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RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication.
Song, Bowen; Wang, Xuan; Liang, Zhanmin; Ma, Jiongming; Huang, Daiyun; Wang, Yue; de Magalhães, João Pedro; Rigden, Daniel J; Meng, Jia; Liu, Gang; Chen, Kunqi; Wei, Zhen.
  • Song B; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • Wang X; Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • Liang Z; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L7 8TX, UK.
  • Ma J; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • Huang D; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • Wang Y; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • de Magalhães JP; Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L7 8TX, UK.
  • Rigden DJ; Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • Meng J; Department of Computer Science, University of Liverpool, Liverpool L7 8TX, UK.
  • Liu G; Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
  • Chen K; Department of Computer Science, University of Liverpool, Liverpool L7 8TX, UK.
  • Wei Z; Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L7 8TX, UK.
Nucleic Acids Res ; 2022 Sep 05.
Article in English | MEDLINE | ID: covidwho-2235809
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.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Variants Language: English Year: 2022 Document Type: Article Affiliation country: Nar

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Topics: Variants Language: English Year: 2022 Document Type: Article Affiliation country: Nar