Authors
Steven G Sutcliffe, Susanne A Kraemer, Isaac Ellmen, Jennifer J Knapp, Alyssa K Overton, Delaney Nash, Jozef I Nissimov, Trevor C Charles, David Dreifuss, Ivan Topolsky, Pelin I Baykal, Lara Fuhrmann, Kim P Jablonski, Niko Beerenwinkel, Joshua I Levy, Abayomi S Olabode, Devan G Becker, Gopi Gugan, Erin Brintnell, Art FY Poon, Renan Valieris, Rodrigo D Drummond, Alexandre Defelicibus, Emmanuel Dias-Neto, Rafael A Rosales, Israel Tojal da Silva, Aspasia Orfanou, Fotis Psomopoulos, Nikolaos Pechlivanis, Lenore Pipes, Zihao Chen, Jasmijn A Baaijens, Michael Baym, B Jesse Shapiro
Publication date
2024/5/24
Journal
Microbial Genomics
Volume
10
Issue
5
Pages
001249
Publisher
Microbiology Society
Description
Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated …
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