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Pham NT, Zhang Y, Rakkiyappan R, Manavalan B. HOTGpred: Enhancing human O-linked threonine glycosylation prediction using integrated pretrained protein language model-based features and multi-stage feature selection approach. Comput Biol Med 2024; 179:108859. [PMID: 39029431 DOI: 10.1016/j.compbiomed.2024.108859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/19/2024] [Accepted: 07/06/2024] [Indexed: 07/21/2024]
Abstract
O-linked glycosylation is a complex post-translational modification (PTM) in human proteins that plays a critical role in regulating various cellular metabolic and signaling pathways. In contrast to N-linked glycosylation, O-linked glycosylation lacks specific sequence features and maintains an unstable core structure. Identifying O-linked threonine glycosylation sites (OTGs) remains challenging, requiring extensive experimental tests. While bioinformatics tools have emerged for predicting OTGs, their reliance on limited conventional features and absence of well-defined feature selection strategies limit their effectiveness. To address these limitations, we introduced HOTGpred (Human O-linked Threonine Glycosylation predictor), employing a multi-stage feature selection process to identify the optimal feature set for accurately identifying OTGs. Initially, we assessed 25 different feature sets derived from various pretrained protein language model (PLM)-based embeddings and conventional feature descriptors using nine classifiers. Subsequently, we integrated the top five embeddings linearly and determined the most effective scoring function for ranking hybrid features, identifying the optimal feature set through a process of sequential forward search. Among the classifiers, the extreme gradient boosting (XGBT)-based model, using the optimal feature set (HOTGpred), achieved 92.03 % accuracy on the training dataset and 88.25 % on the balanced independent dataset. Notably, HOTGpred significantly outperformed the current state-of-the-art methods on both the balanced and imbalanced independent datasets, demonstrating its superior prediction capabilities. Additionally, SHapley Additive exPlanations (SHAP) and ablation analyses were conducted to identify the features contributing most significantly to HOTGpred. Finally, we developed an easy-to-navigate web server, accessible at https://balalab-skku.org/HOTGpred/, to support glycobiologists in their research on glycosylation structure and function.
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Affiliation(s)
- Nhat Truong Pham
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea
| | - Ying Zhang
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Rajan Rakkiyappan
- Department of Mathematics, Bharathiar University, Coimbatore, 641046, Tamil Nadu, India.
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, Republic of Korea.
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2
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Wang M, Ali H, Xu Y, Xie J, Xu S. BiPSTP: Sequence feature encoding method for identifying different RNA modifications with bidirectional position-specific trinucleotides propensities. J Biol Chem 2024; 300:107140. [PMID: 38447795 PMCID: PMC10997841 DOI: 10.1016/j.jbc.2024.107140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/17/2024] [Accepted: 02/25/2024] [Indexed: 03/08/2024] Open
Abstract
RNA modification, a posttranscriptional regulatory mechanism, significantly influences RNA biogenesis and function. The accurate identification of modification sites is paramount for investigating their biological implications. Methods for encoding RNA sequence into numerical data play a crucial role in developing robust models for predicting modification sites. However, existing techniques suffer from limitations, including inadequate information representation, challenges in effectively integrating positional and sequential information, and the generation of irrelevant or redundant features when combining multiple approaches. These deficiencies hinder the effectiveness of machine learning models in addressing the performance challenges associated with predicting RNA modification sites. Here, we introduce a novel RNA sequence feature representation method, named BiPSTP, which utilizes bidirectional trinucleotide position-specific propensities. We employ the parameter ξ to denote the interval between the current nucleotide and its adjacent forward or backward dinucleotide, enabling the extraction of positional and sequential information from RNA sequences. Leveraging the BiPSTP method, we have developed the prediction model mRNAPred using support vector machine classifier to identify multiple types of RNA modification sites. We evaluate the performance of our BiPSTP method and mRNAPred model across 12 distinct RNA modification types. Our experimental results demonstrate the superiority of the mRNAPred model compared to state-of-art models in the domain of RNA modification sites identification. Importantly, our BiPSTP method enhances the robustness and generalization performance of prediction models. Notably, it can be applied to feature extraction from DNA sequences to predict other biological modification sites.
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Affiliation(s)
- Mingzhao Wang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Haider Ali
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yandi Xu
- School of Computer Science, Shaanxi Normal University, Xi'an, China; College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an, China.
| | - Shengquan Xu
- College of Life Sciences, Shaanxi Normal University, Xi'an, China.
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3
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Ji Y, Sun J, Xie J, Wu W, Shuai SC, Zhao Q, Chen W. m5UMCB: Prediction of RNA 5-methyluridine sites using multi-scale convolutional neural network with BiLSTM. Comput Biol Med 2024; 168:107793. [PMID: 38048661 DOI: 10.1016/j.compbiomed.2023.107793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/06/2023]
Abstract
As a prevalent RNA modification, 5-methyluridine (m5U) plays a critical role in diverse biological processes and disease pathogenesis. High-throughput identification of m5U typically relies on labor-intensive biochemical experiments using various sequencing-based techniques, which are not only time-consuming but also expensive. Consequently, there is a pressing need for more efficient and cost-effective computational methods to complement these high-throughput techniques. In this study, we present m5UMCB, a novel approach that harnesses a multi-scale convolutional neural network (CNN) in tandem with bidirectional long short-term memory (BiLSTM) to recognize m5U sites. Our method involves segmenting RNA sequences into smaller fragments based on a 3-mer length and subsequently mapping each fragment to a lower-dimensional vector representation using the global vectors for word representation (GloVe) technique. Through a series of multi-scale convolution and pooling operations, local features are extracted from RNA sequences and transformed into abstract, high-level features. The feature matrix is then inputted into a BiLSTM network, enabling the capture of contextual information and long-term dependencies within the sequence. Ultimately, a fully connected layer is employed to classify m5U sites. The validation results from 5-fold cross-validation (5-fold CV) test indicate that m5UMCB outperforms existing state-of-the-art predictive methods, demonstrating a 1.98% increase in the area under ROC curve (AUC) and significant improvements in relevant evaluation metrics. We are confident that m5UMCB will serve as a valuable tool for m5U prediction.
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Affiliation(s)
- Yingshan Ji
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China
| | - Jingxuan Xie
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Wei Wu
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
| | - Stella C Shuai
- Biological Science, Northwestern University, Evanston, IL, 60208, USA
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
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4
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Wang L, Zhou Y. MRM-BERT: a novel deep neural network predictor of multiple RNA modifications by fusing BERT representation and sequence features. RNA Biol 2024; 21:1-10. [PMID: 38357904 PMCID: PMC10877979 DOI: 10.1080/15476286.2024.2315384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2024] [Indexed: 02/16/2024] Open
Abstract
RNA modifications play crucial roles in various biological processes and diseases. Accurate prediction of RNA modification sites is essential for understanding their functions. In this study, we propose a hybrid approach that fuses a pre-trained sequence representation with various sequence features to predict multiple types of RNA modifications in one combined prediction framework. We developed MRM-BERT, a deep learning method that combined the pre-trained DNABERT deep sequence representation module and the convolutional neural network (CNN) exploiting four traditional sequence feature encodings to improve the prediction performance. MRM-BERT was evaluated on multiple datasets of 12 commonly occurring RNA modifications, including m6A, m5C, m1A and so on. The results demonstrate that our hybrid model outperforms other models in terms of area under receiver operating characteristic curve (AUC) for all 12 types of RNA modifications. MRM-BERT is available as an online tool (http://117.122.208.21:8501) or source code (https://github.com/abhhba999/MRM-BERT), which allows users to predict RNA modification sites and visualize the results. Overall, our study provides an effective and efficient approach to predict multiple RNA modifications, contributing to the understanding of RNA biology and the development of therapeutic strategies.
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Affiliation(s)
- Linshu Wang
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing, China
- Department of Biomedical Informatics, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
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Ren J, Chen X, Zhang Z, Shi H, Wu S. DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features. Front Genet 2023; 14:1334132. [PMID: 38169665 PMCID: PMC10758487 DOI: 10.3389/fgene.2023.1334132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 11/29/2023] [Indexed: 01/05/2024] Open
Abstract
Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA. Methods: To identify D in epitranscriptome, we provided a prediction framework named "DPred_3S" based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time. Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study. Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.
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Affiliation(s)
- Jinjin Ren
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, Fujian, China
| | - Xiaozhen Chen
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Zhengqian Zhang
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
| | - Haoran Shi
- Institute of Applied Microbiology, Research Center for BioSystems, Land Use, and Nutrition (IFZ), Justus-Liebig-University Giessen, Giessen, Germany
| | - Shuxiang Wu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, Fujian Medical University, Fuzhou, Fujian, China
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Xu Z, Wang X, Meng J, Zhang L, Song B. m5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features. Front Microbiol 2023; 14:1277099. [PMID: 37937221 PMCID: PMC10627201 DOI: 10.3389/fmicb.2023.1277099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 11/09/2023] Open
Abstract
5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.
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Affiliation(s)
- Zhongxing Xu
- Department of Public Health, School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
- School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Xuan Wang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Lin Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Bowen Song
- Department of Public Health, School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, China
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7
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Yu L, Zhang Y, Xue L, Liu F, Jing R, Luo J. Evaluation and development of deep neural networks for RNA 5-Methyluridine classifications using autoBioSeqpy. Front Microbiol 2023; 14:1175925. [PMID: 37275146 PMCID: PMC10232852 DOI: 10.3389/fmicb.2023.1175925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/27/2023] [Indexed: 06/07/2023] Open
Abstract
Post-transcriptionally RNA modifications, also known as the epitranscriptome, play crucial roles in the regulation of gene expression during development. Recently, deep learning (DL) has been employed for RNA modification site prediction and has shown promising results. However, due to the lack of relevant studies, it is unclear which DL architecture is best suited for some pyrimidine modifications, such as 5-methyluridine (m5U). To fill this knowledge gap, we first performed a comparative evaluation of various commonly used DL models for epigenetic studies with the help of autoBioSeqpy. We identified optimal architectural variations for m5U site classification, optimizing the layer depth and neuron width. Second, we used this knowledge to develop Deepm5U, an improved convolutional-recurrent neural network that accurately predicts m5U sites from RNA sequences. We successfully applied Deepm5U to transcriptomewide m5U profiling data across different sequencing technologies and cell types. Third, we showed that the techniques for interpreting deep neural networks, including LayerUMAP and DeepSHAP, can provide important insights into the internal operation and behavior of models. Overall, we offered practical guidance for the development, benchmark, and analysis of deep learning models when designing new algorithms for RNA modifications.
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Affiliation(s)
- Lezheng Yu
- School of Chemistry and Materials Science, Guizhou Education University, Guiyang, China
| | - Yonglin Zhang
- Department of Pharmacy, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Li Xue
- School of Public Health, Southwest Medical University, Luzhou, China
| | - Fengjuan Liu
- School of Geography and Resources, Guizhou Education University, Guiyang, China
| | - Runyu Jing
- School of Cyber Science and Engineering, Sichuan University, Chengdu, China
| | - Jiesi Luo
- Basic Medical College, Southwest Medical University, Luzhou, China
- Sichuan Key Medical Laboratory of New Drug Discovery and Druggability Evaluation, Luzhou Key Laboratory of Activity Screening and Druggability Evaluation for Chinese Materia Medica, Southwest Medical University, Luzhou, China
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8
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Ao C, Ye X, Sakurai T, Zou Q, Yu L. m5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation. BMC Biol 2023; 21:93. [PMID: 37095510 PMCID: PMC10127088 DOI: 10.1186/s12915-023-01596-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/12/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND RNA 5-methyluridine (m5U) modifications are obtained by methylation at the C5 position of uridine catalyzed by pyrimidine methylation transferase, which is related to the development of human diseases. Accurate identification of m5U modification sites from RNA sequences can contribute to the understanding of their biological functions and the pathogenesis of related diseases. Compared to traditional experimental methods, computational methods developed based on machine learning with ease of use can identify modification sites from RNA sequences in an efficient and time-saving manner. Despite the good performance of these computational methods, there are some drawbacks and limitations. RESULTS In this study, we have developed a novel predictor, m5U-SVM, based on multi-view features and machine learning algorithms to construct predictive models for identifying m5U modification sites from RNA sequences. In this method, we used four traditional physicochemical features and distributed representation features. The optimized multi-view features were obtained from the four fused traditional physicochemical features by using the two-step LightGBM and IFS methods, and then the distributed representation features were fused with the optimized physicochemical features to obtain the new multi-view features. The best performing classifier, support vector machine, was identified by screening different machine learning algorithms. Compared with the results, the performance of the proposed model is better than that of the existing state-of-the-art tool. CONCLUSIONS m5U-SVM provides an effective tool that successfully captures sequence-related attributes of modifications and can accurately predict m5U modification sites from RNA sequences. The identification of m5U modification sites helps to understand and delve into the related biological processes and functions.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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RNADSN: Transfer-Learning 5-Methyluridine (m5U) Modification on mRNAs from Common Features of tRNA. Int J Mol Sci 2022; 23:ijms232113493. [PMID: 36362279 PMCID: PMC9655583 DOI: 10.3390/ijms232113493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/24/2022] [Accepted: 09/29/2022] [Indexed: 11/06/2022] Open
Abstract
One of the most abundant non-canonical bases widely occurring on various RNA molecules is 5-methyluridine (m5U). Recent studies have revealed its influences on the development of breast cancer, systemic lupus erythematosus, and the regulation of stress responses. The accurate identification of m5U sites is crucial for understanding their biological functions. We propose RNADSN, the first transfer learning deep neural network that learns common features between tRNA m5U and mRNA m5U to enhance the prediction of mRNA m5U. Without seeing the experimentally detected mRNA m5U sites, RNADSN has already outperformed the state-of-the-art method, m5UPred. Using mRNA m5U classification as an additional layer of supervision, our model achieved another distinct improvement and presented an average area under the receiver operating characteristic curve (AUC) of 0.9422 and an average precision (AP) of 0.7855. The robust performance of RNADSN was also verified by cross-technical and cross-cellular validation. The interpretation of RNADSN also revealed the sequence motif of common features. Therefore, RNADSN should be a useful tool for studying m5U modification.
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Huang D, Chen K, Song B, Wei Z, Su J, Coenen F, de Magalhães JP, Rigden DJ, Meng J. Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation. Nucleic Acids Res 2022; 50:10290-10310. [PMID: 36155798 PMCID: PMC9561283 DOI: 10.1093/nar/gkac830] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 08/26/2022] [Accepted: 09/15/2022] [Indexed: 12/25/2022] Open
Abstract
As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3′UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.
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Affiliation(s)
- Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Department of Computer Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - Kunqi Chen
- Key Laboratory of Gastrointestinal Cancer (Fujian Medical University), Ministry of Education, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, PR China
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Zhen Wei
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jionglong Su
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China
| | - Frans Coenen
- Department of Computer Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - João Pedro de Magalhães
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 7ZB, UK
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou 215123, PR China
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11
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Meng J, Zuo Z, Lee TY, Liu Z, Huang Y. Bioinformatics resources for understanding RNA modifications. Methods 2022; 206:53-55. [PMID: 35988901 DOI: 10.1016/j.ymeth.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022] Open
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