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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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2
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Tu G, Wang X, Xia R, Song B. m6A-TCPred: a web server to predict tissue-conserved human m 6A sites using machine learning approach. BMC Bioinformatics 2024; 25:127. [PMID: 38528499 PMCID: PMC10962094 DOI: 10.1186/s12859-024-05738-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level. RESULTS We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively. CONCLUSION Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .
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Affiliation(s)
- Gang Tu
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Xuan Wang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L7 8TX, UK.
| | - Rong Xia
- Department of Financial and Actuarial Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
| | - Bowen Song
- Department of Public Health, School of Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China
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3
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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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4
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Benak D, Kolar F, Zhang L, Devaux Y, Hlavackova M. RNA modification m 6Am: the role in cardiac biology. Epigenetics 2023; 18:2218771. [PMID: 37331009 DOI: 10.1080/15592294.2023.2218771] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 05/19/2023] [Accepted: 05/23/2023] [Indexed: 06/20/2023] Open
Abstract
Epitranscriptomic modifications have recently emerged into the spotlight of researchers due to their vast regulatory effects on gene expression and thereby cellular physiology and pathophysiology. N6,2'-O-dimethyladenosine (m6Am) is one of the most prevalent chemical marks on RNA and is dynamically regulated by writers (PCIF1, METTL4) and erasers (FTO). The presence or absence of m6Am in RNA affects mRNA stability, regulates transcription, and modulates pre-mRNA splicing. Nevertheless, its functions in the heart are poorly known. This review summarizes the current knowledge and gaps about m6Am modification and its regulators in cardiac biology. It also points out technical challenges and lists the currently available techniques to measure m6Am. A better understanding of epitranscriptomic modifications is needed to improve our knowledge of the molecular regulations in the heart which may lead to novel cardioprotective strategies.
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Affiliation(s)
- Daniel Benak
- Laboratory of Developmental Cardiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
- Department of Physiology, Faculty of Science, Charles University, Prague, Czech Republic
| | - Frantisek Kolar
- Laboratory of Developmental Cardiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Lu Zhang
- Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Yvan Devaux
- Cardiovascular Research Unit, Department of Population Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Marketa Hlavackova
- Laboratory of Developmental Cardiology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
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Jia J, Wei Z, Sun M. EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning. BMC Bioinformatics 2023; 24:397. [PMID: 37880673 PMCID: PMC10598967 DOI: 10.1186/s12859-023-05543-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 10/20/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND N6, 2'-O-dimethyladenosine (m6Am) is an abundant RNA methylation modification on vertebrate mRNAs and is present in the transcription initiation region of mRNAs. It has recently been experimentally shown to be associated with several human disorders, including obesity genes, and stomach cancer, among others. As a result, N6,2'-O-dimethyladenosine (m6Am) site will play a crucial part in the regulation of RNA if it can be correctly identified. RESULTS This study proposes a novel deep learning-based m6Am prediction model, EMDL_m6Am, which employs one-hot encoding to expressthe feature map of the RNA sequence and recognizes m6Am sites by integrating different CNN models via stacking. Including DenseNet, Inflated Convolutional Network (DCNN) and Deep Multiscale Residual Network (MSRN), the sensitivity (Sn), specificity (Sp), accuracy (ACC), Mathews correlation coefficient (MCC) and area under the curve (AUC) of our model on the training data set reach 86.62%, 88.94%, 87.78%, 0.7590 and 0.8778, respectively, and the prediction results on the independent test set are as high as 82.25%, 79.72%, 80.98%, 0.6199, and 0.8211. CONCLUSIONS In conclusion, the experimental results demonstrated that EMDL_m6Am greatly improved the predictive performance of the m6Am sites and could provide a valuable reference for the next part of the study. The source code and experimental data are available at: https://github.com/13133989982/EMDL-m6Am .
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Affiliation(s)
- Jianhua Jia
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
| | - Zhangying Wei
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
| | - Mingwei Sun
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China
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Kong Y, Yu J, Ge S, Fan X. Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape. Innovation (N Y) 2023; 4:100452. [PMID: 37485079 PMCID: PMC10362524 DOI: 10.1016/j.xinn.2023.100452] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 05/23/2023] [Indexed: 07/25/2023] Open
Abstract
An immunosuppressive state is a typical feature of the tumor microenvironment. Despite the dramatic success of immune checkpoint inhibitor (ICI) therapy in preventing tumor cell escape from immune surveillance, primary and acquired resistance have limited its clinical use. Notably, recent clinical trials have shown that epigenetic drugs can significantly improve the outcome of ICI therapy in various cancers, indicating the importance of epigenetic modifications in immune regulation of tumors. Recently, RNA modifications (N6-methyladenosine [m6A], N1-methyladenosine [m1A], 5-methylcytosine [m5C], etc.), novel hotspot areas of epigenetic research, have been shown to play crucial roles in protumor and antitumor immunity. In this review, we provide a comprehensive understanding of how m6A, m1A, and m5C function in tumor immunity by directly regulating different immune cells as well as indirectly regulating tumor cells through different mechanisms, including modulating the expression of immune checkpoints, inducing metabolic reprogramming, and affecting the secretion of immune-related factors. Finally, we discuss the current status of strategies targeting RNA modifications to prevent tumor immune escape, highlighting their potential.
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Affiliation(s)
- Yuxin Kong
- Department of Ophthalmology, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200001, China
| | - Jie Yu
- Department of Ophthalmology, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200001, China
| | - Shengfang Ge
- Department of Ophthalmology, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200001, China
| | - Xianqun Fan
- Department of Ophthalmology, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai 200001, China
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7
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Liu Z, Lan P, Liu T, Liu X, Liu T. m6Aminer: Predicting the m6Am Sites on mRNA by Fusing Multiple Sequence-Derived Features into a CatBoost-Based Classifier. Int J Mol Sci 2023; 24:ijms24097878. [PMID: 37175594 PMCID: PMC10177809 DOI: 10.3390/ijms24097878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
As one of the most important post-transcriptional modifications, m6Am plays a fairly important role in conferring mRNA stability and in the progression of cancers. The accurate identification of the m6Am sites is critical for explaining its biological significance and developing its application in the medical field. However, conventional experimental approaches are time-consuming and expensive, making them unsuitable for the large-scale identification of the m6Am sites. To address this challenge, we exploit a CatBoost-based method, m6Aminer, to identify the m6Am sites on mRNA. For feature extraction, nine different feature-encoding schemes (pseudo electron-ion interaction potential, hash decimal conversion method, dinucleotide binary encoding, nucleotide chemical properties, pseudo k-tuple composition, dinucleotide numerical mapping, K monomeric units, series correlation pseudo trinucleotide composition, and K-spaced nucleotide pair frequency) were utilized to form the initial feature space. To obtain the optimized feature subset, the ExtraTreesClassifier algorithm was adopted to perform feature importance ranking, and the top 300 features were selected as the optimal feature subset. With different performance assessment methods, 10-fold cross-validation and independent test, m6Aminer achieved average AUC of 0.913 and 0.754, demonstrating a competitive performance with the state-of-the-art models m6AmPred (0.905 and 0.735) and DLm6Am (0.897 and 0.730). The prediction model developed in this study can be used to identify the m6Am sites in the whole transcriptome, laying a foundation for the functional research of m6Am.
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Affiliation(s)
- Ze Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
| | - Pengfei Lan
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
| | - Ting Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
- Department of Mechanical Engineering, Faculty of Engineering, The University of Hong Kong, Hong Kong 999077, China
| | - Xudong Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Tao Liu
- College of Water Resources and Architectural Engineering, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China
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8
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Zou J, Liu H, Tan W, Chen YQ, Dong J, Bai SY, Wu ZX, Zeng Y. Dynamic regulation and key roles of ribonucleic acid methylation. Front Cell Neurosci 2022; 16:1058083. [PMID: 36601431 PMCID: PMC9806184 DOI: 10.3389/fncel.2022.1058083] [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: 09/30/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Ribonucleic acid (RNA) methylation is the most abundant modification in biological systems, accounting for 60% of all RNA modifications, and affects multiple aspects of RNA (including mRNAs, tRNAs, rRNAs, microRNAs, and long non-coding RNAs). Dysregulation of RNA methylation causes many developmental diseases through various mechanisms mediated by N 6-methyladenosine (m6A), 5-methylcytosine (m5C), N 1-methyladenosine (m1A), 5-hydroxymethylcytosine (hm5C), and pseudouridine (Ψ). The emerging tools of RNA methylation can be used as diagnostic, preventive, and therapeutic markers. Here, we review the accumulated discoveries to date regarding the biological function and dynamic regulation of RNA methylation/modification, as well as the most popularly used techniques applied for profiling RNA epitranscriptome, to provide new ideas for growth and development.
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Affiliation(s)
- Jia Zou
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Hui Liu
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Wei Tan
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Yi-qi Chen
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Jing Dong
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Shu-yuan Bai
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China
| | - Zhao-xia Wu
- Community Health Service Center, Wuchang Hospital, Wuhan, China
| | - Yan Zeng
- Community Health Service Center, Geriatric Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China,Brain Science and Advanced Technology Institute, School of Medicine, Wuhan University of Science and Technology, Wuhan, China,School of Public Health, Wuhan University of Science and Technology, Wuhan, China,*Correspondence: Yan Zeng,
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9
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DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2′-O-Dimethyladenosine Sites in RNA Sequences. Int J Mol Sci 2022; 23:ijms231911026. [PMID: 36232325 PMCID: PMC9570463 DOI: 10.3390/ijms231911026] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/10/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022] Open
Abstract
N6,2′-O-dimethyladenosine (m6Am) is a post-transcriptional modification that may be associated with regulatory roles in the control of cellular functions. Therefore, it is crucial to accurately identify transcriptome-wide m6Am sites to understand underlying m6Am-dependent mRNA regulation mechanisms and biological functions. Here, we used three sequence-based feature-encoding schemes, including one-hot, nucleotide chemical property (NCP), and nucleotide density (ND), to represent RNA sequence samples. Additionally, we proposed an ensemble deep learning framework, named DLm6Am, to identify m6Am sites. DLm6Am consists of three similar base classifiers, each of which contains a multi-head attention module, an embedding module with two parallel deep learning sub-modules, a convolutional neural network (CNN) and a Bi-directional long short-term memory (BiLSTM), and a prediction module. To demonstrate the superior performance of our model’s architecture, we compared multiple model frameworks with our method by analyzing the training data and independent testing data. Additionally, we compared our model with the existing state-of-the-art computational methods, m6AmPred and MultiRM. The accuracy (ACC) for the DLm6Am model was improved by 6.45% and 8.42% compared to that of m6AmPred and MultiRM on independent testing data, respectively, while the area under receiver operating characteristic curve (AUROC) for the DLm6Am model was increased by 4.28% and 5.75%, respectively. All the results indicate that DLm6Am achieved the best prediction performance in terms of ACC, Matthews correlation coefficient (MCC), AUROC, and the area under precision and recall curves (AUPR). To further assess the generalization performance of our proposed model, we implemented chromosome-level leave-out cross-validation, and found that the obtained AUROC values were greater than 0.83, indicating that our proposed method is robust and can accurately predict m6Am sites.
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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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11
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Deng MS, Chen KJ, zhang DD, Li GH, Weng CM, Wang JM. m6A RNA Methylation Regulators Contribute to Predict and as a Therapy Target of Pulmonary Fibrosis. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:2425065. [PMID: 35497924 PMCID: PMC9050297 DOI: 10.1155/2022/2425065] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 03/31/2022] [Indexed: 01/04/2023]
Abstract
Background Pulmonary fibrosis is difficult to treat. Early diagnosis and finding potential drug therapy targets of pulmonary fibrosis are particularly important. There were still various problems with existing pulmonary fibrosis markers, so it is particularly important to find new biomarkers and drug treatment targets. m6A (N6,2'-O-dimethyladenosine) RNA methylation was the cause of many diseases, and it is regulated by m6A methylation regulators. So, whether RNA methylation regulators can be a diagnostic marker and potential drug therapy target of early pulmonary fibrosis needs to be explored. Materials and Methods Using GSE110147 and GSE33566 in the GEO database to predict the m6A methylation regulators that may be related to the development of pulmonary fibrosis, we used 10 mg/ml bleomycin to induce mouse pulmonary fibrosis models and human pulmonary fibrosis samples, to confirm whether this indicator can be an early diagnostic marker of pulmonary fibrosis. Results According to the database prediction results, METTL3 can predict the occurrence and development of pulmonary fibrosis, and the results of MASSON and HE staining show that the fibrosis model of mice is successful, and the fibrosis of human samples is obvious. The results of immunohistochemistry showed that the expression of METTL3 was significantly reduced in pulmonary fibrosis. Conclusions The m6A methylation regulator METTL3 can be considered as an important biomarker for diagnosing pulmonary fibrosis occurrence, furthermore it could be considered as a drug target because of its low expression in pulmonary fibrosis.
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Affiliation(s)
- Meng-Sheng Deng
- State Key Laboratory of Trauma, Burn and Combined Injury, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Kui-Jun Chen
- State Key Laboratory of Trauma, Burn and Combined Injury, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Dong-Dong zhang
- State Key Laboratory of Trauma, Burn and Combined Injury, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Guan-Hua Li
- State Key Laboratory of Trauma, Burn and Combined Injury, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Chang-Mei Weng
- State Key Laboratory of Trauma, Burn and Combined Injury, Daping Hospital, Army Medical University, Chongqing 400042, China
| | - Jian-Min Wang
- State Key Laboratory of Trauma, Burn and Combined Injury, Daping Hospital, Army Medical University, Chongqing 400042, China
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12
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Wang H, Wang S, Zhang Y, Bi S, Zhu X. A brief review of machine learning methods for RNA methylation sites prediction. Methods 2022; 203:399-421. [DOI: 10.1016/j.ymeth.2022.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/15/2022] [Accepted: 03/01/2022] [Indexed: 02/07/2023] Open
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13
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Song Z, Huang D, Song B, Chen K, Song Y, Liu G, Su J, Magalhães JPD, Rigden DJ, Meng J. Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications. Nat Commun 2021; 12:4011. [PMID: 34188054 PMCID: PMC8242015 DOI: 10.1038/s41467-021-24313-3] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 06/07/2021] [Indexed: 02/08/2023] Open
Abstract
Recent studies suggest that epi-transcriptome regulation via post-transcriptional RNA modifications is vital for all RNA types. Precise identification of RNA modification sites is essential for understanding the functions and regulatory mechanisms of RNAs. Here, we present MultiRM, a method for the integrated prediction and interpretation of post-transcriptional RNA modifications from RNA sequences. Built upon an attention-based multi-label deep learning framework, MultiRM not only simultaneously predicts the putative sites of twelve widely occurring transcriptome modifications (m6A, m1A, m5C, m5U, m6Am, m7G, Ψ, I, Am, Cm, Gm, and Um), but also returns the key sequence contents that contribute most to the positive predictions. Importantly, our model revealed a strong association among different types of RNA modifications from the perspective of their associated sequence contexts. Our work provides a solution for detecting multiple RNA modifications, enabling an integrated analysis of these RNA modifications, and gaining a better understanding of sequence-based RNA modification mechanisms. RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.
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Affiliation(s)
- Zitao Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | - Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China. .,Department of Computer Sciences, University of Liverpool, Liverpool, United Kingdom.
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Kunqi Chen
- Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, PR China
| | - Yiyou Song
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | - Gang Liu
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, PR China
| | | | - Daniel J Rigden
- 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, PR China. .,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom. .,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, PR China.
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Ao C, Zou Q, Yu L. RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features. Methods 2021; 203:32-39. [PMID: 34033879 DOI: 10.1016/j.ymeth.2021.05.016] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 05/04/2021] [Accepted: 05/20/2021] [Indexed: 12/31/2022] Open
Abstract
N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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Zhang SY, Zhang SW, Zhang T, Fan XN, Meng J. Recent advances in functional annotation and prediction of the epitranscriptome. Comput Struct Biotechnol J 2021; 19:3015-3026. [PMID: 34136099 PMCID: PMC8175281 DOI: 10.1016/j.csbj.2021.05.030] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/16/2021] [Accepted: 05/18/2021] [Indexed: 12/17/2022] Open
Abstract
RNA modifications, in particular N6-methyladenosine (m6A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.
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Affiliation(s)
- Song-Yao Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Teng Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xiao-Nan Fan
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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