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Open Access Software

Visualization and Exploration of Conserved Regulatory Modules Using ReXSpecies 2

Stephan Struckmann12*, Daniel Esch3, Hans Schöler34 and Georg Fuellen1*

Author Affiliations

1 University of Rostock, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Heydemannstrasse 8, 18057 Rostock, Germany

2 E.M.A. University of Greifswald, Institute for Mathematics and Computer Science, Rathenaustrasse 47, 17487 Greifswald, Germany

3 Department of Cell and Developmental Biology, Max Planck Institute for Molecular Biomedicine, Röntgenstrasse 20, 48149 Münster, Germany

4 Medical Faculty, University of Münster, Domagkstrasse 3, 48149 Münster, Germany

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BMC Evolutionary Biology 2011, 11:267  doi:10.1186/1471-2148-11-267

Published: 24 September 2011

Abstract

Background

The prediction of transcription factor binding sites is difficult for many reasons. Thus, filtering methods are needed to enrich for biologically relevant (true positive) matches in the large amount of computational predictions that are frequently generated from promoter sequences.

Results

ReXSpecies 2 filters predictions of transcription factor binding sites and generates a set of figures displaying them in evolutionary context. More specifically, it uses position specific scoring matrices to search for motifs that specify transcription factor binding sites. It removes redundant matches and filters the remaining matches by the phylogenetic group that the matrices belong to. It then identifies potential transcriptional modules, and generates figures that highlight such modules, taking evolution into consideration. Module formation, scoring by evolutionary criteria and visual clues reduce the amount of predictions to a manageable scale. Identification of transcription factor binding sites of particular functional importance is left to expert filtering. ReXSpecies 2 interacts with genome browsers to enable scientists to filter predictions together with other sequence-related data.

Conclusions

Based on ReXSpecies 2, we derive plausible hypotheses about the regulation of pluripotency. Our tool is designed to analyze transcription factor binding site predictions considering their common pattern of occurrence, highlighting their evolutionary history.