DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs

• Published in: Briefings in bioinformatics 2024-07, Vol.25 (5)
• Main Authors: Zhao, Jian, Chen, Zhewei, Zhang, Meng, Zou, Lingxiao, He, Shan, Liu, Jingjing, Wang, Quan, Song, Xiaofeng, Wu, Jing
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; Bioinformatics; Cellular structure; Computational Biology - methods; Conserved sequence; Deep Learning; Experimental methods; Gene sequencing; Humans; Internal ribosome entry site; Internal Ribosome Entry Sites; Machine learning; mRNA; Neural networks; Neural Networks, Computer; Nucleotide sequence; Predictions; Problem Solving Protocol; Ribonucleic acid; RNA; RNA, Messenger - genetics; RNA, Messenger - metabolism; RNA, Viral - genetics; RNA, Viral - metabolism
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbae439

Abstract The internal ribosome entry site (IRES) is a cis-regulatory element that can initiate translation in a cap-independent manner. It is often related to cellular processes and many diseases. Thus, identifying the IRES is important for understanding its mechanism and finding potential therapeutic strategies for relevant diseases since identifying IRES elements by experimental method is time-consuming and laborious. Many bioinformatics tools have been developed to predict IRES, but all these tools are based on structure similarity or machine learning algorithms. Here, we introduced a deep learning model named DeepIRES for precisely identifying IRES elements in messenger RNA (mRNA) sequences. DeepIRES is a hybrid model incorporating dilated 1D convolutional neural network blocks, bidirectional gated recurrent units, and self-attention module. Tenfold cross-validation results suggest that DeepIRES can capture deeper relationships between sequence features and prediction results than other baseline models. Further comparison on independent test sets illustrates that DeepIRES has superior and robust prediction capability than other existing methods. Moreover, DeepIRES achieves high accuracy in predicting experimental validated IRESs that are collected in recent studies. With the application of a deep learning interpretable analysis, we discover some potential consensus motifs that are related to IRES activities. In summary, DeepIRES is a reliable tool for IRES prediction and gives insights into the mechanism of IRES elements.