MSRCall: a multi-scale deep neural network to basecall Oxford Nanopore sequences

Abstract

Motivation: MinION, a third-generation sequencer from Oxford Nanopore Technologies, is a portable device that can provide long-nucleotide read data in real-time. It primarily aims to deduce the makeup of nucleotide sequences from the ionic current signals generated when passing DNA/RNA fragments through nanopores charged with a voltage difference. To determine nucleotides from measured signals, a translation process known as basecalling is required. However, compared to NGS basecallers, the calling accuracy of MinION still needs to be improved.

Results: In this work, a simple but powerful neural network architecture called multi-scale recurrent caller (MSRCall) is proposed. MSRCall comprises a multi-scale structure, recurrent layers, a fusion block and a connectionist temporal classification decoder. To better identify both short-and long-range dependencies, the recurrent layer is redesigned to capture various time-scale features with a multi-scale structure. The results show that MSRCall outperforms other basecallers in terms of both read and consensus accuracies.

Availability and implementation: MSRCall is available at: https://github.com/d05943006/MSRCall.

Supplementary information: Supplementary data are available at Bioinformatics online.