The entire organization of transcription units on the Bacillus subtilis genome.
BACKGROUND
In the post-genomic era, comprehension of cellular processes and systems requires global and non-targeted approaches to handle vast amounts of biological information.


RESULTS
The present study predicts transcription units (TUs) in Bacillus subtilis, based on an integrated approach involving DNA sequence and transcriptome analyses. First, co-expressed gene clusters are predicted by calculating the Pearson correlation coefficients of adjacent genes for all the genes in a series that are transcribed in the same direction with no intervening gene transcribed in the opposite direction. Transcription factor (TF) binding sites are then predicted by detecting statistically significant TF binding sequences on the genome using a position weight matrix. This matrix is a convenient way to identify sites that are more highly conserved than others in the entire genome because any sequence that differs from a consensus sequence has a lower score. We identify genes regulated by each of the TFs by comparing gene expression between wild-type and TF mutants using a one-sided test. By applying the integrated approach to 11 sigma factors and 17 TFs of B. subtilis, we are able to identify fewer candidates for genes regulated by the TFs than were identified using any single approach, and also detect the known TUs efficiently.


CONCLUSIONS
This integrated approach is, therefore, an efficient tool for narrowing searches for candidate genes regulated by TFs, identifying TUs, and estimating roles of the sigma factors and TFs in cellular processes and functions of genes composing the TUs.
Kobayashi
Hirokazu
H
Department of Bioinformatics and Genomes, Graduate School of Information Sciences, Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan. skanaya@gtc.naist.jp
Akitomi
Joe
J
Fujii
Nobuyuki
N
Kobayashi
Kazuo
K
Altaf-Ul-Amin
Md
M
Kurokawa
Ken
K
Ogasawara
Naotake
N
Kanaya
Shigehiko
S
D000465:Algorithms; D001412:Bacillus subtilis / Q000235:genetics*; D019295:Computational Biology; D015964:Gene Expression Regulation, Bacterial; D005798:Genes, Bacterial / Q000235:genetics*; D016678:Genome; D016680:Genome, Bacterial; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D005810:Multigene Family; D050436:Regulatory Elements, Transcriptional; D012808:Sigma Factor / Q000235:genetics; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
D012808:Sigma Factor; D014157:Transcription Factors