XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites.

As one of the most popular post-transcriptional modifications, pseudouridine (Ψ) participates in a series of biological processes. Therefore, the efficient detection of pseudouridine sites is very important in revealing its functions in biological processes. Although experimental techniques have been proposed for identifying Ψ sites at single-base resolution, they are still labor intensive and expensive. Recently, to fill the experimental method's gap, computational methods have been proposed for identifying Ψ sites. However, their performances are still unsatisfactory. In this paper, we proposed an eXtreme Gradient Boosting (xgboost)-based method, called XG-PseU, to identify Ψ sites based on the optimal features obtained using the forward feature selection together with increment feature selection method. Our results demonstrated that XG-PseU is superior or at least complementary to existing methods for identifying pseudouridine sites. Finally, a freely available online web server for XG-PseU was established at http://www.bioml.cn/. We wish that XG-PseU will become a useful tool for computationally identifying Ψ sites.