%0 Journal Article
%T Evaluation of phylogenetic footprint discovery for predicting bacterial cis-regulatory elements and revealing their evolution
%A Rekin's Janky
%A Jacques van Helden
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-37
%X 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.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.A major challenge of current genomics is to decipher the regulation of the expression for all the genes of a genome. Transcriptional regulation is mediated by interactions between transcription factors (TF) and specific cis-acting elements. The identification of putative transcription factor binding sites (TFBS) is far from trivial, given their short size (typically 6 to 25 bp), and the low level of information in the signal (typically restricted to 5¨C10 informative nucleotides). For this reason, specific algorithms have been developed to detect cis-acting elements in non-coding sequences. Several pattern discovery algorithms were developed to discover putative regulato
%U http://www.biomedcentral.com/1471-2105/9/37