Correlated clustering and virtual display of gene expression patterns in the wheat life cycle by large-scale statistical analyses of expressed sequence tags.

Compared to rice, wheat exhibits characteristic growth habits and contains complex genome constituents. To assess global changes in gene expression patterns in the wheat life cycle, we conducted large-scale analysis of expressed sequence tags (ESTs) in common wheat. Ten wheat tissues were used to construct cDNA libraries: crown and root from 14-day-old seedlings; spikelet from early and late flowering stages; spike at the booting stage, heading date and flowering date; pistil at the heading date; and seeds at 10 and 30 days post-anthesis. Several thousand colonies were randomly selected from each of these 10 cDNA libraries and sequenced from both 5' and 3' ends. Consequently, a total of 116 232 sequences were accumulated and classified into 25 971 contigs based on sequence homology. By computing abundantly expressed ESTs, correlated expression patterns of genes across the tissues were identified. Furthermore, relationships of gene expression profiles among the 10 wheat tissues were inferred from global gene expression patterns. Genes with similar functions were grouped with one another by clustering gene expression profiles. This technique might enable estimation of the functions of anonymous genes. Multidimensional analysis of EST data that is analogous to the microarray experiments may offer new approaches to functional genomics of plants.