Software

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

Yao Yu^1,2†, Kang Tu^1†, Siyuan Zheng¹, Yun Li¹, Guohui Ding¹, Jie Ping⁴, Pei Hao^1,3* and Yixue Li^1,2,3,4,5*

• * Corresponding authors: Pei Hao phao@sibs.ac.cn - Yixue Li yxli@sibs.ac.cn

• † Equal contributors

Author Affiliations

¹ Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China

² Graduate School of the Chinese Academy of Sciences, Shanghai 200031, PR China

³ Shanghai Center for Bioinformation Technology, 100 Qinzhou Road, Shanghai 200235, PR China

⁴ College of life science and biotechnology, Shanghai Jiaotong University, Shanghai 200240, PR China

⁵ College of life science and biotechnology, Shanghai Tongji University, Shanghai 200331, PR China

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BMC Bioinformatics 2009, 10:264 doi:10.1186/1471-2105-10-264

Published: 25 August 2009

Abstract

Background

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.

Results

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.

Conclusion

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: http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020 webcite