DNA microarray technology provides us with a first step toward the goal of uncovering gene functions on a genomic scale. In the last few years, vast amounts of gene expression data were collected, and much of these data are available in public databases, such as the Gene Expression Omnibus (GEO). To date, most researchers have been manually retrieving data from databases through web browsers using accession numbers (IDs) or keywords, but gene expression patterns are not considered when retrieving data from such databases. The data retrieved using keywords or IDs is usually limited by experimental conditions such as microarray platform, reagent, and cell type.
GEM-TREND (Gene Expression Data Mining Toward Relevant Network Discovery) was developed to retrieve gene expression data from GEO by comparing gene-expression signature of queries with those of GEO gene expression data and to provide network visualization. The comparison methods are based on the nonparametric, rank-based pattern matching approach of Lamb et al. (Science 2006) with the additional calculation of statistical significance. Retrieved gene expression data can then be viewed as a co-expression network with gene ontology (GO) annotation where genes and annotations are dynamically linked to external data repositories.
GEM-TREND provides a new way of data retrieval that is not using keywords (e.g., gene annotation, pre-computed profile characteristics) or IDs, and results are not restricted by experimental conditions. It is expected to support knowledge discovery.