Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning
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 intera...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-11-01
|
| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/24/22/4035 |
| _version_ | 1819020663814356992 |
|---|---|
| author | Zhengfeng Wang Xiujuan Lei Fang-Xiang Wu |
| author_facet | Zhengfeng Wang Xiujuan Lei Fang-Xiang Wu |
| author_sort | Zhengfeng Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T03:54:48Z |
| format | Article |
| id | doaj.art-294f57f3d3384e7da284d7a4da24b313 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-21T03:54:48Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-294f57f3d3384e7da284d7a4da24b3132022-12-21T19:16:52ZengMDPI AGMolecules1420-30492019-11-012422403510.3390/molecules24224035molecules24224035Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep LearningZhengfeng Wang0Xiujuan Lei1Fang-Xiang Wu2School of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaDepartment of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, CanadaCircular 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.https://www.mdpi.com/1420-3049/24/22/4035circrnarna binding proteincancer-specificconvolutional neural network |
| spellingShingle | Zhengfeng Wang Xiujuan Lei Fang-Xiang Wu Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning Molecules circrna rna binding protein cancer-specific convolutional neural network |
| title | Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning |
| title_full | Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning |
| title_fullStr | Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning |
| title_full_unstemmed | Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning |
| title_short | Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning |
| title_sort | identifying cancer specific circrna rbp binding sites based on deep learning |
| topic | circrna rna binding protein cancer-specific convolutional neural network |
| url | https://www.mdpi.com/1420-3049/24/22/4035 |
| work_keys_str_mv | AT zhengfengwang identifyingcancerspecificcircrnarbpbindingsitesbasedondeeplearning AT xiujuanlei identifyingcancerspecificcircrnarbpbindingsitesbasedondeeplearning AT fangxiangwu identifyingcancerspecificcircrnarbpbindingsitesbasedondeeplearning |