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Efficient Double Fragmentation ChIP-seq Provides Nucleotide Resolution Protein-DNA Binding Profiles

Overview of attention for article published in PLOS ONE, November 2010
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Title
Efficient Double Fragmentation ChIP-seq Provides Nucleotide Resolution Protein-DNA Binding Profiles
Published in
PLOS ONE, November 2010
DOI 10.1371/journal.pone.0015092
Pubmed ID
Authors

Michal Mokry, Pantelis Hatzis, Ewart de Bruijn, Jan Koster, Rogier Versteeg, Jurian Schuijers, Marc van de Wetering, Victor Guryev, Hans Clevers, Edwin Cuppen

Abstract

Immunoprecipitated crosslinked protein-DNA fragments typically range in size from several hundred to several thousand base pairs, with a significant part of chromatin being much longer than the optimal length for next-generation sequencing (NGS) procedures. Because these larger fragments may be non-random and represent relevant biology that may otherwise be missed, but also because they represent a significant fraction of the immunoprecipitated material, we designed a double-fragmentation ChIP-seq procedure. After conventional crosslinking and immunoprecipitation, chromatin is de-crosslinked and sheared a second time to concentrate fragments in the optimal size range for NGS. Besides the benefits of increased chromatin yields, the procedure also eliminates a laborious size-selection step. We show that the double-fragmentation ChIP-seq approach allows for the generation of biologically relevant genome-wide protein-DNA binding profiles from sub-nanogram amounts of TCF7L2/TCF4, TBP and H3K4me3 immunoprecipitated material. Although optimized for the AB/SOLiD platform, the same approach may be applied to other platforms.

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Mendeley readers

The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Germany 2 2%
Spain 2 2%
Austria 1 <1%
Australia 1 <1%
Italy 1 <1%
Canada 1 <1%
Netherlands 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 103 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 36%
Student > Ph. D. Student 36 31%
Other 6 5%
Professor > Associate Professor 6 5%
Student > Master 6 5%
Other 14 12%
Unknown 8 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 69 58%
Biochemistry, Genetics and Molecular Biology 26 22%
Medicine and Dentistry 5 4%
Computer Science 3 3%
Immunology and Microbiology 2 2%
Other 3 3%
Unknown 10 8%