Alcov: Estimating Variant of Concern Abundance from SARS-CoV-2 Wastewater Sequencing Data

Abstract

Detection of SARS-CoV-2 in wastewater is an important strategy for community level surveillance. Variants of concern (VOCs) can be detected in the wastewater samples using next generation sequencing, however it can be challenging to determine the relative abundance of different VOCs since the reads cannot be assembled into complete genomes. Here, we present Alcov (abundance learning of SARS-CoV-2 variants), a tool that uses mutation frequencies in SARS-CoV-2 sequencing data to predict the distribution of VOC lineages in the sample. We used Alcov to predict the distributions of lineages from three wastewater samples which agreed well with clinical data. By predicting not just which VOCs are present, but their relative abundances in the population, Alcov extracts a more complete snapshot of the variants which are circulating in a community.

QuaID: Enabling Earlier Detection of Recently Emerged SARS-CoV-2 Variants of Concern in Wastewater

“…Current approaches for VoC detection in wastewater samples typically require sufficient depth and breadth of coverage of the variant genomes 9,12 , and therefore depend on a large fraction of the sample representing the variant genotype 10 , hampering early detection. Furthermore, most of the current approaches discard insertion and deletion (indel) information and only rely on single nucleotide variants (SNVs) associated with the VoC 9,12 . Finally, all approaches that rely on a database of previously collected SARS-CoV-2 genomes are biased by the contents of the database 26,27 , which can lead to both false negative and false positive calls at the inference stage 28 .…”

“…Wastewater monitoring is an invaluable tool for SARS-CoV-2 surveillance [1][2][3][4][5][6][7][8] . Despite multiple recent successes in VoC monitoring and detection from wastewater sequencing data [9][10][11][12][13][14][15] , there are multiple challenges associated with the nature of the environmental data. Since wastewater represents a pooled sample of multiple hosts, it harbors a diversity of SARS-CoV-2 variants that are currently circulating in the population 1,2,10,13 , including potentially previously unreported genotypes 16 .…”

“…Despite these challenges, detection of VoCs in wastewater samples is important for monitoring the emergence and spread of variants and informing public health response 4,5,11,24,25 . Current approaches for VoC detection in wastewater samples typically require sufficient depth and breadth of coverage of the variant genomes 9,12 , and therefore depend on a large fraction of the sample representing the variant genotype 10 , hampering early detection. Furthermore, most of the current approaches discard insertion and deletion (indel) information and only rely on single nucleotide variants (SNVs) associated with the VoC 9,12 .…”

VirPool: Model-Based Estimation of SARS-CoV-2 Variant Proportions in Wastewater Samples

“…the sum of proportions of individual variants is greater than one) and can be biased by mutations shared by multiple variants, an issue which is likely to be exacerbated given the increased occurrence of convergent evolution events between different lineages due to selective pressures. To account for these shared mutations, Ellmen et al (2021) estimate the proportions of variants by optimising the L2 metric between a mixture of base frequencies of individual variants and observed frequencies of specific mutation sites. Amman et al (2022) pushed this idea further by estimating the proportions jointly for multiple samples, taking the time of their collection into account.…”

Lineage abundance estimation for SARS-CoV-2 in wastewater using transcriptome quantification techniques

“…Given the urgency of wastewater surveillance during the ongoing pandemic, several tools are under development to tackle the problem of lineage (or variant) quantification from wastewater sequencing data. For example, Ellmen et al define an optimization problem to combine individual mutation frequencies into lineage frequencies [15] and Karthikeyan et al propose an algorithm based on a regression problem to minimize the edit distance between sequences and a reference [16]. Each of these approaches under development relies on the discovery and quantification of individual mutations using popular tools like V-pipe [17] or iVar [18], a process that is highly error-prone given the nature of wastewater sequencing data.…”