Literature DB >> 29165544

RNA methylation and diseases: experimental results, databases, Web servers and computational models.

Xing Chen1, Ya-Zhou Sun2, Hui Liu1, Lin Zhang1, Jian-Qiang Li2, Jia Meng3.   

Abstract

Ribonucleic acid (RNA) methylation is a type of posttranscriptional modifications occurring in all kingdoms of life. It is strongly related to important biological process, thus making it linked to a number of human diseases. Owing to the development of high-throughput sequencing technology, plenty of achievement had been obtained in RNA methylation research recently. Meanwhile, various computational models have been developed to analyze and mining increasing RNA methylation data. In this review, we first made a brief introduction about eight types of most popular RNA methylation, the biological functions of RNA methylation, the relationship between RNA methylation and disease and five important RNA methylation-related diseases. The research of RNA methylation is based on sequencing data processing, and effective bioinformatics techniques can benefit better understanding of RNA methylation. We further introduced seven publicly available RNA methylation-related databases, and some important publicly available RNA-methylation-related Web servers and software for RNA methylation site identification, differential analysis and so on. Furthermore, we provided detailed analysis of the state-of-the-art computational models used in these Web servers and software. We also analyzed the limitations of these models and discussed the future directions of developing computational models for RNA methylation research.
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Entities:  

Keywords:  RNA methylation; Web server and software; biological function; computational model; database; disease

Mesh:

Substances:

Year:  2019        PMID: 29165544     DOI: 10.1093/bib/bbx142

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  27 in total

1.  IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction.

Authors:  Qi Zhao; Yue Zhang; Huan Hu; Guofei Ren; Wen Zhang; Hongsheng Liu
Journal:  Front Genet       Date:  2018-07-04       Impact factor: 4.599

2.  WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

Authors:  Kunqi Chen; Zhen Wei; Qing Zhang; Xiangyu Wu; Rong Rong; Zhiliang Lu; Jionglong Su; João Pedro de Magalhães; Daniel J Rigden; Jia Meng
Journal:  Nucleic Acids Res       Date:  2019-04-23       Impact factor: 16.971

3.  Differential analysis of RNA methylation regulators in gastric cancer based on TCGA data set and construction of a prognostic model.

Authors:  Jing Li; Zhifan Zuo; Shusheng Lai; Zhendong Zheng; Bo Liu; Yuan Wei; Tao Han
Journal:  J Gastrointest Oncol       Date:  2021-08

4.  YTHDF1 Protects Auditory Hair Cells from Cisplatin-Induced Damage by Activating Autophagy via the Promotion of ATG14 Translation.

Authors:  Yuyu Huang; Dekun Gao; Yan Wu; Lianhua Sun; Jianyong Chen; Junmin Chen; Xingxu Huang; Jun Yang; Shuna Li
Journal:  Mol Neurobiol       Date:  2022-09-13       Impact factor: 5.682

5.  Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

Authors:  Daiyun Huang; Kunqi Chen; Bowen Song; Zhen Wei; Jionglong Su; Frans Coenen; João Pedro de Magalhães; Daniel J Rigden; Jia Meng
Journal:  Nucleic Acids Res       Date:  2022-10-14       Impact factor: 19.160

Review 6.  Circular RNAs and complex diseases: from experimental results to computational models.

Authors:  Chun-Chun Wang; Chen-Di Han; Qi Zhao; Xing Chen
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 11.622

7.  Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization.

Authors:  Bin-Sheng He; Jia Qu; Qi Zhao
Journal:  Front Genet       Date:  2018-08-07       Impact factor: 4.599

Review 8.  Regulatory Mechanisms of the RNA Modification m6A and Significance in Brain Function in Health and Disease.

Authors:  Justine Mathoux; David C Henshall; Gary P Brennan
Journal:  Front Cell Neurosci       Date:  2021-05-19       Impact factor: 5.505

9.  Identifying RNA N6-Methyladenosine Sites in Escherichia coli Genome.

Authors:  Jidong Zhang; Pengmian Feng; Hao Lin; Wei Chen
Journal:  Front Microbiol       Date:  2018-05-14       Impact factor: 5.640

10.  trumpet: transcriptome-guided quality assessment of m6A-seq data.

Authors:  Teng Zhang; Shao-Wu Zhang; Lin Zhang; Jia Meng
Journal:  BMC Bioinformatics       Date:  2018-07-13       Impact factor: 3.169

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