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Journal Article Ppublish 2 citations

PubMed: 39115959 DOI: 10.1093/gigascience/giae051

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

Gigascience 13

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

Affiliations

  • Genome Competence Center (MF1), Robert Koch Institute, Berlin 13353, Germany.
  • Center for Artificial Intelligence in Public Health Research (ZKI-PH), Robert Koch Institute, Berlin 13353, Germany.
  • Chair of Water and Environmental Biotechnology, Institute IWAR, Department of Civil and Environmental Engineering Sciences, Technical University of Darmstadt, Darmstadt 64287, Germany.
  • Gastroenteritis and Hepatitis Pathogens and Enteroviruses, Robert Koch Institute, Berlin 13353, Germany.

Abstract

BACKGROUND

Sequencing 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.

RESULTS

Here, 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.

CONCLUSIONS

Our 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.

MeSH

Medical Informatics, Sars-Cov-2, Viruses, Sewage, Severe Acute Respiratory Syndrome-Related Coronavirus, Mutation, Rna

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