Benchmarking and optimisation of bait-capture metagenomics for sequencing of respiratory viruses at scale
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Published version (article in press)
Author(s)
Type
Journal Article
Abstract
Background
Sequencing respiratory virus genomes is essential for public health surveillance and research. Although shotgun metagenomics is pathogen agnostic, its sensitivity is limited by abundant off-target host nucleic acids. Hybridization bait capture overcomes this limitation by selectively enriching viral sequences prior to sequencing.
Methods
We evaluated three respiratory viral bait capture workflows (veSEQ, RVI-seq, and Illumina) to compare their performance and assess their suitability for scalable respiratory virus sequencing. Synthetic RNA controls and clinical samples containing SARS-CoV-2, influenza A, influenza B, human parainfluenza virus, and respiratory syncytial virus (RSV) were analysed.
Results
All three workflows demonstrated high efficiency, reproducibility and broadly comparable performance across respiratory viruses. Complete viral genomes were consistently recovered from samples containing 10,000 viral copies, while viral reads remained detectable at substantially lower viral loads, including approximately 100 copies. Workflow optimisation reduced reagent costs and enabled laboratory automation without compromising sequencing sensitivity.
Conclusion
Hybridization bait capture provides an effective and scalable approach for respiratory virus genome sequencing. Cost reductions and automation can be implemented without compromising performance, supporting its use in routine genomic surveillance and public health preparedness.
Sequencing respiratory virus genomes is essential for public health surveillance and research. Although shotgun metagenomics is pathogen agnostic, its sensitivity is limited by abundant off-target host nucleic acids. Hybridization bait capture overcomes this limitation by selectively enriching viral sequences prior to sequencing.
Methods
We evaluated three respiratory viral bait capture workflows (veSEQ, RVI-seq, and Illumina) to compare their performance and assess their suitability for scalable respiratory virus sequencing. Synthetic RNA controls and clinical samples containing SARS-CoV-2, influenza A, influenza B, human parainfluenza virus, and respiratory syncytial virus (RSV) were analysed.
Results
All three workflows demonstrated high efficiency, reproducibility and broadly comparable performance across respiratory viruses. Complete viral genomes were consistently recovered from samples containing 10,000 viral copies, while viral reads remained detectable at substantially lower viral loads, including approximately 100 copies. Workflow optimisation reduced reagent costs and enabled laboratory automation without compromising sequencing sensitivity.
Conclusion
Hybridization bait capture provides an effective and scalable approach for respiratory virus genome sequencing. Cost reductions and automation can be implemented without compromising performance, supporting its use in routine genomic surveillance and public health preparedness.
Date Issued
2026-07-30
Date Acceptance
2026-07-13
Citation
Genome Medicine, 2026
ISSN
1756-994X
Publisher
BMC
Journal / Book Title
Genome Medicine
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Publication Status
Published online
Date Publish Online
2026-07-30