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

被引:12
|
作者
Sapoval, Nicolae [1 ]
Liu, Yunxi [1 ]
Lou, Esther G. G. [2 ]
Hopkins, Loren [3 ,4 ]
Ensor, Katherine B. [4 ]
Schneider, Rebecca [3 ]
Stadler, Lauren B. [2 ]
Treangen, Todd J. [1 ]
机构
[1] Rice Univ, Dept Comp Sci, 6100 Main St, Houston, TX 77005 USA
[2] Rice Univ, Dept Civil & Environm Engn, 6100 Main St, Houston, TX 77005 USA
[3] Houston Hlth Dept, 8000 N Stadium Dr, Houston, TX 77054 USA
[4] Rice Univ, Dept Stat, 6100 Main St, Houston, TX 77005 USA
基金
美国国家科学基金会;
关键词
D O I
10.1038/s41467-023-38184-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Sapoval et al. introduce QuaID, a bioinformatics tool for SARS-CoV-2 variant detection based on quasi-unique mutations. QuaID leverages all mutations, including insertions and deletions, and provides precise detection of variants early in their spread. 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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页数:7
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