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Lookup NU author(s): Dr Mathew BrownORCiD, Dr Matthew WadeORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Wastewater-based epidemiology has been used extensively throughout the COVID-19 (coronavirus disease 19) pandemic to detect and monitor the spread and prevalence of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) and its variants. It has proven an excellent, complementary tool to clinical sequencing, supporting the insights gained and helping to make informed public-health decisions. Consequently, many groups globally have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations is critical in this process and in the assignment of circulating variants; yet, to date, the performance of variant-calling algorithms in wastewater samples has not been investigated. To address this, we compared the performance of six variant callers (VarScan, iVar, GATK, FreeBayes, LoFreq and BCFtools), used widely in bioinformatics pipelines, on 19 synthetic samples with known ratios of three different SARS-CoV-2 variants of concern (VOCs) (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15th and 18th December 2021. We used the fundamental parameters of recall (sensitivity) and precision (specificity) to confirm the presence of mutational profiles defining specific variants across the six variant callers. Our results show that BCFtools, FreeBayes and VarScan found the expected variants with higher precision and recall than GATK or iVar, although the latter identified more expected defining mutations than other callers. LoFreq gave the least reliable results due to the high number of false-positive mutations detected, resulting in lower precision. Similar results were obtained for both the synthetic and wastewater samples.
Author(s): Bassano I, Ramachandran VK, Khalifa MS, Lilley CJ, Brown MR, van Aerle R, Denise H, Rowe W, George A, Cairns E, Wierzbicki C, Pickwell ND, Carlile M, Holmes N, Payne A, Loose M, Burke TA, Paterson S, Wade MJ, Grimsley JMS
Publication type: Article
Publication status: Published
Journal: Microbial Genomics
Year: 2023
Volume: 9
Issue: 4
Print publication date: 01/04/2023
Online publication date: 19/04/2023
Acceptance date: 16/11/2022
Date deposited: 05/05/2023
ISSN (electronic): 2057-5858
Publisher: The Microbiology Society
URL: https://doi.org/10.1099/mgen.0.000933
DOI: 10.1099/mgen.0.000933
PubMed id: 37074153
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