Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data

Abstract

Sequencing of SARS-CoV-2 RNA from wastewater samples has emerged as a valuable tool for detecting the presence and relative abundances of SARS-CoV-2 variants in a community. By analyzing the viral genetic material present in wastewater, public health officials can gain early insights into the spread of the virus and inform timely intervention measures. The construction of reference datasets from known SARS-CoV-2 lineages and their mutation profiles has become state-of-the-art for assigning viral lineages and their relative abundances from wastewater sequencing data. However, the selection of reference sequences or mutations directly affects the predictive power. Here, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark three data sets: 1) synthetic 'spike-in' mixtures, 2) German samples from early 2021, mainly comprising Alpha, and 3) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The two approaches differ in sub-lineage detection, with the marker-mutation-based method, in particular, being challenged by the increasing number of mutations and lineages. However, the estimations of both approaches depend on selecting representative references and optimized parameter settings. By performing parameter escalation experiments, we demonstrate the effects of reference size and alternative allele frequency cutoffs for abundance estimation. We show how different parameter settings can lead to different results for our test data sets, and illustrate the effects of virus lineage composition of wastewater samples and references. Here, we compare a mutation- and sequence-based reference construction and assignment for SARS-CoV-2 abundance estimation from wastewater samples. Our study highlights current computational challenges, focusing on the general reference design, which significantly and directly impacts abundance allocations. We illustrate advantages and disadvantages that may be relevant for further developments in the wastewater community and in the context of higher standardization.

A Gold Standard Dataset for Lineage Abundance Estimation from Wastewater

“…Kallisto takes a set of reference sequences that contains multiple genomic sequences per lineage, with the recommendation to include multiple sequences per lineages to reduce biases relating to within-lineage variation [27]. As a consequence, the composition of the reference set can significantly affect the performance of a reference based deconvolution tool [49]. We developed two databases to test how the performance of kallisto varies with respect to the database used.…”

Variations in the persistence of 5′-end genomic and subgenomic SARS-CoV-2 RNAs in wastewater from aircraft, airports and wastewater treatment plants

“…Interestingly, a study on stool samples [ 18 ] showed that SARS-CoV could survive up to 4 days in an alkaline pH of 9 while viruses in acidic stool samples with a pH of 6 are undetectable within 3 h. The conventional SARS-CoV-2 wastewater surveillance strategy relies predominantly on rapid surveys of the viral burden in the community [ 4 , 5 ] using a reverse transcription (RT)-qPCR-based approach. Wastewater sequencing [ [23] , [24] , [25] , [26] ] [ [23] , [24] , [25] , [26] ] [ [23] , [24] , [25] , [26] ] can complement this approach and allow the deciphering and detection of small-scale community spread. In addition, wastewater sequencing approaches allow in-depth studies of all kinds of genomic features of the SARS-CoV-2 RNA that persist in wastewater, e.g.…”