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


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 These results indicate that the proposed method holds a potential to become an elegant tool in identifying m6A site in A. thaliana.


m6Ring structure Hydrogen bond Chemical functionality Support vector machine 



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.


  1. Cantara WA, Crain PF, Rozenski J, McCloskey JA, Harris KA, Zhang X, Vendeix FA, Fabris D, Agris PF (2011) The RNA modification database, RNAMDB: 2011 update. Nucleic Acids Res 39:D195–D201CrossRefPubMedGoogle Scholar
  2. Cao R, Wang Z, Cheng J (2014a) Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment. BMC Struct Biol 14:13CrossRefPubMedPubMedCentralGoogle Scholar
  3. Cao R, Wang Z, Wang Y, Cheng J (2014b) SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines. BMC Bioinform 15:120CrossRefGoogle Scholar
  4. Chen W, Feng P, Lin H (2012) Prediction of replication origins by calculating DNA structural properties. FEBS Lett 586:934–938CrossRefPubMedGoogle Scholar
  5. Chen W, Feng PM, Lin H, Chou KC (2013) iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41:e68CrossRefPubMedPubMedCentralGoogle Scholar
  6. Chen W, Feng PM, Deng EZ, Lin H, Chou KC (2014a) iTIS-PseTNC: a sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition. Anal Biochem 462:76–83CrossRefPubMedGoogle Scholar
  7. Chen W, Feng PM, Lin H, Chou KC (2014b) iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Res Int 2014:623149PubMedPubMedCentralGoogle Scholar
  8. Chen T, Hao YJ, Zhang Y, Li MM, Wang M, Han W, Wu Y, Lv Y, Hao J, Wang L, Li A, Yang Y, Jin KX, Zhao X, Li Y, Ping XL, Lai WY, Wu LG, Jiang G, Wang HL, Sang L, Wang XJ, Yang YG, Zhou Q (2015a) m(6)A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell 16:289–301CrossRefPubMedGoogle Scholar
  9. Chen W, Feng P, Ding H, Lin H, Chou KC (2015b) iRNA-Methyl: identifying N(6)-methyladenosine sites using pseudo nucleotide composition. Anal Biochem 490:26–33CrossRefPubMedGoogle Scholar
  10. Chen W, Tran H, Liang Z, Lin H, Zhang L (2015c) Identification and analysis of the N(6)-methyladenosine in the Saccharomyces cerevisiae transcriptome. Sci Rep 5:13859CrossRefPubMedPubMedCentralGoogle Scholar
  11. Chen W, Feng P, Tang H, Ding H, Lin H (2016a) Identifying 2′-O-methylationation sites by integrating nucleotide chemical properties and nucleotide compositions. Genomics 107:255–258CrossRefPubMedGoogle Scholar
  12. Chen W, Tang H, Ye J, Lin H, Chou KC (2016b) iRNA-PseU: identifying RNA pseudouridine sites. Mol Ther Nucleic Acids 5:e332Google Scholar
  13. Chen W, Tang H, Lin H (2016) MethyRNA: a web server for identification of N6-methyladenosine sites. J Biomol Struct Dyn. doi: 10.1080/07391102.2016.1157761 Google Scholar
  14. Chou KC (2011) Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol 273:236–247CrossRefPubMedGoogle Scholar
  15. Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, Sorek R, Rechavi G (2012) Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485:201–206CrossRefPubMedGoogle Scholar
  16. Feng P, Jiang N, Liu N (2014a) Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions. Sci World J 2014:740506Google Scholar
  17. Feng P, Lin H, Chen W, Zuo Y (2014b) Predicting the types of J-proteins using clustered amino acids. Biomed Res Int 2014:935719PubMedPubMedCentralGoogle Scholar
  18. Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20:2479–2481CrossRefPubMedGoogle Scholar
  19. Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28:3150–3152CrossRefPubMedPubMedCentralGoogle Scholar
  20. Geula S, Moshitch-Moshkovitz S, Dominissini D, Mansour AA, Kol N, Salmon-Divon M, Hershkovitz V, Peer E, Mor N, Manor YS, Ben-Haim MS, Eyal E, Yunger S, Pinto Y, Jaitin DA, Viukov S, Rais Y, Krupalnik V, Chomsky E, Zerbib M, Maza I, Rechavi Y, Massarwa R, Hanna S, Amit I, Levanon EY, Amariglio N, Stern-Ginossar N, Novershtern N, Rechavi G, Hanna JH (2015) Stem cells. m6A mRNA methylation facilitates resolution of naive pluripotency toward differentiation. Science 347:1002–1006CrossRefPubMedGoogle Scholar
  21. Jia G, Fu Y, Zhao X, Dai Q, Zheng G, Yang Y, Yi C, Lindahl T, Pan T, Yang YG, He C (2011) N 6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat Chem Biol 7:885–887CrossRefPubMedPubMedCentralGoogle Scholar
  22. Lin H, Chen W, Ding H (2013) AcalPred: a sequence-based tool for discriminating between acidic and alkaline enzymes. PLoS One 8:e75726CrossRefPubMedPubMedCentralGoogle Scholar
  23. Linder B, Grozhik AV, Olarerin-George AO, Meydan C, Mason CE, Jaffrey SR (2015) Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat Methods 12:767–772CrossRefPubMedPubMedCentralGoogle Scholar
  24. Liu J, Yue Y, Han D, Wang X, Fu Y, Zhang L, Jia G, Yu M, Lu Z, Deng X, Dai Q, Chen W, He C (2014) A METTL3-METTL14 complex mediates mammalian nuclear RNA N 6-adenosine methylation. Nat Chem Biol 10:93–95CrossRefPubMedGoogle Scholar
  25. Luo GZ, MacQueen A, Zheng G, Duan H, Dore LC, Lu Z, Liu J, Chen K, Jia G, Bergelson J, He C (2014) Unique features of the m6A methylome in Arabidopsis thaliana. Nat Commun 5:5630CrossRefPubMedPubMedCentralGoogle Scholar
  26. Meyer KD, Jaffrey SR (2014) The dynamic epitranscriptome: N 6-methyladenosine and gene expression control. Nat Rev Mol Cell Biol 15:313–326CrossRefPubMedPubMedCentralGoogle Scholar
  27. Nilsen TW (2014) Molecular biology. Internal mRNA methylation finally finds functions. Science 343:1207–1208CrossRefPubMedGoogle Scholar
  28. Schwartz S, Agarwala SD, Mumbach MR, Jovanovic M, Mertins P, Shishkin A, Tabach Y, Mikkelsen TS, Satija R, Ruvkun G, Carr SA, Lander ES, Fink GR, Regev A (2013) High-resolution mapping reveals a conserved, widespread, dynamic mRNA methylation program in yeast meiosis. Cell 155:1409–1421CrossRefPubMedPubMedCentralGoogle Scholar
  29. Zhou Y, Zeng P, Li YH, Zhang Z, Cui Q (2016) SRAMP: prediction of mammalian N 6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res 44:e91CrossRefPubMedPubMedCentralGoogle Scholar

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

Personalised recommendations