Song, Bowen, Wang, Xuan, Liang, Zhanmin, Ma, Jiongming, Huang, Daiyun, Wang, Yue, de Magalhaes, Joao Pedro 
ORCID: 0000-0002-6363-2465, Rigden, Daniel J ORCID: 0000-0002-7565-8937, Meng, Jia ORCID: 0000-0003-3455-205X, Liu, Gang et al (show 2 more authors)
(2023)
RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.
NUCLEIC ACIDS RESEARCH, 51 (D1).
D1388-D1396.
Abstract
Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Animals, Humans, RNA Processing, Post-Transcriptional, Phenotype, Databases, Factual, Epigenomics, SARS-CoV-2 |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Sep 2022 14:28 |
| Last Modified: | 27 Nov 2023 02:24 |
| DOI: | 10.1093/nar/gkac750 |
| Open Access URL: | https://doi.org/10.1093/nar/gkac750 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3164039 |
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