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Molecular Genetics and Genomics

, Volume 291, Issue 6, pp 2225–2229 | Cite as

Identifying N 6-methyladenosine sites in the Arabidopsis thaliana transcriptome

  • Wei ChenEmail author
  • Pengmian Feng
  • Hui Ding
  • Hao LinEmail author
Methods Paper

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.

Keywords

m6Ring structure Hydrogen bond Chemical functionality Support vector machine 

Notes

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).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Physics, School of Sciences, and Center for Genomics and Computational BiologyNorth China University of Science and TechnologyTangshanChina
  2. 2.School of Public HealthNorth China University of Science and TechnologyTangshanChina
  3. 3.Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics and Center for Information in Biomedicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina

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