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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891306/ https://www.ncbi.nlm.nih.gov/pubmed/31703384 http://dx.doi.org/10.3390/molecules24224035 |
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author | Wang, Zhengfeng Lei, Xiujuan Wu, Fang-Xiang |
author_facet | Wang, Zhengfeng Lei, Xiujuan Wu, Fang-Xiang |
author_sort | Wang, Zhengfeng |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-6891306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68913062019-12-12 Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning Wang, Zhengfeng Lei, Xiujuan Wu, Fang-Xiang Molecules Article 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. MDPI 2019-11-07 /pmc/articles/PMC6891306/ /pubmed/31703384 http://dx.doi.org/10.3390/molecules24224035 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Zhengfeng Lei, Xiujuan Wu, Fang-Xiang Identifying Cancer-Specific circRNA–RBP Binding Sites Based on Deep Learning |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891306/ https://www.ncbi.nlm.nih.gov/pubmed/31703384 http://dx.doi.org/10.3390/molecules24224035 |
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