Evidence map›Paper›PMID 39252069›Full record

ArticleGenome medicine2024

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses.

Sarah Buddle, Leysa Forrest, Naomi Akinsuyi, Luz Marina Martin Bernal, Tony Brooks, Cristina Venturini, Charles Miller, Julianne R Brown, Nathaniel Storey, Laura Atkinson and 11 more

Abstract read
In one paragraph

Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed.

  1. Article
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  8. Integrating metagenomics and metatranscriptomics intoThe Journal of general virology · 2026
    Review
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  11. Article
  12. Article
  13. Review
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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

21 authors.

Sarah Buddle *Infection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK.ORCID 0000-0002-4738-9469
Leysa Forrest *Genetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Naomi AkinsuyiInfection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Luz Marina Martin BernalGenetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Tony BrooksGenetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Cristina VenturiniInfection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK.ORCID 0000-0002-4769-7912
Charles MillerDepartment of Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Julianne R BrownDepartment of Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Nathaniel StoreyDepartment of Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Laura AtkinsonDepartment of Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Timothy BestDepartment of Microbiology, Virology and Infection Prevention & Control, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Sunando RoyGenetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Sian GoldsworthyGenetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Sergi CastellanoGenetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Peter SimmondsNuffield Department of Medicine, University of Oxford, Oxford, UK.
Heli HarvalaRadcliffe Department of Medicine, University of Oxford, Oxford, UK.
Tanya GolubchikNuffield Department of Medicine, University of Oxford, Oxford, UK.
Rachel WilliamsGenetics and Genomic Medicine Department, Great Ormond Street Institute of Child Health, University College London, London, UK.
Judith Breuer *Infection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK. j.breuer@ucl.ac.uk.ORCID 0000-0001-8246-0534
Sofia Morfopoulou *Infection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK. sofia.morfopoulou.10@ucl.ac.uk.ORCID 0000-0001-8181-4548
Oscar Enrique Torres Montaguth *Infection, Immunity and Inflammation Department, Great Ormond Street Institute of Child Health, University College London, London, UK. oscar.torres@ucl.ac.uk.ORCID 0000-0003-4234-6037

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity.

methodsWe evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers.

resultsCapture with the Twist CVRP increased sensitivity by at least 10-100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600-6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT.

conclusionsMetagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Indexed as

MetagenomicsVirusesHigh-Throughput Nucleotide SequencingHumansMetagenomeSensitivity and SpecificityVirus DiseasesClinical metagenomicsEpidemiological surveillanceNext-generation sequencingPathogen detectionViral diagnostics

Identifiers

PMID39252069
PMCPMC11382446

What OpenQuestion holds

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LicenceCC BY
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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.