Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater

Nicolae Sapoval, Yunxi Liu, Esther G. Lou, Loren Hopkins, Katherine B. Ensor, Rebecca Schneider, Lauren B. Stadler () and Todd J. Treangen ()
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Nicolae Sapoval: Rice University
Yunxi Liu: Rice University
Esther G. Lou: Rice University
Loren Hopkins: 8000 N. Stadium Dr.
Katherine B. Ensor: Rice University
Rebecca Schneider: 8000 N. Stadium Dr.
Lauren B. Stadler: Rice University
Todd J. Treangen: Rice University

Nature Communications, 2023, vol. 14, issue 1, 1-7

Abstract: Abstract As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions).

Date: 2023
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DOI: 10.1038/s41467-023-38184-3

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