Transcriptome characterization and differentially expressed genes under flooding and drought stress in the biomass grasses Phalaris arundinacea and Dactylis glomerata
• Background and Aims Perennial grasses are a global resource as forage, and for alternative uses in bioenergy and as raw materials for the processing industry. Marginal lands can be valuable for perennial biomass grass production, if perennial biomass grasses can cope with adverse abiotic environmental stresses such as drought and waterlogging. • Methods In this study, two perennial grass species, reed canary grass (Phalaris arundinacea) and cocksfoot (Dactylis glomerata) were subjected to drought and waterlogging stress to study their responses for insights to improving environmental stress tolerance. Physiological responses were recorded, reference transcriptomes established and differential gene expression investigated between control and stress conditions. We applied a robust non-parametric method, RoDEO, based on rank ordering of transcripts to investigate differential gene expression. Furthermore, we extended and validated vRoDEO for comparing samples with varying sequencing depths. • Key Results This allowed us to identify expressed genes under drought and waterlogging whilst using only a limited number of RNA sequencing experiments. Validating the methodology, several differentially expressed candidate genes involved in the stage 3 step-wise scheme in detoxification and degradation of xenobiotics were recovered, while several novel stress-related genes classified as of unknown function were discovered. • Conclusions Reed canary grass is a species coping particularly well with flooding conditions, but this study adds novel information on how its transcriptome reacts under drought stress. We built extensive transcriptomes for the two investigated C3 species cocksfoot and reed canary grass under both extremes of water stress to provide a clear comparison amongst the two species to broaden our horizon for comparative studies, but further confirmation of the data would be ideal to obtain a more detailed picture.