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

Abstract

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.

• Authors: Li Fang, Man Zhang, Yanhui Li, Yan Liu, Qinghua Cui, Nanping Wang
• Journal: PPAR research (PPAR Res) Vol. 2016 Pg. 6042162 ( 2016) ISSN: 1687-4757 [Print] United States
• PMID: 27148361 (Publication Type: Journal Article)