Abstract
Coexpression analysis data sets generated from comprehensively collected transcriptome data sets have become an efficient resource capable of facilitating the discovery of genes closely correlated in their expression patterns. In order to construct coexpression network of barley, we analyzed publicly available 45 experimental series, which are composed of 1347 GeneChip data in barley. On the basis of gene-to-gene weighted correlation coefficient, we constructed global barley coexpression network. Then, we classified the global co-expression network into clusters of sub-network modules by using the MCODE algorithm. The resulting clusters are candidate for functional regulatory modules in the barley transcriptome. To annotate each of the clusters, we applied functional annotation of genes in Arabidopsis and in rice as well as in Brachypodium distachyon (Brachypodium). On the basis of comparative analysis between barley and these model species, we investigated functional properties from the representative distributions of genes encoding transcription factors and of the Gene Ontology(GO) terms.
