We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites.
- Authors
Ramsey, Stephen A.; Knijnenburg, Theo A.; Kennedy, Kathleen A.; Zak, Daniel E.; Gilchrist, Mark; Gold, Elizabeth S.; Johnson, Carrie D.; Lampano, Aaron E.; Litvak, Vladimir; Navarro, Garnet; Stolyar, Tetyana; Aderem, Alan; Shmulevich, Ilya
- 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 ChIPSeq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motifbased 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.
- Subjects
GENOMES; HISTONES; ACETYLATION; TRANSCRIPTION factors; MAMMALS; MACROPHAGES; BINDING sites
- Publication
Bioinformatics, 2010, Vol 26, Issue 17, p2071
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btq405