English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons73761

Mammana,  Alessandro
Computational Epigenetics (Ho-Ryun Chung), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

/persons/resource/persons50124

Chung,  Ho-Ryun
Computational Epigenetics (Ho-Ryun Chung), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

Fulltext (public)

Mammana.pdf
(Publisher version), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

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: 16:151. doi:10.1186/s13059-015-0708-z.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002A-5FC6-F
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.