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Updated: Jan 4, 2026
Identification of Circular RNAs using RNA Sequencing
Published on: November 14, 2019
Identifying Cancer-Specific circRNA-RBP Binding Sites Based on Deep Learning
Zhengfeng Wang1,2, Xiujuan Lei1, Fang-Xiang Wu3
1School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.
Abstract:
Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA-RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.
Insights
A new deep learning method, CSCRSites, identifies cancer-specific circular RNA-RNA binding protein (circRNA-RBP) binding sites using only nucleotide sequences. This tool aids in understanding circRNA functions in cancer by predicting binding interactions.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Circular RNAs (circRNAs) are prevalent in cells and tissues, regulating biological processes and human diseases.
- circRNAs can act as RNA binding protein (RBP) sponges, and RBPs influence circRNA back-splicing.
- Understanding circRNA-RBP interactions is vital for elucidating circRNA functions, particularly in cancer.
Purpose of the Study:
- To develop a novel deep learning-based method for identifying cancer-specific circRNA-RBP binding sites.
- To utilize only nucleotide sequences as input for the prediction model.
- To facilitate the functional analysis of circRNAs associated with human cancers.
Main Methods:
- A deep learning architecture, CSCRSites, employing multiple convolution layers to extract features from raw circRNA sequences.
- A fully connected layer with softmax output for identifying circRNA-RBP binding sites.
- Comparison of CSCRSites performance against conventional machine learning and other deep learning methods.
Main Results:
- CSCRSites demonstrated superior performance compared to existing methods on benchmark datasets.
- Learned features from CSCRSites were converted into sequence motifs.
- Identified motifs showed similarity to known human RNA motifs implicated in diseases, especially cancer.
Conclusions:
- CSCRSites is an effective deep learning tool for predicting cancer-specific circRNA-RBP binding sites.
- The method contributes to the functional analysis of cancer-associated circRNAs.
- The findings highlight the potential of deep learning in uncovering complex RNA-protein interactions in disease contexts.
