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Tracking SARS-CoV-2 genomic variants in wastewater sequencing data with LolliPop
Preprint
in English
| medRxiv
| ID: ppmedrxiv-22281825
ABSTRACT
During the COVID-19 pandemic, wastewater-based epidemiology has progressively taken a central role as a pathogen surveillance tool. Tracking viral loads and variant outbreaks in sewage offers advantages over clinical surveillance methods by providing unbiased estimates and enabling early detection. However, wastewater-based epidemiology poses new computational research questions that need to be solved in order for this approach to be implemented broadly and successfully. Here, we address the variant deconvolution problem, where we aim to estimate the relative abundances of genomic variants from next-generation sequencing data of a mixed wastewater sample. We introduce LolliPop, a computational method to solve the variant deconvolution problem by simultaneously solving least squares problems and kernel-based smoothing of relative variant abundances from wastewater time series sequencing data. We derive multiple approaches to compute confidence bands, and demonstrate the application of our method to data from the Swiss wastewater surveillance efforts.
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Experimental_studies
/
Prognostic study
Language:
English
Year:
2022
Document type:
Preprint