A multiple-feature framework for modelling and predicting transcription factor binding sites.
BACKGROUND
The identification of transcription factor binding sites in promoter sequences is an important problem, since it reveals information about the transcriptional regulation of genes. For analysing transcriptional regulation, computational approaches for predicting putative binding sites are applied. Commonly used stochastic models for binding sites are position-specific score matrices, which show weak predictive power.


RESULTS
We have developed a probabilistic modelling approach, which allows to consider diverse characteristic binding site properties to obtain more accurate representations of binding sites. These properties are modelled as random variables in Bayesian networks, which are capable of dealing with dependencies among binding site properties. Cross-validation on several datasets shows improvements in the false positive error rate and the significance (P-value) of true binding sites.
Pudimat
Rainer
R
Institut für Informatik, Friedrich-Schiller-Universität Ernst-Abbe-Platz 3, D-07743 Jena, Germany.
Schukat-Talamazzini
Ernst-Günter
EG
Backofen
Rolf
R
D000465:Algorithms; D001665:Binding Sites; D003198:Computer Simulation; D008956:Models, Chemical; D008957:Models, Genetic; D015233:Models, Statistical; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D014157:Transcription Factors / Q000032:analysis / Q000737:chemistry* / Q000235:genetics
D014157:Transcription Factors