Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome

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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

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Mammana, A., & Chung, H. R. (2015). Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome. Genome Biology, 16(1). https://doi.org/10.1186/s13059-015-0708-z

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