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Bioinformatics. 2010 Sep 1;26(17):2071-5. doi: 10.1093/bioinformatics/btq405. Epub 2010 Jul 27.

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

Author information

  • 1Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103, USA. aderem@systemsbiology.org

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 approximately 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.

PMID:
20663846
[PubMed - indexed for MEDLINE]
PMCID:
PMC2922897
Free PMC Article

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