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Nucleic Acids Res. Jul 15, 1999; 27(14): 2981–2989.
PMCID: PMC148515

Computer analysis of transcription regulatory patterns in completely sequenced bacterial genomes.


Recognition of transcription regulation sites (operators) is a hard problem in computational molecular biology. In most cases, small sample size and low degree of sequence conservation preclude the construction of reliable recognition rules. We suggest an approach to this problem based on simultaneous analysis of several related genomes. It appears that as long as a gene coding for a transcription regulator is conserved in the compared bacterial genomes, the regulation of the respective group of genes (regulons) also tends to be maintained. Thus a gene can be confidently predicted to belong to a particular regulon in case not only itself, but also its orthologs in other genomes have candidate operators in the regulatory regions. This provides for a greater sensitivity of operator identification as even relatively weak signals are likely to be functionally relevant when conserved. We use this approach to analyze the purine (PurR), arginine (ArgR) and aromatic amino acid (TrpR and TyrR) regulons of Escherichia coli and Haemophilus influenzae. Candidate binding sites in regulatory regions of the respective H.influenzae genes are identified, a new family of purine transport proteins predicted to belong to the PurR regulon is described, and probable regulation of arginine transport by ArgR is demonstrated. Differences in the regulation of some orthologous genes in E.coli and H.influenzae, in particular the apparent lack of the autoregulation of the purine repressor gene in H.influenzae, are demonstrated.

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