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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...

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Autores principales: Wang, Zhengfeng, Lei, Xiujuan, Wu, Fang-Xiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
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.
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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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