Recognition of cis-regulatory elements with vombat.
Variable order Markov models and variable order Bayesian trees have been proposed for the recognition of cis-regulatory elements, and it has been demonstrated that they outperform traditional models such as position weight matrices, Markov models, and Bayesian trees for the recognition of binding sites in prokaryotes. Here, we study to which degree variable order models can improve the recognition of eukaryotic cis-regulatory elements. We find that variable order models can improve the recognition of binding sites of all the studied transcription factors. To ease a systematic evaluation of different model combinations based on problem-specific data sets and allow genomic scans of cis-regulatory elements based on fixed and variable order Markov models and Bayesian trees, we provide the VOMBATserver to the public community.
Posch
Stefan
S
Institute of Computer Science, University Halle, 06099 Halle (Saale), Germany.
Grau
Jan
J
Gohr
Andre
A
Ben-Gal
Irad
I
Kel
Alexander E
AE
Grosse
Ivo
I
D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D050436:Regulatory Elements, Transcriptional / Q000235:genetics*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software; D014157:Transcription Factors / Q000235:genetics*
D014157:Transcription Factors