DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads

TL;DR

DeepNano introduces a deep recurrent neural network-based base caller for Oxford Nanopore's MinION device, significantly reducing sequencing errors and enhancing genome sequencing and clinical application potential.

Contribution

It provides the first open-source base caller for MinION using RNNs, improving accuracy over the default manufacturer tool.

Findings

Improved base calling accuracy with RNNs

Open-source tool available for community use

Potential to enhance genome sequencing applications

Abstract

Motivation: The MinION device by Oxford Nanopore is the first portable sequencing device. MinION is able to produce very long reads (reads over 100~kBp were reported), however it suffers from high sequencing error rate. In this paper, we show that the error rate can be reduced by improving the base calling process. Results: We present the first open-source DNA base caller for the MinION sequencing platform by Oxford Nanopore. By employing carefully crafted recurrent neural networks, our tool improves the base calling accuracy compared to the default base caller supplied by the manufacturer. This advance may further enhance applicability of MinION for genome sequencing and various clinical applications. Availability: DeepNano can be downloaded at http://compbio.fmph.uniba.sk/deepnano/. Contact: boza@fmph.uniba.sk