AtLASS: A Scheme for End-to-End Prediction of Splice Sites using Attention-based Bi-LSTM

• Item type: SIG Technical Reports(1)
• 公開日: 2023-06-22
• タイトル: AtLASS: A Scheme for End-to-End Prediction of Splice Sites using Attention-based Bi-LSTM
• タイトル / 言語: en
• タイトル: AtLASS: A Scheme for End-to-End Prediction of Splice Sites using Attention-based Bi-LSTM
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: バイオ情報学1
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Science and Technology, University of Tsukuba／Graduate School of Life and Environmental Sciences, University of Tsukuba
• 著者所属: Department of Clinical Medicine, University of Tsukuba
• 著者所属: Center for Computational Sciences, University of Tsukuba
• 著者所属: Center for Computational Sciences, University of Tsukuba
• 著者所属: Center for Computational Sciences, University of Tsukuba
• 著者所属: Center for Computational Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Technology, University of Tsukuba / Graduate School of Life and Environmental Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Department of Clinical Medicine, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Center for Computational Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Center for Computational Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Center for Computational Sciences, University of Tsukuba
• 著者所属(英): en
• 著者所属(英): Center for Computational Sciences, University of Tsukuba
• 著者名: Ryo, Harada; Keitaro, Kume; Kazumasa, Horie; Takuro, Nakayama; Yuji, Inagaki; Toshiyuki, Amagasa
• 著者名(英): Ryo, Harada; Keitaro, Kume; Kazumasa, Horie; Takuro, Nakayama; Yuji, Inagaki; Toshiyuki, Amagasa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Eukaryotic genomes contain exons and introns, and it is necessary to accurately identify exon-intron boundaries, i.e., splice sites, to annotate genomes. To address this problem, many previous works have proposed annotation methods/tools based on RNA-seq evidence. Many recent works exploit neural networks (NNs) as their prediction models, but only a few can be used to generate new genome annotation in practice. In this study, we propose AtLASS, a fully automated method for predicting splice sites from genomic and RNA-seq data using attention-based Bi-LSTM (Bidirectional Long Short-Term Memory). We exploit two-stage training on RNA-seq data to address the problem of biased label problem, thereby reducing the false positives. The experiments on the genomes of three species show that the performance of the proposed method itself is comparable to that of existing methods, but we can achieve better performance by combining the outputs of the proposed method and the existing method. The proposed method is the first program specialized in end-to-end splice site prediction using NNs.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Eukaryotic genomes contain exons and introns, and it is necessary to accurately identify exon-intron boundaries, i.e., splice sites, to annotate genomes. To address this problem, many previous works have proposed annotation methods/tools based on RNA-seq evidence. Many recent works exploit neural networks (NNs) as their prediction models, but only a few can be used to generate new genome annotation in practice. In this study, we propose AtLASS, a fully automated method for predicting splice sites from genomic and RNA-seq data using attention-based Bi-LSTM (Bidirectional Long Short-Term Memory). We exploit two-stage training on RNA-seq data to address the problem of biased label problem, thereby reducing the false positives. The experiments on the genomes of three species show that the performance of the proposed method itself is comparable to that of existing methods, but we can achieve better performance by combining the outputs of the proposed method and the existing method. The proposed method is the first program specialized in end-to-end splice site prediction using NNs.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN10505667
• 書誌情報: 研究報告数理モデル化と問題解決（MPS）; 巻 2023-MPS-143, 号 43, p. 1-6, 発行日 2023-06-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8833
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会