Biology

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

N. Sapoval, Y. Liu, et al.

Discover QualD, a groundbreaking bioinformatics tool designed to detect SARS-CoV-2 variants in wastewater, offering faster detection up to three weeks earlier and exceptional accuracy over 95%. Developed by Nicolae Sapoval, Yunxi Liu, Esther G. Lou, Loren Hopkins, Katherine B. Ensor, Rebecca Schneider, Lauren B. Stadler, and Todd J. Treangen, QualD outperforms traditional tools, setting a new standard in public health monitoring.

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Abstract

This paper introduces QualD, a novel bioinformatics tool for detecting SARS-CoV-2 variants of concern (VoCs) in wastewater. QualD offers three key advantages: earlier VoC detection (up to 3 weeks earlier), high accuracy (over 95% precision in simulated benchmarks), and the ability to utilize all mutational signatures, including insertions and deletions. The tool's performance is demonstrated on real Houston wastewater data, showing improved precision and earlier detection compared to the state-of-the-art tool Freyja.

Publisher

Nature Communications

Published On

May 17, 2023

Authors

Nicolae Sapoval, Yunxi Liu, Esther G. Lou, Loren Hopkins, Katherine B. Ensor, Rebecca Schneider, Lauren B. Stadler, Todd J. Treangen

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