School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.
College of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China.
Molecules. 2019 Nov 7;24(22):4035. doi: 10.3390/molecules24224035.
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.
环状 RNA（circRNAs）在细胞和组织中广泛表达，并在人类疾病和生物过程中发挥关键作用。最近的研究报告称，circRNAs 可以作为 RNA 结合蛋白（RBP）的海绵，同时 RBP 也可以参与反式剪接。与 RBP 的相互作用也被认为是研究 circRNAs 功能的一个重要因素。因此，有必要了解 circRNAs 和 RBPs 的相互作用机制，特别是在人类癌症中。在这里，我们提出了一种基于深度学习的新方法，用于识别仅使用核苷酸序列作为输入的癌症特异性 circRNA-RBP 结合位点（CSCRSites）。在 CSCRSites 中，利用具有多个卷积层的架构来检测原始 circRNA 序列片段的特征，并通过具有 softmax 输出的全连接层进一步识别结合位点。实验结果表明，CSCRSites 在基准数据上优于传统的机器学习分类器和一些有代表性的深度学习方法。此外，CSCRSites 学习到的特征被转换为序列基序，其中一些可以与涉及人类疾病（特别是癌症）的人类已知 RNA 基序匹配。因此，作为一种基于深度学习的工具，CSCRSites 可以为癌症相关 circRNAs 的功能分析做出重大贡献。
