Dynamic, adaptive sampling during nanopore sequencing using Bayesian experimental design

TL;DR

This paper introduces a new method for nanopore sequencing that dynamically selects DNA molecules to sequence, improving coverage and variant detection in microbial communities.

Contribution

The novel contribution is the BOSS-RUNS framework, which uses Bayesian experimental design to adaptively select DNA molecules during sequencing.

Findings

BOSS-RUNS reduces low-coverage sites in species at 1% abundance by 87.5%.

The method detects 12.5% more single-nucleotide polymorphisms compared to standard approaches.

Dynamic molecule selection improves variant calling and reduces coverage bias in microbial communities.

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.

Nanopore selective sequencing with real-time decision updates mitigates coverage bias.

Figures