RESULTS: Here, we have undertaken further analysis of role of OsFAD2-1 in the developing rice grain. The use of Illumina-based NGS transcriptomics analysis of developing rice grain reveals that knockdown of Os-FAD2-1 gene expression was accompanied by the down regulation of the expression of a number of key genes in the lipid biosynthesis pathway in the HO rice line. A slightly higher level of oil accumulation was also observed in the HO-RBO.
CONCLUSION: Prominent among the down regulated genes were those that coded for FatA, LACS, SAD2, SAD5, caleosin and steroleosin. It may be possible to further increase the oleic acid content in rice oil by altering the expression of the lipid biosynthetic genes that are affected in the HO line.
FINDINGS: We optimized the assembly of a Hevea bark transcriptome based on 16 Gb Illumina PE RNA-Seq reads using the Oases assembler across a range of k-mer sizes. We then assessed assembly quality based on transcript N50 length and transcript mapping statistics in relation to (a) known Hevea cDNAs with complete open reading frames, (b) a set of core eukaryotic genes and (c) Hevea genome scaffolds. This was followed by a systematic transcript mapping process where sub-assemblies from a series of incremental amounts of bark transcripts were aligned to transcripts from the entire bark transcriptome assembly. The exercise served to relate read amounts to the degree of transcript mapping level, the latter being an indicator of the coverage of gene transcripts expressed in the sample. As read amounts or datasize increased toward 16 Gb, the number of transcripts mapped to the entire bark assembly approached saturation. A colour matrix was subsequently generated to illustrate sequencing depth requirement in relation to the degree of coverage of total sample transcripts.
CONCLUSIONS: We devised a procedure, the "transcript mapping saturation test", to estimate the amount of RNA-Seq reads needed for deep coverage of transcriptomes. For Hevea de novo assembly, we propose generating between 5-8 Gb reads, whereby around 90% transcript coverage could be achieved with optimized k-mers and transcript N50 length. The principle behind this methodology may also be applied to other non-model plants, or with reads from other second generation sequencing platforms.
SIGNIFICANCE: Plant proteomics study is a rapidly growing area of biological research that is positively impacting plant science. With the recent advances in new technologies, proteomics not only allows us to comprehensively analyses crop proteins, but also help us to understand the functions of the genes. In this review, we highlighted recent proteomic studies in commercial crops and updated the advances in our understanding of the proteomes of these crops. We believe that proteomic-based research will continue to grow and contribute to the improvement of crops.