A nucleosome-guided map of transcription factor binding sites in yeast.

Loading...
Thumbnail Image

Date

2007-11

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

150
views
105
downloads

Citation Stats

Attention Stats

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.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1371/journal.pcbi.0030215

Publication Info

Narlikar, Leelavati, Raluca Gordân and Alexander J Hartemink (2007). A nucleosome-guided map of transcription factor binding sites in yeast. PLoS Comput Biol, 3(11). p. 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.

Scholars@Duke

Hartemink

Alexander J. Hartemink

Professor 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


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.