Affiliation:
1. School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China
2. Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
Abstract
AbstractMotivationRNA modifications play critical roles in a series of cellular and developmental processes. Knowledge about the distributions of RNA modifications in the transcriptomes will provide clues to revealing their functions. Since experimental methods are time consuming and laborious for detecting RNA modifications, computational methods have been proposed for this aim in the past five years. However, there are some drawbacks for both experimental and computational methods in simultaneously identifying modifications occurred on different nucleotides.ResultsTo address such a challenge, in this article, we developed a new predictor called iMRM, which is able to simultaneously identify m6A, m5C, m1A, ψ and A-to-I modifications in Homo sapiens, Mus musculus and Saccharomyces cerevisiae. In iMRM, the feature selection technique was used to pick out the optimal features. The results from both 10-fold cross-validation and jackknife test demonstrated that the performance of iMRM is superior to existing methods for identifying RNA modifications.Availability and implementationA user-friendly web server for iMRM was established at http://www.bioml.cn/XG_iRNA/home. The off-line command-line version is available at https://github.com/liukeweiaway/iMRM.Contactgreatchen@ncst.edu.cnSupplementary informationSupplementary data are available at Bioinformatics online.
Funder
National Nature Scientific Foundation of China
Natural Science Foundation for Distinguished Young Scholar of Hebei Province
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
121 articles.
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