Characterizing RNA Pseudouridylation by Convolutional Neural Networks

Pseudouridine (Psi) is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and mRNAs. However, the functions, mechanisms, and precise distribution of Psi s (especially in mRNAs) still remain largely unclear. The landscape of Psi s across the transcriptome has not yet been fully delineated. Here, we present a highly effective model based on a convolutional neural network (CNN), called PseudoUridyLation Site Estimator (PULSE), to analyze large-scale profiling data of Psi sites and characterize the contextual sequence features of pseudouridylation. PULSE, consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network, can automatically learn the hidden patterns of pseudouridylation from the local sequence information. Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy, thus enabling us to further characterize the transcriptome-wide landscape of Psi sites. We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation, such as the regulations of RNA secondary structure, codon usage, translation, and RNA stability, and the connection to single nucleotide variants. The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.

Automated summary

The study introduced a model named PULSE based on convolutional neural network for analyzing large-scale Psi site data and characterizing the sequence features of pseudouridylation. The validation tests showed that PULSE outperformed other prediction methods, providing novel insights into the functional roles of pseudouridylation and enabling further research on the transcriptome-wide landscape of Psi sites.