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Enabling accurate and early detection of recently emerged SARS-CoV-2 variants of concern in wastewater.
Sapoval, Nicolae; Liu, Yunxi; Lou, Esther G; Hopkins, Loren; Ensor, Katherine B; Schneider, Rebecca; Stadler, Lauren B; Treangen, Todd J.
  • Sapoval N; Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
  • Liu Y; Department of Computer Science, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
  • Lou EG; Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
  • Hopkins L; Houston Health Department, 8000 N. Stadium Dr., Houston, TX, 77054, USA.
  • Ensor KB; Department of Statistics, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
  • Schneider R; Department of Statistics, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
  • Stadler LB; Houston Health Department, 8000 N. Stadium Dr., Houston, TX, 77054, USA.
  • Treangen TJ; Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA. lauren.stadler@rice.edu.
Nat Commun ; 14(1): 2834, 2023 05 17.
Article in English | MEDLINE | ID: covidwho-2326063
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).
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2023 Document Type: Article Affiliation country: S41467-023-38184-3

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Topics: Variants Limits: Humans Language: English Journal: Nat Commun Journal subject: Biology / Science Year: 2023 Document Type: Article Affiliation country: S41467-023-38184-3