Bayesian joint prediction of associated transcription factors in Bacillus subtilis.
Sigma factors, often in conjunction with other transcription factors, regulate gene expression in prokaryotes at the transcriptional level. Specific transcription factors tend to co-occur with specific sigma factors. To predict new members of the transcription factor regulon, we applied Bayes rule to combine the Bayesian probability of sigma factor prediction calculated from microarray data and the sigma factor binding sequence motif, the motif score of the transcription factor associated with the sigma factor, the empirically determined distance between the transcription start site to the cis-regulatory region, and the tendency for specific sigma factors and transcription factors to co-occur. By combining these information sources, we improve the accuracy of predicting regulation by transcription factors, and also confirm the sigma factor prediction. We applied our proposed method to all genes in Bacillus subtilis to find currently unknown gene regulations by transcription factors and sigma factors.
Makita
Y
Y
Human Genome Center, Institute of Medical Science, University of Tokyo Minato-ku, Tokyo 108-8639, Japan.
De Hoon
M J L
MJ
Ogasawara
N
N
Miyano
S
S
Nakai
K
K
D001412:Bacillus subtilis / Q000235:genetics*; D001426:Bacterial Proteins / Q000235:genetics* / Q000378:metabolism; D001499:Bayes Theorem; D001665:Binding Sites; D008957:Models, Genetic; D012808:Sigma Factor / Q000235:genetics* / Q000378:metabolism; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism; D014158:Transcription, Genetic
D001426:Bacterial Proteins; C093265:SigX protein, Bacillus subtilis; D012808:Sigma Factor; D014157:Transcription Factors