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Detecting negative selection on recurrent mutations using gene genealogy

Overview of attention for article published in BMC Genomic Data, May 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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7 X users

Citations

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4 Dimensions

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30 Mendeley
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Title
Detecting negative selection on recurrent mutations using gene genealogy
Published in
BMC Genomic Data, May 2013
DOI 10.1186/1471-2156-14-37
Pubmed ID
Authors

Kiyoshi Ezawa, Giddy Landan, Dan Graur

Abstract

Whether or not a mutant allele in a population is under selection is an important issue in population genetics, and various neutrality tests have been invented so far to detect selection. However, detection of negative selection has been notoriously difficult, partly because negatively selected alleles are usually rare in the population and have little impact on either population dynamics or the shape of the gene genealogy. Recently, through studies of genetic disorders and genome-wide analyses, many structural variations were shown to occur recurrently in the population. Such "recurrent mutations" might be revealed as deleterious by exploiting the signal of negative selection in the gene genealogy enhanced by their recurrence.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 17%
Student > Ph. D. Student 4 13%
Student > Postgraduate 4 13%
Student > Bachelor 3 10%
Professor > Associate Professor 2 7%
Other 2 7%
Unknown 10 33%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 37%
Biochemistry, Genetics and Molecular Biology 4 13%
Veterinary Science and Veterinary Medicine 1 3%
Arts and Humanities 1 3%
Physics and Astronomy 1 3%
Other 1 3%
Unknown 11 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 August 2013.
All research outputs
#7,896,932
of 25,374,917 outputs
Outputs from BMC Genomic Data
#280
of 1,204 outputs
Outputs of similar age
#64,018
of 205,282 outputs
Outputs of similar age from BMC Genomic Data
#7
of 25 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 76% 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 205,282 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.