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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-5FC6-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-5FC7-D
Genre: Journal Article

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© Mammana and Chung. 2015

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 Creators:
Mammana, Alessandro1, Author              
Chung, Ho-Ryun1, Author              
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1Computational Epigenetics (Ho-Ryun Chung), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479658              

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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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Language(s): eng - English
 Dates: 2015-07-242015
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1186/s13059-015-0708-z
ISSN: 1474-760X (Electronic)1474-7596 (Print)
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Title: Genome Biology
Source Genre: Journal
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Publ. Info: London : BioMed Central Ltd.
Pages: - Volume / Issue: 16 Sequence Number: 16:151 Start / End Page: - Identifier: ISSN: 1465-6906
CoNE: /journals/resource/1000000000224390_1