Guo Xudong, Li Fuyi, Song Jiangning
College of Information Engineering, Northwest A&F University, Yangling, China.
Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia.
Methods Mol Biol. 2023;2624:139-151. doi: 10.1007/978-1-0716-2962-8_10.
Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.
假尿苷是一种普遍存在的RNA修饰，在许多生物过程中发挥着关键作用。然而，使用昂贵且耗时的实验研究来识别假尿苷位点仍然是一项具有挑战性的任务。为此，我们提出了Porpoise，一种从RNA序列数据中识别假尿苷位点的计算方法。Porpoise基于具有多个信息特征的堆叠集成学习框架构建，与现有最先进的方法相比，具有竞争力的性能。本协议详细阐述了Porpoise本地独立版本和网络服务器的逐步使用和执行。此外，我们还提供了一个通用的机器学习框架，该框架可以帮助使用基于特征的不同组合来识别最佳堆叠集成学习模型。这个通用的机器学习框架可以方便用户使用他们的内部数据集构建自己的假尿苷预测器。
