master-refs.bib

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@STRING{AHG = {Annals of Human Genetics}}

@STRING{AJHG = {American Journal of Human Genetics}}

@STRING{ARB = {Annual Review of Biochemistry}}

@STRING{ARCB = {Annual Review of Cell Biology}}

@STRING{BI = {Bioinformatics}}

@STRING{BIOGEN = {Biochemical Genetics}}

@STRING{BJLS = {Biological Journal of the Linnean Society}}

@STRING{BMB = {Bulletin of Mathematical Biology}}

@STRING{BMCBI = {BMC Bioinformatics}}

@STRING{CABIOS = {Computer Applications in the Biosciences}}

@STRING{CACM = {Communications of the ACM}}

@STRING{CELL = {Cell}}

@STRING{COCB = {Current Opinion in Cell Biology}}

@STRING{COGD = {Current Opinion in Genetics and Development}}

@STRING{ComputChem = {Computers and Chemistry}}

@STRING{COSB = {Current Opinion in Structural Biology}}

@STRING{CSHSQB = {Cold Spring Harbor Symposia Quantitative Biology}}

@STRING{EMBO = {EMBO Journal}}

@STRING{EVO = {Evolution}}

@STRING{GB = {Genome Biology}}

@STRING{GEN = {Genetics}}

@STRING{GR = {Genome Research}}

@STRING{JBSD = {Journal of Biomolecular Structure and Dynamics}}

@STRING{JCB = {Journal of Computational Biology}}

@STRING{JMB = {Journal of Molecular Biology}}

@STRING{JME = {Journal of Molecular Evolution}}

@STRING{JRSS = {Journal of the Royal Statistical Society, B}}

@STRING{JTB = {Journal of Theoretical Biology}}

@STRING{MBE = {Molecular Biology and Evolution}}

@STRING{MBIO = {Mathematical Biosciences}}

@STRING{MCB = {Molecular Cell Biology}}

@STRING{ME = {Methods in Enzymology}}

@STRING{MPE = {Molecular Phylogenetics and Evolution}}

@STRING{NAR = {Nucleic Acids Research}}

@STRING{Nature = {Nature}}

@STRING{NB = {Nature Biotechnology}}

@STRING{NC = {Neural Computation}}

@STRING{NG = {Nature Genetics}}

@STRING{NNB = {Nature New Biology}}

@STRING{PE = {Protein Engineering}}

@STRING{PHTRANSRB = {Philosophical Transactions of the Royal Society B}}

@STRING{PLOSCOMPBIO = {PLoS Computational Biology}}

@STRING{PNAS = {Proceedings of the National Academy of Sciences, USA}}

@STRING{PROCROYB = {Proceedings of the Royal Society B}}

@STRING{PROT = {Proteins}}

@STRING{PROTSCI = {Protein Science}}

@STRING{Science = {Science}}

@STRING{S = Science}

@STRING{SIAM = {SIAM Journal of Applied Mathematics}}

@STRING{SYSB = {Systematic Biology}}

@STRING{SZ = {Systematic Zoology}}

@STRING{TIBTECH = {Trends in Biotechnology}}

@STRING{TIGS = {Trends in Genetics}}

@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{Ray2000,
author = {Ray, StuartÊC. and Arthur, RayÊR. and Carella, Anthony and Bukh, Jens and Thomas, DavidÊL.},
doi = {10.1086/315786},
file = {:Users/nicmuell/Library/Application Support/Mendeley Desktop/Downloaded/Ray et al. - 2000 - Genetic Epidemiology of Hepatitis C Virus throughout Egypt.pdf:pdf},
issn = {0022-1899},
journal = {The Journal of Infectious Diseases},
mendeley-groups = {Diseases and Healthcare,SkylineTutorial},
month = {sep},
number = {3},
pages = {698--707},
publisher = {Oxford University Press},
title = {Genetic Epidemiology of Hepatitis C Virus throughout Egypt},
url = {http://jid.oxfordjournals.org/lookup/doi/10.1086/315786},
volume = {182},
year = {2000}
}
@article{Pybus2003,
author = {Pybus, O. G. and Drummond, A. J. and Nakano, T. and Robertson, B. H. and Rambaut, A.},
doi = {10.1093/molbev/msg043},
file = {:Users/nicmuell/Library/Application Support/Mendeley Desktop/Downloaded/Pybus et al. - 2003 - The Epidemiology and Iatrogenic Transmission of Hepatitis C Virus in Egypt A Bayesian Coalescent Approach(3).pdf:pdf},
issn = {07374038},
journal = {Molecular Biology and Evolution},
mendeley-groups = {Skyline,Diseases and Healthcare,SkylineTutorial},
month = {mar},
number = {3},
pages = {381--387},
publisher = {Oxford University Press},
title = {The Epidemiology and Iatrogenic Transmission of Hepatitis C Virus in Egypt: A Bayesian Coalescent Approach},
url = {http://mbe.oupjournals.org/cgi/doi/10.1093/molbev/msg043},
volume = {20},
year = {2003}
}
@article{Drummond2005,
abstract = {We introduce the Bayesian skyline plot, a new method for estimating past population dynamics through time from a sample of molecular sequences without dependence on a prespecified parametric model of demographic history. We describe a Markov chain Monte Carlo sampling procedure that efficiently samples a variant of the generalized skyline plot, given sequence data, and combines these plots to generate a posterior distribution of effective population size through time. We apply the Bayesian skyline plot to simulated data sets and show that it correctly reconstructs demographic history under canonical scenarios. Finally, we compare the Bayesian skyline plot model to previous coalescent approaches by analyzing two real data sets (hepatitis C virus in Egypt and mitochondrial DNA of Beringian bison) that have been previously investigated using alternative coalescent methods. In the bison analysis, we detect a severe but previously unrecognized bottleneck, estimated to have occurred 10,000 radiocarbon years ago, which coincides with both the earliest undisputed record of large numbers of humans in Alaska and the megafaunal extinctions in North America at the beginning of the Holocene.},
author = {Drummond, A J and Rambaut, A and Shapiro, B and Pybus, O G},
doi = {10.1093/molbev/msi103},
issn = {0737-4038},
journal = {Molecular biology and evolution},
keywords = {Algorithms,Animals,Bayes Theorem,Bison,Bison: genetics,DNA, Mitochondrial,DNA, Mitochondrial: genetics,Egypt,Egypt: epidemiology,Evolution, Molecular,Genetics, Population,Hepacivirus,Hepacivirus: genetics,Hepacivirus: pathogenicity,Hepatitis C,Hepatitis C: epidemiology,Hepatitis C: transmission,Humans,Markov Chains,Models, Genetic,Monte Carlo Method,Population Density,Population Dynamics,Time Factors},
mendeley-groups = {Skyline,SkylineTutorial},
month = {may},
number = {5},
pages = {1185--92},
pmid = {15703244},
title = {Bayesian coalescent inference of past population dynamics from molecular sequences.},
url = {http://mbe.oxfordjournals.org/content/22/5/1185.abstract},
volume = {22},
year = {2005}
}
@article{Stadler2013,
author = {Stadler, T. and Kuhnert, D. and Bonhoeffer, S. and Drummond, A. J.},
doi = {10.1073/pnas.1207965110},
file = {:Users/nicmuell/Library/Application Support/Mendeley Desktop/Downloaded/Stadler et al. - 2013 - Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV).pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
mendeley-groups = {Skyline,SkylineTutorial},
month = {jan},
number = {1},
pages = {228--233},
publisher = {National Acad Sciences},
title = {Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV)},
url = {http://www.pnas.org/cgi/doi/10.1073/pnas.1207965110},
volume = {110},
year = {2013}
}
@article{Heled2008,
author = {Heled, Joseph and Drummond, Alexei J},
doi = {10.1186/1471-2148-8-289},
issn = {1471-2148},
journal = {BMC Evolutionary Biology},
mendeley-groups = {Skyline,SkylineTutorial},
number = {1},
pages = {289},
publisher = {BioMed Central},
title = {Bayesian inference of population size history from multiple loci},
url = {http://bmcevolbiol.biomedcentral.com/articles/10.1186/1471-2148-8-289},
volume = {8},
year = {2008}
}
@article{Minin2008,
abstract = {Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. Because researchers are rarely able to choose a priori a deterministic model describing effective population size dynamics for data at hand, nonparametric curve-fitting methods based on multiple change-point (MCP) models have been developed. We propose an alternative to change-point modeling that exploits Gaussian Markov random fields to achieve temporal smoothing of the effective population size in a Bayesian framework. The main advantage of our approach is that, in contrast to MCP models, the explicit temporal smoothing does not require strong prior decisions. To approximate the posterior distribution of the population dynamics, we use efficient, fast mixing Markov chain Monte Carlo algorithms designed for highly structured Gaussian models. In a simulation study, we demonstrate that the proposed temporal smoothing method, named Bayesian skyride, successfully recovers "true" population size trajectories in all simulation scenarios and competes well with the MCP approaches without evoking strong prior assumptions. We apply our Bayesian skyride method to 2 real data sets. We analyze sequences of hepatitis C virus contemporaneously sampled in Egypt, reproducing all key known aspects of the viral population dynamics. Next, we estimate the demographic histories of human influenza A hemagglutinin sequences, serially sampled throughout 3 flu seasons.},
author = {Minin, Vladimir N and Bloomquist, Erik W and Suchard, Marc A},
doi = {10.1093/molbev/msn090},
file = {:Users/nicmuell/Library/Application Support/Mendeley Desktop/Downloaded/Minin, Bloomquist, Suchard - 2008 - Smooth skyride through a rough skyline Bayesian coalescent-based inference of population dynamics.pdf:pdf},
issn = {1537-1719},
journal = {Molecular biology and evolution},
mendeley-groups = {Skyline,SkylineTutorial},
month = {jul},
number = {7},
pages = {1459--71},
pmid = {18408232},
title = {Smooth skyride through a rough skyline: Bayesian coalescent-based inference of population dynamics.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18408232 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3302198},
volume = {25},
year = {2008}
}
@BOOK{BEAST2book2014,
  title = {Bayesian evolutionary analysis with {BEAST} 2},
  publisher = {Cambridge University Press},
  year = {2014},
  author = {Alexei J. Drummond and Remco R. Bouckaert}
}
@article{altekar2004parallel,
  title={Parallel metropolis coupled Markov chain Monte Carlo for Bayesian phylogenetic inference},
  author={Altekar, Gautam and Dwarkadas, Sandhya and Huelsenbeck, John P and Ronquist, Fredrik},
  journal={Bioinformatics},
  volume={20},
  number={3},
  pages={407--415},
  year={2004},
  publisher={Oxford University Press}
}
@article{muller2020bayesian,
  title={Bayesian inference of reassortment networks reveals fitness benefits of reassortment in human influenza viruses},
  author={M{\"u}ller, Nicola F and Stolz, Ugn{\.e} and Dudas, Gytis and Stadler, Tanja and Vaughan, Timothy G},
  journal={Proceedings of the National Academy of Sciences},
  year={2020},
  publisher={National Acad Sciences}
}
@Article{vaughan2017icytree,
  author    = {Timothy G. Vaughan},
  title     = {{IcyTree}: {R}apid browser-based visualization for phylogenetic trees and networks},
  journal   = {Bioinformatics},
  year      = {2017},
  month     = {apr},
  doi       = {10.1093/bioinformatics/btx155},
  groups    = {Software},
  owner     = {tvaughan},
  publisher = {Oxford University Press ({OUP})},
  timestamp = {2017.04.13},
}