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Cui L, Zheng J, Lin Y, Lin P, Lu Y, Zheng Y, Guo B, Zhao X. Decoding the ribosome's hidden language: rRNA modifications as key players in cancer dynamics and targeted therapies. Clin Transl Med 2024; 14:e1705. [PMID: 38797935 PMCID: PMC11128715 DOI: 10.1002/ctm2.1705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/05/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
Ribosomal RNA (rRNA) modifications, essential components of ribosome structure and function, significantly impact cellular proteomics and cancer biology. These chemical modifications transcend structural roles, critically shaping ribosome functionality and influencing cellular protein profiles. In this review, the mechanisms by which rRNA modifications regulate both rRNA functions and broader cellular physiological processes are critically discussed. Importantly, by altering the translational output, rRNA modifications can shift the cellular equilibrium towards oncogenesis, thus playing a key role in cancer development and progression. Moreover, a special focus is placed on the functions of mitochondrial rRNA modifications and their aberrant expression in cancer, an area with profound implications yet largely uncharted. Dysregulation in these modifications can lead to metabolic dysfunction and apoptosis resistance, hallmark traits of cancer cells. Furthermore, the current challenges and future perspectives in targeting rRNA modifications are highlighted as a therapeutic approach for cancer treatment. In conclusion, rRNA modifications represent a frontier in cancer research, offering novel insights and therapeutic possibilities. Understanding and harnessing these modifications can pave the way for breakthroughs in cancer treatment, potentially transforming the approach to combating this complex disease.
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Affiliation(s)
- Li Cui
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhouGuangdongChina
- Division of Oral Biology and Medicine, School of DentistryUniversity of
California, Los AngelesLos AngelesUSA
| | - Jiarong Zheng
- Department of Dentistry, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Yunfan Lin
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhouGuangdongChina
| | - Pei Lin
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhouGuangdongChina
| | - Ye Lu
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhouGuangdongChina
| | - Yucheng Zheng
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhouGuangdongChina
| | - Bing Guo
- Department of Dentistry, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
| | - Xinyuan Zhao
- Stomatological Hospital, School of StomatologySouthern Medical UniversityGuangzhouGuangdongChina
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2
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Wang R, Chung CR, Lee TY. Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species. Int J Mol Sci 2024; 25:2869. [PMID: 38474116 DOI: 10.3390/ijms25052869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/19/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species. Therefore, a versatile computational approach is necessary for interpretable analysis of RNA modifications across species. A multi-scale biological language-based deep learning model is proposed for interpretable, sequential-level prediction of diverse RNA modifications. Benchmark comparisons across species demonstrate the model's superiority in predicting various RNA methylation types over current state-of-the-art methods. The cross-species validation and attention weight visualization also highlight the model's capability to capture sequential and functional semantics from genomic backgrounds. Our analysis of RNA modifications helps us find the potential existence of "biological grammars" in each modification type, which could be effective for mapping methylation-related sequential patterns and understanding the underlying biological mechanisms of RNA modifications.
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Affiliation(s)
- Rulan Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Chia-Ru Chung
- Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan
| | - Tzong-Yi Lee
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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3
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Guo X, Li F, Song J. Predicting Pseudouridine Sites with Porpoise. Methods Mol Biol 2023; 2624:139-151. [PMID: 36723814 DOI: 10.1007/978-1-0716-2962-8_10] [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] [Indexed: 06/18/2023]
Abstract
Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.
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Affiliation(s)
- Xudong Guo
- College of Information Engineering, Northwest A&F University, Yangling, China
| | - Fuyi Li
- College of Information Engineering, Northwest A&F University, Yangling, China.
- Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
| | - Jiangning Song
- Biomedicine Discovery Institute, Monash University, Melbourne, VIC, Australia.
- Monash Data Futures Institute, Monash University, Melbourne, VIC, Australia.
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4
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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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5
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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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Huang S, Zhang W, Katanski CD, Dersh D, Dai Q, Lolans K, Yewdell J, Eren AM, Pan T. Interferon inducible pseudouridine modification in human mRNA by quantitative nanopore profiling. Genome Biol 2021; 22:330. [PMID: 34872593 PMCID: PMC8646010 DOI: 10.1186/s13059-021-02557-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 11/23/2021] [Indexed: 01/28/2023] Open
Abstract
Pseudouridine (Ψ) is an abundant mRNA modification in mammalian transcriptome, but its functions have remained elusive due to the difficulty of transcriptome-wide mapping. We develop a nanopore native RNA sequencing method for quantitative Ψ prediction (NanoPsu) that utilizes native content training, machine learning modeling, and single-read linkage analysis. Biologically, we find interferon inducible Ψ modifications in interferon-stimulated gene transcripts which are consistent with a role of Ψ in enabling efficacy of mRNA vaccines.
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Affiliation(s)
- Sihao Huang
- Department of Biochemistry & Molecular Biology, University of Chicago, Chicago, IL 60637 USA
| | - Wen Zhang
- Department of Biochemistry & Molecular Biology, University of Chicago, Chicago, IL 60637 USA
| | - Christopher D. Katanski
- Department of Biochemistry & Molecular Biology, University of Chicago, Chicago, IL 60637 USA
| | - Devin Dersh
- Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892 USA
| | - Qing Dai
- Department of Chemistry, University of Chicago, Chicago, IL 60637 USA
| | - Karen Lolans
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Jonathan Yewdell
- Laboratory of Viral Diseases, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892 USA
| | - A. Murat Eren
- Department of Medicine, University of Chicago, Chicago, IL 60637 USA
| | - Tao Pan
- Department of Biochemistry & Molecular Biology, University of Chicago, Chicago, IL 60637 USA
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7
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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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WHISTLE server: A high-accuracy genomic coordinate-based machine learning platform for RNA modification prediction. Methods 2021; 203:378-382. [PMID: 34245870 DOI: 10.1016/j.ymeth.2021.07.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 06/28/2021] [Accepted: 07/05/2021] [Indexed: 01/12/2023] Open
Abstract
The primary sequences of DNA, RNA and protein have been used as the dominant information source of existing machine learning tools, especially for contexts not fully explored by wet-experimental approaches. Since molecular markers are profoundly orchestrated in the living organisms, those markers that cannot be unambiguously recovered from the primary sequence often help to predict other biological events. To the best of our knowledge, there is no current tool to build and deploy machine learning models that consider genomic evidence. We therefore developed the WHISTLE server, the first machine learning platform based on genomic coordinates. It features convenient covariate extraction and model web deployment with 46 distinct genomic features integrated along with the conventional sequence features. We showed that, when predicting m6A sites from SRAMP project, the model integrating genomic features substantially outperformed those based on only sequence features. The WHISTLE server should be a useful tool for studying biological attributes specifically associated with genomic coordinates, and is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/whi2.
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9
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Zhang SY, Zhang SW, Zhang T, Fan XN, Meng J. Recent advances in functional annotation and prediction of the epitranscriptome. Comput Struct Biotechnol J 2021; 19:3015-3026. [PMID: 34136099 PMCID: PMC8175281 DOI: 10.1016/j.csbj.2021.05.030] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/16/2021] [Accepted: 05/18/2021] [Indexed: 12/17/2022] Open
Abstract
RNA modifications, in particular N6-methyladenosine (m6A), participate in every stages of RNA metabolism and play diverse roles in essential biological processes and disease pathogenesis. Thanks to the advances in sequencing technology, tens of thousands of RNA modification sites can be identified in a typical high-throughput experiment; however, it remains a major challenge to decipher the functional relevance of these sites, such as, affecting alternative splicing, regulation circuit in essential biological processes or association to diseases. As the focus of RNA epigenetics gradually shifts from site discovery to functional studies, we review here recent progress in functional annotation and prediction of RNA modification sites from a bioinformatics perspective. The review covers naïve annotation with associated biological events, e.g., single nucleotide polymorphism (SNP), RNA binding protein (RBP) and alternative splicing, prediction of key sites and their regulatory functions, inference of disease association, and mining the diagnosis and prognosis value of RNA modification regulators. We further discussed the limitations of existing approaches and some future perspectives.
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Affiliation(s)
- Song-Yao Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shao-Wu Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Teng Zhang
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xiao-Nan Fan
- Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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10
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Song B, Chen K, Tang Y, Wei Z, Su J, de Magalhães JP, Rigden DJ, Meng J. ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome. Brief Bioinform 2021; 22:6276017. [PMID: 33993206 DOI: 10.1093/bib/bbab088] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/04/2021] [Accepted: 02/24/2021] [Indexed: 12/15/2022] Open
Abstract
Motivation N6-methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. Evidence increasingly demonstrates its crucial importance in essential molecular mechanisms and various diseases. With recent advances in sequencing techniques, tens of thousands of m6A sites are identified in a typical high-throughput experiment, posing a key challenge to distinguish the functional m6A sites from the remaining 'passenger' (or 'silent') sites. Results: We performed a comparative conservation analysis of the human and mouse m6A epitranscriptomes at single site resolution. A novel scoring framework, ConsRM, was devised to quantitatively measure the degree of conservation of individual m6A sites. ConsRM integrates multiple information sources and a positive-unlabeled learning framework, which integrated genomic and sequence features to trace subtle hints of epitranscriptome layer conservation. With a series validation experiments in mouse, fly and zebrafish, we showed that ConsRM outperformed well-adopted conservation scores (phastCons and phyloP) in distinguishing the conserved and unconserved m6A sites. Additionally, the m6A sites with a higher ConsRM score are more likely to be functionally important. An online database was developed containing the conservation metrics of 177 998 distinct human m6A sites to support conservation analysis and functional prioritization of individual m6A sites. And it is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/con.
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Affiliation(s)
- Bowen Song
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, United Kingdom
| | - Kunqi Chen
- Key Laboratory of Ministry of Education of Gastrointestinal Cancer, School of Basic Medical Science, Fujian Medical University, Fuzhou, China
| | - Yujiao Tang
- Xi'an Jiaotong-Liverpool University, L7 8TX, Liverpool, United Kingdom
| | - Zhen Wei
- Department of Biological Science, Xi'an Jiaotong-Liverpool University, L7 8TX, Liverpool, United Kingdom
| | - Jionglong Su
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, L7 8TX, Liverpool, United Kingdom
| | - João Pedro de Magalhães
- Institute of Ageing & Chronic Disease, University of Liverpool, L7 8TX, Liverpool, United Kingdom
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX, Liverpool, United Kingdom
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, L7 8TX, Liverpool, United Kingdom
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11
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Chen K, Song B, Tang Y, Wei Z, Xu Q, Su J, de Magalhães JP, Rigden DJ, Meng J. RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis. Nucleic Acids Res 2021; 49:D1396-D1404. [PMID: 33010174 PMCID: PMC7778951 DOI: 10.1093/nar/gkaa790] [Citation(s) in RCA: 64] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/08/2020] [Accepted: 09/11/2020] [Indexed: 12/11/2022] Open
Abstract
Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.
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Affiliation(s)
- 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, UK
| | - Bowen Song
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK.,Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Yujiao Tang
- 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, UK
| | - Zhen Wei
- 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, UK
| | - Qingru Xu
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Jionglong Su
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | | | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L7 8TX Liverpool, UK
| | - Jia Meng
- 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, UK.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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