Biomedical subjects

Toshiaki Katayama

Publications and source records attributed to Toshiaki Katayama.

4 recordsLinked to original sources

PubMed2006

ODB: a database of operons accumulating known operons across multiple genomes.

Operon structures play an important role in co-regulation in prokaryotes. Although over 200 complete genome sequences are now available, databases providing genome-wide operon information have been limited to certain specific genomes. Thus, we have developed an ODB (Operon DataBase), which provides a data retrieval system of known operons among the many complete genomes. Additionally, putative operons that are conserved in terms of known operons are also provided. The current version of our database contains about 2000 known operon information in more than 50 genomes and about 13 000 putative operons in more than 200 genomes. This system integrates four types of associations: genome context, gene co-expression obtained from microarray data, functional links in biological pathways and the conservation of gene order across the genomes. These associations are indicators of the genes that organize an operon, and the combination of these indicators allows us to predict more reliable operons. Furthermore, our system validates these predictions using known operon information obtained from the literature. This database integrates known literature-based information and genomic data. In addition, it provides an operon prediction tool, which make the system useful for both bioinformatics researchers and experimental biologists. Our database is accessible at http://odb.kuicr.kyoto-u.ac.jp/.

Databases, Nucleic Acid↗

PubMed2006

From genomics to chemical genomics: new developments in KEGG.

The increasing amount of genomic and molecular information is the basis for understanding higher-order biological systems, such as the cell and the organism, and their interactions with the environment, as well as for medical, industrial and other practical applications. The KEGG resource (http://www.genome.jp/kegg/) provides a reference knowledge base for linking genomes to biological systems, categorized as building blocks in the genomic space (KEGG GENES) and the chemical space (KEGG LIGAND), and wiring diagrams of interaction networks and reaction networks (KEGG PATHWAY). A fourth component, KEGG BRITE, has been formally added to the KEGG suite of databases. This reflects our attempt to computerize functional interpretations as part of the pathway reconstruction process based on the hierarchically structured knowledge about the genomic, chemical and network spaces. In accordance with the new chemical genomics initiatives, the scope of KEGG LIGAND has been significantly expanded to cover both endogenous and exogenous molecules. Specifically, RPAIR contains curated chemical structure transformation patterns extracted from known enzymatic reactions, which would enable analysis of genome-environment interactions, such as the prediction of new reactions and new enzyme genes that would degrade new environmental compounds. Additionally, drug information is now stored separately and linked to new KEGG DRUG structure maps.

Biotransformation↗

PubMed2005

Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

Bayes Theorem↗

PubMed2004

An interactive gene network for securin-separase, condensin, cohesin, Dis1/Mtc1 and histones constructed by mass transformation.

The small genome of fission yeast Schizosaccharomyces pombe contains 4824 predicted genes and gene disruption suggests that approximately 850 are essential for viability. To obtain information on interactions among genes required for chromosome segregation, an approach called Strategy B was taken using mass transformation of the 1015 temperature-sensitive (ts) mutants that were made by random mutagenesis and transformed by plasmids carrying the genes for securin, separase, condensin, cohesin, kinetochore microtubule-binding proteins Dis1/Mtc1 or histones. Mutant strains whose phenotypes were either suppressed or inhibited by plasmids were selected. Each plasmid interacted positively or negatively with the average 14 strains. Identification of the mutant gene products by cloning revealed many hitherto unknown interactions. The interactive networks of segregation therefore may consist of genes with a variety of functions. For example, separase/Cut1 interacts with Cdc48/p97/VCP, which stabilizes securin and separase. Surprisingly, S. pombe cdc48 mutants displayed the mitotic phenotype highly similar to separase/cut1 mutants. This approach also provides a novel way of mutant isolation, resulting in two histone H2B strains and a cohesion mutant with a new phenotype.

Adenosine Triphosphatases↗