{"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}
引用次数: 0
Abstract
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.