Evidence map›Paper›PMID 37058515›Full record

ArticlePloS one2023

Optimised protocol for monitoring SARS-CoV-2 in wastewater using reverse complement PCR-based whole-genome sequencing.

Harry T Child, Paul A O'Neill, Karen Moore, William Rowe, Hubert Denise, David Bass, Matthew J Wade, Matt Loose, Steve Paterson, Ronny van Aerle and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
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

8 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Article
  3. Laboratory and In-Field Metagenomics for Environmental Monitoring.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Article
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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

11 authors at 5 institutions in 1 country.

Harry T ChildBiosciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom.ORCID 0000-0002-9127-2647
Paul A O'NeillBiosciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom.
Karen MooreBiosciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom.ORCID 0000-0002-0146-0653
William RoweAnalytics & Data Science Directorate, UK Health Security Agency, London, United Kingdom.
Hubert DeniseAnalytics & Data Science Directorate, UK Health Security Agency, London, United Kingdom.ORCID 0000-0001-9862-5890
David BassInternational Centre of Excellence for Aquatic Animal Health, Weymouth, United Kingdom.
Matthew J WadeAnalytics & Data Science Directorate, UK Health Security Agency, London, United Kingdom.ORCID 0000-0001-9824-7121
Matt LooseDeep Seq, Centre for Genetics and Genomics, Queen's Medical Centre, The University of Nottingham, Nottingham, United Kingdom.
Steve PatersonInstitute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Ronny van AerleInternational Centre of Excellence for Aquatic Animal Health, Weymouth, United Kingdom.ORCID 0000-0002-2605-6518
Aaron R JeffriesBiosciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom.ORCID 0000-0002-1235-8291
University of Exeter · GBUK Health Security Agency · GBCentre for Environment, Fisheries and Aquaculture Science · GBUniversity of Liverpool · GBUniversity of Nottingham · GB

Funding

Wellcome Trust 218247/Z/19/Z
6 · The paper itself

Abstract

Monitoring the spread of viral pathogens in the population during epidemics is crucial for mounting an effective public health response. Understanding the viral lineages that constitute the infections in a population can uncover the origins and transmission patterns of outbreaks and detect the emergence of novel variants that may impact the course of an epidemic. Population-level surveillance of viruses through genomic sequencing of wastewater captures unbiased lineage data, including cryptic asymptomatic and undiagnosed infections, and has been shown to detect infection outbreaks and novel variant emergence before detection in clinical samples. Here, we present an optimised protocol for quantification and sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in influent wastewater, used for high-throughput genomic surveillance in England during the COVID-19 pandemic. This protocol utilises reverse compliment PCR for library preparation, enabling tiled amplification across the whole viral genome and sequencing adapter addition in a single step to enhance efficiency. Sequencing of synthetic SARS-CoV-2 RNA provided evidence validating the efficacy of this protocol, while data from high-throughput sequencing of wastewater samples demonstrated the sensitivity of this method. We also provided guidance on the quality control steps required during library preparation and data analysis. Overall, this represents an effective method for high-throughput sequencing of SARS-CoV-2 in wastewater which can be applied to other viruses and pathogens of humans and animals.

Indexed as

COVID-19SARS-CoV-2AnimalsComplement System ProteinsCOVID-19 TestingHumansPandemicsPolymerase Chain ReactionRNA, ViralWastewaterComplement System ProteinsRNA, ViralWastewater

Identifiers

PMID37058515
PMCPMC10104291
OpenAlexW4365483690

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.