An Introduction to Microarrays
Microarray technology enables the concurrent collection and mining of expression data from thousands of genes (Brown and Botstein, 1999; Kerr and Churchill, 2001). This technology has enabled analyses of gene expression in a diverse range of biological processes. Examples are measurement of gene expression responses to pathogens allowing drug development (Grunblatt, 2004; Shultz et al., 2004) , the study of fruit firmness in strawberries (Salentijn et al., 2003) , investigating diurnal patterns in expression (Schaffer et al., 2001; Qian et al., 2003) , and the analysis of gene expression in developmental mutants (Moon et al., 2003; Ohgishi et al., 2004) .
Detection of transcript by microarrays
The use of microarrays enable the detection of mRNA transcripts at a given moment, providing an indication of protein abundance, though mRNA and protein levels do not always correlate (Gygi et al., 1999) . A recent poll of microarray users by the National Cancer Institute suggests that differences in protein amounts correlate with mRNA levels in only fifty percent of cases (http://www.cancer.gov/tarp). Although microarrays provide information on the expression of a large number of genes (over 24000 gene sequences (~94% of the total gene complement) in the case of the Affymetrix full Arabidopsis genome chip ATH1), there are also disadvantages associated with the technology. The number of genes analysed can make interpretation of data difficult, individual hybridisations can be noisy generating variations between experiments, and single data points may prove unreliable. This is particularly the case for genes with low expression levels. Furthermore, the most highly expressed genes or those showing the largest differences in expression in a particular comparison may not be the most biologically relevant. Often genes with known biological functions show a slight, though significant change in transcript levels (Causton et al., 2003).