Journal Article

Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks

Shen, Zhen, Liu, Wei, Yuan, Lin, Shao, Yan Ling, Zhang, Qinhu

2022

Overview

Background Circular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism of CircRNAs formation can help reveal the role of CircRNAs in various biological processes mentioned above. Back-splicing is important for CircRNAs formation. Back-splicing sites prediction helps uncover the mysteries of CircRNAs formation. Several methods were proposed for back-splicing sites prediction or circRNA-realted prediction tasks. Model performance was constrained by poor feature learning and using ability. Results In this study, CircCNN was proposed to predict pre-mRNA back-splicing sites. Convolution neural network and batch normalization are the main parts of CircCNN. Experimental results on three datasets show that CircCNN outperforms other baseline models. Moreover, PPM (Position Probability Matrix) features extract by CircCNN were converted as motifs. Further analysis reveals that some of motifs found by CircCNN match known motifs involved in gene expression regulation, the distribution of motif and special short sequence is important for pre-mRNA back-splicing. Conclusions In general, the findings in this study provide a new direction for exploring CircRNA-related gene expression regulatory mechanism and identifying potential targets for complex malignant diseases. The datasets and source code of this study are freely available at: https://github.com/szhh521/CircCNN .

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Animal Genetics and Genomics / Artificial neural networks / Back-splicing sites prediction / Batch normalization / Biological activity / Biomedical and Life Sciences / Cellular control mechanisms / CircRNA / Convolutional neural networks / Datasets / Deep learning / Feature extraction / Gene expression / Gene regulation / Genetic aspects / Genetic research / Genomics / Health aspects / Life Sciences / Microarrays / Microbial Genetics and Genomics / MicroRNAs / Neural networks / Plant Genetics and Genomics / Predictions / Proteomics / Regulatory mechanisms (biology) / RNA splicing / Source code / Splicing / Stem cells