ABSTRACT

Versatile RNA modifications play important roles in post-transcriptional regulations of gene expression, among which glycosylation modifications on small RNAs emerge as a novel clade whose characteristics need further interrogations. Here, we demonstrated that the sequence pattern around RNA glycosylation sites was not random and could be exploited for glycosylation site prediction. A machine learning predictor, GlyinsRNA, which integrated multiple RNA sequence representation encodings, was established. GlyinsRNA achieved AUROC (area under the receiver operating characteristic curve) of 0.7933 and 0.7979 in five-fold cross-validation and independent tests, respectively. GlyinsRNA was implemented as an online webserver, where both the predicted glycosylation sites and the overrepresented RNA-binding protein (RBP)-related motifs were annotated to facilitate the users. GlyinsRNA webserver is freely available at http://www.rnanut.net/glyinsrna.

摘要

多功能 RNA 修饰在基因表达的转录后调控中发挥重要作用，其中对小 RNA 的糖基化修饰作为一种新的进化枝出现，其特征需要进一步研究。在这里，我们证明了 RNA 糖基化位点周围的序列模式不是随机的，可以用于糖基化位点预测。建立了一种机器学习预测器 GlyinsRNA，它集成了多个 RNA 序列表示编码。GlyinsRNA 在五重交叉验证和独立测试中分别达到了 0.7933 和 0.7979 的 AUROC（受试者工作特征曲线下面积）。GlyinsRNA 被实现为在线网络服务器，其中预测的糖基化位点和过多的 RNA 结合蛋白 (RBP) 相关基序都被注释以方便用户。GlyinsRNA 网络服务器可在 http://www.rnanut.net/glyinsrna 免费获得。