Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.
Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions such as gene expression, RNA structural stability,...
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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.0320077 |
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| author | Bo Han Sudan Bai Yang Liu Jiezhang Wu Xin Feng Ruihao Xin |
| author_facet | Bo Han Sudan Bai Yang Liu Jiezhang Wu Xin Feng Ruihao Xin |
| author_sort | Bo Han |
| collection | DOAJ |
| description | Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions such as gene expression, RNA structural stability, and various diseases. Therefore, accurate identification of pseudouridine sites can effectively explain the functional mechanism of this modification site. Due to the rapid increase of genomics data, traditional biological experimental methods to identify RNA modification sites can no longer meet the practical needs, and it is necessary to accurately identify pseudouridine sites from high-throughput RNA sequence data by computational methods. In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, Homo sapiens, Saccharomyces cerevisiae and Mus musculus. The method incorporates two sequence coding schemes, including NCP and One-hot, and then feeds the extracted RNA sequence features into a deep learning model constructed from CNN, GRU and Attention. The benchmark dataset contains data from three species, H. sapiens, S. cerevisiae and M. musculus, and the results using 10-fold cross-validation show that Definer significantly outperforms other existing methods. Meanwhile, the data sets of two species, H. sapiens and S. cerevisiae, were tested independently to further demonstrate the predictive ability of the model. In summary, our method, Definer, can accurately identify pseudouridine modification sites in RNA. |
| format | Article |
| id | doaj-art-c5a67fbc1a17472c8fd574662a5025a5 |
| 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-c5a67fbc1a17472c8fd574662a5025a52025-08-20T02:30:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01204e032007710.1371/journal.pone.0320077Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning.Bo HanSudan BaiYang LiuJiezhang WuXin FengRuihao XinPseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions such as gene expression, RNA structural stability, and various diseases. Therefore, accurate identification of pseudouridine sites can effectively explain the functional mechanism of this modification site. Due to the rapid increase of genomics data, traditional biological experimental methods to identify RNA modification sites can no longer meet the practical needs, and it is necessary to accurately identify pseudouridine sites from high-throughput RNA sequence data by computational methods. In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, Homo sapiens, Saccharomyces cerevisiae and Mus musculus. The method incorporates two sequence coding schemes, including NCP and One-hot, and then feeds the extracted RNA sequence features into a deep learning model constructed from CNN, GRU and Attention. The benchmark dataset contains data from three species, H. sapiens, S. cerevisiae and M. musculus, and the results using 10-fold cross-validation show that Definer significantly outperforms other existing methods. Meanwhile, the data sets of two species, H. sapiens and S. cerevisiae, were tested independently to further demonstrate the predictive ability of the model. In summary, our method, Definer, can accurately identify pseudouridine modification sites in RNA.https://doi.org/10.1371/journal.pone.0320077 |
| spellingShingle | Bo Han Sudan Bai Yang Liu Jiezhang Wu Xin Feng Ruihao Xin Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. PLoS ONE |
| title | Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. |
| title_full | Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. |
| title_fullStr | Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. |
| title_full_unstemmed | Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. |
| title_short | Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning. |
| title_sort | definer a computational method for accurate identification of rna pseudouridine sites based on deep learning |
| url | https://doi.org/10.1371/journal.pone.0320077 |
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