Abstract
Similarities of gene expression pattern have been used widely to classify gene functions, because genes in the same, or related, biological function often show similar expression patterns. Large-scale gene expression data from various experimental conditions have an important role to evaluate the statistical significance in terms of genome-wide gene expressions. Such data can facilitate the identification of genes with similar expression patterns. In addition, it allows us to construct gene expression network.
To elucidate gene functions and their transcription factors in rice, we aimed to identify gene sets which show similar expression patterns and to develop a gene expression network database. From the GEO, we obtained 244 data sets of microarray platform 'Affymetrix Rice Genome Array', normalized, and calculated Pearson's correlation coefficients between each gene (probe) pair. Based on the statistical indices, gene expression network was analyzed and converted into an interactive graphical viewer. It will contribute to simultaneously search the gene expression similarities and functional annotations. All data obtained here are available in our database 'OryzaExpress'.
