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Inferring positive selection in humans from genomic data

Overview of attention for article published in Investigative Genetics, January 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)

Mentioned by

7 tweeters
3 Facebook pages


21 Dimensions

Readers on

115 Mendeley
1 CiteULike
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Inferring positive selection in humans from genomic data
Published in
Investigative Genetics, January 2015
DOI 10.1186/s13323-015-0023-1
Pubmed ID

Andreas Wollstein, Wolfgang Stephan


Adaptation can be described as an evolutionary process that leads to an adjustment of the phenotypes of a population to their environment. In the classical view, new mutations can introduce novel phenotypic features into a population that leave footprints in the genome after fixation, such as selective sweeps. Alternatively, existing genetic variants may become beneficial after an environmental change and increase in frequency. Although they may not reach fixation, they may cause a shift of the optimum of a phenotypic trait controlled by multiple loci. With the availability of polymorphism data from various organisms, including humans and chimpanzees, it has become possible to detect molecular evidence of adaptation and to estimate the strength and target of positive selection. In this review, we discuss the two competing models of adaptation and suitable approaches for detecting the footprints of positive selection on the molecular level.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters 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 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 2 2%
Mexico 1 <1%
United States 1 <1%
Austria 1 <1%
Unknown 110 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 33%
Researcher 18 16%
Student > Bachelor 15 13%
Student > Master 13 11%
Professor 5 4%
Other 19 17%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 67 58%
Biochemistry, Genetics and Molecular Biology 23 20%
Mathematics 5 4%
Social Sciences 2 2%
Unspecified 2 2%
Other 8 7%
Unknown 8 7%

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 24 January 2016.
All research outputs
of 21,192,559 outputs
Outputs from Investigative Genetics
of 97 outputs
Outputs of similar age
of 241,267 outputs
Outputs of similar age from Investigative Genetics
of 1 outputs
Altmetric has tracked 21,192,559 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 97 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.0. This one is in the 32nd percentile – i.e., 32% 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 241,267 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 70% 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