{"title":"基于图卷积网络和 DNA 图构建预测 DNA 序列剪接位点","authors":"Luo Rentao, Li Yelin, Guan Lixin, Li Mengshan","doi":"10.1016/j.jksuci.2024.102089","DOIUrl":null,"url":null,"abstract":"<div><p>Identifying splice sites is essential for gene structure analysis and eukaryotic genome annotation. Recently, computational and deep learning approaches for splice site detection have advanced, focusing on reducing false positives by distinguishing true from pseudo splice sites. This paper introduces GraphSplice, a method using graph convolutional neural networks. It encodes DNA sequences into directed graphs to extract features and predict splice sites. Tested across multiple datasets, GraphSplice consistently achieved high accuracy (91%-94%) and F1Scores (92%-94%), outperforming state-of-the-art models by up to 9.16% for donors and 5.64% for acceptors. Cross-species experiments also show GraphSplice’s capability to annotate splice sites in under-trained genomic datasets, proving its wide applicability as a tool for DNA splice site analysis.</p></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1319157824001782/pdfft?md5=a168124b3f808fa8741574d862f7a5a1&pid=1-s2.0-S1319157824001782-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Predicting DNA sequence splice site based on graph convolutional network and DNA graph construction\",\"authors\":\"Luo Rentao, Li Yelin, Guan Lixin, Li Mengshan\",\"doi\":\"10.1016/j.jksuci.2024.102089\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Identifying splice sites is essential for gene structure analysis and eukaryotic genome annotation. Recently, computational and deep learning approaches for splice site detection have advanced, focusing on reducing false positives by distinguishing true from pseudo splice sites. This paper introduces GraphSplice, a method using graph convolutional neural networks. It encodes DNA sequences into directed graphs to extract features and predict splice sites. Tested across multiple datasets, GraphSplice consistently achieved high accuracy (91%-94%) and F1Scores (92%-94%), outperforming state-of-the-art models by up to 9.16% for donors and 5.64% for acceptors. Cross-species experiments also show GraphSplice’s capability to annotate splice sites in under-trained genomic datasets, proving its wide applicability as a tool for DNA splice site analysis.</p></div>\",\"PeriodicalId\":48547,\"journal\":{\"name\":\"Journal of King Saud University-Computer and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1319157824001782/pdfft?md5=a168124b3f808fa8741574d862f7a5a1&pid=1-s2.0-S1319157824001782-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of King Saud University-Computer and Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1319157824001782\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of King Saud University-Computer and Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1319157824001782","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
识别剪接位点对于基因结构分析和真核基因组注释至关重要。最近,用于剪接位点检测的计算和深度学习方法取得了进展,重点是通过区分真假剪接位点来减少假阳性。本文介绍的 GraphSplice 是一种使用图卷积神经网络的方法。它将 DNA 序列编码为有向图,以提取特征并预测剪接位点。在多个数据集上进行测试后,GraphSplice 始终保持着较高的准确率(91%-94%)和 F1Scores(92%-94%),在供体和受体方面分别比最先进的模型高出 9.16% 和 5.64%。跨物种实验还显示了 GraphSplice 在训练不足的基因组数据集中注释剪接位点的能力,证明了它作为 DNA 剪接位点分析工具的广泛适用性。
Predicting DNA sequence splice site based on graph convolutional network and DNA graph construction
Identifying splice sites is essential for gene structure analysis and eukaryotic genome annotation. Recently, computational and deep learning approaches for splice site detection have advanced, focusing on reducing false positives by distinguishing true from pseudo splice sites. This paper introduces GraphSplice, a method using graph convolutional neural networks. It encodes DNA sequences into directed graphs to extract features and predict splice sites. Tested across multiple datasets, GraphSplice consistently achieved high accuracy (91%-94%) and F1Scores (92%-94%), outperforming state-of-the-art models by up to 9.16% for donors and 5.64% for acceptors. Cross-species experiments also show GraphSplice’s capability to annotate splice sites in under-trained genomic datasets, proving its wide applicability as a tool for DNA splice site analysis.
期刊介绍:
In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.