PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes

PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes

The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, an...

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Main Authors: Fang, Li, Zhang, Man, Li, Yanhui, Liu, Yan, Cui, Qinghua, Wang, Nanping
Format: Online
Language:English
Published: Hindawi Publishing Corporation 2016
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842375/
id pubmed-4842375
recordtype oai_dc
spelling pubmed-48423752016-05-04 PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes Fang, Li Zhang, Man Li, Yanhui Liu, Yan Cui, Qinghua Wang, Nanping Research Article The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, and wound healing. Here, we describe the first database of PPAR target genes, PPARgene. Among the 225 experimentally verified PPAR target genes, 83 are for PPARα, 83 are for PPARβ/δ, and 104 are for PPARγ. Detailed information including tissue types, species, and reference PubMed IDs was also provided. In addition, we developed a machine learning method to predict novel PPAR target genes by integrating in silico PPAR-responsive element (PPRE) analysis with high throughput gene expression data. Fivefold cross validation showed that the performance of this prediction method was significantly improved compared to the in silico PPRE analysis method. The prediction tool is also implemented in the PPARgene database. Hindawi Publishing Corporation 2016 2016-04-11 /pmc/articles/PMC4842375/ /pubmed/27148361 http://dx.doi.org/10.1155/2016/6042162 Text en Copyright © 2016 Li Fang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Fang, Li
Zhang, Man
Li, Yanhui
Liu, Yan
Cui, Qinghua
Wang, Nanping
spellingShingle Fang, Li
Zhang, Man
Li, Yanhui
Liu, Yan
Cui, Qinghua
Wang, Nanping
PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
author_facet Fang, Li
Zhang, Man
Li, Yanhui
Liu, Yan
Cui, Qinghua
Wang, Nanping
author_sort Fang, Li
title PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
title_short PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
title_full PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
title_fullStr PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
title_full_unstemmed PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes
title_sort ppargene: a database of experimentally verified and computationally predicted ppar target genes
description The peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors of the nuclear receptor superfamily. Upon ligand binding, PPARs activate target gene transcription and regulate a variety of important physiological processes such as lipid metabolism, inflammation, and wound healing. Here, we describe the first database of PPAR target genes, PPARgene. Among the 225 experimentally verified PPAR target genes, 83 are for PPARα, 83 are for PPARβ/δ, and 104 are for PPARγ. Detailed information including tissue types, species, and reference PubMed IDs was also provided. In addition, we developed a machine learning method to predict novel PPAR target genes by integrating in silico PPAR-responsive element (PPRE) analysis with high throughput gene expression data. Fivefold cross validation showed that the performance of this prediction method was significantly improved compared to the in silico PPRE analysis method. The prediction tool is also implemented in the PPARgene database.
publisher Hindawi Publishing Corporation
publishDate 2016
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842375/
_version_ 1613569969389305856

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