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BMC Bioinformatics. 2008 Jan 23;9:37. doi: 10.1186/1471-2105-9-37.

Evaluation of phylogenetic footprint discovery for predicting bacterial cis-regulatory elements and revealing their evolution.

Author information

  • 1Laboratoire de Bioinformatique des Génomes et des Réseaux, Université Libre de Bruxelles (ULB), Campus Plaine, CP 263, Boulevard du Triomphe, 1050 Bruxelles, Belgium. rekins@scmbb.ulb.ac.be

Abstract

BACKGROUND:

The detection of conserved motifs in promoters of orthologous genes (phylogenetic footprints) has become a common strategy to predict cis-acting regulatory elements. Several software tools are routinely used to raise hypotheses about regulation. However, these tools are generally used as black boxes, with default parameters. A systematic evaluation of optimal parameters for a footprint discovery strategy can bring a sizeable improvement to the predictions.

RESULTS:

We evaluate the performances of a footprint discovery approach based on the detection of over-represented spaced motifs. This method is particularly suitable for (but not restricted to) Bacteria, since such motifs are typically bound by factors containing a Helix-Turn-Helix domain. We evaluated footprint discovery in 368 Escherichia coli K12 genes with annotated sites, under 40 different combinations of parameters (taxonomical level, background model, organism-specific filtering, operon inference). Motifs are assessed both at the levels of correctness and significance. We further report a detailed analysis of 181 bacterial orthologs of the LexA repressor. Distinct motifs are detected at various taxonomical levels, including the 7 previously characterized taxon-specific motifs. In addition, we highlight a significantly stronger conservation of half-motifs in Actinobacteria, relative to Firmicutes, suggesting an intermediate state in specificity switching between the two Gram-positive phyla, and thereby revealing the on-going evolution of LexA auto-regulation.

CONCLUSION:

The footprint discovery method proposed here shows excellent results with E. coli and can readily be extended to predict cis-acting regulatory signals and propose testable hypotheses in bacterial genomes for which nothing is known about regulation.

PMID:
18215291
[PubMed - indexed for MEDLINE]
PMCID:
PMC2248561
Free PMC Article

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