Copertina

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

Salvato in:
Pubblicato in:PLoS One vol. 20, no. 4 (Apr 2025), p. e0320077
Autore principale: Han, Bo
Altri autori: Bai, Sudan, Liu, Yang, Wu, Jiezhang, Feng, Xin, Ruihao Xin
Pubblicazione:
Public Library of Science
Soggetti:
Machine learning
Software
Deep learning
Datasets
Experimental methods
Hydrogen bonds
Identification methods
Gene expression
Ribonucleic acid > RNA
Neural networks
Feature selection
Computer applications
Algorithms
Yeast
Nucleotide sequence
Biological activity
Performance evaluation
RNA modification
Structural stability
Saccharomyces cerevisiae
Environmental
Accesso online:Citation/Abstract
Full Text
Full Text - PDF
Tags: Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
Abstract:Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions such as gene expression, RNA structural stability, and various diseases. Therefore, accurate identification of pseudouridine sites can effectively explain the functional mechanism of this modification site. Due to the rapid increase of genomics data, traditional biological experimental methods to identify RNA modification sites can no longer meet the practical needs, and it is necessary to accurately identify pseudouridine sites from high-throughput RNA sequence data by computational methods. In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, Homo sapiens, Saccharomyces cerevisiae and Mus 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 contains data from three species, H. sapiens, S. cerevisiae and M. musculus, and the results using 10-fold cross-validation show that Definer significantly outperforms 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. In summary, our method, Definer, can accurately identify pseudouridine modification sites in RNA.
ISSN:1932-6203
DOI:10.1371/journal.pone.0320077
Fonte:Health & Medical Collection