refs.bib

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@article{Bouckaert2014,
abstract = {We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format.},
author = {Bouckaert, Remco and Heled, Joseph and K{\"{u}}hnert, Denise and Vaughan, Tim and Wu, Chieh-Hsi and Xie, Dong and Suchard, Marc A and Rambaut, Andrew and Drummond, Alexei J},
doi = {10.1371/journal.pcbi.1003537},
file = {:Users/nicmuell/Library/Application Support/Mendeley Desktop/Downloaded/Bouckaert et al. - 2014 - BEAST 2 a software platform for Bayesian evolutionary analysis.pdf:pdf},
issn = {1553-7358},
journal = {PLoS computational biology},
mendeley-groups = {Methods,SkylineTutorial},
month = {apr},
number = {4},
pages = {e1003537},
pmid = {24722319},
publisher = {Public Library of Science},
title = {BEAST 2: a software platform for Bayesian evolutionary analysis.},
url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003537},
volume = {10},
year = {2014}
}

@article{Kuhnert2016,
author = {Kühnert, Denise and Stadler, Tanja and Vaughan, Timothy G. and Drummond, Alexei J.}, 
title = {Phylodynamics with Migration: A Computational Framework to Quantify Population Structure from Genomic Data},
year = {2016}, 
doi = {10.1093/molbev/msw064}, 
abstract ={When viruses spread, outbreaks can be spawned in previously unaffected regions. Depending on the time and mode of introduction, each regional outbreak can have its own epidemic dynamics. The migration and phylodynamic processes are often intertwined and need to be taken into account when analyzing temporally and spatially structured virus data. In this article, we present a fully probabilistic approach for the joint reconstruction of phylodynamic history in structured populations (such as geographic structure) based on a multitype birth–death process. This approach can be used to quantify the spread of a pathogen in a structured population. Changes in epidemic dynamics through time within subpopulations are incorporated through piecewise constant changes in transmission parameters.We analyze a global human influenza H3N2 virus data set from a geographically structured host population to demonstrate how seasonal dynamics can be inferred simultaneously with the phylogeny and migration process. Our results suggest that the main migration path among the northern, tropical, and southern region represented in the sample analyzed here is the one leading from the tropics to the northern region. Furthermore, the time-dependent transmission dynamics between and within two HIV risk groups, heterosexuals and injecting drug users, in the Latvian HIV epidemic are investigated. Our analyses confirm that the Latvian HIV epidemic peaking around 2001 was mainly driven by the injecting drug user risk group.}, 
URL = {http://mbe.oxfordjournals.org/content/early/2016/05/20/molbev.msw064.abstract}, 
eprint = {http://mbe.oxfordjournals.org/content/early/2016/05/20/molbev.msw064.full.pdf+html}, 
journal = {Molecular Biology and Evolution} 
}

@article{Vaughan2014,
author = {Vaughan, Timothy G. and Kühnert, Denise and Popinga, Alex and Welch, David and Drummond, Alexei J.}, 
title = {Efficient Bayesian inference under the structured coalescent},
volume = {30}, 
number = {16}, 
pages = {2272-2279}, 
year = {2014}, 
doi = {10.1093/bioinformatics/btu201}, 
abstract ={Motivation: Population structure significantly affects evolutionary dynamics. Such structure may be due to spatial segregation, but may also reflect any other gene-flow-limiting aspect of a model. In combination with the structured coalescent, this fact can be used to inform phylogenetic tree reconstruction, as well as to infer parameters such as migration rates and subpopulation sizes from annotated sequence data. However, conducting Bayesian inference under the structured coalescent is impeded by the difficulty of constructing Markov Chain Monte Carlo (MCMC) sampling algorithms (samplers) capable of efficiently exploring the state space.Results: In this article, we present a new MCMC sampler capable of sampling from posterior distributions over structured trees: timed phylogenetic trees in which lineages are associated with the distinct subpopulation in which they lie. The sampler includes a set of MCMC proposal functions that offer significant mixing improvements over a previously published method. Furthermore, its implementation as a BEAST 2 package ensures maximum flexibility with respect to model and prior specification. We demonstrate the usefulness of this new sampler by using it to infer migration rates and effective population sizes of H3N2 influenza between New Zealand, New York and Hong Kong from publicly available hemagglutinin (HA) gene sequences under the structured coalescent.Availability and implementation: The sampler has been implemented as a publicly available BEAST 2 package that is distributed under version 3 of the GNU General Public License at http://compevol.github.io/MultiTypeTree.Contact: tgvaughan@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.}, 
URL = {http://bioinformatics.oxfordjournals.org/content/30/16/2272.abstract}, 
eprint = {http://bioinformatics.oxfordjournals.org/content/30/16/2272.full.pdf+html}, 
journal = {Bioinformatics} 
}

@BOOK{BEAST2book2014,
  title = {Bayesian evolutionary analysis with {BEAST} 2},
  publisher = {Cambridge University Press},
  year = {2014},
  author = {Alexei J. Drummond and Remco R. Bouckaert}
}