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Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites

Authors :
Elizabeth S. Gold
Alan Aderem
Ilya Shmulevich
Tetyana Stolyar
Daniel E. Zak
Carrie D. Johnson
Vladimir Litvak
Stephen A. Ramsey
Aaron E. Lampano
Theo A. Knijnenburg
Mark Gilchrist
Garnet Navarro
Kathleen A. Kennedy
Source :
Bioinformatics
Publication Year :
2010
Publisher :
Oxford University Press, 2010.

Abstract

Motivation: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation. Results: We tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by ∼50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01. Availability: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac. Contact: aderem@systemsbiology.org; ishmulevich@systemsbiology.org Supplementary information: Supplementary data are available at Bioinformatics online.

Details

Language :
English
ISSN :
13674811 and 13674803
Volume :
26
Issue :
17
Database :
OpenAIRE
Journal :
Bioinformatics
Accession number :
edsair.doi.dedup.....07b8916aed98443b44c4c4ece71cd8b6