Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network
Introduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites. These methods predict whether the CircRNA sequence includes protein binding sites from the sequ...
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2023.1283404/full |
| _version_ | 1797665370994114560 |
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| author | Zhen Shen Wei Liu ShuJun Zhao QinHu Zhang SiGuo Wang Lin Yuan Lin Yuan Lin Yuan |
| author_facet | Zhen Shen Wei Liu ShuJun Zhao QinHu Zhang SiGuo Wang Lin Yuan Lin Yuan Lin Yuan |
| author_sort | Zhen Shen |
| collection | DOAJ |
| description | Introduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites. These methods predict whether the CircRNA sequence includes protein binding sites from the sequence level, and primarily concentrate on analysing the sequence specificity of CircRNA-protein binding. For model performance, these methods are unsatisfactory in accurately predicting motif sites that have special functions in gene expression.Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).Results: CPBFCN provides a new path to predict CircRNA motifs. Based on the MEME tool, the existing CircRNA-related and protein-related database, we analysed the motif functions discovered by CPBFCN. We also investigated the correlation between CircRNA sponge and motif distribution. Furthermore, by comparing the motif distribution with different input sequence lengths, we found that some motifs in the flanking sequences of CircRNA-protein binding region may contribute to CircRNA-protein binding.Conclusion: This study contributes to identify circRNA-protein binding and provides help in understanding the role of circRNA-protein binding in gene expression regulation. |
| first_indexed | 2024-03-11T19:42:52Z |
| format | Article |
| id | doaj.art-cf114b82a6454fcf9f410f737cb5deae |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-03-11T19:42:52Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-cf114b82a6454fcf9f410f737cb5deae2023-10-06T06:57:33ZengFrontiers Media S.A.Frontiers in Genetics1664-80212023-10-011410.3389/fgene.2023.12834041283404Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural networkZhen Shen0Wei Liu1ShuJun Zhao2QinHu Zhang3SiGuo Wang4Lin Yuan5Lin Yuan6Lin Yuan7School of Computer and Software, Nanyang Institute of Technology, Nanyang, Henan, ChinaSchool of Computer and Software, Nanyang Institute of Technology, Nanyang, Henan, ChinaSchool of Computer and Software, Nanyang Institute of Technology, Nanyang, Henan, ChinaEIT Institute for Advanced Study, Ningbo, Zhejiang, ChinaEIT Institute for Advanced Study, Ningbo, Zhejiang, ChinaKey Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaShandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, ChinaIntroduction: CircRNA-protein binding plays a critical role in complex biological activity and disease. Various deep learning-based algorithms have been proposed to identify CircRNA-protein binding sites. These methods predict whether the CircRNA sequence includes protein binding sites from the sequence level, and primarily concentrate on analysing the sequence specificity of CircRNA-protein binding. For model performance, these methods are unsatisfactory in accurately predicting motif sites that have special functions in gene expression.Methods: In this study, based on the deep learning models that implement pixel-level binary classification prediction in computer vision, we viewed the CircRNA-protein binding sites prediction as a nucleotide-level binary classification task, and use a fully convolutional neural networks to identify CircRNA-protein binding motif sites (CPBFCN).Results: CPBFCN provides a new path to predict CircRNA motifs. Based on the MEME tool, the existing CircRNA-related and protein-related database, we analysed the motif functions discovered by CPBFCN. We also investigated the correlation between CircRNA sponge and motif distribution. Furthermore, by comparing the motif distribution with different input sequence lengths, we found that some motifs in the flanking sequences of CircRNA-protein binding region may contribute to CircRNA-protein binding.Conclusion: This study contributes to identify circRNA-protein binding and provides help in understanding the role of circRNA-protein binding in gene expression regulation.https://www.frontiersin.org/articles/10.3389/fgene.2023.1283404/fullCircRNA-protein binding sites predictiondeep learningfully convolutional neural networkshard negative mining lossnucleotide-level prediction |
| spellingShingle | Zhen Shen Wei Liu ShuJun Zhao QinHu Zhang SiGuo Wang Lin Yuan Lin Yuan Lin Yuan Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network Frontiers in Genetics CircRNA-protein binding sites prediction deep learning fully convolutional neural networks hard negative mining loss nucleotide-level prediction |
| title | Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network |
| title_full | Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network |
| title_fullStr | Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network |
| title_full_unstemmed | Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network |
| title_short | Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network |
| title_sort | nucleotide level prediction of circrna protein binding based on fully convolutional neural network |
| topic | CircRNA-protein binding sites prediction deep learning fully convolutional neural networks hard negative mining loss nucleotide-level prediction |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2023.1283404/full |
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