Finding regulatory DNA motifs using alignment-free evolutionary conservation information.
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As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at detecting regulatory elements in intergenic sequences are becoming more prevalent. Most comparative methods for transcription factor (TF) binding site discovery make use of global or local alignments of orthologous regulatory regions to assess whether a particular DNA site is conserved across related organisms, and thus more likely to be functional. Since binding sites are usually short, sometimes degenerate, and often independent of orientation, alignment algorithms may not align them correctly. Here, we present a novel, alignment-free approach for using conservation information for TF binding site discovery. We relax the definition of conserved sites: we consider a DNA site within a regulatory region to be conserved in an orthologous sequence if it occurs anywhere in that sequence, irrespective of orientation. We use this definition to derive informative priors over DNA sequence positions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery. Our approach is simple and fast. It requires neither sequence alignments nor the phylogenetic relationships between the orthologous sequences, yet it is more effective on real biological data than methods that do.
Molecular Sequence Data
Promoter Regions, Genetic
Sequence Analysis, DNA
Published Version (Please cite this version)10.1093/nar/gkp1166
Publication InfoGordân, Raluca; Narlikar, Leelavati; & Hartemink, Alexander J (2010). Finding regulatory DNA motifs using alignment-free evolutionary conservation information. Nucleic Acids Res, 38(6). pp. e90. 10.1093/nar/gkp1166. Retrieved from https://hdl.handle.net/10161/15158.
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Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection
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