Project description:Gene-to-gene coexpression analysis is a powerful approach to infer function of uncharacterized genes. To perform non-targeted coexpression analysis of tomato genes, we collected a developmental gene expression dataset using various tissues of tomato plant. Expression data are collected from 24 different tissue types including root, hypocotyl, cotyledon, leaf at different stages, and fruit tissues at 4 different ripening stages from 4 different Solanum lycopersicum cultivars. Fruits were separated to the flesh and the peel. These two tissue types indeed showed remarkably different gene expression profiles. We also collected data from 4 different ripening stages (mature green, yellow, orange, and red) to detail the changes during ripening. By using this gene expression dataset, we calculated pair-wise Pearson’s correlation coefficients, and performed network-based coexpression analysis. The analysis generated a number of coexpression modules, some of which showed an enrichment of genes associated with specific functional categories. This result will be useful in inferring functions of uncharacterized tomato genes, and in prioritizing genes for further experimental analysis. We used Affymetrix GeneChip Tomato genome Arrays to detail the global gene expression change using 24 different tomato tissue types (67 hybridizations).
Project description:Using RNA-seq, we recently investigated the transcriptomic dynamics of rose flower under treatment of various plant hormones, including ethylene, 2,4-D, NAA, cytok, gibberellins, abscisic acid, brassinosteroids, salicylic acid, jasmonates, as well as ethylene inhibitor 1-MCP and AgNO. We obtained approximately 240GB data and dissected the transcriptional network with the aim of exploring the transcriptional variation of rose responses towards those plant hormones. Our data will be useful to all those working with the analysis of rose gene expression.
Project description:Hormones effect various plant developmental processes by altering gene expression. The expression of several genes is regulated by plant hormones and many of these genes are regulated commonly and specifically by various hormones. We used microarrays to study the global effect of plant hormones on rice gene expression and identify the genes involved in operlapping and specific transcriptional responses. Rice seedlings of IR64 variety were grown hydroponically for 7-days in a culture room with a daily photoperiodic cycle of 14h light and 10h dark. Seedlings were incubated in water (control) or 50 µM solution of indole-3-acetic acid (auxin, IAA) and benzyl aminopurine (cytokinin, BAP) and 100 µM solution of abscisic acid (ABA), 1-aminocyclopropane-1-carboxylic acid (ethylene derivative, ACC), salicylic acid (SA) and jasmonic acid (JA) for 3h. The 5 micrograms of total RNA sample isolated from each tissue sample was processed for microarray analysis according to Affymetrix protocol. Two biological replicates for each sample (two controls, IAA, BAP, ABA, ACC, SA and JA) were used for microarray analysis.