Meta-PseU: A Meta-Classifier for Robust Prediction of RNA Pseudouridine Modification Sites from Long Sequences

Abstract

Pseudouridine ({Psi}) represents one of the most abundant and evolutionarily conserved RNA modifications. {Psi} provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of {Psi} sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, current machine-learning and deep-learning predictors suffer from limitations such as small datasets and limited generalizability. To overcome these issues, we have constructed new long-sequence datasets derived from RMBase 3.0 and developed Meta-PseU, a logistic regression-based meta-classifier that stacks multiple single-feature or baseline classifiers across three species of human, mouse, and yeast. Meta-PseU substantially reduced the performance gaps between training and independent test datasets, presenting superior generalization. Meta-PseU substantially outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. This work offers a new framework for robust {Psi}-site identification by using long sequences. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU.