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ABLE: blockwise site frequency spectra for inferring complex population histories and recombination

Overview of attention for article published in Genome Biology, September 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (69th percentile)

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87 Mendeley
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Title
ABLE: blockwise site frequency spectra for inferring complex population histories and recombination
Published in
Genome Biology, September 2018
DOI 10.1186/s13059-018-1517-y
Pubmed ID
Authors

Champak R. Beeravolu, Michael J. Hickerson, Laurent A. F. Frantz, Konrad Lohse

Abstract

We introduce ABLE (Approximate Blockwise Likelihood Estimation), a novel simulation-based composite likelihood method that uses the blockwise site frequency spectrum to jointly infer past demography and recombination. ABLE is explicitly designed for a wide variety of data from unphased diploid genomes to genome-wide multi-locus data (for example, RADSeq) and can also accommodate arbitrarily large samples. We use simulations to demonstrate the accuracy of this method to infer complex histories of divergence and gene flow and reanalyze whole genome data from two species of orangutan. ABLE is available for download at https://github.com/champost/ABLE .

X Demographics

X Demographics

The data shown below were collected from the profiles of 54 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 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 87 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 29%
Researcher 16 18%
Student > Master 12 14%
Student > Bachelor 7 8%
Other 5 6%
Other 8 9%
Unknown 14 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 38%
Biochemistry, Genetics and Molecular Biology 23 26%
Environmental Science 5 6%
Medicine and Dentistry 2 2%
Veterinary Science and Veterinary Medicine 1 1%
Other 3 3%
Unknown 20 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 05 February 2021.
All research outputs
#1,341,630
of 25,385,509 outputs
Outputs from Genome Biology
#1,050
of 4,468 outputs
Outputs of similar age
#28,408
of 350,858 outputs
Outputs of similar age from Genome Biology
#26
of 85 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,468 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. 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 350,858 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 85 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 69% of its contemporaries.