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Identifying N6-methyladenosine sites in the Arabidopsis thaliana transcriptome

Overview of attention for article published in Molecular Genetics and Genomics, September 2016
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Title
Identifying N6-methyladenosine sites in the Arabidopsis thaliana transcriptome
Published in
Molecular Genetics and Genomics, September 2016
DOI 10.1007/s00438-016-1243-7
Pubmed ID
Authors

Wei Chen, Pengmian Feng, Hui Ding, Hao Lin

Abstract

N (6)-Methyladenosine (m(6)A) plays important roles in many biological processes. The knowledge of the distribution of m(6)A is helpful for understanding its regulatory roles. Although the experimental methods have been proposed to detect m(6)A, 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 m(6)A 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 m(6)A 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 m(6)A site in A. thaliana.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 3%
Canada 1 3%
Unknown 37 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 23%
Student > Master 4 10%
Student > Bachelor 4 10%
Researcher 3 8%
Lecturer 2 5%
Other 5 13%
Unknown 12 31%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 31%
Agricultural and Biological Sciences 6 15%
Computer Science 2 5%
Medicine and Dentistry 2 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 2 5%
Unknown 14 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 October 2016.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Molecular Genetics and Genomics
#3,135
of 3,319 outputs
Outputs of similar age
#308,122
of 347,928 outputs
Outputs of similar age from Molecular Genetics and Genomics
#18
of 20 outputs
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