Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design
Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design
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使用贝叶斯实验设计在纳米孔测序过程中动态、自适应采样
DOI：
10.1101/2020.02.07.938670
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Weilguny L
中科院分区：
文献类型：
--
作者：
Weilguny L
Real-time selective sequencing of individual DNA fragments, or ‘Read Until’, allows the focusing of Oxford Nanopore Technology sequencing on pre-selected genomic regions. This can lead to large improvements in DNA sequencing performance in many scenarios where only part of the DNA content of a sample is of interest. This approach is based on the idea of deciding whether to sequence a fragment completely after having sequenced only a small initial part of it. If, based on this small part, the fragment is not deemed of (sufficient) interest it is rejected and sequencing is continued on a new fragment. To date, only simple decision strategies based on location within a genome have been proposed to determine what fragments are of interest. We present a new mathematical model and algorithm for the real-time assessment of the value of prospective fragments. Our decision framework is based not only on which genomic regions area prioriinteresting, but also on which fragments have so far been sequenced, and so on the current information available regarding the genome being sequenced. As such, our strategy can adapt dynamically during each run, focusing sequencing efforts in areas of highest uncertainty (typically areas currently low coverage). We show that our approach can lead to considerable savings of time and materials, providing high-confidence genome reconstruction sooner than a standard sequencing run, and resulting in more homogeneous coverage across the genome, even when entire genomes are of interest.Author SummaryAn existing technique called ‘Read Until’ allows selective sequencing of DNA fragments with an Oxford Nanopore Technology (ONT) sequencer. With Read Until it is possible to enrich coverage of areas of interest within a sequenced genome. We propose a new use of this technique: combining a mathematical model of read utility and an algorithm to select an optimal dynamic decision strategy (i.e. one that can be updated in real time, and so react to the data generated so far in an experiment), we show that it possible to improve the efficiency of a sequencing run by focusing effort on areas of highest uncertainty.
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DOI：
--
发表时间：
2021
期刊：
bioRxiv
影响因子：
--
作者：
M. Sereika;R. Kirkegaard;S. Karst;T. Michaelsen;E. A. Sørensen;R. Wollenberg;M. Albertsen
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DOI：
--
发表时间：
2021
期刊：
影响因子：
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作者：
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Masinde Muliro
影响因子：
12.3
作者：
Martin S;Heavens D;Lan Y;Horsfield S;Clark MD;Leggett RM
通讯作者：
Leggett RM
影响因子：
48
作者：
Sereika M;Kirkegaard RH;Karst SM;Michaelsen TY;Sørensen EA;Wollenberg RD;Albertsen M
通讯作者：
Albertsen M
影响因子：
4.6
作者：
Tan, Ge;Opitz, Lennart;Rehrauer, Hubert
通讯作者：
Rehrauer, Hubert