GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction

• Main Authors: Ping Jie, Ding Guohui, Li Yun, Zheng Siyuan, Tu Kang, Yu Yao, Hao Pei, Li Yixue
• Format: Article
• Language: English
• Published: BMC 2009-08-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/10/264

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches.</p> <p>Results</p> <p>In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis – GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value.</p> <p>Conclusion</p> <p>This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: <url>http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020</url></p>