FITBAR: a web tool for the robust prediction of prokaryotic regulons.
BACKGROUND
The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.


RESULTS
FITBAR (Fast Investigation Tool for Bacterial and Archaeal Regulons) is a web service designed to identify new protein binding sites on fully sequenced prokaryotic genomes. This tool consists in a workbench where the significance of the predictions can be compared using different statistical methods, a feature not found in existing resources. The Local Markov Model and the Compound Importance Sampling algorithms have been implemented to compute the P-value of newly discovered binding sites. In addition, FITBAR provides two optimized genomic scanning algorithms using either log-odds or entropy-weighted position-specific scoring matrices. Other significant features include the production of a detailed genomic context map for each detected binding site and the export of the search results in spreadsheet and portable document formats. FITBAR discovery of a high affinity Escherichia coli NagC binding site was validated experimentally in vitro as well as in vivo and published.


CONCLUSIONS
FITBAR was developed in order to allow fast, accurate and statistically robust predictions of prokaryotic regulons. This feature constitutes the main advantage of this web tool over other matrix search programs and does not impair its performance. The web service is available at http://archaea.u-psud.fr/fitbar.
Université Paris-Sud 11, Centre National de la Recherche Scientifique, UMR 8621, Institut de Génétique et Microbiologie, Orsay, France. jacques.oberto@igmors.u-psud.fr
Jacques
J
Oberto
D000465:Algorithms; D001665:Binding Sites; D020745:Genome, Archaeal; D016680:Genome, Bacterial; D020407:Internet; D018085:Regulon; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
D014157:Transcription Factors