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medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.06.06.22275866


Wastewater-based epidemiology (WBE) has been extensively used during the COVID-19 pandemic to detect and monitor the spread of the SARS-CoV-2 virus and its variants. It has also proven to be an excellent tool to complement and support insights gained from reported clinical data. Globally, many groups have developed bioinformatics pipelines to analyse sequencing data from wastewater. Accurate calling of mutations from RNA extracted from wastewater samples is key in supporting clinical data to make informed decisions on the prevalence of variants, as well as in the use of WBE as a molecular surveillance tool. However, wastewater samples can be challenging to extract and sequence, and performance of variant-calling algorithms in this context has, so far, not been investigated. Analysis of the data and assignment of circulating variants depends heavily on the accuracy of the variant caller, particularly given the degraded nature of the viral RNA and the heterogeneous nature of metagenomic samples. 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 a known mix of three different SARS-CoV-2 variant genomes (Alpha, Beta and Delta), as well as 13 wastewater samples collected in London between the 15 th and 18 th December 2021. Using the Quasimodo benchmarking tool to compare the six variant callers, we assessed the fundamental parameters of recall (sensitivity) and precision (specificity) in confirming the presence of a variant within the population. Our results show that BCFtools, FreeBayes and VarScan called the expected mutations with higher precision and recall than iVAR or GATK, although the latter identified more expected defining mutations. 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.

medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.02.16.22269810


Genomic surveillance of SARS-CoV-2 has been essential to provide an evidence base for public health decisions throughout the SARS-CoV-2 pandemic. Sequencing data from clinical cases has provided data crucial to understanding disease transmission and the detection, surveillance, and containment of outbreaks of novel variants, which continue to pose fresh challenges. However, genomic wastewater surveillance can provide important complementary information by providing estimates of variant frequencies which do not suffer from sampling bias, and capturing all variants circulating in a population. Here we show that genomic SARS-CoV-2 wastewater surveillance can detect fine-scale differences within urban centres, specifically within the city of Liverpool, UK, during the emergence of Alpha and Delta variants between November 2020 and June 2021. Overall, the correspondence between wastewater and clinical variant frequencies demonstrates the reliability of wastewater surveillance. Yet, discrepancies between the two approaches in when the Alpha variant was first detected emphasises that wastewater monitoring can also capture missing information resulting from asymptomatic cases or communities less engaged with testing programmes, as found by a simultaneous surge testing effort across the city.