Literature DB >> 27590733

Identifying N 6-methyladenosine sites in the Arabidopsis thaliana transcriptome.

Wei Chen1, Pengmian Feng2, Hui Ding3, Hao Lin4.   

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.

Entities:  

Keywords:  Chemical functionality; Hydrogen bond; Ring structure; Support vector machine; m6A

Mesh:

Substances:

Year:  2016        PMID: 27590733     DOI: 10.1007/s00438-016-1243-7

Source DB:  PubMed          Journal:  Mol Genet Genomics        ISSN: 1617-4623            Impact factor:   3.291


  29 in total

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  19 in total

1.  WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

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2.  HLMethy: a machine learning-based model to identify the hidden labels of m6A candidates.

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4.  Rama: a machine learning approach for ribosomal protein prediction in plants.

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10.  Identifying RNA N6-Methyladenosine Sites in Escherichia coli Genome.

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Journal:  Front Microbiol       Date:  2018-05-14       Impact factor: 5.640

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