Identification of transcription factor binding sites with variable-order Bayesian networks

Identification of transcription factor binding sites with variable-order Bayesian networks

MOTIVATION We propose a new class of variable-order Bayesian network (VOBN) models for the identification of transcription factor binding sites (TFBSs). The proposed models generalize the widely used position weight matrix (PWM) models, Markov models and Bayesian network models. In contrast to these models, where for each position a fixed subset of the remaining positions is used to model depen...

VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees

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 or...

Predicting Transcription Factor Binding Sites Using a Bayesian Allocation Method

PromoterSweep: a tool for identification of transcription factor binding sites

There are many tools available for the prediction of potential promoter regions and the transcription factor binding sites (TFBS) harboured by them. Unfortunately, these tools cannot really avoid the prediction of vast amounts of false positives, the greatest problem in promoter analysis. The combination of different methods and algorithms has shown an improvement in prediction accuracy for sim...

Identification of human transcription factor binding sites by comparative genomics

Understanding transcriptional regulation of gene expression is one of the greatest challenges of modern molecular biology. A central role in this mechanism is played by transcription factors (TF) which typically bind to specific, short DNA sequence motifs which are usually located in the upstream region of the regulated genes. We discuss here a simple and powerful approach for the identificatio...