Evidence map›Paper›PMID 39115959›Full record

ArticleGigaScience2024

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

Eva Aßmann, Shelesh Agrawal, Laura Orschler, Sindy Böttcher, Susanne Lackner, Martin Hölzer

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Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Eva AßmannGenome Competence Center (MF1), Robert Koch Institute, Berlin 13353, Germany.ORCID 0000-0002-7249-069X
Shelesh AgrawalChair of Water and Environmental Biotechnology, Institute IWAR, Department of Civil and Environmental Engineering Sciences, Technical University of Darmstadt, Darmstadt 64287, Germany.ORCID 0000-0001-9365-5951
Laura OrschlerChair of Water and Environmental Biotechnology, Institute IWAR, Department of Civil and Environmental Engineering Sciences, Technical University of Darmstadt, Darmstadt 64287, Germany.ORCID 0000-0001-7867-4353
Sindy BöttcherGastroenteritis and Hepatitis Pathogens and Enteroviruses, Robert Koch Institute, Berlin 13353, Germany.ORCID 0000-0002-7521-093X
Susanne LacknerChair of Water and Environmental Biotechnology, Institute IWAR, Department of Civil and Environmental Engineering Sciences, Technical University of Darmstadt, Darmstadt 64287, Germany.ORCID 0000-0002-9163-9541
Martin HölzerGenome Competence Center (MF1), Robert Koch Institute, Berlin 13353, Germany.ORCID 0000-0001-7090-8717

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSequencing of severe acute respiratory syndrome coronavirus 2 (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, researchers and public health authorities can gain early insights into the spread of virus lineages and emerging mutations. Constructing 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, selecting reference sequences or mutations directly affects the predictive power.

resultsHere, we show the impact of a mutation- and sequence-based reference reconstruction for SARS-CoV-2 abundance estimation. We benchmark 3 datasets: (i) synthetic "spike-in"' mixtures; (ii) German wastewater samples from early 2021, mainly comprising Alpha; and (iii) samples obtained from wastewater at an international airport in Germany from the end of 2021, including first signals of Omicron. The 2 approaches differ in sublineage 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 datasets and illustrate the effects of virus lineage composition of wastewater samples and references.

conclusionsOur study highlights current computational challenges, focusing on the general reference design, which 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 defining robust quality metrics.

Indexed as

COVID-19MutationSARS-CoV-2WastewaterGenome, ViralHumansRNA, ViralRNA, ViralWastewaterabundance estimation, next-generation sequencing, benchmarkSARS-CoV-2sewagewastewater

Identifiers

PMID39115959
PMCPMC11308188

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.