QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
04 Aug 2022
Historique:
pubmed: 29 7 2022
medline: 29 7 2022
entrez: 28 7 2022
Statut: epublish

Résumé

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variants of concern (VoC) in communities. Multiple recent studies support that wastewater-based SARS-CoV-2 detection of circulating VoC can precede clinical cases by up to two weeks. Furthermore, wastewater based epidemiology enables wide population-based screening and study of viral evolutionary dynamics. However, highly sensitive detection of emerging variants remains a complex task due to the pooled nature of environmental samples and genetic material degradation. In this paper we propose quasi-unique mutations for VoC identification, implemented in a novel bioinformatics tool (QuaID) for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3 week earlier VoC detection compared to existing approaches, (ii) enables more sensitive VoC detection, which is shown to be tolerant of >50% mutation drop-out, and (iii) leverages all mutational signatures, including insertions & deletions.

Identifiants

pubmed: 35898338
doi: 10.1101/2021.09.08.21263279
pmc: PMC9327636
pii:
doi:

Types de publication

Preprint

Langues

eng

Subventions

Organisme : NIAID NIH HHS
ID : P01 AI152999
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES028819
Pays : United States

Commentaires et corrections

Type : UpdateIn

Déclaration de conflit d'intérêts

Competing interests Authors declare no competing interests.

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Auteurs

Nicolae Sapoval (N)

Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Yunxi Liu (Y)

Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Esther G Lou (EG)

Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Loren Hopkins (L)

Houston Health Department, 8000 N. Stadium Dr., Houston, TX 77054.
Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Katherine B Ensor (KB)

Department of Statistics, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Rebecca Schneider (R)

Houston Health Department, 8000 N. Stadium Dr., Houston, TX 77054.

Lauren B Stadler (LB)

Department of Civil and Environmental Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Todd J Treangen (TJ)

Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA.

Classifications MeSH