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Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies

Overview of attention for article published in Genome Biology, April 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)

Mentioned by

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11 X users
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1 research highlight platform

Citations

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34 Dimensions

Readers on

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56 Mendeley
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6 CiteULike
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Title
Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies
Published in
Genome Biology, April 2014
DOI 10.1186/gb-2014-15-4-r61
Pubmed ID
Authors

Jong Wha J Joo, Jae Hoon Sul, Buhm Han, Chun Ye, Eleazar Eskin

Abstract

Expression quantitative trait loci (eQTL) mapping is a tool that can systematically identify genetic variation affecting gene expression. eQTL mapping studies have shown that certain genomic locations, referred to as regulatory hotspots, may affect the expression levels of many genes. Recently, studies have shown that various confounding factors may induce spurious regulatory hotspots. Here, we introduce a novel statistical method that effectively eliminates spurious hotspots while retaining genuine hotspots. Applied to simulated and real datasets, we validate that our method achieves greater sensitivity while retaining low false discovery rates compared to previous methods.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 9%
Unknown 51 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 38%
Researcher 18 32%
Professor 5 9%
Professor > Associate Professor 3 5%
Student > Master 2 4%
Other 4 7%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 28 50%
Biochemistry, Genetics and Molecular Biology 10 18%
Computer Science 5 9%
Mathematics 4 7%
Environmental Science 2 4%
Other 2 4%
Unknown 5 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 29 December 2014.
All research outputs
#5,210,739
of 25,373,627 outputs
Outputs from Genome Biology
#2,846
of 4,467 outputs
Outputs of similar age
#48,588
of 241,187 outputs
Outputs of similar age from Genome Biology
#39
of 46 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 36th percentile – i.e., 36% 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 241,187 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.