Transcriptome Analysis Reveals Drought-Responsive Pathways and Key Genes of Two Oat (Avena sativa) Varieties.

To cope with the yield loss caused by drought stress, new oat varieties with greater drought tolerance need to be selected. In this study, two oat varieties with different drought tolerances were selected for analysis of their phenotypes and physiological indices under moderate and severe soil drought stress. The results revealed significant differences in the degree of wilting, leaf relative water content (RWC), and SOD and CAT activity between the two oat genotypes under severe soil drought stress; moreover, the drought-tolerant variety exhibited a significant increase in the number of stomata and wax crystals on the surface of both the leaf and guard cells; additionally, the morphology of the guard cells was normal, and there was no significant disruption of the grana lamella membrane or the nuclear envelope. Furthermore, transcriptome analysis revealed that the expression of genes related to the biosynthesis of waxes and cell-wall components, as well as those of the WRKY family, significantly increased in the drought-tolerant variety. These findings suggest that several genes involved in the antioxidant pathway could improve drought tolerance in plants by regulating the increase/decrease in wax and cell-wall constituents and maintaining normal cellular water potential, as well as improving the ability of the antioxidant system to scavenge peroxides in oats.

Linear regression model of short k-word: a similarity distance suitable for biological sequences with various lengths.

Originating from sequences' length difference, both k-word based methods and graphical representation approaches have uncovered biological information in their distinct ways. However, it is less likely that the mechanisms of information storage vary with sequences' length. A similarity distance suitable for sequences with various lengths will be much near to the mechanisms of information storage. In this paper, new sub-sequences of k-word were extracted from biological sequences under a one-to-one mapping. The new sub-sequences were evaluated by a linear regression model. Moreover, a new distance was defined on the invariants from the linear regression model. With comparison to other alignment-free distances, the results of four experiments demonstrated that our similarity distance was more efficient.

A simple k-word interval method for phylogenetic analysis of DNA sequences.

In this study, the normalized k-word average interval distance is proposed to extract phylogenetic information from DNA sequences. The phylogenetic trees of 30 mammalian species based on Euclidean distance measure are reconstructed with k ranging from 2 to 9. Comparison of our results with other methods shows that our method is efficient and powerful for phylogenetic analysis. In addition, for a fixed k, 4(k) distinct k-words are divided into n classes based on a new proposed indicator, where n is the number of DNA sequences. The effect of each k-word class on phylogeny is discussed.

A novel statistical measure for sequence comparison on the basis of k-word counts.

Numerous efficient methods based on word counts for sequence analysis have been proposed to characterize DNA sequences to help in comparison, retrieval from the databases and reconstructing evolutionary relations. However, most of them seem unrelated to any intrinsic characteristics of DNA. In this paper, we proposed a novel statistical measure for sequence comparison on the basis of k-word counts. This new measure removed the influence of sequences' lengths and uncovered bulk property of DNA sequences. The proposed measure was tested by similarity search and phylogenetic analysis. The experimental assessment demonstrated that our similarity measure was efficient.