Tracking SARS-CoV-2 genomic variants in wastewater sequencing data with<i>LolliPop</i>

Dreifuss, David; Topolsky, Ivan; Baykal, Pelin Icer; Beerenwinkel, Niko

doi:10.1101/2022.11.02.22281825

Cited by 7 publications

(9 citation statements)

References 22 publications

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“…In our study we had access to one week of wastewater surveillance data, but we believe that longer sampling time frames could positively impact variant abundance estimation. Since the variant deconvolution approach used in this study is tailored to time series data by allowing for kernel smoothing of variant abundance estimates over time 31 , we expect that smoothing over longer surveillance time periods would further decrease the divergence in variant abundance estimates between Illumina and Nanopore data. This prediction can be tested in the future by analysing data from longer time periods than one week.…”

Section: Discussionmentioning

confidence: 99%

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

John,

Dreifuss,

Kang

et al. 2024

Preprint

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Wastewater-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.

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Section: Discussionmentioning

confidence: 99%

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

John,

Dreifuss,

Kang

et al. 2024

Preprint

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“…Freyja [40], kallisto [26], LCS [28] and Kraken2/Bracken [29] were selected due to their use in the CFSAN’S C-WAP pipeline and LolliPop [30] was selected for its use in V-pipe. In addition to these, LineageSpot [34], Alcov [33], and VaQuERo [11] were selected to look at different and newer approaches to deconvolution.…”

Section: Methodsmentioning

confidence: 99%

A Gold Standard Dataset for Lineage Abundance Estimation from Wastewater

Ferdous Moon,

Kunkleman,

Taylor

et al. 2024

Preprint

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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.

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“…Mutations with prevalence of >=90 % for each VOC investigated here (Alpha, B.1.351, Gamma, Delta, Lambda, Mu, BA.1, BA.2, BA.3) based on the GISAID msa from 27-03-2022. This included 300 mutations associated with lineages including Alpha (28), B.1.351 (19), Gamma (32), Delta (25), Lambda (25), Mu (26), BA.1 (42), BA.2 (62), and BA.3 (41). Of these, 253 were unique mutations with 40,56,18 associated with BA.1, BA.2 and Delta, respectively.…”

Section: Basicmentioning

confidence: 99%

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

Sutcliffe,

Kraemer,

Ellmen

et al. 2024

Microbial Genomics

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Wastewater-based surveillance (WBS) is an important epidemiological and public health tool for tracking pathogens across the scale of a building, neighbourhood, city, or region. WBS gained widespread adoption globally during the SARS-CoV-2 pandemic for estimating community infection levels by qPCR. Sequencing pathogen genes or genomes from wastewater adds information about pathogen genetic diversity, which can be used to identify viral lineages (including variants of concern) that are circulating in a local population. Capturing the genetic diversity by WBS sequencing is not trivial, as wastewater samples often contain a diverse mixture of viral lineages with real mutations and sequencing errors, which must be deconvoluted computationally from short sequencing reads. In this study we assess nine different computational tools that have recently been developed to address this challenge. We simulated 100 wastewater sequence samples consisting of SARS-CoV-2 BA.1, BA.2, and Delta lineages, in various mixtures, as well as a Delta–Omicron recombinant and a synthetic ‘novel’ lineage. Most tools performed well in identifying the true lineages present and estimating their relative abundances and were generally robust to variation in sequencing depth and read length. While many tools identified lineages present down to 1 % frequency, results were more reliable above a 5 % threshold. The presence of an unknown synthetic lineage, which represents an unclassified SARS-CoV-2 lineage, increases the error in relative abundance estimates of other lineages, but the magnitude of this effect was small for most tools. The tools also varied in how they labelled novel synthetic lineages and recombinants. While our simulated dataset represents just one of many possible use cases for these methods, we hope it helps users understand potential sources of error or bias in wastewater sequencing analysis and to appreciate the commonalities and differences across methods.

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Tracking SARS-CoV-2 genomic variants in wastewater sequencing data withLolliPop

Cited by 7 publications

References 22 publications

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

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

A Gold Standard Dataset for Lineage Abundance Estimation from Wastewater

Tracking SARS-CoV-2 variants of concern in wastewater: an assessment of nine computational tools using simulated genomic data

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