Abstract
N 6-Methyladenosine (m6A) plays important roles in many biological processes. The knowledge of the distribution of m6A is helpful for understanding its regulatory roles. Although the experimental methods have been proposed to detect m6A, the resolutions of these methods are still unsatisfying especially for Arabidopsis thaliana. Benefitting from the experimental data, in the current work, a support vector machine-based method was proposed to identify m6A sites in A. thaliana transcriptome. The proposed method was validated on a benchmark dataset using jackknife test and was also validated by identifying strain-specific m6A sites in A. thaliana. The obtained predictive results indicate that the proposed method is quite promising. For the convenience of experimental biologists, an online webserver for the proposed method was built, which is freely available at http://lin.uestc.edu.cn/server/M6ATH. These results indicate that the proposed method holds a potential to become an elegant tool in identifying m6A site in A. thaliana.
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Acknowledgments
This work was supported by Program for the Top Young Innovative Talents of Higher Learning Institutions of Hebei Province (No. BJ2014028), the Outstanding Youth Foundation of North China University of Science and Technology (No. JP201502), China Postdoctoral Science Foundation (No. 2015M582533), the Scientific Research Foundation of the Education Department of Sichuan Province (No. 2015JY0100), and the Fundamental Research Funds for the Central Universities, China (Nos. ZYGX2015J144, ZYGX2015Z006).
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Communicated by S. Hohmann.
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Chen, W., Feng, P., Ding, H. et al. Identifying N 6-methyladenosine sites in the Arabidopsis thaliana transcriptome. Mol Genet Genomics 291, 2225–2229 (2016). https://doi.org/10.1007/s00438-016-1243-7
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DOI: https://doi.org/10.1007/s00438-016-1243-7