These authors contributed equally to this work.
The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access
Article first published online: 21 MAY 2008
© 2008 The Authors. Journal compilation © 2008 Blackwell Publishing Ltd
The Plant Journal
Volume 55, Issue 3, pages 526–542, August 2008
How to Cite
Goda, H., Sasaki, E., Akiyama, K., Maruyama-Nakashita, A., Nakabayashi, K., Li, W., Ogawa, M., Yamauchi, Y., Preston, J., Aoki, K., Kiba, T., Takatsuto, S., Fujioka, S., Asami, T., Nakano, T., Kato, H., Mizuno, T., Sakakibara, H., Yamaguchi, S., Nambara, E., Kamiya, Y., Takahashi, H., Hirai, M. Y., Sakurai, T., Shinozaki, K., Saito, K., Yoshida, S. and Shimada, Y. (2008), The AtGenExpress hormone and chemical treatment data set: experimental design, data evaluation, model data analysis and data access. The Plant Journal, 55: 526–542. doi: 10.1111/j.1365-313X.2008.03510.x
- Issue published online: 23 JUL 2008
- Article first published online: 21 MAY 2008
- Received 22 February 2008; accepted 20 March 2008; published online 21 May 2008.
- chemical genomics;
- phytohormone network analysis;
- co-expression network analysis;
- systems biology;
We analyzed global gene expression in Arabidopsis in response to various hormones and in related experiments as part of the AtGenExpress project. The experimental agents included seven basic phytohormones (auxin, cytokinin, gibberellin, brassinosteroid, abscisic acid, jasmonate and ethylene) and their inhibitors. In addition, gene expression was investigated in hormone-related mutants and during seed germination and sulfate starvation. Hormone-inducible genes were identified from the hormone response data. The effects of each hormone and the relevance of the gene lists were verified by comparing expression profiles for the hormone treatments and related experiments using Pearson’s correlation coefficient. This approach was also used to analyze the relationships among expression profiles for hormone responses and those included in the AtGenExpress stress-response data set. The expected correlations were observed, indicating that this approach is useful to monitor the hormonal status in the stress-related samples. Global interactions among hormones-inducible genes were analyzed in a pairwise fashion, and several known and novel hormone interactions were detected. Genome-wide transcriptional gene-to-gene correlations, analyzed by hierarchical cluster analysis (HCA), indicated that our data set is useful for identification of clusters of co-expressed genes, and to predict the functions of unknown genes, even if a gene’s function is not directly related to the experiments included in AtGenExpress. Our data are available online from AtGenExpressJapan; the results of genome-wide HCA are available from PRIMe. The data set presented here will be a versatile resource for future hormone studies, and constitutes a reference for genome-wide gene expression in Arabidopsis.