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PMID: 18237374 Published · epublish English Journal Article Research Support, Non-U.S. Gov't

SIGffRid: a tool to search for sigma factor binding sites in bacterial genomes using comparative approach and biologically driven statistics.

BMC bioinformatics ·Vol. 9 ·2008-01-31 ·Pages 73

Touzain F, Schbath S, Debled-Rennesson I, Aigle B, Kucherov G, Leblond P

Abstract

Many programs have been developed to identify transcription factor binding sites. However, most of them are not able to infer two-word motifs with variable spacer lengths. This case is encountered for RNA polymerase Sigma (sigma) Factor Binding Sites (SFBSs) usually composed of two boxes, called -35 and -10 in reference to the transcription initiation point. Our goal is to design an algorithm detecting SFBS by using combinational and statistical constraints deduced from biological observations. We describe a new approach to identify SFBSs by comparing two related bacterial genomes. The method, named SIGffRid (SIGma Factor binding sites Finder using R'MES to select Input Data), performs a simultaneous analysis of pairs of promoter regions of orthologous genes. SIGffRid uses a prior identification of over-represented patterns in whole genomes as selection criteria for potential -35 and -10 boxes. These patterns are then grouped using pairs of short seeds (of which one is possibly gapped), allowing a variable-length spacer between them. Next, the motifs are extended guided by statistical considerations, a feature that ensures a selection of motifs with statistically relevant properties. We applied our method to the pair of related bacterial genomes of Streptomyces coelicolor and Streptomyces avermitilis. Cross-check with the well-defined SFBSs of the SigR regulon in S. coelicolor is detailed, validating the algorithm. SFBSs for HrdB and BldN were also found; and the results suggested some new targets for these sigma factors. In addition, consensus motifs for BldD and new SFBSs binding sites were defined, overlapping previously proposed consensuses. Relevant tests were carried out also on bacteria with moderate GC content (i.e. Escherichia coli/Salmonella typhimurium and Bacillus subtilis/Bacillus licheniformis pairs). Motifs of house-keeping sigma factors were found as well as other SFBSs such as that of SigW in Bacillus strains. We demonstrate that our approach combining statistical and biological criteria was successful to predict SFBSs. The method versatility authorizes the recognition of other kinds of two-box regulatory sites.

MeSH Terms
Algorithms Binding Sites Chromosome Mapping/methods Genome, Bacterial/genetics Pattern Recognition, Automated/methods Protein Binding Sequence Analysis, DNA/methods Sigma Factor/genetics Software
Chemicals
Sigma Factor
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Touzain Fabrice
Laboratoire Lorrain de Recherche en Informatique et ses Applications, Campus Scientifique, B,P, 239, UMR CNRS-INPL-INRIA-Nancy 2-UHP 7503, 54506 Vandoeuvre-lès-Nancy, France. touzain@loria.fr
Schbath Sophie
Debled-Rennesson Isabelle
Aigle Bertrand
Kucherov Gregory
Leblond Pierre
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Article Info
Journal
BMC bioinformatics
Abbr.
BMC Bioinformatics
ISSN
1471-2105
DOI
10.1186/1471-2105-9-73
Email
touzain@loria.fr
Published
2008-01-31
Epub
2008-00-31
Pages
73
Language
English
Region
England
NLM ID
100965194
PMCID
PMC2375139
Subset
IM
External Links
PubMed source DOI: 10.1186/1471-2105-9-73 PMC Full Text View journal details
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