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Sequence-Specific Capture of Protein-DNA Complexes for Mass Spectrometric Protein Identification

Overview of attention for article published in PLOS ONE, October 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

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12 patents

Citations

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30 Dimensions

Readers on

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195 Mendeley
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2 CiteULike
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Title
Sequence-Specific Capture of Protein-DNA Complexes for Mass Spectrometric Protein Identification
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0026217
Pubmed ID
Authors

Cheng-Hsien Wu, Siyuan Chen, Michael R. Shortreed, Gloria M. Kreitinger, Yuan Yuan, Brian L. Frey, Yi Zhang, Shama Mirza, Lisa A. Cirillo, Michael Olivier, Lloyd M. Smith

Abstract

The regulation of gene transcription is fundamental to the existence of complex multicellular organisms such as humans. Although it is widely recognized that much of gene regulation is controlled by gene-specific protein-DNA interactions, there presently exists little in the way of tools to identify proteins that interact with the genome at locations of interest. We have developed a novel strategy to address this problem, which we refer to as GENECAPP, for Global ExoNuclease-based Enrichment of Chromatin-Associated Proteins for Proteomics. In this approach, formaldehyde cross-linking is employed to covalently link DNA to its associated proteins; subsequent fragmentation of the DNA, followed by exonuclease digestion, produces a single-stranded region of the DNA that enables sequence-specific hybridization capture of the protein-DNA complex on a solid support. Mass spectrometric (MS) analysis of the captured proteins is then used for their identification and/or quantification. We show here the development and optimization of GENECAPP for an in vitro model system, comprised of the murine insulin-like growth factor-binding protein 1 (IGFBP1) promoter region and FoxO1, a member of the forkhead rhabdomyosarcoma (FoxO) subfamily of transcription factors, which binds specifically to the IGFBP1 promoter. This novel strategy provides a powerful tool for studies of protein-DNA and protein-protein interactions.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 195 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
France 2 1%
Switzerland 1 <1%
Austria 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Belgium 1 <1%
United States 1 <1%
Unknown 185 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 26%
Researcher 40 21%
Student > Bachelor 24 12%
Student > Master 19 10%
Student > Doctoral Student 14 7%
Other 29 15%
Unknown 19 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 90 46%
Biochemistry, Genetics and Molecular Biology 54 28%
Chemistry 12 6%
Medicine and Dentistry 3 2%
Chemical Engineering 2 1%
Other 11 6%
Unknown 23 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 02 April 2024.
All research outputs
#2,336,115
of 23,577,654 outputs
Outputs from PLOS ONE
#29,432
of 202,026 outputs
Outputs of similar age
#12,130
of 141,143 outputs
Outputs of similar age from PLOS ONE
#323
of 2,570 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 202,026 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one has done well, scoring higher than 85% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 141,143 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 2,570 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.