circ2CBA: prediction of circRNA-RBP binding sites combining deep learning and attention mechanism

Abstract

Circular RNAs (circRNAs) are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins (RBPs). Existing methods for predicting these interactions have limitations in feature learning. In view of this, we propose a method named circ2CBA, which uses only sequence information of circRNAs to predict circRNA-RBP binding sites. We have constructed a data set which includes eight sub-datasets. First, circ2CBA encodes circRNA sequences using the one-hot method. Next, a two-layer convolutional neural network (CNN) is used to initially extract the features. After CNN, circ2CBA uses a layer of bidirectional long and short-term memory network (BiLSTM) and the self-attention mechanism to learn the features. The AUC value of circ2CBA reaches 0.8987. Comparison of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.

Graphical abstract

Keywords

circRNAs / RBPs / CNN / BiLSTM / self-attention mechanism

Yajing Guo received the BS degree in School of Computer Science from Shaanxi Normal University, China in 2020, where she is currently pursuing the MS degree. Her current research interests include bioinformatics and deep learning

Xiujuan Lei received the MS and PhD degrees from Northwestern Polytechnical University, China in 2001 and 2005, respectively. She is currently a Professor at the School of Computer Science, Shaanxi Normal University, China. Her research interests include bioinformatics, swarm intelligent optimization, data mining, and deep learning

Lian Liu, received the PhD from Northwestern Polytechnical University, China in 2018. She is currently an associate research fellow at the School of Computer Science, Shaanxi Normal University, China. Her current research interests include bioinformatics, pattern recognition and machine learning

Yi Pan is currently a professor of the Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China. He has served as Chair of Computer Science Department at Georgia State University, USA during 2005 to 2020. He received his Bachelor’s degree and Master’s degree in computer engineering from Tsinghua University, China in 1982 and 1984, respectively, and his PhD degree in computer science from the University of Pittsburgh, USA in 1991. His current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61972451, 61902230) and the Fundamental Research Funds for the Central Universities, Shaanxi Normal University (GK202103091). We would like to express our gratitude to EditSprings for the expert linguistic services provided.

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