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Kurata H, Harun-Or-Roshid M, Mehedi Hasan M, Tsukiyama S, Maeda K, Manavalan B. MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models. Methods 2024; 227:37-47. [PMID: 38729455 DOI: 10.1016/j.ymeth.2024.05.004] [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: 09/22/2023] [Revised: 04/22/2024] [Accepted: 05/06/2024] [Indexed: 05/12/2024] Open
Abstract
RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.
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Affiliation(s)
- Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| | - Md Harun-Or-Roshid
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Md Mehedi Hasan
- Division of Biotetecnology and Molecular Medicine, Department of Pathobiological Science, School of Veterinary Medicine, Lousiana State University, Baton Rouge, LA 70803, USA
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Kazuhiro Maeda
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Balachandran Manavalan
- Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
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2
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Aslam I, Shah S, Jabeen S, ELAffendi M, A Abdel Latif A, Ul Haq N, Ali G. A CNN based m5c RNA methylation predictor. Sci Rep 2023; 13:21885. [PMID: 38081880 PMCID: PMC10713599 DOI: 10.1038/s41598-023-48751-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
Post-transcriptional modifications of RNA play a key role in performing a variety of biological processes, such as stability and immune tolerance, RNA splicing, protein translation and RNA degradation. One of these RNA modifications is m5c which participates in various cellular functions like RNA structural stability and translation efficiency, got popularity among biologists. By applying biological experiments to detect RNA m5c methylation sites would require much more efforts, time and money. Most of the researchers are using pre-processed RNA sequences of 41 nucleotides where the methylated cytosine is in the center. Therefore, it is possible that some of the information around these motif may have lost. The conventional methods are unable to process the RNA sequence directly due to high dimensionality and thus need optimized techniques for better features extraction. To handle the above challenges the goal of this study is to employ an end-to-end, 1D CNN based model to classify and interpret m5c methylated data sites. Moreover, our aim is to analyze the sequence in its full length where the methylated cytosine may not be in the center. The evaluation of the proposed architecture showed a promising results by outperforming state-of-the-art techniques in terms of sensitivity and accuracy. Our model achieve 96.70% sensitivity and 96.21% accuracy for 41 nucleotides sequences while 96.10% accuracy for full length sequences.
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Affiliation(s)
- Irum Aslam
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, KPK, Pakistan
| | - Sajid Shah
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Rafha, Riyadh, 12435, Saudi Arabia
| | - Saima Jabeen
- College of Engineering, AI Research Center, Alfaisal University, Riyadh, 50927, Saudi Arabia.
| | - Mohammed ELAffendi
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Rafha, Riyadh, 12435, Saudi Arabia
| | - Asmaa A Abdel Latif
- Public Health and Community Medicine Department (Industrial medicine and occupational health specialty, Faculty of Medicine, Menoufia University, Shibîn el Kôm, Egypt
| | - Nuhman Ul Haq
- Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, KPK, Pakistan
| | - Gauhar Ali
- EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Rafha, Riyadh, 12435, Saudi Arabia
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3
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Liu Y, Liu S, Yan L, Zhang Q, Liu W, Huang X, Liu S. Contribution of m5C RNA Modification-Related Genes to Prognosis and Immunotherapy Prediction in Patients with Ovarian Cancer. Mediators Inflamm 2023; 2023:1400267. [PMID: 38022687 PMCID: PMC10661868 DOI: 10.1155/2023/1400267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/03/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Background 5-Methylcytosine (m5C) RNA modification is closely implicated in the occurrence of a variety of cancers. Here, we established a novel prognostic signature for ovarian cancer (OC) patients based on m5C RNA modification-related genes and explored the correlation between these genes with the tumor immune microenvironment. Methods Methylated-RNA immunoprecipitation sequencing helped us to identify candidate genes related to m5C RNA modification at first. Based on TCGA database, we screened the differentially expressed candidate genes related to the prognosis and constructed a prognostic model using LASSO Cox regression analyses. Notably, the accuracy of the model was evaluated by Kaplan-Meier analysis and receiver operator characteristic curves. Independent prognostic risk factors were investigated by Cox proportional hazard model. Furthermore, we also analyzed the biological functions and pathways involved in the signature. Finally, the immune response of the model was visualized in great detail. Results Totally, 2,493 candidate genes proved to be involved in m5C modification of RNA for OC. We developed a signature with prognostic value consisting of six m5C RNA modification-related genes. Specially, samples have been split into two cohorts with low- and high-risk scores according to the model, in which the low-risk OC patients exhibited dramatically better overall survival time than those with high-risk scores. Besides, not only was this model a prognostic factor independent of other clinical characteristics but it predicted the intensity of the immune response in OC. Significantly, the accuracy and availability of the signature were verified by ICGC database. Conclusions Our study bridged the gap between m5C RNA modification and the prognosis of OC and was expected to provide an effective breakthrough for immunotherapy in OC patients.
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Affiliation(s)
- Yibin Liu
- Department of Gynecology, The Second Hospital of Hebei Medical University, 215 Heping West Road, Shijiazhuang, Hebei 050011, China
| | - Shouze Liu
- Department of Gynecology, The Second Hospital of Hebei Medical University, 215 Heping West Road, Shijiazhuang, Hebei 050011, China
- Department of Gynecology III, Cangzhou Central Hospital, Cangzhou, Hebei 061000, China
| | - Lu Yan
- Department of Gynecology, The Second Hospital of Hebei Medical University, 215 Heping West Road, Shijiazhuang, Hebei 050011, China
| | - Qianqian Zhang
- Department of Gynecology and Obstetrics, Beijing Tsinghua Changgung Hospital, Beijing 102218, China
| | - Wenhua Liu
- Department of Pain, Cangzhou Hospital of Integrated TCM-WM Hebei, Cangzhou, Hebei 061001, China
| | - Xianghua Huang
- Department of Gynecology, The Second Hospital of Hebei Medical University, 215 Heping West Road, Shijiazhuang, Hebei 050011, China
| | - Shikai Liu
- Department of Gynecology III, Cangzhou Central Hospital, Cangzhou, Hebei 061000, China
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Jia J, Wei Z, Cao X. EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention. Front Genet 2023; 14:1232038. [PMID: 37519885 PMCID: PMC10372626 DOI: 10.3389/fgene.2023.1232038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 06/29/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction: N4-acetylcytidine (ac4C) is a critical acetylation modification that has an essential function in protein translation and is associated with a number of human diseases. Methods: The process of identifying ac4C sites by biological experiments is too cumbersome and costly. And the performance of several existing computational models needs to be improved. Therefore, we propose a new deep learning tool EMDL-ac4C to predict ac4C sites, which uses a simple one-hot encoding for a unbalanced dataset using a downsampled ensemble deep learning network to extract important features to identify ac4C sites. The base learner of this ensemble model consists of a modified DenseNet and Squeeze-and-Excitation Networks. In addition, we innovatively add a convolutional residual structure in parallel with the dense block to achieve the effect of two-layer feature extraction. Results: The average accuracy (Acc), mathews correlation coefficient (MCC), and area under the curve Area under curve of EMDL-ac4C on ten independent testing sets are 80.84%, 61.77%, and 87.94%, respectively. Discussion: Multiple experimental comparisons indicate that EMDL-ac4C outperforms existing predictors and it greatly improved the predictive performance of the ac4C sites. At the same time, EMDL-ac4C 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/EMDLac4C.
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Affiliation(s)
- Jianhua Jia
- *Correspondence: Jianhua Jia, ; Zhangying Wei,
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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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6
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Abstract
The epitranscriptome, defined as RNA modifications that do not involve alterations in the nucleotide sequence, is a popular topic in the genomic sciences. Because we need massive computational techniques to identify epitranscriptomes within individual transcripts, many tools have been developed to infer epitranscriptomic sites as well as to process datasets using high-throughput sequencing. In this review, we summarize recent developments in epitranscriptome spatial detection and data analysis and discuss their progression.
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Affiliation(s)
- Y-H Taguchi
- Department of Physics, Chuo University, Tokyo, Japan
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7
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Wang Y, Wang X, Cui X, Meng J, Rong R. Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:411-420. [PMID: 36845339 PMCID: PMC9945750 DOI: 10.1016/j.omtn.2023.01.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023]
Abstract
Dihydrouridine (D) is a modified pyrimidine nucleotide universally found in viral, prokaryotic, and eukaryotic species. It serves as a metabolic modulator for various pathological conditions, and its elevated levels in tumors are associated with a series of cancers. Precise identification of D sites on RNA is vital for understanding its biological function. A number of computational approaches have been developed for predicting D sites on tRNAs; however, none have considered mRNAs. We present here DPred, the first computational tool for predicting D on mRNAs in yeast from the primary RNA sequences. Built on a local self-attention layer and a convolutional neural network (CNN) layer, the proposed deep learning model outperformed classic machine learning approaches (random forest, support vector machines, etc.) and achieved reasonable accuracy and reliability with areas under the curve of 0.9166 and 0.9027 in jackknife cross-validation and on an independent testing dataset, respectively. Importantly, we showed that distinct sequence signatures are associated with the D sites on mRNAs and tRNAs, implying potentially different formation mechanisms and putative divergent functionality of this modification on the two types of RNA. DPred is available as a user-friendly Web server.
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Affiliation(s)
- Yue Wang
- Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China,Department of Computer Science, University of Liverpool, L69 7ZB Liverpool, UK
| | - Xuan Wang
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Xiaodong Cui
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China
| | - Jia Meng
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China,AI University Research Centre, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L69 7ZB Liverpool, UK
| | - Rong Rong
- Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China,Corresponding author: Rong Rong, Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, 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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Li X, Zhang S, Shi H. An improved residual network using deep fusion for identifying RNA 5-methylcytosine sites. Bioinformatics 2022; 38:4271-4277. [PMID: 35866985 DOI: 10.1093/bioinformatics/btac532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/30/2022] [Accepted: 07/21/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION 5-Methylcytosine (m5C) is a crucial post-transcriptional modification. With the development of technology, it is widely found in various RNAs. Numerous studies have indicated that m5C plays an essential role in various activities of organisms, such as tRNA recognition, stabilization of RNA structure, RNA metabolism and so on. Traditional identification is costly and time-consuming by wet biological experiments. Therefore, computational models are commonly used to identify the m5C sites. Due to the vast computing advantages of deep learning, it is feasible to construct the predictive model through deep learning algorithms. RESULTS In this study, we construct a model to identify m5C based on a deep fusion approach with an improved residual network. First, sequence features are extracted from the RNA sequences using Kmer, K-tuple nucleotide frequency component (KNFC), Pseudo dinucleotide composition (PseDNC) and Physical and chemical property (PCP). Kmer and KNFC extract information from a statistical point of view. PseDNC and PCP extract information from the physicochemical properties of RNA sequences. Then, two parts of information are fused with new features using bidirectional long- and short-term memory and attention mechanisms, respectively. Immediately after, the fused features are fed into the improved residual network for classification. Finally, 10-fold cross-validation and independent set testing are used to verify the credibility of the model. The results show that the accuracy reaches 91.87%, 95.55%, 92.27% and 95.60% on the training sets and independent test sets of Arabidopsis thaliana and M.musculus, respectively. This is a considerable improvement compared to previous studies and demonstrates the robust performance of our model. AVAILABILITY AND IMPLEMENTATION The data and code related to the study are available at https://github.com/alivelxj/m5c-DFRESG.
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Affiliation(s)
- Xinjie Li
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, P. R. China
| | - Shengli Zhang
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, P. R. China
| | - Hongyan Shi
- School of Mathematics and Statistics, Xidian University, Xi'an 710071, P. R. China
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10
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Asim MN, Ibrahim MA, Imran Malik M, Dengel A, Ahmed S. Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction. Int J Mol Sci 2022; 23:ijms23158221. [PMID: 35897818 PMCID: PMC9329987 DOI: 10.3390/ijms23158221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 07/20/2022] [Indexed: 02/04/2023] Open
Abstract
Circular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs.
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Affiliation(s)
- Muhammad Nabeel Asim
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
- Correspondence:
| | - Muhammad Ali Ibrahim
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Muhammad Imran Malik
- School of Computer Science & Electrical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
| | - Andreas Dengel
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany; (M.A.I.); (A.D.); (S.A.)
- DeepReader GmbH, Trippstadter Str. 122, 67663 Kaiserslautern, Germany
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11
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Hasan MM, Tsukiyama S, Cho JY, Kurata H, Alam MA, Liu X, Manavalan B, Deng HW. Deepm5C: A deep learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy. Mol Ther 2022; 30:2856-2867. [PMID: 35526094 PMCID: PMC9372321 DOI: 10.1016/j.ymthe.2022.05.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 11/30/2022] Open
Abstract
As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C), plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method to identify RNA m5C sites in the throughout human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature encoding algorithms and a feature derived from word embedding approaches. Afterwards, four variants of deep learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a 1-D convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and formulate the novel testable biological hypothesis.
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Affiliation(s)
- Md Mehedi Hasan
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA.
| | - Sho Tsukiyama
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Jae Youl Cho
- Molecular Immunology Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Xiaowen Liu
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA
| | - Balachandran Manavalan
- Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Korea.
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, 70112 USA.
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12
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Liu HY, Du PF. i5hmCVec: Identifying 5-Hydroxymethylcytosine Sites of Drosophila RNA Using Sequence Feature Embeddings. Front Genet 2022; 13:896925. [PMID: 35591855 PMCID: PMC9110757 DOI: 10.3389/fgene.2022.896925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 03/30/2022] [Indexed: 01/27/2023] Open
Abstract
5-Hydroxymethylcytosine (5hmC), one of the most important RNA modifications, plays an important role in many biological processes. Accurately identifying RNA modification sites helps understand the function of RNA modification. In this work, we propose a computational method for identifying 5hmC-modified regions using machine learning algorithms. We applied a sequence feature embedding method based on the dna2vec algorithm to represent the RNA sequence. The results showed that the performance of our model is better that of than state-of-art methods. All dataset and source codes used in this study are available at: https://github.com/liu-h-y/5hmC_model.
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13
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He Z, Xu J, Shi H, Wu S. m5CRegpred: Epitranscriptome Target Prediction of 5-Methylcytosine (m5C) Regulators Based on Sequencing Features. Genes (Basel) 2022; 13:genes13040677. [PMID: 35456483 PMCID: PMC9025882 DOI: 10.3390/genes13040677] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/02/2022] [Accepted: 04/05/2022] [Indexed: 02/04/2023] Open
Abstract
5-methylcytosine (m5C) is a common post-transcriptional modification observed in a variety of RNAs. m5C has been demonstrated to be important in a variety of biological processes, including RNA structural stability and metabolism. Driven by the importance of m5C modification, many projects focused on the m5C sites prediction were reported before. To better understand the upstream and downstream regulation of m5C, we present a bioinformatics framework, m5CRegpred, to predict the substrate of m5C writer NSUN2 and m5C readers YBX1 and ALYREF for the first time. After features comparison, window lengths selection and algorism comparison on the mature mRNA model, our model achieved AUROC scores 0.869, 0.724 and 0.889 for NSUN2, YBX1 and ALYREF, respectively in an independent test. Our work suggests the substrate of m5C regulators can be distinguished and may help the research of m5C regulators in a special condition, such as substrates prediction of hyper- or hypo-expressed m5C regulators in human disease.
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Affiliation(s)
- Zhizhou He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; (Z.H.); (J.X.)
- Department of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | - Jing Xu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; (Z.H.); (J.X.)
| | - Haoran Shi
- Research Center for BioSystems, Land Use, and Nutrition (IFZ), Institute of Applied Microbiology, Justus-Liebig-University Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
- Correspondence: (H.S.); (S.W.)
| | - Shuxiang Wu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China; (Z.H.); (J.X.)
- Fujian Key Laboratory of Tumor Microbiology, Department of Medical Microbiology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350004, China
- Correspondence: (H.S.); (S.W.)
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14
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Xiao X, Shao YT, Luo ZT, Qiu WR. m5C-HPromoter: An Ensemble Deep Learning Predictor for Identifying 5-methylcytosine Sites in Human Promoters. Curr Bioinform 2022. [DOI: 10.2174/1574893617666220330150259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
This paper is intended to identify 5-methylcytosine Sites in Human Promoters.
Background:
Aberrant DNA methylation patterns are often associated with tumor development, hypermethylation inhibits expression of tumor suppressor genes, and hypomethylation stimulates expression of certain oncogenes. Most DNA methylation occurs on the CpG island of gene promoter region.
Objective:
Therefore, a comprehensive display of the methylation status of the promoter region of human gene is extremely important for understanding cancer pathogenesis and function of post-transcriptional modification.
Method:
This paper constructed three human promoter methylation datasets, a total of 3 million sample sequences, of small cell lung cancer, non-small cell lung cancer, and hepatocellular carcinoma from Cancer Cell Line Encyclopedia (CCLE) database. Frequency-based One-Hot Encoding was used to encode the sample sequence, and an innovative stacking-based ensemble deep learning classifier was applied to establish the m5C-HPromoter predictor.
Result:
Taking the average of 10 times of 5-fold cross-validation, m5C-HPromoter obtained a good result of Accuracy (Acc) = 0.9270, Matthew's correlation coefficient (MCC) = 0.7234, Sensitivity (Sn) = 0.9123, and Specificity (Sp) = 0.9290.
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Affiliation(s)
- Xuan Xiao
- Department of Computer, Jing-De-Zhen Ceramic Institute, 333046, Jing-De-Zhen, China
| | - Yu-Tao Shao
- Department of Computer, Jing-De-Zhen Ceramic Institute, 333046, Jing-De-Zhen, China
| | - Zhen-Tao Luo
- Department of Computer, Jing-De-Zhen Ceramic Institute, 333046, Jing-De-Zhen, China
| | - Wang-Ren Qiu
- Department of Computer, Jing-De-Zhen Ceramic Institute, 333046, Jing-De-Zhen, China
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15
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Immune Infiltrates of m5C RNA Methylation-Related LncRNAs in Uterine Corpus Endometrial Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:1531474. [PMID: 35392434 PMCID: PMC8983181 DOI: 10.1155/2022/1531474] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 01/08/2022] [Indexed: 11/18/2022]
Abstract
Aberrant 5-methylcytidine (m5C) modification plays an essential role in the progression of different cancers. More and more researchers are focusing on developing a lncRNA-based risk model to assess the clinical prognosis of cancer patients. However, the impact of m5C-related lncRNAs on the prognosis of patients with uterine corpus endometrial carcinoma (UCEC), as well as the immune microenvironment of UCEC, remains unclear. Here, we comprehensively analyzed the predictive value of m5C-associated lncRNAs in UCEC and their association with the tumor immune microenvironment, according to the information extracted from the TCGA-UCEC dataset. We identified a total of 32 m5C-associated lncRNAs that were significantly correlated with the prognosis of UCEC patients. Two molecular subtypes were determined by consensus clustering analysis of these 32 m5C-associated prognostic lncRNAs. Further data showed that cluster 1 was associated with poor clinical prognosis, advanced tumor grade, higher PD-L1 expression levels, higher ESTIMATEScore, and higher immuneScore, as well as the immune cell infiltration. Then, 17 m5C-associated lncRNAs with prognostic values were obtained using LASSO regression analysis. And a risk model was constructed based on these 17 lncRNAs. It was revealed that the risk model could be used as an independent factor for UCEC prognosis. In addition, patients with UCEC in the high-risk group had higher tumor grades and immune scores. The risk model based on m5C-related lncRNAs was also closely associated with infiltrating immune cells. In conclusion, our study elucidated the crucial roles of the identified m5C-related lncRNAs in the UCEC patients' prognoses, as well as in the immune microenvironment in UCEC. The results suggest that the components of risk models based on the m5C-related lncRNAs may serve as important mediators of the immune microenvironment in UCEC.
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16
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Kang Q, Meng J, Luan Y. RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction. Brief Bioinform 2022; 23:6555402. [PMID: 35352114 DOI: 10.1093/bib/bbac107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/22/2022] [Accepted: 03/02/2022] [Indexed: 11/12/2022] Open
Abstract
Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.
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Affiliation(s)
- Qiang Kang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116024, China
| | - Yushi Luan
- School of Bioengineering, Dalian University of Technology, Dalian, Liaoning, 116024, China
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17
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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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18
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Ao C, Jiao S, Wang Y, Yu L, Zou Q. Biological Sequence Classification: A Review on Data and General Methods. RESEARCH 2022. [DOI: 10.34133/research.0011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (
http://lab.malab.cn/~acy/BioseqData/home.html
), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.
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Affiliation(s)
- Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi’an, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Shihu Jiao
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
| | - Yansu Wang
- 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
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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19
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Wang K, Zhong W, Long Z, Guo Y, Zhong C, Yang T, Wang S, Lai H, Lu J, Zheng P, Mao X. 5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer. Front Mol Biosci 2021; 8:775304. [PMID: 34926580 PMCID: PMC8672116 DOI: 10.3389/fmolb.2021.775304] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/01/2021] [Indexed: 02/02/2023] Open
Abstract
The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.
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Affiliation(s)
- Ke Wang
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.,Department of Urology, The Hospital of Trade-Business in Hunan Province, Changsha, China
| | - Weibo Zhong
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Zining Long
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yufei Guo
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chuanfan Zhong
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Taowei Yang
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Shuo Wang
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Houhua Lai
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Jianming Lu
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Pengxiang Zheng
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.,Department of Urology, Fuqing City Hospital Affiliated with Fujian Medical University, Fuzhou, China
| | - Xiangming Mao
- Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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20
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Cheng X, Wang J, Li Q, Liu T. BiLSTM-5mC: A Bidirectional Long Short-Term Memory-Based Approach for Predicting 5-Methylcytosine Sites in Genome-Wide DNA Promoters. Molecules 2021; 26:molecules26247414. [PMID: 34946497 PMCID: PMC8704614 DOI: 10.3390/molecules26247414] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 12/04/2021] [Indexed: 12/04/2022] Open
Abstract
An important reason of cancer proliferation is the change in DNA methylation patterns, characterized by the localized hypermethylation of the promoters of tumor-suppressor genes together with an overall decrease in the level of 5-methylcytosine (5mC). Therefore, identifying the 5mC sites in the promoters is a critical step towards further understanding the diverse functions of DNA methylation in genetic diseases such as cancers and aging. However, most wet-lab experimental techniques are often time consuming and laborious for detecting 5mC sites. In this study, we proposed a deep learning-based approach, called BiLSTM-5mC, for accurately identifying 5mC sites in genome-wide DNA promoters. First, we randomly divided the negative samples into 11 subsets of equal size, one of which can form the balance subset by combining with the positive samples in the same amount. Then, two types of feature vectors encoded by the one-hot method, and the nucleotide property and frequency (NPF) methods were fed into a bidirectional long short-term memory (BiLSTM) network and a full connection layer to train the 22 submodels. Finally, the outputs of these models were integrated to predict 5mC sites by using the majority vote strategy. Our experimental results demonstrated that BiLSTM-5mC outperformed existing methods based on the same independent dataset.
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Affiliation(s)
- Xin Cheng
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; (X.C.); (Q.L.)
| | - Jun Wang
- School of Software Technology, Zhejiang University, Ningbo 315048, China;
| | - Qianyue Li
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; (X.C.); (Q.L.)
| | - Taigang Liu
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China; (X.C.); (Q.L.)
- Correspondence: ; Tel.: +86-21-61900624
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21
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Guo G, Pan K, Fang S, Ye L, Tong X, Wang Z, Xue X, Zhang H. Advances in mRNA 5-methylcytosine modifications: Detection, effectors, biological functions, and clinical relevance. MOLECULAR THERAPY. NUCLEIC ACIDS 2021; 26:575-593. [PMID: 34631286 PMCID: PMC8479277 DOI: 10.1016/j.omtn.2021.08.020] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
5-methylcytosine (m5C) post-transcriptional modifications affect the maturation, stability, and translation of the mRNA molecule. These modifications play an important role in many physiological and pathological processes, including stress response, tumorigenesis, tumor cell migration, embryogenesis, and viral replication. Recently, there has been a better understanding of the biological implications of m5C modification owing to the rapid development and optimization of detection technologies, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and RNA-BisSeq. Further, predictive models (such as PEA-m5C, m5C-PseDNC, and DeepMRMP) for the identification of potential m5C modification sites have also emerged. In this review, we summarize the current experimental detection methods and predictive models for mRNA m5C modifications, focusing on their advantages and limitations. We systematically surveyed the latest research on the effectors related to mRNA m5C modifications and their biological functions in multiple species. Finally, we discuss the physiological effects and pathological significance of m5C modifications in multiple diseases, as well as their therapeutic potential, thereby providing new perspectives for disease treatment and prognosis.
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Affiliation(s)
- Gangqiang Guo
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Kan Pan
- First Clinical College, Wenzhou Medical University, Wenzhou, China
| | - Su Fang
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Lele Ye
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xinya Tong
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Zhibin Wang
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Xiangyang Xue
- Wenzhou Collaborative Innovation Center of Gastrointestinal Cancer in Basic Research and Precision Medicine, Wenzhou Key Laboratory of Cancer-related Pathogens and Immunity, Department of Microbiology and Immunology, Institute of Molecular Virology and Immunology, Institute of Tropical Medicine, School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, China
| | - Huidi Zhang
- Department of Nephrology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
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22
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Staem5: A novel computational approachfor accurate prediction of m5C site. MOLECULAR THERAPY. NUCLEIC ACIDS 2021; 26:1027-1034. [PMID: 34786208 PMCID: PMC8571400 DOI: 10.1016/j.omtn.2021.10.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/27/2021] [Accepted: 10/06/2021] [Indexed: 12/25/2022]
Abstract
5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.
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23
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Zhou W, Wang X, Chang J, Cheng C, Miao C. The molecular structure and biological functions of RNA methylation, with special emphasis on the roles of RNA methylation in autoimmune diseases. Crit Rev Clin Lab Sci 2021; 59:203-218. [PMID: 34775884 DOI: 10.1080/10408363.2021.2002256] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Autoimmune diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and systemic vasculitis are caused by the body's immune response to autoantigens. The pathogenesis of autoimmune diseases is complex. RNA methylation is known to play a key role in disease progression as it regulates almost all aspects of RNA processing, including RNA nuclear export, translation, splicing, and noncoding RNA processing. This review summarizes the mechanisms, molecular structures of RNA methylations and their roles in biological functions. Similar to the roles of RNA methylation in cancers, RNA methylation in RA and SLE involves "writers" that deposit methyl groups to form N6-methyladenosine (m6A) and 5-methylcytosine (m5C), "erasers" that remove these modifications, and "readers" that further affect mRNA splicing, export, translation, and degradation. Recent advances in detection methods have identified N1-methyladenosine (m1A), N6,2-O-dimethyladenosine (m6Am), and 7-methylguanosine (m7G) RNA modifications, and their roles in RA and SLE need to be further studied. The relationship between RNA methylation and other autoimmune diseases has not been reported, and the roles and mechanisms of RNA modifications in these diseases need to be explored in the future.
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Affiliation(s)
- Wanwan Zhou
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Xiao Wang
- Department of Clinical Nursing, School of Nursing, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Jun Chang
- Department of Orthopaedics, Fourth Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
| | - Chenglong Cheng
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, China
| | - Chenggui Miao
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, Anhui, China.,Institute of Prevention and Treatment of Rheumatoid Arthritis, Anhui University of Chinese Medicine, Hefei, Anhui, China.,Department of Pharmacy, School of Life and Health Sciences, Anhui University of Science and Technology, Fengyang, Anhui Province, China
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24
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El Allali A, Elhamraoui Z, Daoud R. Machine learning applications in RNA modification sites prediction. Comput Struct Biotechnol J 2021; 19:5510-5524. [PMID: 34712397 PMCID: PMC8517552 DOI: 10.1016/j.csbj.2021.09.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/24/2021] [Accepted: 09/25/2021] [Indexed: 12/15/2022] Open
Abstract
Ribonucleic acid (RNA) modifications are post-transcriptional chemical composition changes that have a fundamental role in regulating the main aspect of RNA function. Recently, large datasets have become available thanks to the recent development in deep sequencing and large-scale profiling. This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA modification types, namely,m 1 A ,m 6 A ,m 5 C , 5 hmC , ψ , 2 ' - O - Me , ac 4 C ,m 7 G , A - to - I ,m 2 G , and D . This review covers the life cycle of machine learning methods to predict RNA modification sites including available benchmark datasets, feature extraction, and classification algorithms. We compare available methods in terms of datasets, target species, approach, and accuracy for each RNA modification type. Finally, we discuss the advantages and limitations of the reviewed approaches and suggest future perspectives.
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Affiliation(s)
- A. El Allali
- African Genome Center, University Mohamed VI Polytechnic, Morocco
| | - Zahra Elhamraoui
- African Genome Center, University Mohamed VI Polytechnic, Morocco
| | - Rachid Daoud
- African Genome Center, University Mohamed VI Polytechnic, Morocco
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25
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Zhang Q, Liu F, Chen W, Miao H, Liang H, Liao Z, Zhang Z, Zhang B. The role of RNA m 5C modification in cancer metastasis. Int J Biol Sci 2021; 17:3369-3380. [PMID: 34512153 PMCID: PMC8416729 DOI: 10.7150/ijbs.61439] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 07/19/2021] [Indexed: 12/26/2022] Open
Abstract
Epigenetic modification plays a crucial regulatory role in the biological processes of eukaryotic cells. The recent characterization of DNA and RNA methylation is still ongoing. Tumor metastasis has long been an unconquerable feature in the fight against cancer. As an inevitable component of the epigenetic regulatory network, 5-methylcytosine is associated with multifarious cellular processes and systemic diseases, including cell migration and cancer metastasis. Recently, gratifying progress has been achieved in determining the molecular interactions between m5C writers (DNMTs and NSUNs), demethylases (TETs), readers (YTHDF2, ALYREF and YBX1) and RNAs. However, the underlying mechanism of RNA m5C methylation in cell mobility and metastasis remains unclear. The functions of m5C writers and readers are believed to regulate gene expression at the post-transcription level and are involved in cellular metabolism and movement. In this review, we emphatically summarize the recent updates on m5C components and related regulatory networks. The content will be focused on writers and readers of the RNA m5C modification and potential mechanisms in diseases. We will discuss relevant upstream and downstream interacting molecules and their associations with cell migration and metastasis.
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Affiliation(s)
- Qiaofeng Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Furong Liu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Wei Chen
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Hongrui Miao
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Huifang Liang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Zhibin Liao
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Zhanguo Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Bixiang Zhang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.,Hubei Province for the Clinical Medicine Research Center of Hepatic Surgery, Wuhan, Hubei 430030, China.,Hubei key laboratory of Hepato-Pancreato-Biliary Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
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Zhou W, Wang C, Chang J, Huang Y, Xue Q, Miao C, Wu P. RNA Methylations in Cardiovascular Diseases, Molecular Structure, Biological Functions and Regulatory Roles in Cardiovascular Diseases. Front Pharmacol 2021; 12:722728. [PMID: 34489709 PMCID: PMC8417252 DOI: 10.3389/fphar.2021.722728] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 08/09/2021] [Indexed: 01/05/2023] Open
Abstract
Cardiovascular diseases (CVDs) are the leading cause of morbidity and mortality in the world. Despite considerable progress in the diagnosis, treatment and prognosis of CVDs, new diagnostic biomarkers and new therapeutic measures are urgently needed to reduce the mortality of CVDs and improve the therapeutic effect. RNA methylations regulate almost all aspects of RNA processing, such as RNA nuclear export, translation, splicing and non-coding RNA processing. In view of the importance of RNA methylations in the pathogenesis of diseases, this work reviews the molecular structures, biological functions of five kinds of RNA methylations (m6A, m5C, m1a, m6am and m7G) and their effects on CVDs, including pulmonary hypertension, hypertension, vascular calcification, cardiac hypertrophy, heart failure. In CVDs, m6A “writers” catalyze the installation of m6A on RNAs, while “erasers” remove these modifications. Finally, the “readers” of m6A further influence the mRNA splicing, nuclear export, translation and degradation. M5C, m1A, m6Am and m7G are new types of RNA methylations, their roles in CVDs need to be further explored. RNA methylations have become a new research hotspot and the roles in CVDs is gradually emerging, the review of the molecular characteristics, biological functions and effects of RNA methylation on CVDs will contribute to the elucidation of the pathological mechanisms of CVDs and the discovery of new diagnostic markers and therapeutic targets of CVDs.
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Affiliation(s)
- Wanwan Zhou
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
| | - Changhui Wang
- Department of Cardiology, The First Affiliated Hospital, Anhui Medical University, Hefei, China
| | - Jun Chang
- Department of Orthopaedics, The Fourth Affiliated Hospital, Anhui Medical University, Hefei, China
| | - Yurong Huang
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
| | - Qiuyun Xue
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
| | - Chenggui Miao
- Department of Pharmacology, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China.,Anhui Provincial Key Laboratory of Applied Basis and Development of Modern Internal Medicine of Traditional Chinese Medicine, The First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, China
| | - Peng Wu
- Department of Anatomy, School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, China
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He Y, Yu X, Zhang M, Guo W. Pan-cancer analysis of m 5C regulator genes reveals consistent epigenetic landscape changes in multiple cancers. World J Surg Oncol 2021; 19:224. [PMID: 34325709 PMCID: PMC8323224 DOI: 10.1186/s12957-021-02342-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 07/21/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND 5-Methylcytosine (m5C) is a reversible modification to both DNA and various cellular RNAs. However, its roles in developing human cancers are poorly understood, including the effects of mutant m5C regulators and the outcomes of modified nucleobases in RNAs. METHODS Based on The Cancer Genome Atlas (TCGA) database, we uncovered that mutations and copy number variations (CNVs) of m5C regulatory genes were significantly correlated across many cancer types. We then assessed the correlation between the expression of individual m5C regulators and the activity of related hallmark pathways of cancers. RESULTS After validating m5C regulators' expression based on their contributions to cancer development and progression, we observed their upregulation within tumor-specific processes. Notably, our research connected aberrant alterations to m5C regulatory genes with poor clinical outcomes among various tumors that may drive cancer pathogenesis and/or survival. CONCLUSION Our results offered strong evidence and clinical implications for the involvement of m5C regulators.
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Affiliation(s)
- Yuting He
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, Zhengzhou, 450052, China.
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation At Henan Universities, Zhengzhou, 450052, China.
- Henan Key Laboratory of Digestive Organ Transplantation, Zhengzhou, 450052, China.
| | - Xiao Yu
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, Zhengzhou, 450052, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation At Henan Universities, Zhengzhou, 450052, China
- Henan Key Laboratory of Digestive Organ Transplantation, Zhengzhou, 450052, China
| | - Menggang Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, Zhengzhou, 450052, China
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation At Henan Universities, Zhengzhou, 450052, China
- Henan Key Laboratory of Digestive Organ Transplantation, Zhengzhou, 450052, China
| | - Wenzhi Guo
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, Zhengzhou, 450052, China.
- Key Laboratory of Hepatobiliary and Pancreatic Surgery and Digestive Organ Transplantation of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Open and Key Laboratory of Hepatobiliary & Pancreatic Surgery and Digestive Organ Transplantation At Henan Universities, Zhengzhou, 450052, China.
- Henan Key Laboratory of Digestive Organ Transplantation, Zhengzhou, 450052, China.
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Chantsalnyam T, Siraj A, Tayara H, Chong KT. ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning. Genomics 2021; 113:3030-3038. [PMID: 34242708 DOI: 10.1016/j.ygeno.2021.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 06/29/2021] [Accepted: 07/03/2021] [Indexed: 12/14/2022]
Abstract
With the rapidly growing importance of biological research, non-coding RNAs (ncRNA) attract more attention in biology and bioinformatics. They play vital roles in biological processes such as transcription and translation. Classification of ncRNAs is essential to our understanding of disease mechanisms and treatment design. Many approaches to ncRNA classification have been developed, several of which use machine learning and deep learning. In this paper, we construct a novel deep learning-based architecture, ncRDense, to effectively classify and distinguish ncRNA families. In a comparative study, our model produces comparable results with existing state-of-the-art methods. Finally, we built a freely accessible web server for the ncRDense tool, which is available at http://nsclbio.jbnu.ac.kr/tools/ncRDense/.
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Affiliation(s)
- Tuvshinbayar Chantsalnyam
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
| | - Arslan Siraj
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea.
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.
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29
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Zhu W, Guo Y, Zou Q. Prediction of presynaptic and postsynaptic neurotoxins based on feature extraction. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:5943-5958. [PMID: 34517517 DOI: 10.3934/mbe.2021297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A neurotoxin is essentially a protein that mainly acts on the nervous system; it has a selective toxic effect on the central nervous system and neuromuscular nodes, can cause muscle paralysis and respiratory paralysis, and has strong lethality. According to their principle of action, neurotoxins are divided into presynaptic neurotoxins and postsynaptic neurotoxins. Correctly identifying presynaptic and postsynaptic nerve toxins provides important clues for future drug development and the discovery of drug targets. Therefore, a predictive model, Neu_LR, was constructed in this paper. The monoMonokGap method was used to extract the frequency characteristics of presynaptic and postsynaptic neurotoxin sequences and carry out feature selection, then, based on the important features obtained after dimensionality reduction, the prediction model Neu_LR was constructed using a logistic regression algorithm, and ten-fold cross-validation and independent test set validation were used. The final accuracy rates were 99.6078 and 94.1176%, respectively, which proved that the Neu_LR model had good predictive performance and robustness, and could meet the prediction requirements of presynaptic and postsynaptic neurotoxins. The data and source code of the model can be freely download from https://github.com/gyx123681/.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Yuxin Guo
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China
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30
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Pan J, Huang Z, Xu Y. m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma. Front Cell Dev Biol 2021; 9:671821. [PMID: 34268304 PMCID: PMC8277384 DOI: 10.3389/fcell.2021.671821] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 06/01/2021] [Indexed: 12/24/2022] Open
Abstract
Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.
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Affiliation(s)
- Junfan Pan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
| | - Zhidong Huang
- Quanzhou First Hospital, Fujian Medical University, Quanzhou, China
| | - Yiquan Xu
- Department of Thoracic Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
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31
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Jiang J, Song B, Chen K, Lu Z, Rong R, Zhong Y, Meng J. m6AmPred: Identifying RNA N6, 2'-O-dimethyladenosine (m 6A m) sites based on sequence-derived information. Methods 2021; 203:328-334. [PMID: 33540081 DOI: 10.1016/j.ymeth.2021.01.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/14/2021] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
N6,2'-O-dimethyladenosine (m6Am) is a reversible modification widely occurred on varied RNA molecules. The biological function of m6Am is yet to be known though recent studies have revealed its influences in cellular mRNA fate. Precise identification of m6Am sites on RNA is vital for the understanding of its biological functions. We present here m6AmPred, the first web server for in silico identification of m6Am sites from the primary sequences of RNA. Built upon the eXtreme Gradient Boosting with Dart algorithm (XgbDart) and EIIP-PseEIIP encoding scheme, m6AmPred achieved promising prediction performance with the AUCs greater than 0.954 when tested by 10-fold cross-validation and independent testing datasets. To critically test and validate the performance of m6AmPred, the experimentally verified m6Am sites from two data sources were cross-validated. The m6AmPred web server is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/m6am, and it should make a useful tool for the researchers who are interested in N6,2'-O-dimethyladenosine RNA modification.
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Affiliation(s)
- Jie Jiang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, United Kingdom
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, United Kingdom.
| | - Kunqi Chen
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China; Institute of Ageing & Chronic Disease, University of Liverpool, L7 8TX Liverpool, United Kingdom
| | - Zhiliang Lu
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Rong Rong
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Yu Zhong
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China; AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, United Kingdom
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Zhuang J, Liu D, Lin M, Qiu W, Liu J, Chen S. PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm. Front Genet 2021; 12:773882. [PMID: 34868261 PMCID: PMC8637112 DOI: 10.3389/fgene.2021.773882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 11/16/2022] Open
Abstract
Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming. Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification. Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.
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Affiliation(s)
- Jujuan Zhuang
- College of Science, Dalian Maritime University, Dalian, China
| | - Danyang Liu
- College of Science, Dalian Maritime University, Dalian, China
| | - Meng Lin
- College of Science, Dalian Maritime University, Dalian, China
| | - Wenjing Qiu
- Electrical and Information Engineering, Anhui University of Technology, Anhui, China
- Geneis (Beijing) Co., Ltd., Beijing, China
| | | | - Size Chen
- Department of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Guangdong Provincial Engineering Research Center for Esophageal Cancer Precise Therapy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- Central Laboratory, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- *Correspondence: Size Chen,
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