Flouri, T;
Rannala, B;
Yang, Z;
(2020)
A tutorial on the use of BPP for species tree estimation and species delimitation.
In: Scornavacca, C and Delsuc, F and Galtier, N, (eds.)
Phylogenetics in the Genomic Era.
(5.6:1-5.6:16).
Self published
Preview |
Text
2020Flouri-bpptutorial.pdf Download (298kB) | Preview |
Abstract
BPP is a Bayesian Markov chain Monte Carlo program for analyzing multilocus sequence data under the multispecies coalescent (MSC) model with and without introgression. Among the analyses that can be conducted are estimation of population size and species divergence times, species tree estimation, species delimitation and estimation of cross-species introgression intensity. The program can also be used to simulate gene trees and sequence alignments under the MSC model with, or without, migration. In this tutorial, we illustrate the use of BPP for species tree estimation and species delimitation. We also provide practical guidelines on running BPP on multicore systems. As BPP is continuously updated, the most up-to-date version of this tutorial, as well as the data files, are available at http://github.com/bpp/tutorial.
| Type: | Book chapter |
|---|---|
| Title: | A tutorial on the use of BPP for species tree estimation and species delimitation |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://hal.inria.fr/PGE/ |
| Language: | English |
| Additional information: | Licensed under Creative Commons License CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10097365 |
Archive Staff Only
 |
View Item |
