RNA independent fragment partition method based on deep learning for RNA secondary structure prediction
Abstract The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-30124-x |
| _version_ | 1797865027089203200 |
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| author | Qi Zhao Qian Mao Zheng Zhao Wenxuan Yuan Qiang He Qixuan Sun Yudong Yao Xiaoya Fan |
| author_facet | Qi Zhao Qian Mao Zheng Zhao Wenxuan Yuan Qiang He Qixuan Sun Yudong Yao Xiaoya Fan |
| author_sort | Qi Zhao |
| collection | DOAJ |
| description | Abstract The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar . |
| first_indexed | 2024-04-09T23:01:27Z |
| format | Article |
| id | doaj.art-25dc3b85a11f4062af00d80ffa232980 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T23:01:27Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-25dc3b85a11f4062af00d80ffa2329802023-03-22T10:55:39ZengNature PortfolioScientific Reports2045-23222023-02-0113111410.1038/s41598-023-30124-xRNA independent fragment partition method based on deep learning for RNA secondary structure predictionQi Zhao0Qian Mao1Zheng Zhao2Wenxuan Yuan3Qiang He4Qixuan Sun5Yudong Yao6Xiaoya Fan7College of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Light Industry, Liaoning UniversityCollege of Artificial Intelligence, Dalian Maritime UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityCollege of Medicine and Biological Information Engineering, Northeastern UniversityDepartment of Electrical and Computer Engineering, Stevens Institute of TechnologySchool of Software, Dalian University of Technology, Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceAbstract The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods. The prediction of the structures of long RNA sequences with high precision and reasonable computational cost remains challenging. Here, we propose a deep learning model, RNA-par, which could partition an RNA sequence into several independent fragments (i-fragments) based on its exterior loops. Each i-fragment secondary structure predicted individually could be further assembled to acquire the complete RNA secondary structure. In the examination of our independent test set, the average length of the predicted i-fragments was 453 nt, which was considerably shorter than that of complete RNA sequences (848 nt). The accuracy of the assembled structures was higher than that of the structures predicted directly using the state-of-the-art RNA secondary structure prediction methods. This proposed model could serve as a preprocessing step for RNA secondary structure prediction for enhancing the predictive performance (especially for long RNA sequences) and reducing the computational cost. In the future, predicting the secondary structure of long-sequence RNA with high accuracy can be enabled by developing a framework combining RNA-par with various existing RNA secondary structure prediction algorithms. Our models, test codes and test data are provided at https://github.com/mianfei71/RNAPar .https://doi.org/10.1038/s41598-023-30124-x |
| spellingShingle | Qi Zhao Qian Mao Zheng Zhao Wenxuan Yuan Qiang He Qixuan Sun Yudong Yao Xiaoya Fan RNA independent fragment partition method based on deep learning for RNA secondary structure prediction Scientific Reports |
| title | RNA independent fragment partition method based on deep learning for RNA secondary structure prediction |
| title_full | RNA independent fragment partition method based on deep learning for RNA secondary structure prediction |
| title_fullStr | RNA independent fragment partition method based on deep learning for RNA secondary structure prediction |
| title_full_unstemmed | RNA independent fragment partition method based on deep learning for RNA secondary structure prediction |
| title_short | RNA independent fragment partition method based on deep learning for RNA secondary structure prediction |
| title_sort | rna independent fragment partition method based on deep learning for rna secondary structure prediction |
| url | https://doi.org/10.1038/s41598-023-30124-x |
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