Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design - Nature Biotechnology

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Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design - Nature Biotechnology
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Nanopore selective sequencing with real-time decision updates mitigates coverage bias

First, we capture the amount of information at each site of one or multiple genomes by considering a probability distribution over all possible genotypes. The prior of this distribution can be informed by reference genomes and is subsequently updated as we collect data throughout the experiment—that is, we calculate a posterior probability distribution based on the observed nucleotides at that position. Additionally, ploidy and sequencing error probabilities are taken into account.

Using the expected benefit of reads, we can define criteria for making decisions about which fragments to sequence fully and which to reject from nanopores. Note that, in line with common usage, we refer to DNA molecules and their translation into sequence space interchangeably as ‘fragments’ and ‘reads’. Our aim is to optimize the rate of accumulation of information—that is, of expected benefit—across all pores and over time.

We call our approach of finding an optimal strategy BOSS-RUNS: ‘Benefit-Optimising Short-term Strategy for Read Until Nanopore Sequencing’. Further methodological details, overview of parameters and variables in the model and proof of optimality are given in the

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