Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.

Grain quality is one of the main targets that rice breeders focus on to improve elite rice varieties. Several characteristics are considered when determine rice grain quality, such as aroma, amylose content (AC), gelatinization temperature (GT) and, especially, lengthwise grain elongation (GE). GE is a desirable feature in premium rice of high quality, such as India and Pakistan’ Basmati. Inheritance of GE in rice has not been clearly elucidated due to its complex and inconsistent pattern. In this study, we identified QTLs for GE in rice using bulk-segregant analysis (BSA) and whole-genome sequencing based on an F₂ population segregated for GE as well as AC and GT. We identified two QTLs on chromosome 6, qGE6.1 and qGE6.2, and another QTL on chromosome 4, qGE4.1. qGE6.1 and qGE6.2 were located near starch synthase IIa (SSIIa) and starch branching enzyme III (SBEIII), respectively, and qGE4.1 was located near starch branching enzyme IIa (SBEIIa). qGE6.1 was considered to be the major QTL for GE based on this population, and SSIIa was suggested to be the best candidate gene associated with the GE trait. The results of this study may be useful for breeding rice with increased grain elongation and different starch properties.

The specification of vascular patterning in plants has interested plant biologists for many years. In the last decade a new context has emerged for this interest. Specifically, recent proposals to engineer C4 traits into C3 plants such as rice require an understanding of how the distinctive venation pattern in the leaves of C4 plants is determined. High vein density with Kranz anatomy, whereby photosynthetic cells are arranged in encircling layers around vascular bundles, is one of the major traits that differentiate C4 species from C3 species. To identify genetic factors that specify C4 leaf anatomy, we generated ethyl methanesulfonate- and γ-ray-mutagenized populations of the C4 species sorghum (Sorghum bicolor), and screened for lines with reduced vein density. Two mutations were identified that conferred low vein density. Both mutations segregated in backcrossed F2 populations as homozygous recessive alleles. Bulk segregant analysis using next-generation sequencing revealed that, in both cases, the mutant phenotype was associated with mutations in the CYP90D2 gene, which encodes an enzyme in the brassinosteroid biosynthesis pathway. Lack of complementation in allelism tests confirmed this result. These data indicate that the brassinosteroid pathway promotes high vein density in the sorghum leaf, and suggest that differences between C4 and C3 leaf anatomy may arise in part through differential activity of this pathway in the two leaf types