A Gold Standard Dataset for Lineage Abundance Estimation from Wastewater

Abstract

During the SARS-CoV-2 pandemic, genome-based wastewater surveillance sequencing has been a powerful tool for public health to monitor circulating and emerging viral variants. As a medium, wastewater is very complex because of its mixed matrix nature, which makes the deconvolution of wastewater samples more difficult. Here we introduce a gold standard dataset constructed from synthetic viral control mixtures of known composition, spiked into a wastewater RNA matrix and sequenced on the Oxford Nanopore Technologies platform. We compare the performance of eight of the most commonly used deconvolution tools in identifying SARS-CoV-2 variants present in these mixtures. The software evaluated was primarily chosen for its relevance to the CDC wastewater surveillance reporting protocol, which until recently employed a pipeline that incorporates results from four deconvolution methods: Freyja, kallisto, Kraken2/Bracken, and LCS. We also tested Lollipop, a deconvolution method used by the Swiss SARS-CoV2 Sequencing Consortium, and three recently-published methods: lineagespot, Alcov, and VaQuERo. We found that the commonly used software Freyja outperformed the other CDC pipeline tools in correct identification of lineages present in the control mixtures, and that the newer method VaQuERo was similarly accurate, with minor differences in the ability of the two methods to avoid false negatives and suppress false positives. These results provide insight into the effect of the tiling primer scheme and wastewater RNA extract matrix on viral sequencing and data deconvolution outcomes.