Computational Biology
@ Comenius University in Bratislava

Askar Gafurov, Andrej Balaz, Fabian Amman, Kristina Borsova, Viktoria Cabanova, Boris Klempa, Andreas Bergthaler, Tomas Vinar, Brona Brejova. VirPool: model-based estimation of SARS-CoV-2 variant proportions in wastewater samples. BMC Bioinformatics, 23(1):551. 2022.

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Download from publisher: https://doi.org/10.1186/s12859-022-05100-3

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Bibliography entry: BibTeX

See also: early version

Abstract:

BACKGROUND: The genomes of SARS-CoV-2 are classified into variants, some of which 
are monitored as variants of concern (e.g. the Delta variant B.1.617.2 or Omicron 
variant B.1.1.529). Proportions of these variants circulating in a human 
population are typically estimated by large-scale sequencing of individual 
patient samples. Sequencing a mixture of SARS-CoV-2 RNA molecules from wastewater 
provides a cost-effective alternative, but requires methods for estimating 
variant proportions in a mixed sample. RESULTS: We propose a new method based on 
a probabilistic model of sequencing reads, capturing sequence diversity present 
within individual variants, as well as sequencing errors. The algorithm is 
implemented in an open source Python program called VirPool. We evaluate the 
accuracy of VirPool on several simulated and real sequencing data sets from both 
Illumina and nanopore sequencing platforms, including wastewater samples from 
Austria and France monitoring the onset of the Alpha variant. CONCLUSIONS: 
VirPool is a versatile tool for wastewater and other mixed-sample analysis that 
can handle both short- and long-read sequencing data. Our approach does not 
require pre-selection of characteristic mutations for variant profiles, it is 
able to use the entire length of reads instead of just the most informative 
positions, and can also capture haplotype dependencies within a single read.