CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features

序列(生物学) RNA结合蛋白 计算机科学 计算生物学 稳健性(进化) 代表(政治) 嵌入 核糖核酸 人工智能 生物 遗传学 基因 政治学 政治 法学
作者
Lishen Zhang,Chengqian Lu,Min Zeng,Yaohang Li,Jianxin Wang
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:24 (1) 被引量:8
标识
DOI:10.1093/bib/bbac530
摘要

Circular RNAs (circRNAs) are reverse-spliced and covalently closed RNAs. Their interactions with RNA-binding proteins (RBPs) have multiple effects on the progress of many diseases. Some computational methods are proposed to identify RBP binding sites on circRNAs but suffer from insufficient accuracy, robustness and explanation. In this study, we first take the characteristics of both RNA and RBP into consideration. We propose a method for discriminating circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features, called CRMSS. For circRNAs, we use sequence ${k}\hbox{-}{mer}$ embedding and the forming probabilities of local secondary structures as features. For RBPs, we combine sequence and structure frequencies of RNA-binding domain regions to generate features. We capture binding patterns with multi-scale residual blocks. With BiLSTM and attention mechanism, we obtain the contextual information of high-level representation for circRNA-RBP binding. To validate the effectiveness of CRMSS, we compare its predictive performance with other methods on 37 RBPs. Taking the properties of both circRNAs and RBPs into account, CRMSS achieves superior performance over state-of-the-art methods. In the case study, our model provides reliable predictions and correctly identifies experimentally verified circRNA-RBP pairs. The code of CRMSS is freely available at https://github.com/BioinformaticsCSU/CRMSS.
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