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Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites.
Bioinformatics. 2010 Jul 27;
Authors: Ramsey SA, Knijnenburg TA, Kennedy KA, Zak DE, Gilchrist M, Gold ES, Johnson CD, Lampano A, Litvak V, Navarro G, Stolyar T, Aderem A, Shmulevich I
MOTIVATION: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor 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 transcription factors 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 data set, we used ChIP-Seq-derived transcription factor (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 motifbased performance for 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 at magnet.systemsbiology.net/hac. CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary methods, figures, and tables are available in the online Supplementary Material document.
PMID: 20663846 [PubMed - as supplied by publisher]
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Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites.
Bioinformatics. 2010 Jul 27;
Authors: Ramsey SA, Knijnenburg TA, Kennedy KA, Zak DE, Gilchrist M, Gold ES, Johnson CD, Lampano A, Litvak V, Navarro G, Stolyar T, Aderem A, Shmulevich I
MOTIVATION: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor 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 transcription factors 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 data set, we used ChIP-Seq-derived transcription factor (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 motifbased performance for 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 at magnet.systemsbiology.net/hac. CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary methods, figures, and tables are available in the online Supplementary Material document.
PMID: 20663846 [PubMed - as supplied by publisher]
More...