Journal Article

DistanceScan: a tool for promoter modeling

Vladimir Shelest, Daniela Albrecht and Ekaterina Shelest

in Bioinformatics

Volume 26, issue 11, pages 1460-1462
Published in print June 2010 | ISSN: 1367-4803
Published online March 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq132
DistanceScan: a tool for promoter modeling

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Summary: The state of the art in promoter modeling for higher eukaryotes is predicting not single transcription factor binding sites (TFBSs), but their combinations. The new tool utilizes a previously developed method of distance distributions of TFBS pairs. We model the random distribution of distances and compare it with the distribution observed in the query sequences. Comparison of the profiles allows filtering out the ‘noise’ and retaining the potentially functional combinations. This approach has proved its usefulness as a filtering technique for the selection of TFBS pairs for promoter modeling and is now implemented as a tool in R. As an input, it can use the outputs of three different TFBS- and motif-predictive tools (Gibbs Sampler for motifs, MatchTM and MEME/FIMO for PWM-based search). The output is a list of predicted pairs on overrepresented distances with assigned scores, P-values and plots showing the distribution of pairs in the input sequences.

Availability: The tool is available at https://www.omnifung.hki-jena.de/Rpad/Distance_Scan/index.htm

Contact: ekaterina.shelest@hki.jena.de

Supplementary information: Supplementary Data are available at Bioinformatics online.

Journal Article.  1610 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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