Title:A Mini-review of the Computational Methods Used in Identifying RNA 5- Methylcytosine Sites
VOLUME: 21 ISSUE: 1
Author(s):Jianwei Li, Yan Huang and Yuan Zhou*
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
Keywords:5-methylcytosine, RNA modification, machine learning, prediction, sequence encoding, pseudo dinucleotide
composition.
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.