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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


Lukas Weilguny

Nicola De Maio

Rory Munro

Charlotte Manser

Ewan Birney

Professor of Developmental and Computational Biology

Nick Goldman


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.


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,

Journal Article Type Article
Acceptance Date Oct 18, 2022
Online Publication Date Jan 2, 2023
Publication Date Jan 2, 2023
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
Keywords Biomedical Engineering; Molecular Medicine; Applied Microbiology and Biotechnology; Bioengineering; Biotechnology
Public URL


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