A Gold Standard Dataset for Lineage Abundance Estimation from Wastewater

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. Highlights ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement Funding for this project was provided by the North Carolina Department of Health and Human Services. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at * SARS : Severe Acute Respiratory Syndrome COVID-19 : Coronavirus Disease 2019 WBE : wastewater-based epidemiology WB : water background NWRB : SARS-CoV-2 negative wastewater RNA extract background PWRB : SARS-CoV-2 positive wastewater RNA extract background NWSS : National Wastewater Surveillance System CFSAN : Center for Food Safety and Applied Nutrition C-WAP : CFSAN Wastewater Analysis Pipeline ONT : Oxford Nanopore Technologies NFW : nuclease-free water RNA : ribonucleic acid SNV : single nucleotide variant NCBI : National Center for Biotechnology Information PCR : polymerase chain reaction ddPCR : droplet digital PCR Pangolin : Phylogenetic Assignment of Named Global Outbreak Lineages VOC : variant of concern DCIPHER : Data Collation and Integration for Public Health Event Responses S3C : Swiss SARS-CoV-2 Sequencing Consortium SIB : Swiss Institute of Bioinformatics