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Nucleic Acids Res. 2018 Jan 4;46(D1):D139-D145. doi: 10.1093/nar/gkx895.

m6AVar: a database of functional variants involved in m6A modification.

Zheng Y1,2, Nie P2, Peng D2, He Z2, Liu M2, Xie Y2, Miao Y2, Zuo Z1, Ren J1,2,3.

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Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University, Guangzhou 510060, China.
State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong 510275, China.
Collaborative Innovation Center of High Performance Computing, National University of Defense Technology, Changsha 410073, China.


Identifying disease-causing variants among a large number of single nucleotide variants (SNVs) is still a major challenge. Recently, N6-methyladenosine (m6A) has become a research hotspot because of its critical roles in many fundamental biological processes and a variety of diseases. Therefore, it is important to evaluate the effect of variants on m6A modification, in order to gain a better understanding of them. Here, we report m6AVar (, a comprehensive database of m6A-associated variants that potentially influence m6A modification, which will help to interpret variants by m6A function. The m6A-associated variants were derived from three different m6A sources including miCLIP/PA-m6A-seq experiments (high confidence), MeRIP-Seq experiments (medium confidence) and transcriptome-wide predictions (low confidence). Currently, m6AVar contains 16 132 high, 71 321 medium and 326 915 low confidence level m6A-associated variants. We also integrated the RBP-binding regions, miRNA-targets and splicing sites associated with variants to help users investigate the effect of m6A-associated variants on post-transcriptional regulation. Because it integrates the data from genome-wide association studies (GWAS) and ClinVar, m6AVar is also a useful resource for investigating the relationship between the m6A-associated variants and disease. Overall, m6AVar will serve as a useful resource for annotating variants and identifying disease-causing variants.

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