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ChIP-seq: Using high-throughput sequencing to discover protein–DNA interactions

Overview of attention for article published in Methods, July 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#38 of 2,340)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

blogs
2 blogs
twitter
1 tweeter
patent
10 patents

Citations

dimensions_citation
397 Dimensions

Readers on

mendeley
775 Mendeley
citeulike
16 CiteULike
Title
ChIP-seq: Using high-throughput sequencing to discover protein–DNA interactions
Published in
Methods, July 2009
DOI 10.1016/j.ymeth.2009.03.001
Pubmed ID
Authors

Dominic Schmidt, Michael D. Wilson, Christiana Spyrou, Gordon D. Brown, James Hadfield, Duncan T. Odom

Abstract

Chromatin immunoprecipitation (ChIP) allows specific protein-DNA interactions to be isolated. Combining ChIP with high-throughput sequencing reveals the DNA sequence involved in these interactions. Here, we describe how to perform ChIP-seq starting with whole tissues or cell lines, and ending with millions of short sequencing tags that can be aligned to the reference genome of the species under investigation. We also outline additional procedures to recover ChIP-chip libraries for ChIP-seq and discuss contemporary issues in data analysis.

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 775 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 16 2%
United Kingdom 8 1%
Germany 7 <1%
Italy 4 <1%
Sweden 4 <1%
Finland 2 <1%
Spain 2 <1%
Netherlands 1 <1%
France 1 <1%
Other 9 1%
Unknown 721 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 217 28%
Researcher 184 24%
Student > Master 103 13%
Student > Bachelor 77 10%
Student > Postgraduate 30 4%
Other 102 13%
Unknown 62 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 391 50%
Biochemistry, Genetics and Molecular Biology 206 27%
Medicine and Dentistry 40 5%
Computer Science 15 2%
Engineering 7 <1%
Other 40 5%
Unknown 76 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 13 April 2021.
All research outputs
#1,466,862
of 20,775,181 outputs
Outputs from Methods
#38
of 2,340 outputs
Outputs of similar age
#12,351
of 173,337 outputs
Outputs of similar age from Methods
#1
of 25 outputs
Altmetric has tracked 20,775,181 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,340 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 98% 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 173,337 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 92% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.