Probabilistic approaches to transcription factor binding site prediction.
Many different computer programs for the prediction of transcription factor binding sites have been developed over the last decades. These programs differ from each other by pursuing different objectives and by taking into account different sources of information. For methods based on statistical approaches, these programs differ at an elementary level from each other by the statistical models used for individual binding sites and flanking sequences and by the learning principles employed for estimating the model parameters. According to our experience, both the models and the learning principles should be chosen with great care, depending on the specific task at hand, but many existing programs do not allow the user to choose them freely. Hence, we developed Jstacs, an object-oriented Java framework for sequence analysis, which allows the user to combine different statistical models and different learning principles in a modular manner with little effort. In this chapter we explain how Jstacs can be used for the recognition of transcription factor binding sites.
Posch
Stefan
S
Institute of Computer Science, Martin Luther University, Halle-Wittenberg, Germany.
Grau
Jan
J
Gohr
André
A
Keilwagen
Jens
J
Grosse
Ivo
I
D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D011401:Promoter Regions, Genetic / Q000235:genetics; D011987:Receptors, Steroid / Q000378:metabolism; D015203:Reproducibility of Results; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
D011987:Receptors, Steroid; D014157:Transcription Factors