Germinated seed from cereal crops including barley (Hordeum vulgare L.) is an important tissue to extract RNA and analyze expression levels of genes that control aspects of germination. These tissues are rich in polysaccharides and most methods for RNA extraction are not suitable to handle the excess polysaccharides. Here, we compare the current methods for RNA extraction applicable to germinated barley tissue. We found that although some of these standard methods produced high-quality RNA, the process of extraction was drastically slow, mostly because the frozen seed tissue powder from liquid N2 grinding became recalcitrant to buffer mixing. Our suggested modifications to the protocols removed the need for liquid N2 grinding and significantly increased the output efficiency of RNA extraction. Our modified protocol has applications in other cereal tissues rich in polysaccharides, including oat.

Reference genes are selected based on the assumption of temporal and spatial expression stability and on their widespread use in model species. They are often used without validation in the studied species. For barley (Hordeum vulgare L.), previous reference gene validation studies have been limited by the number of tissues, cultivars, and/or reference gene candidates assayed. Publically available bioinformatic resources are now available that enable efficient assessment of much larger numbers of candidate genes than is possible with empirical approaches. Expression sequence tag profile viewer data from the UniGene library were used to predict the expression stability of 655 barley genes. The expression profiles of 20 gene candidates predicted to be stable across tissue types were evaluated in eight tissues of the cultivar ‘Conlon’ by real‐time quantitative polymerase chain reaction. The five most stable genes were then tested in the barley cultivars ‘Golden Promise’ and ‘Harrington’ and (to test potential applicability to other Poaceae species) in the oat (Avena sativa L.) cultivar ‘Hifi’. The traditional reference gene actin and the candidate genes elongation factor EF‐2 and malate dehydrogenase demonstrated the most stable expression. The predictive capacity of bioinformatics to identify suitable reference genes was demonstrated. Four genes with better stability than all except one traditionally used reference gene were discovered.