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Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks

Overview of attention for article published in BMC Bioinformatics, December 2008
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1 tweeter

Citations

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172 Dimensions

Readers on

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232 Mendeley
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23 CiteULike
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3 Connotea
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Title
Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks
Published in
BMC Bioinformatics, December 2008
DOI 10.1186/1471-2105-9-523
Pubmed ID
Authors

David A Nix, Samir J Courdy, Kenneth M Boucher

Abstract

High throughput signature sequencing holds many promises, one of which is the ready identification of in vivo transcription factor binding sites, histone modifications, changes in chromatin structure and patterns of DNA methylation across entire genomes. In these experiments, chromatin immunoprecipitation is used to enrich for particular DNA sequences of interest and signature sequencing is used to map the regions to the genome (ChIP-Seq). Elucidation of these sites of DNA-protein binding/modification are proving instrumental in reconstructing networks of gene regulation and chromatin remodelling that direct development, response to cellular perturbation, and neoplastic transformation.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 232 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 6%
Germany 4 2%
United Kingdom 4 2%
China 2 <1%
Australia 2 <1%
Italy 2 <1%
Belgium 1 <1%
India 1 <1%
Sweden 1 <1%
Other 8 3%
Unknown 192 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 70 30%
Student > Ph. D. Student 67 29%
Student > Master 26 11%
Professor > Associate Professor 17 7%
Professor 14 6%
Other 31 13%
Unknown 7 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 154 66%
Biochemistry, Genetics and Molecular Biology 32 14%
Computer Science 15 6%
Engineering 5 2%
Medicine and Dentistry 5 2%
Other 10 4%
Unknown 11 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 January 2013.
All research outputs
#11,231,298
of 17,351,915 outputs
Outputs from BMC Bioinformatics
#4,373
of 6,150 outputs
Outputs of similar age
#67,708
of 101,391 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 17,351,915 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,150 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
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