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Evaluation of variant calling algorithms for wastewater-based epidemiology using mixed populations of SARS-CoV-2 variants in synthetic and wastewater samples

Lookup NU author(s): Dr Mathew Brown, 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.

Publication metadata

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


DOI: 10.1099/mgen.0.000933

PubMed id: 37074153


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