Pseudouridine synthase binds to uridine sites and catalyzes the conversion of uridine to pseudouridine (Ψ). This binding takes place in a specific context and in the conformation of nucleotides. Most machine-learning methods for Ψ site classification use nucleotide frequency as a feature, which may not fully depict the relevant conformation around a Ψ site. Using the power of deep learning and raw sequence, as well as secondary structure features, our tool MU-PseUDeep is designed to capture both the sequence and secondary structure context, which inputs the raw RNA sequence and the predicted secondary structure to two sets of convolutional neural networks. It has shown considerable improvement in Ψ site prediction over existing tools, XG-PseU, PseUI, and iRNA-PseU for both balanced and imbalanced datasets. To the best of our knowledge, this is the most accurate tool for Ψ site prediction. We also used MU-PseUDeep to scan the human transcriptome, which shows that the genes with predicted Ψ sites are enriched in nucleotide and protein binding, as well as in neurodegeneration pathways. The tool is open source, available at https://github.com/smk5g5/MU-PseUDeep.

假尿苷合酶与尿苷位点结合并催化尿苷向假尿苷的转化。这种结合在特定的背景下和核苷酸的构象中发生。大多数用于methods位点分类的机器学习方法都使用核苷酸频率作为特征，这可能无法完全描述Ψ位点周围的相关构象。利用深度学习和原始序列的强大功能以及二级结构功能，我们的工具MU-PseUDeep旨在捕获序列和二级结构上下文，将原始RNA序列和预测的二级结构输入到两组卷积中神经网络。与平衡和不平衡数据集的现有工具XG-PseU，PseUI和iRNA-PseU相比，它在Ψ站点预测中已显示出相当大的进步。据我们所知，这是Ψ站点预测的最准确工具。我们还使用MU-PseUDeep扫描了人类的转录组，这表明具有预测的Ψ位点的基因富含核苷酸和蛋白质结合以及神经退行性途径。该工具是开源的，可从https://github.com/smk5g5/MU-PseUDeep获得。