Reduced representation sequencing of plant stress transcriptomes

Abstract

Plants, as any other organisms, possess evolutionary old mechanisms to cope with the various stresses they are exposed to day by day. The management of stresses and their consequences requires substantial energy, which is frequently subtracted from biomass (in crops: yield). Therefore, a deeper understanding of stress biology has been, is, and will be of paramount importance for plant breeding. One goal of plant stress research centers around the transcriptome, the entirety of transcripts from expressed genes, and aims at identifying major genes in the stress management of the inflicted plant. The development of appropriate technologies to quantitatively study the transcriptomes (indeed the various sub-transcriptomes) in stressed plants and to extract biological meaning from the massive data will be demonstrated here. In particular, reduced complexity sequencing techniques such as deepSuperSAGE and MACE (massive analysis of cDNA ends) and their potential in stress biology are portrayed.