↓ Skip to main content

Species Tree Inference with BPP Using Genomic Sequences and the Multispecies Coalescent

Overview of attention for article published in Molecular Biology and Evolution, July 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

twitter
60 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
45 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Species Tree Inference with BPP Using Genomic Sequences and the Multispecies Coalescent
Published in
Molecular Biology and Evolution, July 2018
DOI 10.1093/molbev/msy147
Pubmed ID
Authors

Tomáš Flouri, Xiyun Jiao, Bruce Rannala, Ziheng Yang

Abstract

The multispecies coalescent (MSC) provides a natural framework for accommodating ancestral genetic polymorphism and coalescent processes that can cause different genomic regions to have different genealogical histories. The Bayesian program bpp includes a full-likelihood implementation of the MSC, using trans-model Markov chain Monte Carlo (MCMC) to calculate the posterior probabilities of different species trees. Bpp is suitable for analyzing multi-locus sequence datasets and it accommodates the heterogeneity of gene trees (both the topology and branch lengths) among loci and gene tree uncertainties due to limited phylogenetic information at each locus. Here we provide a practical guide to the use of bpp in species tree estimation. Bpp is a command-line program that runs on linux, macosx, and windows. This protocol shows how to use both bpp 3.4 (http://abacus.gene.ucl.ac.uk/software/) and bpp 4.0 (https://github.com/bpp/).

Twitter Demographics

The data shown below were collected from the profiles of 60 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 40%
Researcher 6 13%
Student > Master 6 13%
Unspecified 4 9%
Student > Postgraduate 4 9%
Other 7 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 53%
Biochemistry, Genetics and Molecular Biology 10 22%
Unspecified 5 11%
Neuroscience 2 4%
Computer Science 2 4%
Other 2 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 34. 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 06 November 2018.
All research outputs
#424,093
of 12,460,170 outputs
Outputs from Molecular Biology and Evolution
#225
of 3,395 outputs
Outputs of similar age
#18,935
of 269,983 outputs
Outputs of similar age from Molecular Biology and Evolution
#11
of 80 outputs
Altmetric has tracked 12,460,170 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,395 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.8. This one has done particularly well, scoring higher than 93% 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 269,983 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 92% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.