Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design

Weilguny, Lukas; De Maio, Nicola; Munro, Rory; Manser, Charlotte; Birney, Ewan; Loose, Matthew; Goldman, Nick

Abstract

Nanopore sequencers can select which DNA molecules to sequence, rejecting a molecule after analysis of a small initial part. Currently, selection is based on predetermined regions of interest that remain constant throughout an experiment. Sequencing efforts, thus, cannot be re-focused on molecules likely contributing most to experimental success. Here we present BOSS-RUNS, an algorithmic framework and software to generate dynamically updated decision strategies. We quantify uncertainty at each genome position with real-time updates from data already observed. For each DNA fragment, we decide whether the expected decrease in uncertainty that it would provide warrants fully sequencing it, thus optimizing information gain. BOSS-RUNS mitigates coverage bias between and within members of a microbial community, leading to improved variant calling; for example, low-coverage sites of a species at 1% abundance were reduced by 87.5%, with 12.5% more single-nucleotide polymorphisms detected. Such data-driven updates to molecule selection are applicable to many sequencing scenarios, such as enriching for regions with increased divergence or low coverage, reducing time-to-answer.

Citation

Weilguny, L., De Maio, N., Munro, R., Manser, C., Birney, E., Loose, M., & Goldman, N. (2023). Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design. Nature Biotechnology, 41, 1018–1025. https://doi.org/10.1038/s41587-022-01580-z

• Journal Article Type: Article
• Acceptance Date: Oct 18, 2022
• Online Publication Date: Jan 2, 2023
• Publication Date: 2023-07
• Deposit Date: Jan 6, 2023
• Publicly Available Date: Jan 12, 2023
• Journal: Nature Biotechnology
• Print ISSN: 1087-0156
• Electronic ISSN: 1546-1696
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 41
• Pages: 1018–1025
• DOI: https://doi.org/10.1038/s41587-022-01580-z
• Keywords: Data acquisition; Genetics research; Next-generation sequencing
• Public URL: https://nottingham-repository.worktribe.com/output/15718478