iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder

Abstract Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing site...

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Autores principales:	Zhao-Chun Xu, Peng Wang, Wang-Ren Qiu, Xuan Xiao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b553b90ad37b432c8adda3f5815f195d
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spelling	oai:doaj.org-article:b553b90ad37b432c8adda3f5815f195d2021-12-02T12:30:19ZiSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder10.1038/s41598-017-08523-82045-2322https://doaj.org/article/b553b90ad37b432c8adda3f5815f195d2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08523-8https://doaj.org/toc/2045-2322Abstract Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed. To identify the splice donor sites and splice acceptor sites accurately and quickly, a deep sparse auto-encoder model with two hidden layers, called iSS-PC, was constructed based on minimum error law, in which we incorporated twelve physical-chemical properties of the dinucleotides within DNA into PseDNC to formulate given sequence samples via a battery of cross-covariance and auto-covariance transformations. In this paper, five-fold cross-validation test results based on the same benchmark data-sets indicated that the new predictor remarkably outperformed the existing prediction methods in this field. Furthermore, it is expected that many other related problems can be also studied by this approach. To implement classification accurately and quickly, an easy-to-use web-server for identifying slicing sites has been established for free access at: http://www.jci-bioinfo.cn/iSS-PC.Zhao-Chun XuPeng WangWang-Ren QiuXuan XiaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhao-Chun Xu Peng Wang Wang-Ren Qiu Xuan Xiao iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder
description	Abstract Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed. To identify the splice donor sites and splice acceptor sites accurately and quickly, a deep sparse auto-encoder model with two hidden layers, called iSS-PC, was constructed based on minimum error law, in which we incorporated twelve physical-chemical properties of the dinucleotides within DNA into PseDNC to formulate given sequence samples via a battery of cross-covariance and auto-covariance transformations. In this paper, five-fold cross-validation test results based on the same benchmark data-sets indicated that the new predictor remarkably outperformed the existing prediction methods in this field. Furthermore, it is expected that many other related problems can be also studied by this approach. To implement classification accurately and quickly, an easy-to-use web-server for identifying slicing sites has been established for free access at: http://www.jci-bioinfo.cn/iSS-PC.
format	article
author	Zhao-Chun Xu Peng Wang Wang-Ren Qiu Xuan Xiao
author_facet	Zhao-Chun Xu Peng Wang Wang-Ren Qiu Xuan Xiao
author_sort	Zhao-Chun Xu
title	iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder
title_short	iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder
title_full	iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder
title_fullStr	iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder
title_full_unstemmed	iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder
title_sort	iss-pc: identifying splicing sites via physical-chemical properties using deep sparse auto-encoder
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b553b90ad37b432c8adda3f5815f195d
work_keys_str_mv	AT zhaochunxu isspcidentifyingsplicingsitesviaphysicalchemicalpropertiesusingdeepsparseautoencoder AT pengwang isspcidentifyingsplicingsitesviaphysicalchemicalpropertiesusingdeepsparseautoencoder AT wangrenqiu isspcidentifyingsplicingsitesviaphysicalchemicalpropertiesusingdeepsparseautoencoder AT xuanxiao isspcidentifyingsplicingsitesviaphysicalchemicalpropertiesusingdeepsparseautoencoder
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iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder

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