Functional Genomics of Gram-Positive Microoraganisms,
Baveno, Italy, 2003


Genome-wide Analysis of Transcriptional Regulators in Bacillus subtilis

Kazuo Kobayashi, Shigehiko Kanaya and Naotake Ogasawara
Nara Institute of Science and Technology

The B. subtilis genome has 17 sigma factors and about 250 DNA binding transcriptional regulators. In order to develop a comprehensive picture of the regulatory network of gene expression in B. subtilis, a systematic effort to collect expression profiles of wild type cells and knockout mutants of regulator genes is in progress. We first performed comprehensive microarray experiments covering 15 secondary sigma factor genes whose activation conditions are known. In order to reduce a false positive, each microarray analysis was carried out twice by using RNA samples prepared independently. As the result, we found that over 500 genes are regulated by sporulation sigma factors. We have also started microarray analysis of the regulator genes and have already collected data for more than 70 genes whose working conditions are known or suggested from the expression profile in wild type cells. Although the informatics analysis is now in progress, these probably regulate over 1,000 genes. Our progress of the transcriptome analysis will be presented and discussed.

Preciction of Transcription Units in Bacillus subtils Based on Expression Profile and Genome Informatics

Shigehiko Kanaya1, Kazuo Kobayashi1, Akira Ohyama2,3, Naotake Ogasawara1
1Nara Institute of Science and Technology, 2Xanagen Inc., 3Mitsui Knowledge Industry Co., Ltd.

Integration for genome and transcriptome informatics is useful for understanding gene transcription regulation networks in view of genome position. In the present study, we have developed integrated analytical tools for genome and transcriptome informatics mainly focused on (1) gene clusterings by self-organizning mapping, (2) statistical determination methods for transcription units and (3) predicting methods for transcription networks in microarray and science literature. Analyzing gene-expression profiles such as microarray data is important for understanding gene-expression network in the whole cell system. To attain this purpose, it is important to classify genes in high precision by expression profile. We developed butch-learning self-organizing mapping (BL-SOM) for classifying genes based on expression profiles[1]. Some adjacent genes in bacterial genomes are transcribed in a single mRNA molecule (transcription unit) in prokaryotes. Sequence information in the signal sequence, distance between genes, and co-expression pattern from DNA microarray experiments has been used for predicting transcription units in Bacillus subtilis genome.

[1] S. Kanaya, M. Kinouchi, T.Abe, Y. Kudo, Y.Yamada, T. Nishi, T. Mori, T. Ikemura, Gene, 276, 89-99 (2001).


Last updated: June 17, 2003
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