title: Decoding ChIP-seq with a double-binding signal refines binding peaks to single-nucleotides and predicts cooperative interaction.
abstract: The comprehension of protein and DNA binding in vivo is essential to understand gene regulation. Chromatin immunoprecipitation followed by sequencing (ChIP-seq) provides a global map of the regulatory binding network. Most ChIP-seq analysis tools focus on identifying binding regions from coverage enrichment. However, less work has been performed to infer the physical and regulatory details inside the enriched regions. This research extends a previous blind-deconvolution approach to develop a post-peak-calling algorithm that improves binding site resolution and predicts cooperative interactions. At the core of our new method is a physically motivated model that characterizes the binding signal as an extreme value distribution. This model suggests a mathematical framework to study physical properties of DNA shearing from the ChIP-seq coverage. The model explains the ChIP-seq coverage with two signals: The first considers DNA fragments with only a single binding event, whereas the second considers fragments with two binding events (a double-binding signal). The model incorporates motif discovery and is able to detect multiple sites in an enriched region with single-nucleotide resolution, high sensitivity, and high specificity. Our method improves peak caller sensitivity, from less than 45% up to 94%, at a false positive rate < 11% for a set of 47 experimentally validated prokaryotic sites. It also improves resolution of highly enriched regions of large-scale eukaryotic data sets. The double-binding signal provides a novel application in ChIP-seq analysis: the identification of cooperative interaction. Predictions of known cooperative binding sites show a 0.85 area under an ROC curve.
authors.affiliation: Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA;
authors.forename: Antonio L C
authors.initials: AL
authors.lastname: Gomes
authors.affiliation: Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; VIB Department of Plant Systems Biology, Ghent University, 9052 Ghent, Belgium.
authors.forename: Thomas
authors.initials: T
authors.lastname: Abeel
authors.affiliation: Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA;
authors.forename: Matthew
authors.initials: M
authors.lastname: Peterson
authors.affiliation: Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA;
authors.forename: Elham
authors.initials: E
authors.lastname: Azizi
authors.affiliation: Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA;
authors.forename: Anna
authors.initials: A
authors.lastname: Lyubetskaya
authors.affiliation: Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA; Department of Mathematics and Statistics, Boston University, Boston, Massachusetts 02215, USA;
authors.forename: Luís
authors.initials: L
authors.lastname: Carvalho
authors.affiliation: Bioinformatics Program, Boston University, Boston, Massachusetts 02215, USA; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA; Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, USA; antluiz@bu.edu jgalag@bu.edu.
authors.forename: James
authors.initials: J
authors.lastname: Galagan
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000378:metabolism; D008957:Models, Genetic; D009711:Nucleotides / Q000378:metabolism; D017422:Sequence Analysis, DNA
chemical_list: D004268:DNA-Binding Proteins; D009711:Nucleotides