RNA-Seq of Untreated Wastewater to Assess COVID-19 and Endemic Viruses

Stockdale, Stephen R.; Blanchard, Adam A.; Nayak, Amit R.; Husain, Aliabbas A.; Nashine, Rupam; Dudani, Hemanshi; McClure, C. Patrick; Tarr, Alexander W.; Nag, Aditi; Meena, Ekta; Sinha, Vikky; Shrivastava, Sandeep; Hill, Colin; Singer, Andrew C.; Gomes, Rachel L.; Acheampong, Edward; Babu, Chidambaram Saravana; Bhatnagar, Tarun; Vetrivel, Umashankar; Arora, Sudipti; Kashyap, Rajpal S.; Monaghan, Tanya Marie

doi:10.2139/ssrn.4147539

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The SARS-CoV-2 virus is a single-stranded RNA virus that can be shed in wastewater through human waste such as feces, saliva, and urine 13,14 . In comparison to clinical testing, wastewater analyses provide a less-biased approach to viral monitoring, particularly in areas with limited healthcare resources and unclear testing hesitancy rates [15][16][17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Novel SARS-CoV-2 Variants from Urban and Rural Wastewater through Genome Sequencing and Machine Learning

Zhuang,

Vo,

Moshi

et al. 2024

Preprint

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Genome sequencing from wastewater has emerged as an accurate and cost-effective tool for identifying SARS-CoV-2 variants. However, existing methods for analyzing wastewater sequencing data are not designed to detect novel variants that have not been characterized in humans. Here, we present an unsupervised learning approach that clusters co-varying and time-evolving mutation patterns leading to the identification of SARS-CoV-2 variants. To build our model, we sequenced 3,659 wastewater samples collected over a span of more than two years from urban and rural locations in Southern Nevada. We then developed a multivariate independent component analysis (ICA)-based pipeline to transform mutation frequencies into independent sources with co-varying and time-evolving patterns and compared variant predictions to >5,000 SARS-CoV-2 clinical genomes isolated from Nevadans. Using the source patterns as data-driven reference barcodes, we demonstrated the model accuracy by successfully detecting the Delta variant in late 2021, Omicron variants in 2022, and emerging recombinant XBB variants in 2023. Our approach revealed the spatial and temporal dynamics of variants in both urban and rural regions; achieved earlier detection of most variants compared to other computational tools; and uncovered unique co-varying mutation patterns not associated with any known variant. The multivariate nature of our pipeline boosts statistical power and can support accurate and early detection of SARS-CoV-2 variants. This feature offers a unique opportunity for novel variant and pathogen detection, even in the absence of clinical testing.

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