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Model-based Analysis of ChIP-Seq (MACS)

Overview of attention for article published in Genome Biology (Online Edition), January 2008
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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 (92nd percentile)

Mentioned by

1 tweeter
9 patents
4 Wikipedia pages
1 Q&A thread


6042 Dimensions

Readers on

3844 Mendeley
69 CiteULike
11 Connotea
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Model-based Analysis of ChIP-Seq (MACS)
Published in
Genome Biology (Online Edition), January 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 Nussbaum, 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 3,844 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 110 3%
United Kingdom 37 <1%
Germany 25 <1%
France 13 <1%
Italy 12 <1%
Spain 10 <1%
China 10 <1%
Netherlands 9 <1%
Brazil 8 <1%
Other 65 2%
Unknown 3545 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1255 33%
Researcher 902 23%
Student > Master 399 10%
Student > Bachelor 304 8%
Student > Doctoral Student 190 5%
Other 533 14%
Unknown 261 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 1857 48%
Biochemistry, Genetics and Molecular Biology 1039 27%
Medicine and Dentistry 178 5%
Computer Science 167 4%
Neuroscience 63 2%
Other 216 6%
Unknown 324 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 12 April 2019.
All research outputs
of 13,600,958 outputs
Outputs from Genome Biology (Online Edition)
of 3,032 outputs
Outputs of similar age
of 90,877 outputs
Outputs of similar age from Genome Biology (Online Edition)
of 2 outputs
Altmetric has tracked 13,600,958 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 3,032 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.1. This one has gotten more attention than average, scoring higher than 63% 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 90,877 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 2 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