Abstract
BackgroundIn 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.ResultsIn 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.ConclusionThis 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:
Highlights
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
In this report we introduce GEOGLE, an online web service for Gene Expression Omnibus (GEO) dataset mining and biomedical information integration
Public data warehouses such as GEO are high-quality resources for an automatic mining and integration system of gene expression datasets and reference literatures from GEOGLE, which will be a revolution compared to manually collecting experimental data for biological research
Summary
The third part of the pattern is a querying engine where users can perform searching tasks with a friendly web interface. What this miner will perform is to search for similar datasets containing the same (or similar) group of signatures This miner will summarize associated biomedical vocabularies to these GDSes. The format of submitted datasets is very flexible, examples of which could be a gene list with expression values or probe set IDs with FDR values (see Additional file 1). By using 'Vocabulary Miner', we search for 'smoking' related gene expression gene data in human, got a result with 4 GDSes considered to be candidate datasets (GDS1304, GDS1436, GDS1673 and GDS534). Terms like 'Breast Cancer/Estrogen Receptor Signaling' and 'Stress Response to Cellular Damage' are returned with significant p values in pathway section of the results, which suggests that these pathways are closely related to the 'smoking' phenotype Such genes like GALNT1 are identified as signature genes trough all these dataset. The down-regulation of this gene was found to influence both ether lipid and glycerophospholipid pathways, and shift the ratios of plasmalogens to diacyl-phosphatidylcolines
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