https://scholars.lib.ntu.edu.tw/handle/123456789/631791
標題: | MSRCall: A multi-scale deep neural network to basecall Oxford Nanopore sequences | 作者: | Yeh, Yang Ming YI-CHANG LU |
公開日期: | 15-八月-2022 | 出版社: | OXFORD UNIV PRESS | 卷: | 38 | 期: | 16 | 起(迄)頁: | 3877 | 來源出版物: | Bioinformatics | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/631791 | ISSN: | 13674803 | DOI: | 10.1093/bioinformatics/btac435 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。