Introduction N6-methyladenosine (m6A) is one of the most widely studied epigenetic modifications. It plays important roles in various biological processes, such as splicing, RNA localization and degradation, many of which are related to the functions of introns. Although a number of computational approaches have been proposed to predict the m6A sites in different species, none of them were optimized for intronic m6A sites. As existing experimental data overwhelmingly relied on polyA selection in sample preparation and the intronic RNAs are usually underrepresented in the captured RNA library, the accuracy of general m6A sites prediction approaches is limited for intronic m6A sites prediction task. Methodology A computational framework, WITMSG, dedicated to the large-scale prediction of intronic m6A RNA methylation sites in humans has been proposed here for the first time. Based on the random forest algorithm and using only known intronic m6A sites as the training data, WITMSG takes advantage of both conventional sequence features and a variety of genomic characteristics for improved prediction performance of intron-specific m6A sites. Results and Conclusion It has been observed that WITMSG outperformed competing approaches (trained with all the m6A sites or intronic m6A sites only) in 10-fold cross-validation (AUC: 0.940) and when tested on independent datasets (AUC: 0.946). WITMSG was also applied intronome-wide in humans to predict all possible intronic m6A sites, and the prediction results are freely accessible at http://rnamd.com/intron/.

中文翻译：

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WITMSG：根据序列和基因组特征大规模预测人类内含子 m6A RNA 甲基化位点


简介 N6-甲基腺苷 (m6A) 是研究最广泛的表观遗传修饰之一。它在剪接、RNA定位和降解等多种生物过程中发挥着重要作用，其中许多与内含子的功能有关。尽管已经提出了许多计算方法来预测不同物种中的 m6A 位点，但没有一种方法针对内含子 m6A 位点进行了优化。由于现有的实验数据绝大多数依赖于样品制备中的polyA选择，并且内含子RNA在捕获的RNA文库中通常代表性不足，因此一般m6A位点预测方法的准确性对于内含子m6A位点预测任务来说是有限的。方法论 这里首次提出了一种计算框架 WITMSG，致力于大规模预测人类内含子 m6A RNA 甲基化位点。 WITMSG 基于随机森林算法，仅使用已知的内含子 m6A 位点作为训练数据，利用常规序列特征和多种基因组特征来提高内含子特异性 m6A 位点的预测性能。结果和结论 据观察，WITMSG 在 10 倍交叉验证 (AUC：0.940) 和在独立数据集上测试时 (AUC：0.946) 优于竞争方法（使用所有 m6A 位点或仅使用内含子 m6A 位点进行训练）。 WITMSG 还应用于人类内含子范围内，以预测所有可能的内含子 m6A 位点，预测结果可在 http://rnamd.com/intron/ 上免费获取。