|Title||Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites. |
|Publication Type||Journal Article |
|Year of Publication||2010 |
|Authors||Ramsey, SA, Knijnenburg, TA, Kennedy, KA, Zak, DE, Gilchrist, M, Gold, ES, Johnson, CD, Lampano, AE, Litvak, V, Navarro, G, Stolyar, T, Aderem, A, Shmulevich, I |
|Date Published||2010 Sep 01 |
|Keywords||Acetylation, Animals, Binding Sites, Chromatin Immunoprecipitation, Genome, Histones, Macrophage Activation, Mice, Models, Biological, Software, Transcription Factors |
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.
|Alternate Journal||Bioinformatics |
|PubMed ID||20663846 |
|PubMed Central ID||PMC2922897 |
|Grant List||P50GM076547 / GM / NIGMS NIH HHS / United States |
HHSN272200700038C / / PHS HHS / United States
R01 GM072855 / GM / NIGMS NIH HHS / United States
R01GM072855 / GM / NIGMS NIH HHS / United States
K25 HL098807-01 / HL / NHLBI NIH HHS / United States
P50 GM076547 / GM / NIGMS NIH HHS / United States
R01 AI025032 / AI / NIAID NIH HHS / United States
HHSN272200700038C / AI / NIAID NIH HHS / United States
K25HL098807 / HL / NHLBI NIH HHS / United States
K25 HL098807 / HL / NHLBI NIH HHS / United States