Tracking SARS-CoV-2 genomic variants in wastewater sequencing data with LolliPop.

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 estimates not biased by testing practices 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. LolliPop is tailored to

SUBMITTER: Dreifuss D 

PROVIDER: S-EPMC12945317 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature