用于识别RNA 5-甲基胞嘧啶位点的计算方法的小型综述。

Li Jianwei, Huang Yan, Zhou Yuan

1Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China; 2Department of Biomedical Informatics, School of Basic Medical Sciences, Center for Noncoding RNA Medicine, Peking University, Beijing, China.

Curr Genomics. 2020 Jan;21(1):3-10. doi: 10.2174/2213346107666200219124951.

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

RNA 5-甲基胞嘧啶（mC）是转录后修饰（PTCM）的关键组成部分之一。越来越多的证据表明，mC在RNA代谢中起着至关重要的作用。在组织细胞中准确定位RNA mC位点是深入了解mC功能的前提和基础。然而，检测mC位点的主要实验方法都受到不同程度的限制。建立一个计算模型来预测修饰位点是识别mC位点的湿实验的极佳补充。在这篇综述中，我们总结了一些现有的mC预测工具，并讨论了这些方法的特点。