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Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees

Overview of attention for article published in BMC Bioinformatics, September 2015
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Title
Hybrid-Lambda: simulation of multiple merger and Kingman gene genealogies in species networks and species trees
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0721-y
Pubmed ID
Authors

Sha Zhu, James H. Degnan, Sharyn J. Goldstien, Bjarki Eldon

Abstract

There has been increasing interest in coalescent models which admit multiple mergers of ancestral lineages; and to model hybridization and coalescence simultaneously. Hybrid-Lambda is a software package that simulates gene genealogies under multiple merger and Kingman's coalescent processes within species networks or species trees. Hybrid-Lambda allows different coalescent processes to be specified for different populations, and allows for time to be converted between generations and coalescent units, by specifying a population size for each population. In addition, Hybrid-Lambda can generate simulated datasets, assuming the infinitely many sites mutation model, and compute the F ST statistic. As an illustration, we apply Hybrid-Lambda to infer the time of subdivision of certain marine invertebrates under different coalescent processes. Hybrid-Lambda makes it possible to investigate biogeographic concordance among high fecundity species exhibiting skewed offspring distribution.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 3%
United States 1 3%
France 1 3%
Unknown 33 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 33%
Student > Ph. D. Student 6 17%
Professor > Associate Professor 3 8%
Student > Master 3 8%
Other 3 8%
Other 4 11%
Unknown 5 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 42%
Biochemistry, Genetics and Molecular Biology 5 14%
Computer Science 3 8%
Mathematics 3 8%
Environmental Science 2 6%
Other 2 6%
Unknown 6 17%
Attention Score in Context

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 15 September 2015.
All research outputs
#20,291,881
of 22,828,180 outputs
Outputs from BMC Bioinformatics
#6,860
of 7,287 outputs
Outputs of similar age
#225,742
of 268,887 outputs
Outputs of similar age from BMC Bioinformatics
#122
of 128 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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