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Model-based Analysis of ChIP-Seq (MACS)

Overview of attention for article published in Genome Biology, September 2008
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (98th percentile)

Mentioned by

4 news outlets
2 blogs
1 X user
52 patents
7 Wikipedia pages
1 Q&A thread


12752 Dimensions

Readers on

5850 Mendeley
69 CiteULike
11 Connotea
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Model-based Analysis of ChIP-Seq (MACS)
Published in
Genome Biology, September 2008
DOI 10.1186/gb-2008-9-9-r137
Pubmed ID

Yong Zhang, Tao Liu, Clifford A Meyer, Jérôme Eeckhoute, David S Johnson, Bradley E Bernstein, Chad Nusbaum, Richard M Myers, Myles Brown, Wei Li, X Shirley Liu


We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 5,850 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 107 2%
United Kingdom 36 <1%
Germany 24 <1%
France 13 <1%
Italy 12 <1%
Spain 9 <1%
China 9 <1%
Netherlands 8 <1%
Brazil 8 <1%
Other 60 1%
Unknown 5564 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1698 29%
Researcher 1138 19%
Student > Master 579 10%
Student > Bachelor 469 8%
Student > Doctoral Student 301 5%
Other 727 12%
Unknown 938 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 2036 35%
Biochemistry, Genetics and Molecular Biology 1798 31%
Medicine and Dentistry 241 4%
Computer Science 197 3%
Neuroscience 118 2%
Other 408 7%
Unknown 1052 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 62. 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 22 November 2023.
All research outputs
of 24,619,469 outputs
Outputs from Genome Biology
of 4,356 outputs
Outputs of similar age
of 93,724 outputs
Outputs of similar age from Genome Biology
of 4 outputs
Altmetric has tracked 24,619,469 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,356 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.7. This one has done well, scoring higher than 89% 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 93,724 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 98% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them