenzie-Bennett
- Ginkgo Bioworks, Inc.
- Keith Robison
- Ginkgo Bioworks, Inc.
- Alex Plocik
- Ginkgo Bioworks, Inc.
- Rebecca Schilling
- Ginkgo Bioworks, Inc.
- Martha Pierson
- Ginkgo Bioworks, Inc.
- Rebecca Littlefield
- Ginkgo Bioworks, Inc.
- Michelle L. Spencer
- Ginkgo Bioworks, Inc.
- Birgitte B. Simen
- Ginkgo Bioworks, Inc.
- Yale SARS-CoV-2 Genomic Surveillance Initiative
- William P. Hanage
- Center for Communicable Disease Dynamics and Department of Epidemiology, Harvard T.H. Chan School of Public Health
- Nathan D. Grubaugh
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health
- Jordan Peccia
- Department of Chemical and Environmental Engineering, Yale University
- Michael Baym
- Department of Biomedical Informatics, Harvard Medical School
- DOI
- https://doi.org/10.1186/s13059-022-02805-9
- Journal volume & issue
-
Vol. 23,
no. 1
pp. 1 – 20
Abstract
Abstract 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.
