The identification of candidate genes for a reverse genetic analysis of development and function in the Arabidopsis gynoecium

The screening for mutants and their subsequent molecular analysis has permitted the identification of a number of genes of Arabidopsis involved in the development and functions of the gynoecium. However, these processes remain far from completely understood. It is clear that in many cases, genetic redundancy and other factors can limit the efficiency of classical mutant screening. We have taken the alternative approach of a reverse genetic analysis of gene function in the Arabidopsis gynoecium. A high-throughput fluorescent differential display screen performed between two Arabidopsis floral homeotic mutants has permitted the identification of a number of genes that are specifically or preferentially expressed in the gynoecium. Here, we present the results of this screen and a detailed characterization of the expression profiles of the genes identified. Our expression analysis makes novel use of several Arabidopsis floral homeotic mutants to provide floral organ-specific gene expression profiles. The results of these studies permit the efficient targeting of effort into a functional analysis of gynoecium-expressed genes.     

A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors

MOTIVATION: Time series experiments of cDNA microarrays have been commonly used in various biological studies and conducted under a lot of experimental factors. A popular approach of time series microarray analysis is to compare one gene with another in their expression profiles, and clustering expression sequences is a typical example. On the other hand, a practically important issue in gene expression is to identify the general timing difference that is caused by experimental factors. This type of difference can be extracted by comparing a set of time series expression profiles under a factor with those under another factor, and so it would be difficult to tackle this issue by using only a current approach for time series microarray analysis. RESULTS: We have developed a systematic method to capture the timing difference in gene expression under different experimental factors, based on hidden Markov models. Our model outputs a real-valued vector at each state and has a unique state transition diagram. The parameters of our model are trained from a given set of pairwise (generally multiplewise) expression sequences. We evaluated our model using synthetic as well as real microarray datasets. The results of our experiment indicate that our method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.