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BOSS-RUNS: a flexible and practical dynamic read sampling framework for nanopore sequencing

By Nicola De Maio, Charlotte Manser, Rory Munro, Ewan Birney, Matthew W Loose, Nick Goldman

Posted 07 Feb 2020
bioRxiv DOI: 10.1101/2020.02.07.938670

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 are a priori interesting, 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 Summary An 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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