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diffReps: Detecting Differential Chromatin Modification Sites from ChIP-seq Data with Biological Replicates

Overview of attention for article published in PLOS ONE, June 2013
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Title
diffReps: Detecting Differential Chromatin Modification Sites from ChIP-seq Data with Biological Replicates
Published in
PLOS ONE, June 2013
DOI 10.1371/journal.pone.0065598
Pubmed ID
Authors

Li Shen, Ning-Yi Shao, Xiaochuan Liu, Ian Maze, Jian Feng, Eric J. Nestler

Abstract

ChIP-seq is increasingly being used for genome-wide profiling of histone modification marks. It is of particular importance to compare ChIP-seq data of two different conditions, such as disease vs. control, and identify regions that show differences in ChIP enrichment. We have developed a powerful and easy to use program, called diffReps, to detect those differential sites from ChIP-seq data, with or without biological replicates. In addition, we have developed two useful tools for ChIP-seq analysis in the diffReps package: one for the annotation of the differential sites and the other for finding chromatin modification "hotspots". diffReps is developed in PERL programming language and runs on all platforms as a command line script. We tested diffReps on two different datasets. One is the comparison of H3K4me3 between two human cell lines from the ENCODE project. The other is the comparison of H3K9me3 in a discrete region of mouse brain between cocaine- and saline-treated conditions. The results indicated that diffReps is a highly sensitive program in detecting differential sites from ChIP-seq data.

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Mendeley readers

The data shown below were compiled from readership statistics for 290 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 11 4%
France 3 1%
United Kingdom 3 1%
Germany 2 <1%
Italy 1 <1%
Sweden 1 <1%
Mexico 1 <1%
Australia 1 <1%
China 1 <1%
Other 3 1%
Unknown 263 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 89 31%
Researcher 69 24%
Student > Bachelor 24 8%
Student > Master 19 7%
Professor > Associate Professor 15 5%
Other 44 15%
Unknown 30 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 112 39%
Biochemistry, Genetics and Molecular Biology 67 23%
Neuroscience 21 7%
Computer Science 17 6%
Medicine and Dentistry 10 3%
Other 24 8%
Unknown 39 13%