The availability of data produced from various sequencing platforms offer the possibility to answer complex questions in plant research. However, drawbacks can arise when there are gaps in the information generated, and complementary platforms are essential to obtain more comprehensive data sets relating to specific biological process, such as responses to environmental perturbations in plant systems. The investigation of transcriptional regulation raises different challenges, particularly in associating differentially expressed transcription factors with their downstream responsive genes. In this paper, we discuss the integration of transcriptional factor studies through RNA sequencing (RNA-seq) and Chromatin Immunoprecipitation sequencing (ChIP-seq). We show how the data from ChIP-seq can strengthen information generated from RNA-seq in elucidating gene regulatory mechanisms. In particular, we discuss how integration of ChIP-seq and RNA-seq data can help to unravel transcriptional regulatory networks. This review discusses recent advances in methods for studying transcriptional regulation using these two methods. It also provides guidelines for making choices in selecting specific protocols in RNA-seq pipelines for genome-wide analysis to achieve more detailed characterization of specific transcription regulatory pathways via ChIP-seq.
Coloured rice genotypes have greater nutritious value and consumer demand for these varieties is now greater than ever. The documentation of these genotypes is important for the improvement of the rice plant. In this study, 42 coloured rice genotypes were selected for determination of their genetic divergence using 25 simple sequence repeat (SSR) primers and 15 agro-morphological traits. Twenty-one out of the 25 SSR primers showed distinct, reproducible polymorphism. A dendrogram constructed using the SSR primers clustered the 42 coloured rice genotypes into 7 groups. Further, principle component analysis showed 75.28% of total variations were explained by the first-three components. All agro-morphological traits showed significant difference at the (p≤0.05) and (p≤0.01) levels. From the dendrogram constructed using the agro-morphological traits, all the genotypes were clustered into four distinct groups. Pearson's correlation coefficient showed that among the 15 agro-morphological traits, the yield contributing factor had positive correlation with the number of tillers, number of panicles, and panicle length. The heritability of the 15 traits ranged from 17.68 to 99.69%. Yield per plant and harvest index showed the highest value for both heritability and genetic advance. The information on the molecular and agro-morphological traits can be used in rice breeding programmes to improve nutritional value and produce higher yields.
Oil palm is grown in tropical soils with low bioavailability of Pi. A cDNA clone specifically expressed under phosphate-starvation condition in oil palm roots was identified as a high-affinity phosphate transporter (EgPHT1). The deduced amino acid sequence has 6 transmembrane domains each at the N- and C-termini separated by a hydrophilic linker. Comparison of promoter motifs within 1500 bp upstream of ATG of 10 promoters from high- and low-affinity phosphate transporter from both dicots and monocots including EgPHT1 was performed. The EgPHT1 promoter was fused to β-glucuronidase (GUS) reporter gene and its activity was analysed by histochemical and fluorometric GUS assays in transiently transformed oil palm tissues and T3 homozygous transgenic Arabidopsis plants. In response to Pi-starvation, no GUS activity was detected in oil palm leaves, but a strong inducible activity was observed in the roots (1.4 times higher than the CaMV35S promoter). GUS was specifically expressed in transgenic Arabidopsis roots under Pi deficiency and starvation of the other macronutrients (N and K) did not induce GUS activity. Eight motifs including ABRERATCAL (abscisic-acid responsive), RHERPATEXPA7 (root hair-specific), SURECOREATSULTR11 (sulfur-deficiency response), LTRECOREATCOR15 (temperature-stress response), MYB2CONSENSUSAT and ACGTATERD1 (water-stress response) as well as two novel motifs, 3 (TAAAAAAA) and 26 (TTTTATGT) identified through pattern discovery, occur at significantly higher frequency (p