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MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences

Arnold, Phil ; Erb, Ionas ; Pachkov, Mikhail ; Molina, Nacho ; van Nimwegen, Erik

In: Bioinformatics, 2012, vol. 28, no. 4, p. 487-494

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    Title
    • MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences
    Author
    • Arnold, Phil. Biozentrum, University of Basel, Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland, Bioinformatics and Genomics program, Center for Genomic Regulation (CRG) and Pompeu Fabra University (UPF), Barcelona, Spain and School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    • Erb, Ionas. Biozentrum, University of Basel, Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland, Bioinformatics and Genomics program, Center for Genomic Regulation (CRG) and Pompeu Fabra University (UPF), Barcelona, Spain and School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    • Pachkov, Mikhail. Biozentrum, University of Basel, Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland, Bioinformatics and Genomics program, Center for Genomic Regulation (CRG) and Pompeu Fabra University (UPF), Barcelona, Spain and School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    • Molina, Nacho. Biozentrum, University of Basel, Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland, Bioinformatics and Genomics program, Center for Genomic Regulation (CRG) and Pompeu Fabra University (UPF), Barcelona, Spain and School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    • van Nimwegen, Erik. Biozentrum, University of Basel, Swiss Institute of Bioinformatics, Klingelbergstrasse 50-70, 4056 Basel, Switzerland, Bioinformatics and Genomics program, Center for Genomic Regulation (CRG) and Pompeu Fabra University (UPF), Barcelona, Spain and School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    Document Type
    • Postprint
    OAI-PMH Identifier
    • oai:doc.rero.ch:302621
    Summary
    Motivation: Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis-regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework. Results: We present 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 MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction. Availability: Source code, a user manual and files with several example applications are available at www.swissregulon.unibas.ch. Contact: erik.vannimwegen@unibas.ch Supplementary information: Supplementary data are available at Bioinformatics online