| 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/). |
X Demographics
The data shown below were collected from the profiles of 47 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 17% |
| Germany | 4 | 9% |
| United Kingdom | 3 | 6% |
| Australia | 3 | 6% |
| Switzerland | 3 | 6% |
| France | 2 | 4% |
| New Zealand | 1 | 2% |
| Ireland | 1 | 2% |
| Chile | 1 | 2% |
| Other | 4 | 9% |
| Unknown | 17 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 36 | 77% |
| Members of the public | 11 | 23% |
Mendeley readers
The data shown below were compiled from readership statistics for 252 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 252 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 64 | 25% |
| Researcher | 35 | 14% |
| Student > Master | 29 | 12% |
| Student > Bachelor | 26 | 10% |
| Student > Doctoral Student | 13 | 5% |
| Other | 37 | 15% |
| Unknown | 48 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 107 | 42% |
| Biochemistry, Genetics and Molecular Biology | 53 | 21% |
| Environmental Science | 17 | 7% |
| Computer Science | 5 | 2% |
| Mathematics | 3 | 1% |
| Other | 12 | 5% |
| Unknown | 55 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 27. 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 October 2020.
All research outputs
#1,438,788
of 25,658,139 outputs
Outputs from Molecular Biology and Evolution
#671
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Outputs of similar age
#29,507
of 341,674 outputs
Outputs of similar age from Molecular Biology and Evolution
#9
of 57 outputs
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