MSRCall: A multi-scale deep neural network to basecall Oxford Nanopore sequences
Journal
Bioinformatics
Journal Volume
38
Journal Issue
16
Pages
3877
Date Issued
2022-08-15
Author(s)
Yeh, Yang Ming
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.
SDGs
Publisher
OXFORD UNIV PRESS
Type
journal article