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Journal Article Ppublish 8 citations

PubMed: 38785221 DOI: 10.1099/mgen.0.001249

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data.

Microb Genom 10(5)

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 F Y 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

Affiliations

  • Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
  • Environment and Climate Change Canada, Montreal, QC, Canada.
  • Department of Biology, University of Waterloo, Waterloo, ON, Canada.
  • Department of Biosystems Science and Engineering, ETH Zurich, Basel, BS, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, VD, Switzerland.
  • Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA.
  • Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
  • Computational Biology, A.C. Camargo Cancer Center, São Paulo, SP, Brazil.
  • Rutgers University, New Brunswick, NJ, USA.
  • Universidade de São Paulo, São Paulo, SP, Brazil.
  • Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thermi, 57001, Thessaloníki, Greece.
  • Department of Integrative Biology, University of California, Berkeley, CA, USA.
  • School of Mathematical Sciences, Peking University, Beijing, BJ, PR China.
  • Delft University of Technology, Delft, ZH, Netherlands.
  • Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.

Abstract

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 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta-Omicron recombinant and a synthetic 'novel' lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

MeSH

Infections, Genetic Variation, Genomics, Sars Virus, Sars-Cov-2

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