Transcription factors are important gene regulators with distinctive roles in development, cell signaling and cell cycling, and they have been associated with many diseases. The ConTra v2 web server allows easy visualization and exploration of predicted transcription factor binding sites in any genomic region surrounding coding or non-coding genes. In this new version, users can choose from nine reference organisms ranging from human to yeast. ConTra v2 can analyze promoter regions, 5′-UTRs, 3′-UTRs and introns or any other genomic region of interest. Hundreds of position weight matrices are available to choose from, but the user can also upload any other matrices for detecting specific binding sites. A typical analysis is run in four simple steps of choosing the gene, the transcript, the region of interest and then selecting one or more transcription factor binding sites. The ConTra v2 web server is freely available at http://bioit.dmbr.ugent.be/contrav2/index.php.

Transcription factor binding sites (TFBSs) are DNA sequences of 6–15 base pairs. Interaction of these TFBSs with transcription factors (TFs) is largely responsible for most spatiotemporal gene expression patterns. Here, we evaluate to what extent sequence-based prediction of TFBSs can be improved by taking into account the positional dependencies of nucleotides (NPDs) and the nucleotide sequence-dependent structure of DNA. We make use of the random forest algorithm to flexibly exploit both types of information. Results in this study show that both the structural method and the NPD method can be valuable for the prediction of TFBSs. Moreover, their predictive values seem to be complementary, even to the widely used position weight matrix (PWM) method. This led us to combine all three methods. Results obtained for five eukaryotic TFs with different DNA-binding domains show that our method improves classification accuracy for all five eukaryotic TFs compared with other approaches. Additionally, we contrast the results of seven smaller prokaryotic sets with high-quality data and show that with the use of high-quality data we can significantly improve prediction performance. Models developed in this study can be of great use for gaining insight into the mechanisms of TF binding.

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The most important mechanism in the regulation of transcription is the binding of a transcription factor (TF) to a DNA sequence called the TF binding site (TFBS). Most binding sites are short and degenerate, which makes predictions based on their primary sequence alone somewhat unreliable. We present a new web tool that implements a flexible and extensible algorithm for predicting TFBS. The algorithm makes use of both direct (the sequence) and several indirect readout features of protein–DNA complexes (biophysical properties such as bendability or the solvent-excluded surface of the DNA). This algorithm significantly outperforms state-of-the-art approaches for in silico identification of TFBS. Users can submit FASTA sequences for analysis in the PhysBinder integrative algorithm and choose from >60 different TF-binding models. The results of this analysis can be used to plan and steer wet-lab experiments. The PhysBinder web tool is freely available at http://bioit.dmbr.ugent.be/physbinder/index.php.