School of Sciences, Anhui Agricultural University, Hefei, Anhui, China.
School of Sciences, Anhui Agricultural University, Hefei, Anhui, China.
Methods. 2022 Jul;203:399-421. doi: 10.1016/j.ymeth.2022.03.001. Epub 2022 Mar 3.
Thanks to the tremendous advancement of deep sequencing and large-scale profiling, epitranscriptomics has become a rapidly growing field. As one of the most important parts of epitranscriptomics, ribonucleic acid (RNA) methylation has been focused on for years for its fundamental role in regulating the many aspects of RNA function. Thanks to the big data generated in sequencing, machine learning methods have been developed for efficiently identifying methylation sites. In this review, we comprehensively explore machine learning based approaches for predicting 10 types of methylation of RNA, which include m6A, m5C, m7G, 5hmC, m1A, m5U, m6Am, and so on. Firstly, we reviewed three main aspects of machine learning which are data, features and learning algorithms. Then, we summarized all the methods that have been used to predict the 10 types of methylation. Furthermore, the emergent methods which were designed to predict multiple types of methylation were also reviewed. Finally, we discussed the future perspectives for RNA methylation sites prediction.
得益于深度测序和大规模分析技术的巨大进步，表观转录组学已经成为一个快速发展的领域。作为表观转录组学的最重要组成部分之一，RNA 甲基化因其在调节 RNA 功能的许多方面的基本作用而多年来一直受到关注。得益于测序产生的大数据，已经开发出机器学习方法来有效地识别甲基化位点。在这篇综述中，我们全面探讨了基于机器学习的方法，用于预测 RNA 的 10 种甲基化类型，包括 m6A、m5C、m7G、5hmC、m1A、m5U、m6Am 等。首先，我们回顾了机器学习的三个主要方面，即数据、特征和学习算法。然后，我们总结了所有用于预测这 10 种甲基化的方法。此外，还综述了用于预测多种甲基化类型的新兴方法。最后，我们讨论了 RNA 甲基化位点预测的未来展望。
