With the recent availability of massive amounts of DNA sequence data for many organisms, new technologies have emerged to study this information in a high through put and cost effective manner. Microarrays based on spotting of thousands of cDNAs on glass surfaces or synthesis of gene-specific oligonucleotide microarray (OMA) on glass surfaces have allowed the parallel analysis of expression of large numbers of genes (Brown and Botstein 1999; Fodor 1997; Lashkari et al. 1997). For example, new understanding of: (a) stage-specific gene expression during sporulation in yeast (Chu et al. 1998); (b) the functional role of a set of yeast mutants each carrying a deletion in one of 5,916 open reading frames (96.5% of total) (Giaever et al. 2002); (c) the transcriptional hierarchy of metabolic genes in yeast (DeRisi et al. 1997); and (d) gene expression during meiosis (Primig et al. 2000) has resulted from the use of OMAs. Microarrays are currently being used to study global gene expression in Arabidopsis (Zhu et al. 2001a; see later) and rice (Zhu et al. 2001b).
One caution in using these arrays is that they only provide information on gene expression (RNA abundance) while many cellular processes are regulated at the translational or posttranslational levels. Giaever et al. (2002) found that many genes required for fitness through gene knock out did not show increased expression using OMAs. Moreover, many genes that showed increased expression were not required for fitness. In addition, Primig et al. (2000) found very different expression profiles when they compared RNA from different yeast strains undergoing meiosis. The overlapping gene set could be correlated with genes previously known to have a role in meiosis. This result emphasizes the need to use different isolates to find the core set of genes that are needed for a biological process that shows differential gene expression.
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