A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.
The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches often face computational challenges or lack precision...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314837 |
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| author | Loïc Omnes Eric Angel Pierre Bartet François Radvanyi Fariza Tahi |
| author_facet | Loïc Omnes Eric Angel Pierre Bartet François Radvanyi Fariza Tahi |
| author_sort | Loïc Omnes |
| collection | DOAJ |
| description | The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches often face computational challenges or lack precision when dealing with long RNA sequences and/or pseudoknots. To address this, we propose a divide-and-conquer method based on deep learning, called DivideFold, for predicting the secondary structures including pseudoknots of long RNAs. Our approach is able to scale to long RNAs by recursively partitioning sequences into smaller fragments until they can be managed by an existing model able to predict RNA secondary structure including pseudoknots. We show that our approach exhibits superior performance compared to state-of-the-art methods for pseudoknot prediction and secondary structure prediction including pseudoknots for long RNAs. The source code of DivideFold, along with all the datasets used in this study, is accessible at https://evryrna.ibisc.univ-evry.fr/evryrna/dividefold/home. |
| format | Article |
| id | doaj-art-a82d378a02c24065835c3232f71fd5a4 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-a82d378a02c24065835c3232f71fd5a42025-08-20T02:20:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e031483710.1371/journal.pone.0314837A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.Loïc OmnesEric AngelPierre BartetFrançois RadvanyiFariza TahiThe accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches often face computational challenges or lack precision when dealing with long RNA sequences and/or pseudoknots. To address this, we propose a divide-and-conquer method based on deep learning, called DivideFold, for predicting the secondary structures including pseudoknots of long RNAs. Our approach is able to scale to long RNAs by recursively partitioning sequences into smaller fragments until they can be managed by an existing model able to predict RNA secondary structure including pseudoknots. We show that our approach exhibits superior performance compared to state-of-the-art methods for pseudoknot prediction and secondary structure prediction including pseudoknots for long RNAs. The source code of DivideFold, along with all the datasets used in this study, is accessible at https://evryrna.ibisc.univ-evry.fr/evryrna/dividefold/home.https://doi.org/10.1371/journal.pone.0314837 |
| spellingShingle | Loïc Omnes Eric Angel Pierre Bartet François Radvanyi Fariza Tahi A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. PLoS ONE |
| title | A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. |
| title_full | A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. |
| title_fullStr | A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. |
| title_full_unstemmed | A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. |
| title_short | A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. |
| title_sort | divide and conquer approach based on deep learning for long rna secondary structure prediction focus on pseudoknots identification |
| url | https://doi.org/10.1371/journal.pone.0314837 |
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