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D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements.


ABSTRACT:

Background

The binding of transcription factors to DNA plays an essential role in the regulation of gene expression. Numerous experiments elucidated binding sequences which subsequently have been used to derive statistical models for predicting potential transcription factor binding sites (TFBS). The rapidly increasing number of genome sequence data requires sophisticated computational approaches to manage and query experimental and predicted TFBS data in the context of other epigenetic factors and across different organisms.

Results

We have developed D-Light, a novel client-server software package to store and query large amounts of TFBS data for any number of genomes. Users can add small-scale data to the server database and query them in a large scale, genome-wide promoter context. The client is implemented in Java and provides simple graphical user interfaces and data visualization. Here we also performed a statistical analysis showing what a user can expect for certain parameter settings and we illustrate the usage of D-Light with the help of a microarray data set.

Conclusions

D-Light is an easy to use software tool to integrate, store and query annotation data for promoters. A public D-Light server, the client and server software for local installation and the source code under GNU GPL license are available at http://biwww.che.sbg.ac.at/dlight.

SUBMITTER: Laimer J 

PROVIDER: S-EPMC3685601 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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D-Light on promoters: a client-server system for the analysis and visualization of cis-regulatory elements.

Laimer Josef J   Zuzan Clemens J CJ   Ehrenberger Tobias T   Freudenberger Monika M   Gschwandtner Simone S   Lebherz Carina C   Lackner Peter P  

BMC bioinformatics 20130424


<h4>Background</h4>The binding of transcription factors to DNA plays an essential role in the regulation of gene expression. Numerous experiments elucidated binding sequences which subsequently have been used to derive statistical models for predicting potential transcription factor binding sites (TFBS). The rapidly increasing number of genome sequence data requires sophisticated computational approaches to manage and query experimental and predicted TFBS data in the context of other epigenetic  ...[more]

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