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. 2006 Jul 1;34(Web Server issue):W529-33.
doi: 10.1093/nar/gkl212.

VOMBAT: Prediction of Transcription Factor Binding Sites Using Variable Order Bayesian Trees

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Free PMC article

VOMBAT: Prediction of Transcription Factor Binding Sites Using Variable Order Bayesian Trees

Jan Grau et al. Nucleic Acids Res. .
Free PMC article

Abstract

Variable order Markov models and variable order Bayesian trees have been proposed for the recognition of transcription factor binding sites, and it could be demonstrated that they outperform traditional models, such as position weight matrices, Markov models and Bayesian trees. We develop a web server for the recognition of DNA binding sites based on variable order Markov models and variable order Bayesian trees offering the following functionality: (i) given datasets with annotated binding sites and genomic background sequences, variable order Markov models and variable order Bayesian trees can be trained; (ii) given a set of trained models, putative DNA binding sites can be predicted in a given set of genomic sequences and (iii) given a dataset with annotated binding sites and a dataset with genomic background sequences, cross-validation experiments for different model combinations with different parameter settings can be performed. Several of the offered services are computationally demanding, such as genome-wide predictions of DNA binding sites in mammalian genomes or sets of 10(4)-fold cross-validation experiments for different model combinations based on problem-specific data sets. In order to execute these jobs, and in order to serve multiple users at the same time, the web server is attached to a Linux cluster with 150 processors. VOMBAT is available at http://pdw-24.ipk-gatersleben.de:8080/VOMBAT/.

Figures

Figure 1
Figure 1
The form requesting the parameters for a TFBS prediction.
Figure 2
Figure 2
The results of a TFBS prediction. The list of putative TFBSs are displayed as a link to ‘sites.html,’ and the profiles for the sequences are plotted.
Figure 3
Figure 3
The results of a cross-validation.

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