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A Mini-review of the Computational Methods Used in Identifying RNA 5- Methylcytosine Sites

[ Vol. 21 , Issue. 1 ]

Author(s):

Jianwei Li, Yan Huang and Yuan Zhou*   Pages 3 - 10 ( 8 )

Abstract:


RNA 5-methylcytosine (m5C) is one of the pillars of post-transcriptional modification (PTCM). A growing body of evidence suggests that m5C plays a vital role in RNA metabolism. Accurate localization of RNA m5C sites in tissue cells is the premise and basis for the in-depth understanding of the functions of m5C. However, the main experimental methods of detecting m5C sites are limited to varying degrees. Establishing a computational model to predict modification sites is an excellent complement to wet experiments for identifying m5C sites. In this review, we summarized some available m5C predictors and discussed the characteristics of these methods.

Keywords:

5-methylcytosine, RNA modification, machine learning, prediction, sequence encoding, pseudo dinucleotide composition.

Affiliation:

Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, Department of Biomedical Informatics, School of Basic Medical Sciences, Center for Noncoding RNA Medicine, Peking University, Beijing

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