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Model-based Analysis of ChIP-Seq (MACS)

Overview of attention for article published in Genome Biology (Online Edition), 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 (97th percentile)

Mentioned by

2 news outlets
1 blog
1 tweeter
37 patents
6 Wikipedia pages
1 Q&A thread


10166 Dimensions

Readers on

5200 Mendeley
69 CiteULike
11 Connotea
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Model-based Analysis of ChIP-Seq (MACS)
Published in
Genome Biology (Online Edition), 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.

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 5,200 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 25 <1%
France 13 <1%
Italy 12 <1%
Spain 9 <1%
China 9 <1%
Netherlands 8 <1%
Brazil 8 <1%
Other 61 1%
Unknown 4912 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1589 31%
Researcher 1072 21%
Student > Master 540 10%
Student > Bachelor 421 8%
Student > Doctoral Student 266 5%
Other 675 13%
Unknown 637 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 1989 38%
Biochemistry, Genetics and Molecular Biology 1591 31%
Medicine and Dentistry 222 4%
Computer Science 193 4%
Neuroscience 106 2%
Other 361 7%
Unknown 738 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 43. 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 19 July 2022.
All research outputs
of 21,722,534 outputs
Outputs from Genome Biology (Online Edition)
of 3,999 outputs
Outputs of similar age
of 110,662 outputs
Outputs of similar age from Genome Biology (Online Edition)
of 1 outputs
Altmetric has tracked 21,722,534 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,999 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 84% 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 110,662 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 97% of its contemporaries.
We're also able to compare this research output to 1 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