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AdmixKJump: identifying population structure in recently diverged groups

Overview of attention for article published in Source Code for Biology and Medicine, February 2015
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Title
AdmixKJump: identifying population structure in recently diverged groups
Published in
Source Code for Biology and Medicine, February 2015
DOI 10.1186/s13029-014-0031-1
Pubmed ID
Authors

Timothy D O’Connor

Abstract

Correctly modeling population structure is important for understanding recent evolution and for association studies in humans. While pre-existing knowledge of population history can be used to specify expected levels of subdivision, objective metrics to detect population structure are important and may even be preferable for identifying groups in some situations. One such metric for genomic scale data is implemented in the cross-validation procedure of the program ADMIXTURE, but it has not been evaluated on recently diverged and potentially cryptic levels of population structure. Here, I develop a new method, AdmixKJump, and test both metrics under this scenario.

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

Geographical breakdown

Country Count As %
United States 1 9%
Unknown 10 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 45%
Student > Ph. D. Student 4 36%
Lecturer 1 9%
Student > Master 1 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 45%
Medicine and Dentistry 3 27%
Mathematics 1 9%
Psychology 1 9%
Biochemistry, Genetics and Molecular Biology 1 9%
Other 0 0%

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 24 March 2015.
All research outputs
#4,109,504
of 4,912,613 outputs
Outputs from Source Code for Biology and Medicine
#81
of 97 outputs
Outputs of similar age
#121,827
of 145,839 outputs
Outputs of similar age from Source Code for Biology and Medicine
#5
of 5 outputs
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So far Altmetric has tracked 97 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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