Katsuhisa Horimoto

Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling.

MOTIVATIONnRecent advances in DNA microarray technologies have made it possible to measure the expression levels of thousands of genes simultaneously under different conditions. The data obtained by microarray analyses are called expression profile data. One type of important information underlying the expression profile data is the genetic network, that is, the regulatory network among genes. Graphical Gaussian Modeling (GGM) is a widely utilized method to infer or test relationships among a plural of variables.nnnRESULTSnIn this study, we developed a method combining the cluster analysis with GGM for the inference of the genetic network from the expression profile data. The expression profile data of 2467 Saccharomyces cerevisiae genes measured under 79 different conditions (Eisen et al., PROC: Natl Acad. Sci. USA, 95, 14683-14868, 1998) were used for this study. At first, the 2467 genes were classified into 34 clusters by a cluster analysis, as a preprocessing for GGM. Then, the expression levels of the genes in each cluster were averaged for each condition. The averaged expression profile data of 34 clusters were subjected to GGM, and a partial correlation coefficient matrix was obtained as a model of the genetic network of S. cerevisiae. The accuracy of the inferred network was examined by the agreement of our results with the cumulative results of experimental studies.

Statistical estimation of cluster boundaries in gene expression profile data

MOTIVATIONnGene expression profile data are rapidly accumulating due to advances in microarray techniques. The abundant data are analyzed by clustering procedures to extract the useful information about the genes inherent in the data. In the clustering analyses, the systematic determination of the boundaries of gene clusters, instead of by visual inspection and biological knowledge, still remains challenging.nnnRESULTSnWe propose a statistical procedure to estimate the number of clusters in the hierarchical clustering of the expression profiles. Following the hierarchical clustering, the statistical property of the profiles at the node in the dendrogram is evaluated by a statistics-based value: the variance inflation factor in the multiple regression analysis. The evaluation leads to an automatic determination of the cluster boundaries without any additional analyses and any biological knowledge of the measured genes. The performance of the present procedure is demonstrated on the profiles of 2467 yeast genes, with very promising results.nnnAVAILABILITYnA set of programs will be electronically sent upon request.nnnCONTACTnhorimoto@post.saga-med.ac.jp; toh@beri.co.jp

Comprehensive comparison between locations of orthologous genes on archaeal and bacterial genomes

MOTIVATIONnFollowing an extensive search for orthologous genes between the complete genomes from archaea and bacteria, the spatial association of the orthologs has been investigated in terms of synteny, the conservation of the order of neighboring genes. However, the relationships between the relative locations of remote orthologs over entire genomes have not been shown.nnnRESULTSnComprehensive comparisons between the locations of orthologs on nineteen archaeal and bacterial genomes are presented by the location to location correspondence based on the gene-location distance. When the two genomes are rotated such that a pair of orthologs with the shortest distance is set in the same angle, a statistically significant number of orthologs maintain their relative locations between the genomes. Even by the short distances at the 5% significance level, the rotations are restricted within a narrow range, suggesting an intrinsic angle for realizing similar locations between the orthologs in each genome pair. Furthermore, the rotations in the restricted range agree with the replication origin and terminus sites for the analyzed genomes where such sites are known. The relationship between location-maintained orthologs and gene function is also discussed.

A Procedure for Detecting Similarity Between Distantly Related Protein Families

We develop a method to detect similar regions between distantly related families of proteins, in terms of physicochemical properties of amino acid residues. To estimate the similarity between entire sequences, furthermore, similar regions detected by the procedure are aligned by a dynamic programming, and are compared with the structural data. The performance of present procedure is demonstrated by means of TIM barrel protein families that are well known families which mutually show low sequence similarity with a common fold.

Classification of Linear Chromosomes

In order to get an insight into genome duplication, quantification method II (QTM2) has been applied to claasify the chromosomes in Saccharomyces cerevisiae. In the application of QTM2, kind of chromosomes has been used as outside variables, kind of duplicate genes and location of them on the chromosomes have been used as factor items. The duplicate genes have been quantified and the values from the first and the second largest eigen values have been plotted. Six classes of the chromosomes have been emarged by visual inspection of the charts to suggest one time genome duplication during history of the genome evolution in the yeast.