Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning

Abstract

Abstract Background RNA molecules are important biomolecules in cells and play a crucial role in processes such as genetic information transfer and gene expression regulation. However, in addition to their basic nucleotide composition, RNA molecules undergo various modifications, including pseudouridine. Pseudouridine is a critical site of alteration that is found in many non-coding RNAs and has a role in a number of biological processes, including gene expression, RNA structural stability, and the development of several illnesses. Accurate identification of pseudouridine sites in RNA molecules is of significant importance for understanding their functionality and regulatory mechanisms. Traditional experimental methods often rely on techniques such as chemical modifications and mass spectrometry analysis. However, these methods are costly, time-consuming, and limited in terms of sample size. Therefore, the development of an efficient and accurate computational method for identifying pseudouridine sites in RNA holds great scientific significance and practical application value. Results In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, H. sapiens, S. cerevisiae and M. musculus. The method incorporates two sequence coding schemes, including NCP and One-hot, and then feeds the extracted RNA sequence features into a deep learning model constructed from CNN, GRU and Attention. The benchmark dataset contained data from three species, namely H. sapiens, S. cerevisiae and M. musculus, and the results using 10-fold cross-validation showed that the model accuracy reached 82.95, 86.01 and 87.15 for the three species, respectively, with Definer significantly outperforming other existing methods. Meanwhile, the data sets of two species, H. sapiens and S. cerevisiae, were tested independently to further demonstrate the predictive ability of the model. Conclusion It is well known that RNA modifications are an important component of gene regulation and most biological processes depend on RNA modifications. Among them, pseudouridine modification is one of the crucial modification sites, and the accurate identification of pseudouridine sites in RNA is important for understanding their functions and regulatory mechanisms. Therefore, this paper proposes a new predictor, Definer, which can accurately identify pseudouridine sites in three species: H. sapiens, S. cerevisiae, and M. musculus. results on benchmark and independent test sets show that Definer has good performance over other existing methods and can accurately identify pseudouridine sites in cross-species data sets. set can accurately identify pseudouridine loci. In addition, we have developed software that runs on a local computer to provide users with a better presentation of the pseudouridine site prediction process and results for the three species datasets.