Knowledge Enrichment Analysis for Human Tissue-Specific Genes Uncover New Biological Insights

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doi doi:10.2390/biecoll-jib-2012-194
submission May 16, 2012
published July 09, 2012
NCBI PubMed PubMed ID 22773159

Xiu-Jun Gong, Hua Yu, Chun-Bai Yang and Yuan-Fang Li

Correspondence should be addressed to:
Xiu-Jun Gong
School of Computer Science and Technology, Tianjin University, Weijin Rd No. 92, Nankan, Tianjin, 300072, China
nc.ude.ujt@nulljxgnog


Abstract

The expression and regulation of genes in different tissues are fundamental questions to be answered in biology. Knowledge enrichment analysis for tissue specific (TS) and housekeeping (HK) genes may help identify their roles in biological process or diseases and gain new biological insights. In this paper, we performed the knowledge enrichment analysis for 17,343 genes in 84 human tissues using Gene Set Enrichment Analysis (GSEA) and Hypergeometric Analysis (HA) against three biological ontologies: Gene Ontology (GO), KEGG pathways and Disease Ontology (DO) respectively. The analyses results demonstrated that the functions of most gene groups are consistent with their tissue origins. Meanwhile three interesting new associations for HK genes and the skeletal muscle tissue genes are found. Firstly, Hypergeometric analysis against KEGG database for HK genes disclosed that three disease terms (Parkinson's disease, Huntington's disease, Alzheimer's disease) are intensively enriched. Secondly, Hypergeometric analysis against the KEGG database for Skeletal Muscle tissue genes shows that two cardiac diseases of "Hypertrophic cardiomyopathy (HCM)" and "Arrhythmogenic right ventricular cardiomyopathy (ARVC)" are heavily enriched, which are also considered as no relationship with skeletal functions. Thirdly, “Prostate cancer” is intensively enriched in Hypergeometric analysis against the disease ontology (DO) for the Skeletal Muscle tissue genes, which is a much unexpected phenomenon.

Reference

Xiu-Jun Gong, Hua Yu, Chun-Bai Yang and Yuan-Fang Li. Knowledge Enrichment Analysis for Human Tissue-Specific Genes Uncover New Biological Insights. Journal of Integrative Bioinformatics, 9(2):194, 2012. Online Journal: http://journal.imbio.de/index.php?paper_id=194
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