Dynamic Pooling Improves Nanopore Base Calling Accuracy

TL;DR

This paper introduces dynamic pooling, a novel neural network component that adaptively adjusts pooling ratios to improve nanopore base calling accuracy and speed, outperforming existing methods.

Contribution

The paper presents dynamic pooling and two base callers, Heron and Osprey, demonstrating improved accuracy and efficiency over prior approaches in nanopore sequencing.

Findings

Heron surpasses Bonito in accuracy.

Osprey achieves near real-time speed on CPUs.

Dynamic pooling enhances base calling performance.

Abstract

In nanopore sequencing, electrical signal is measured as DNA molecules pass through the sequencing pores. Translating these signals into DNA bases (base calling) is a highly non-trivial task, and its quality has a large impact on the sequencing accuracy. The most successful nanopore base callers to date use convolutional neural networks (CNN) to accomplish the task. Convolutional layers in CNNs are typically composed of filters with constant window size, performing best in analysis of signals with uniform speed. However, the speed of nanopore sequencing varies greatly both within reads and between sequencing runs. Here, we present dynamic pooling, a novel neural network component, which addresses this problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness of dynamic pooling, we developed two base callers: Heron and Osprey. Heron improves the accuracy beyond the experimental high-accuracy base caller Bonito developed by Oxford Nanopore. Osprey is a fast base caller that can compete in accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and achieves a near real-time speed on common desktop CPUs. Availability: https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron Keywords: nanopore sequencing, base calling, convolutional neural networks, pooling