Identifying the sequence specificities of circRNA-binding proteins based on a capsule network architecture
Autor: | Xiujuan Lei, Zhengfeng Wang |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Computer science
RNA-binding protein Computational biology lcsh:Computer applications to medicine. Medical informatics Biochemistry DNA-binding protein RNA Motifs 03 medical and health sciences 0302 clinical medicine Structural Biology Circular RNA Sequence specificities Humans Binding site Molecular Biology lcsh:QH301-705.5 030304 developmental biology Sequence (medicine) 0303 health sciences Network architecture Binding Sites Methodology Article Applied Mathematics Computational Biology RNA-Binding Proteins RNA Circular Convolution (computer science) Computer Science Applications lcsh:Biology (General) 030220 oncology & carcinogenesis lcsh:R858-859.7 DNA microarray Sequence motif Capsule network Algorithms Function (biology) |
Zdroj: | BMC Bioinformatics, Vol 22, Iss 1, Pp 1-16 (2021) BMC Bioinformatics |
ISSN: | 1471-2105 |
Popis: | Background Circular RNAs (circRNAs) are widely expressed in cells and tissues and are involved in biological processes and human diseases. Recent studies have demonstrated that circRNAs can interact with RNA-binding proteins (RBPs), which is considered an important aspect for investigating the function of circRNAs. Results In this study, we design a slight variant of the capsule network, called circRB, to identify the sequence specificities of circRNAs binding to RBPs. In this model, the sequence features of circRNAs are extracted by convolution operations, and then, two dynamic routing algorithms in a capsule network are employed to discriminate between different binding sites by analysing the convolution features of binding sites. The experimental results show that the circRB method outperforms the existing computational methods. Afterwards, the trained models are applied to detect the sequence motifs on the seven circRNA-RBP bound sequence datasets and matched to known human RNA motifs. Some motifs on circular RNAs overlap with those on linear RNAs. Finally, we also predict binding sites on the reported full-length sequences of circRNAs interacting with RBPs, attempting to assist current studies. We hope that our model will contribute to better understanding the mechanisms of the interactions between RBPs and circRNAs. Conclusion In view of the poor studies about the sequence specificities of circRNA-binding proteins, we designed a classification framework called circRB based on the capsule network. The results show that the circRB method is an effective method, and it achieves higher prediction accuracy than other methods. |
Databáze: | OpenAIRE |
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