Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks
Abstract Background Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes mentioned above. Back-splicing is important for Ci...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-022-08820-1 |
| _version_ | 1811320923866791936 |
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| author | Zhen Shen Yan Ling Shao Wei Liu Qinhu Zhang Lin Yuan |
| author_facet | Zhen Shen Yan Ling Shao Wei Liu Qinhu Zhang Lin Yuan |
| author_sort | Zhen Shen |
| collection | DOAJ |
| description | Abstract Background Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes mentioned above. Back-splicing is important for CircRNAs formation. Back-splicing sites prediction helps uncover the mysteries of CircRNAs formation. Several methods were proposed for back-splicing sites prediction or circRNA-realted prediction tasks. Model performance was constrained by poor feature learning and using ability. Results In this study, CircCNN was proposed to predict pre-mRNA back-splicing sites. Convolution neural network and batch normalization are the main parts of CircCNN. Experimental results on three datasets show that CircCNN outperforms other baseline models. Moreover, PPM (Position Probability Matrix) features extract by CircCNN were converted as motifs. Further analysis reveals that some of motifs found by CircCNN match known motifs involved in gene expression regulation, the distribution of motif and special short sequence is important for pre-mRNA back-splicing. Conclusions In general, the findings in this study provide a new direction for exploring CircRNA-related gene expression regulatory mechanism and identifying potential targets for complex malignant diseases. The datasets and source code of this study are freely available at: https://github.com/szhh521/CircCNN . |
| first_indexed | 2024-04-13T13:07:57Z |
| format | Article |
| id | doaj.art-eba010ab804c4515b6236ac3e6923225 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-13T13:07:57Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-eba010ab804c4515b6236ac3e69232252022-12-22T02:45:43ZengBMCBMC Genomics1471-21642022-08-0123111210.1186/s12864-022-08820-1Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networksZhen Shen0Yan Ling Shao1Wei Liu2Qinhu Zhang3Lin Yuan4School of Computer and Software, Nanyang Institute of TechnologySchool of Computer and Software, Nanyang Institute of TechnologySchool of Computer and Software, Nanyang Institute of TechnologyTranslational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji UniversitySchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences)Abstract Background Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes mentioned above. Back-splicing is important for CircRNAs formation. Back-splicing sites prediction helps uncover the mysteries of CircRNAs formation. Several methods were proposed for back-splicing sites prediction or circRNA-realted prediction tasks. Model performance was constrained by poor feature learning and using ability. Results In this study, CircCNN was proposed to predict pre-mRNA back-splicing sites. Convolution neural network and batch normalization are the main parts of CircCNN. Experimental results on three datasets show that CircCNN outperforms other baseline models. Moreover, PPM (Position Probability Matrix) features extract by CircCNN were converted as motifs. Further analysis reveals that some of motifs found by CircCNN match known motifs involved in gene expression regulation, the distribution of motif and special short sequence is important for pre-mRNA back-splicing. Conclusions In general, the findings in this study provide a new direction for exploring CircRNA-related gene expression regulatory mechanism and identifying potential targets for complex malignant diseases. The datasets and source code of this study are freely available at: https://github.com/szhh521/CircCNN .https://doi.org/10.1186/s12864-022-08820-1CircRNABack-splicing sites predictionDeep learningConvolutional neural networksBatch normalization |
| spellingShingle | Zhen Shen Yan Ling Shao Wei Liu Qinhu Zhang Lin Yuan Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks BMC Genomics CircRNA Back-splicing sites prediction Deep learning Convolutional neural networks Batch normalization |
| title | Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks |
| title_full | Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks |
| title_fullStr | Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks |
| title_full_unstemmed | Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks |
| title_short | Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks |
| title_sort | prediction of back splicing sites for circrna formation based on convolutional neural networks |
| topic | CircRNA Back-splicing sites prediction Deep learning Convolutional neural networks Batch normalization |
| url | https://doi.org/10.1186/s12864-022-08820-1 |
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