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HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data

Overview of attention for article published in BMC Bioinformatics, July 2010
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1 X user

Citations

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Readers on

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196 Mendeley
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12 CiteULike
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1 Connotea
Title
HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data
Published in
BMC Bioinformatics, July 2010
DOI 10.1186/1471-2105-11-369
Pubmed ID
Authors

Zhaohui S Qin, Jianjun Yu, Jincheng Shen, Christopher A Maher, Ming Hu, Shanker Kalyana-Sundaram, Jindan Yu, Arul M Chinnaiyan

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

Geographical breakdown

Country Count As %
United States 12 6%
United Kingdom 5 3%
Germany 3 2%
Italy 2 1%
China 2 1%
France 2 1%
Brazil 1 <1%
Australia 1 <1%
Norway 1 <1%
Other 4 2%
Unknown 163 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 65 33%
Student > Ph. D. Student 48 24%
Student > Master 17 9%
Professor > Associate Professor 15 8%
Student > Postgraduate 11 6%
Other 31 16%
Unknown 9 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 120 61%
Biochemistry, Genetics and Molecular Biology 23 12%
Computer Science 14 7%
Engineering 9 5%
Mathematics 7 4%
Other 9 5%
Unknown 14 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 April 2016.
All research outputs
#18,445,779
of 22,854,458 outputs
Outputs from BMC Bioinformatics
#6,323
of 7,292 outputs
Outputs of similar age
#84,416
of 93,978 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 65 outputs
Altmetric has tracked 22,854,458 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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 93,978 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.