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A Nucleosome-Guided Map of Transcription Factor Binding Sites in Yeast

Overview of attention for article published in PLoS Computational Biology, November 2007
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Title
A Nucleosome-Guided Map of Transcription Factor Binding Sites in Yeast
Published in
PLoS Computational Biology, November 2007
DOI 10.1371/journal.pcbi.0030215
Pubmed ID
Authors

Leelavati Narlikar, Raluca Gordân, Alexander J Hartemink

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.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 6%
France 1 1%
Australia 1 1%
United Kingdom 1 1%
Canada 1 1%
Hong Kong 1 1%
China 1 1%
Belgium 1 1%
Spain 1 1%
Other 1 1%
Unknown 78 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 41%
Researcher 32 34%
Professor > Associate Professor 6 6%
Student > Master 4 4%
Professor 3 3%
Other 7 8%
Unknown 3 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 57 61%
Biochemistry, Genetics and Molecular Biology 12 13%
Computer Science 9 10%
Engineering 4 4%
Physics and Astronomy 3 3%
Other 3 3%
Unknown 5 5%