title: Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome.
abstract: Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg.
authors.affiliation: Otto-Warburg-Laboratory, Epigenomics, Max Planck Institute for Molecular Genetics, D-14195, Berlin, Germany. mammana@molgen.mpg.de.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Mammana
authors.affiliation: Otto-Warburg-Laboratory, Epigenomics, Max Planck Institute for Molecular Genetics, D-14195, Berlin, Germany. chung@molgen.mpg.de.
authors.forename: Ho-Ryun
authors.initials: HR
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D002843:Chromatin / Q000378:metabolism*; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods*; D042421:Histone Code; D006801:Humans; D020014:K562 Cells; D015233:Models, Statistical
chemical_list: D002843:Chromatin