A nucleosome-guided map of transcription factor binding sites in yeast.
Abstract
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.
Type
Journal articleSubject
Binding SitesDNA, Fungal
Nucleosomes
Oligonucleotide Array Sequence Analysis
Protein Binding
Protein Interaction Mapping
Saccharomyces cerevisiae
Saccharomyces cerevisiae Proteins
Sequence Analysis, DNA
Transcription Factors
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https://hdl.handle.net/10161/15157Published Version (Please cite this version)
10.1371/journal.pcbi.0030215Publication Info
Narlikar, Leelavati; Gordân, Raluca; & Hartemink, Alexander J (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 https://hdl.handle.net/10161/15157.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Alexander J. Hartemink
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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