BACKGROUND RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'- O-methylation plays an important role in immune recognition and other pathogenesis. OBJECTIVE We aim to design a computational method to identify2'-O-methylation. METHOD Different from the experimental method, we propose a computational workflow identifying the methylation site based on the multi-feature extracting algorithm. RESULTS With a voting procedure based on 7 best feature-classifier combinations, we achieved AUC of 0.80 in 10-fold cross-validation. Furthermore, we optimized features and input the optimized features into SVM. As a results, the AUC reached to 0.813. CONCLUSION The RNA sample, especially the negative samples, used in this study are more objective and strict, so we got more representative result than state-of-arts studies.

中文翻译：

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通过研究多特征提取技术来鉴定2'-O-甲基化位点。

背景技术RNA甲基化是涉及许多生物学过程的可逆转录后修饰。核糖2'-O-甲基化是RNA甲基化的一部分。已经表明核糖2'-O-甲基化在免疫识别和其他发病机理中起重要作用。目的我们旨在设计一种识别2'-O-甲基化的计算方法。方法与实验方法不同，我们提出了一种基于多特征提取算法的甲基化位点识别的计算流程。结果通过基于7种最佳特征分类器组合的投票程序，我们在10倍交叉验证中实现了0.80的AUC。此外，我们优化了功能并将优化后的功能输入SVM。结果，AUC达到0.813。结论RNA样品，尤其是阴性样品，