MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvos novel features significantly improve the accuracy of TFBS prediction, motif inference, and enhancer prediction.
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Phil Arnold, Ionas Erb, Mikhail Pachkov, Nacho Molina and Erik van Nimwegen
MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences
Bioinformatics (2012) 28 (4): 487-494.