A nucleosome-guided map of transcription factor binding sites in yeast.
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Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demonstration that nucleosome occupancy information can be used to improve motif discovery. The improvement is dramatic, even though we are using only a statistical model to predict nucleosome occupancy; we expect our results to improve further as high-resolution genome-wide experimental nucleosome occupancy data becomes increasingly available.
Oligonucleotide Array Sequence Analysis
Protein Interaction Mapping
Saccharomyces cerevisiae Proteins
Sequence Analysis, DNA
Published Version (Please cite this version)10.1371/journal.pcbi.0030215
Publication InfoGordân, R; Hartemink, Alexander J; & Narlikar, L (2007). A nucleosome-guided map of transcription factor binding sites in yeast. PLoS Comput Biol, 3(11). pp. e215. 10.1371/journal.pcbi.0030215. Retrieved from http://hdl.handle.net/10161/15157.
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Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, systems biology, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, computational neurobiology, classification, feature selection