Huang Yuantai, Zhang Luowanyue, Mu Weiping, Zheng Mohan, Bao Xiaoqiong, Li Huiqin, Luo Xiaotong, Ren Jian, Zuo Zhixiang
School of Life Sciences, State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China.
Innovation Center of the Sixth Affiliated hospital, School of Life Sciences, Sun Yat-sen University, Guangzhou 510060, China.
Nucleic Acids Res. 2025 Jan 6;53(D1):D275-D283. doi: 10.1093/nar/gkae924.
Evaluating the impact of genetic variants on RNA modifications (RMs) is crucial for identifying disease-associated variants and understanding the pathogenic mechanisms underlying human diseases. Previously, we developed a database called RMVar to catalog variants linked to RNA modifications in humans and mice. Here, we present an updated version RMVar 2.0 (http://rmvar.renlab.cn). In this updated version, we applied an enhanced analytical pipeline to the latest RNA modification datasets and genetic variant information to identify RM-associated variants. A notable advancement in RMVar 2.0 is our incorporation of allele-specific RNA modification analysis to identify RM-associated variants, a novel approach not utilized in RMVar 1.0 or other comparable databases. Furthermore, the database offers comprehensive annotations for various molecular events, including RNA-binding protein (RBP) interactions, RNA-RNA interactions, splicing events, and circular RNAs (circRNAs), which facilitate investigations into how RM-associated variants influence post-transcriptional regulation. Additionally, we provide disease-related information sourced from ClinVar and GWAS to help researchers explore the connections between RNA modifications and various diseases. We believe that RMVar 2.0 will significantly enhance our understanding of the functional implications of genetic variants affecting RNA modifications within the context of human disease research.
评估基因变异对RNA修饰(RM)的影响对于识别疾病相关变异和理解人类疾病的致病机制至关重要。此前,我们开发了一个名为RMVar的数据库,用于编目与人类和小鼠RNA修饰相关的变异。在此,我们展示了更新版本的RMVar 2.0(http://rmvar.renlab.cn)。在这个更新版本中,我们将增强的分析流程应用于最新的RNA修饰数据集和基因变异信息,以识别与RM相关的变异。RMVar 2.0的一个显著进展是我们纳入了等位基因特异性RNA修饰分析来识别与RM相关的变异,这是RMVar 1.0或其他类似数据库中未使用的新方法。此外,该数据库为各种分子事件提供了全面注释,包括RNA结合蛋白(RBP)相互作用、RNA-RNA相互作用、剪接事件和环状RNA(circRNA),这有助于研究与RM相关的变异如何影响转录后调控。此外,我们提供了来自ClinVar和GWAS的疾病相关信息,以帮助研究人员探索RNA修饰与各种疾病之间的联系。我们相信,RMVar 2.0将显著增强我们在人类疾病研究背景下对影响RNA修饰的基因变异功能意义的理解。