Assessing different next-generation sequencing technologies for wastewater-based epidemiology

Abstract

AbstractWastewater-based epidemiology has proven to be an important public health asset during the COVID-19 pandemic. It can provide less biassed and more cost-effective population-level monitoring of the disease burden as compared to clinical testing. An essential component of SARS-CoV-2 wastewater monitoring is next-generation sequencing, providing genomic data to identify and quantify circulating viral strains rapidly. However, the specific choice of sequencing method influences the quality and timeliness of generated data and hence its usefulness for wastewater-based pathogen surveillance. Here, we systematically benchmarked Illumina Novaseq 6000, Element Aviti, ONT R9.4.1 MinION flow cell, and ONT R9.4.1 Flongle flow cell sequencing data to facilitate the selection of sequencing technology. Using a time series of wastewater samples from influent of six wastewater treatment plants throughout Switzerland, along with spike-in experiments, we show that higher sequencing error rates of ONT Nanopore sequencing reduce the accuracy of estimates of the relative abundance of viral variants, but the overall trend is in good concordance among all technologies. We find that the sequencing runtime for ONT Nanopore flow cells can be reduced to as little as five hours without significant impact on the quality of variant estimates. Our findings suggest that SARS-CoV-2 variant tracking is readily achievable with all tested technologies, albeit with different tradeoffs in terms of cost, timeliness and accuracy.