School of Life Sciences, Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063009, China.
Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
Bioinformatics. 2020 Jun 1;36(11):3336-3342. doi: 10.1093/bioinformatics/btaa155.
RNA 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.
To 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.
A 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.
Supplementary data are available at Bioinformatics online.
RNA 修饰在一系列细胞和发育过程中起着关键作用。了解转录组中 RNA 修饰的分布情况将为揭示其功能提供线索。由于实验方法在检测 RNA 修饰时既耗时又费力，因此在过去五年中，已经提出了计算方法来实现这一目标。然而，实验和计算方法在同时识别不同核苷酸上发生的修饰方面都存在一些缺点。
为了解决这一挑战，在本文中，我们开发了一种名为 iMRM 的新预测器，它能够同时识别人类、小鼠和酿酒酵母中的 m6A、m5C、m1A、ψ 和 A-to-I 修饰。在 iMRM 中，使用特征选择技术来挑选出最佳特征。来自 10 折交叉验证和 Jackknife 测试的结果表明，iMRM 在识别 RNA 修饰方面的性能优于现有方法。
iMRM 的用户友好型网络服务器已在 http://www.bioml.cn/XG_iRNA/home 上建立。离线命令行版本可在 https://github.com/liukeweiaway/iMRM 上获得。
补充数据可在 Bioinformatics 在线获得。
