Evidence map›Paper›PMID 36593407›Full record

ArticleNature biotechnology2023

Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design.

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

Open access · hybridAbstract read
In one paragraph

Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed
11.3field-weighted citation impact, top 1% 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

50 citing papers in PubMed, 89 citations in OpenAlex.

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  16. Dynamic sensor selection for biomarker discovery.Proceedings of the National Academy of Sciences of the United States of America · 2025
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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

7 authors at 2 institutions in 1 country.

Lukas WeilgunyEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.ORCID 0000-0001-6459-0431
Nicola De MaioEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.
Rory MunroDeepSeq, School of Life Sciences, Queen's Medical Centre, University of Nottingham, Nottingham, UK.
Charlotte ManserEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.ORCID 0000-0002-4125-8900
Ewan BirneyEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK.ORCID 0000-0001-8314-8497
Matthew LooseDeepSeq, School of Life Sciences, Queen's Medical Centre, University of Nottingham, Nottingham, UK.ORCID 0000-0002-5264-0929
Nick GoldmanEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK. goldman@ebi.ac.uk.ORCID 0000-0001-8486-2211
European Bioinformatics Institute · GBUniversity of Nottingham · GB

Funding

Biotechnology and Biological Sciences Research Council BB/N017099/1Biotechnology and Biological Sciences Research Council BB/R000492/1
6 · The paper itself

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.

Indexed as

NanoporesNanopore SequencingBayes TheoremGenomeHigh-Throughput Nucleotide SequencingResearch DesignSequence Analysis, DNASoftware

Identifiers

PMID36593407
PMCPMC10344778
OpenAlexW4313453396

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.