tutorefs.bib

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@article{Bedford:2014aa,
	Abstract = {Influenza viruses undergo continual antigenic evolution allowing mutant viruses to evade host immunity acquired to previous virus strains. Antigenic phenotype is often assessed through pairwise measurement of cross-reactivity between influenza strains using the hemagglutination inhibition (HI) assay. Here, we extend previous approaches to antigenic cartography, and simultaneously characterize antigenic and genetic evolution by modeling the diffusion of antigenic phenotype over a shared virus phylogeny. Using HI data from influenza lineages A/H3N2, A/H1N1, B/Victoria and B/Yamagata, we determine patterns of antigenic drift across viral lineages, showing that A/H3N2 evolves faster and in a more punctuated fashion than other influenza lineages. We also show that year-to-year antigenic drift appears to drive incidence patterns within each influenza lineage. This work makes possible substantial future advances in investigating the dynamics of influenza and other antigenically-variable pathogens by providing a model that intimately combines molecular and antigenic evolution. DOI: http://dx.doi.org/10.7554/eLife.01914.001.},
	Author = {Bedford, Trevor and Suchard, Marc A and Lemey, Philippe and Dudas, Gytis and Gregory, Victoria and Hay, Alan J and McCauley, John W and Russell, Colin A and Smith, Derek J and Rambaut, Andrew},
	Date-Added = {2015-12-15 13:46:21 +0000},
	Date-Modified = {2015-12-15 13:46:21 +0000},
	Doi = {10.7554/eLife.01914},
	Journal = {Elife},
	Journal-Full = {eLife},
	Keywords = {Bayesian inference; antigenic cartography; evolution; influenza; multidimensional scaling; phylogenetics},
	Mesh = {Antigens, Viral; Evolution, Molecular; Genetic Drift; Genotype; Humans; Influenza A Virus, H1N1 Subtype; Influenza A Virus, H3N2 Subtype; Influenza B virus; Influenza Vaccines; Influenza, Human; Models, Genetic; Phenotype; Phylogeny; Seasons; Time Factors; Virulence},
	Pages = {e01914},
	Pmc = {PMC3909918},
	Pmid = {24497547},
	Pst = {ppublish},
	Title = {Integrating influenza antigenic dynamics with molecular evolution},
	Volume = {3},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.7554/eLife.01914}}

@article{Smith:2004aa,
	Abstract = {The antigenic evolution of influenza A (H3N2) virus was quantified and visualized from its introduction into humans in 1968 to 2003. Although there was remarkable correspondence between antigenic and genetic evolution, significant differences were observed: Antigenic evolution was more punctuated than genetic evolution, and genetic change sometimes had a disproportionately large antigenic effect. The method readily allows monitoring of antigenic differences among vaccine and circulating strains and thus estimation of the effects of vaccination. Further, this approach offers a route to predicting the relative success of emerging strains, which could be achieved by quantifying the combined effects of population level immune escape and viral fitness on strain evolution.},
	Author = {Smith, Derek J and Lapedes, Alan S and de Jong, Jan C and Bestebroer, Theo M and Rimmelzwaan, Guus F and Osterhaus, Albert D M E and Fouchier, Ron A M},
	Date-Added = {2015-12-15 13:46:11 +0000},
	Date-Modified = {2015-12-15 13:46:11 +0000},
	Doi = {10.1126/science.1097211},
	Journal = {Science},
	Journal-Full = {Science (New York, N.Y.)},
	Mesh = {Amino Acid Substitution; Antigenic Variation; Evolution, Molecular; Genes, Viral; Genetic Drift; Genetic Variation; Hemagglutination Inhibition Tests; Hemagglutinins, Viral; Humans; Influenza A virus; Influenza, Human; Molecular Sequence Data; Mutagenesis, Site-Directed; Mutation; Seasons; Virology},
	Month = {Jul},
	Number = {5682},
	Pages = {371-6},
	Pmid = {15218094},
	Pst = {ppublish},
	Title = {Mapping the antigenic and genetic evolution of influenza virus},
	Volume = {305},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.1126/science.1097211}}

@article{Pybus:2012aa,
	Abstract = {We introduce a conceptual bridge between the previously unlinked fields of phylogenetics and mathematical spatial ecology, which enables the spatial parameters of an emerging epidemic to be directly estimated from sampled pathogen genome sequences. By using phylogenetic history to correct for spatial autocorrelation, we illustrate how a fundamental spatial variable, the diffusion coefficient, can be estimated using robust nonparametric statistics, and how heterogeneity in dispersal can be readily quantified. We apply this framework to the spread of the West Nile virus across North America, an important recent instance of spatial invasion by an emerging infectious disease. We demonstrate that the dispersal of West Nile virus is greater and far more variable than previously measured, such that its dissemination was critically determined by rare, long-range movements that are unlikely to be discerned during field observations. Our results indicate that, by ignoring this heterogeneity, previous models of the epidemic have substantially overestimated its basic reproductive number. More generally, our approach demonstrates that easily obtainable genetic data can be used to measure the spatial dynamics of natural populations that are otherwise difficult or costly to quantify.},
	Author = {Pybus, Oliver G and Suchard, Marc A and Lemey, Philippe and Bernardin, Flavien J and Rambaut, Andrew and Crawford, Forrest W and Gray, Rebecca R and Arinaminpathy, Nimalan and Stramer, Susan L and Busch, Michael P and Delwart, Eric L},
	Date-Added = {2015-12-15 12:10:03 +0000},
	Date-Modified = {2015-12-15 12:10:03 +0000},
	Doi = {10.1073/pnas.1206598109},
	Journal = {Proc Natl Acad Sci U S A},
	Journal-Full = {Proceedings of the National Academy of Sciences of the United States of America},
	Mesh = {Base Sequence; Bayes Theorem; Communicable Diseases, Emerging; Demography; Evolution, Molecular; Humans; Models, Biological; Models, Genetic; Molecular Sequence Data; North America; Phylogeny; Phylogeography; Reverse Transcriptase Polymerase Chain Reaction; Sequence Analysis, DNA; West Nile Fever; West Nile virus},
	Month = {Sep},
	Number = {37},
	Pages = {15066-71},
	Pmc = {PMC3443149},
	Pmid = {22927414},
	Pst = {ppublish},
	Title = {Unifying the spatial epidemiology and molecular evolution of emerging epidemics},
	Volume = {109},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1073/pnas.1206598109}}

@article{Lemey:2014aa,
	Abstract = {Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.},
	Author = {Lemey, Philippe and Rambaut, Andrew and Bedford, Trevor and Faria, Nuno and Bielejec, Filip and Baele, Guy and Russell, Colin A and Smith, Derek J and Pybus, Oliver G and Brockmann, Dirk and Suchard, Marc A},
	Date-Added = {2015-12-14 20:33:17 +0000},
	Date-Modified = {2015-12-14 20:33:17 +0000},
	Doi = {10.1371/journal.ppat.1003932},
	Journal = {PLoS Pathog},
	Journal-Full = {PLoS pathogens},
	Mesh = {Biological Evolution; Human Migration; Humans; Influenza A Virus, H3N2 Subtype; Influenza, Human; Models, Theoretical; Phylogeny},
	Month = {Feb},
	Number = {2},
	Pages = {e1003932},
	Pmc = {PMC3930559},
	Pmid = {24586153},
	Pst = {epublish},
	Title = {Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2},
	Volume = {10},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.ppat.1003932}}

@article{Camacho:2009aa,
	Abstract = {BACKGROUND: Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications.
RESULTS: We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site.
CONCLUSION: The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.},
	Author = {Camacho, Christiam and Coulouris, George and Avagyan, Vahram and Ma, Ning and Papadopoulos, Jason and Bealer, Kevin and Madden, Thomas L},
	Date-Added = {2015-12-14 20:32:24 +0000},
	Date-Modified = {2015-12-14 20:32:24 +0000},
	Doi = {10.1186/1471-2105-10-421},
	Journal = {BMC Bioinformatics},
	Journal-Full = {BMC bioinformatics},
	Mesh = {Computational Biology; Databases, Genetic; Sequence Alignment; Software},
	Pages = {421},
	Pmc = {PMC2803857},
	Pmid = {20003500},
	Pst = {epublish},
	Title = {BLAST+: architecture and applications},
	Volume = {10},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1186/1471-2105-10-421}}

@article{kass:1995a,
	Author = {Kass, Robert E and Raftery, Adrian E},
	Date-Added = {2015-12-14 08:51:36 +0000},
	Date-Modified = {2015-12-14 08:51:36 +0000},
	Journal = {journal of the american Statistical Association},
	Month = {June},
	Number = {430},
	Pages = {773-795},
	Title = {Bayes Factors},
	Volume = {90},
	Year = {1995}}

@article{suchard01,
	Author = {M.A. Suchard and R.E. Weiss and J.S. Sinsheimer},
	Date-Added = {2015-12-14 08:48:15 +0000},
	Date-Modified = {2015-12-14 08:48:15 +0000},
	Journal = {Molecular Biology and Evolution},
	Pages = {1001--1013},
	Title = {Bayesian selection of continuous-time {M}arkov chain evolutionary models},
	Volume = {18},
	Year = {2001}}

@article{Bouckaert:2012aa,
	Abstract = {There are two competing hypotheses for the origin of the Indo-European language family. The conventional view places the homeland in the Pontic steppes about 6000 years ago. An alternative hypothesis claims that the languages spread from Anatolia with the expansion of farming 8000 to 9500 years ago. We used Bayesian phylogeographic approaches, together with basic vocabulary data from 103 ancient and contemporary Indo-European languages, to explicitly model the expansion of the family and test these hypotheses. We found decisive support for an Anatolian origin over a steppe origin. Both the inferred timing and root location of the Indo-European language trees fit with an agricultural expansion from Anatolia beginning 8000 to 9500 years ago. These results highlight the critical role that phylogeographic inference can play in resolving debates about human prehistory.},
	Author = {Bouckaert, Remco and Lemey, Philippe and Dunn, Michael and Greenhill, Simon J and Alekseyenko, Alexander V and Drummond, Alexei J and Gray, Russell D and Suchard, Marc A and Atkinson, Quentin D},
	Date-Added = {2015-12-14 08:35:49 +0000},
	Date-Modified = {2015-12-14 08:35:49 +0000},
	Doi = {10.1126/science.1219669},
	Journal = {Science},
	Journal-Full = {Science (New York, N.Y.)},
	Mesh = {Agriculture; Bayes Theorem; Cultural Evolution; History, Ancient; Humans; Language; Linguistics; Phylogeography; Turkey; Vocabulary},
	Month = {Aug},
	Number = {6097},
	Pages = {957-60},
	Pmc = {PMC4112997},
	Pmid = {22923579},
	Pst = {ppublish},
	Title = {Mapping the origins and expansion of the Indo-European language family},
	Volume = {337},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1126/science.1219669}}

@article{Bielejec:2016aa,
	Author = {Bielejec, Filip and Baele, Guy and Vrancken, Bram and Suchard M.A. and Rambaut, A. and Lemey, Philippe},
	Date-Added = {2015-12-04 21:40:11 +0000},
	Date-Modified = {2015-12-04 21:41:42 +0000},
	Journal = {Molecular Biology and Evolution},
	Title = {SpreaD3: Spatial Phylogenetic Reconstruction of Evolutionary Dynamics 3},
	Year = {2016}}

@article{lemey:2009fk,
	Abstract = {As a key factor in endemic and epidemic dynamics, the
		  geographical distribution of viruses has been frequently
		  interpreted in the light of their genetic histories.
		  Unfortunately, inference of historical dispersal or
		  migration patterns of viruses has mainly been restricted to
		  model-free heuristic approaches that provide little insight
		  into the temporal setting of the spatial dynamics. The
		  introduction of probabilistic models of evolution, however,
		  offers unique opportunities to engage in this statistical
		  endeavor. Here we introduce a Bayesian framework for
		  inference, visualization and hypothesis testing of
		  phylogeographic history. By implementing character mapping
		  in a Bayesian software that samples time-scaled
		  phylogenies, we enable the reconstruction of timed viral
		  dispersal patterns while accommodating phylogenetic
		  uncertainty. Standard Markov model inference is extended
		  with a stochastic search variable selection procedure that
		  identifies the parsimonious descriptions of the diffusion
		  process. In addition, we propose priors that can
		  incorporate geographical sampling distributions or
		  characterize alternative hypotheses about the spatial
		  dynamics. To visualize the spatial and temporal
		  information, we summarize inferences using virtual globe
		  software. We describe how Bayesian phylogeography compares
		  with previous parsimony analysis in the investigation of
		  the influenza A H5N1 origin and H5N1 epidemiological
		  linkage among sampling localities. Analysis of rabies in
		  West African dog populations reveals how virus diffusion
		  may enable endemic maintenance through continuous epidemic
		  cycles. From these analyses, we conclude that our
		  phylogeographic framework will make an important asset in
		  molecular epidemiology that can be easily generalized to
		  infer biogeogeography from genetic data for many
		  organisms.},
	Author = {Lemey, Philippe and Rambaut, Andrew and Drummond, Alexei J and Suchard, Marc A},
	Date-Added = {2015-12-04 20:16:44 +0000},
	Date-Modified = {2015-12-04 20:16:44 +0000},
	Doi = {10.1371/journal.pcbi.1000520},
	Journal = {PLoS Comput Biol},
	Journal-Full = {PLoS computational biology},
	Mesh = {Animals; Bayes Theorem; Computational Biology; Dogs; Geography; Humans; Influenza A Virus, H5N1 Subtype; Influenza, Human; Markov Chains; Models, Biological; Molecular Epidemiology; Phylogeny; Rabies; Rabies virus; Stochastic Processes},
	Month = {Sep},
	Number = {9},
	Pages = {e1000520},
	Pmc = {PMC2740835},
	Pmid = {19779555},
	Pst = {ppublish},
	Title = {Bayesian phylogeography finds its roots},
	Volume = {5},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1000520}}

@article{Lemey:2009aa,
	Abstract = {Here, we present an analysis of the H1N1pdm genetic data sampled over the initial stages in the epidemic. To infer phylodynamic spread in time and space we employ a recently developed Bayesian statistical inference framework (Lemey et al., in press). We model spatial diffusion as a continuous-time Markov chain process along time-measured genealogies. In this analysis, we consider 40 locations for which sequence data were available on 06-Aug-2009. The sampling time interval of the 242 sequences spans from 30-Mar-2009 to 12-Jul-2009. The Bayesian inference typically results in a posterior distribution of phylogenetic trees, each having an estimate of the epidemic locations at the ancestral nodes in the tree. We summarize these trees using the most representative clustering pattern and annotate these clusters with the most probable location states. We can visualize this information as tree that grows over time, seeding locations each time an ancestral node is inferred to exist at a different location. A Bayes factor test provides statistical support for epidemiological linkage throughout the evolutionary history. We demonstrate how our full probabilistic approach efficiently tracks an epidemic based on viral genetic data as it unfolds across the globe.},
	Author = {Lemey, Philippe and Suchard, Marc and Rambaut, Andrew},
	Date-Added = {2015-11-25 15:16:59 +0000},
	Date-Modified = {2015-11-25 15:16:59 +0000},
	Doi = {10.1371/currents.RRN1031},
	Journal = {PLoS Curr},
	Journal-Full = {PLoS currents},
	Pages = {RRN1031},
	Pmc = {PMC2762761},
	Pmid = {20029613},
	Pst = {epublish},
	Title = {Reconstructing the initial global spread of a human influenza pandemic: A Bayesian spatial-temporal model for the global spread of H1N1pdm},
	Volume = {1},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/currents.RRN1031}}

@article{Trovao:2015aa,
	Abstract = {Since its first isolation in 1996 in Guangdong, China, the highly pathogenic avian influenza virus (HPAIV) H5N1 has circulated in avian hosts for almost two decades and spread to more than 60 countries worldwide. The role of different avian hosts and the domestic-wild bird interface has been critical in shaping the complex HPAIV H5N1 disease ecology, but remains difficult to ascertain. To shed light on the large-scale H5N1 transmission patterns and disentangle the contributions of different avian hosts on the tempo and mode of HPAIV H5N1 dispersal, we apply Bayesian evolutionary inference techniques to comprehensive sets of hemagglutinin and neuraminidase gene sequences sampled between 1996 and 2011 throughout Asia and Russia. Our analyses demonstrate that the large-scale H5N1 transmission dynamics are structured according to different avian flyways, and that the incursion of the Central Asian flyway specifically was driven by Anatidae hosts coinciding with rapid rate of spread and an epidemic wavefront acceleration. This also resulted in long-distance dispersal that is likely to be explained by wild bird migration. We identify a significant degree of asymmetry in the large-scale transmission dynamics between Anatidae and Phasianidae, with the latter largely representing poultry as an evolutionary sink. A joint analysis of host dynamics and continuous spatial diffusion demonstrates that the rate of viral dispersal and host diffusivity is significantly higher for Anatidae compared with Phasianidae. These findings complement risk modeling studies and satellite tracking of wild birds in demonstrating a continental-scale structuring into areas of H5N1 persistence that are connected through migratory waterfowl.},
	Author = {Trov{\~a}o, N{\'\i}dia Sequeira and Suchard, Marc A and Baele, Guy and Gilbert, Marius and Lemey, Philippe},
	Date-Added = {2015-11-25 12:40:18 +0000},
	Date-Modified = {2015-11-25 12:40:18 +0000},
	Doi = {10.1093/molbev/msv185},
	Journal = {Mol Biol Evol},
	Journal-Full = {Molecular biology and evolution},
	Keywords = {Bayesian inference; H5N1; disease ecology; phylogeography; viral evolution},
	Month = {Dec},
	Number = {12},
	Pages = {3264-75},
	Pmid = {26341298},
	Pst = {ppublish},
	Title = {Bayesian Inference Reveals Host-Specific Contributions to the Epidemic Expansion of Influenza A H5N1},
	Volume = {32},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/molbev/msv185}}

@article{drummond:2012zr,
	Abstract = {Computational evolutionary biology, statistical
		  phylogenetics, and coalescent-based population genetics are
		  becoming increasingly central to the analysis and
		  understanding of molecular sequence data. We present the
		  Bayesian Evolutionary Analysis by Sampling Trees (BEAST)
		  software package version 1.7, which implements a family of
		  Markov chain Monte Carlo (MCMC) algorithms for Bayesian
		  phylogenetic inference, divergence time dating, coalescent
		  analysis, phylogeography, and related molecular
		  evolutionary analyses. This package includes an enhanced
		  graphical user interface program called Bayesian
		  Evolutionary Analysis Utility (BEAUti) that enables access
		  to advanced models for molecular sequence and phenotypic
		  trait evolution that were previously available to
		  developers only. The package also provides new tools for
		  visualizing and summarizing multispecies coalescent and
		  phylogeographic analyses. BEAUti and BEAST 1.7 are open
		  source under the GNU lesser general public license and
		  available at http://beast-mcmc.googlecode.com and
		  http://beast.bio.ed.ac.uk.},
	Author = {Drummond, Alexei J and Suchard, Marc A and Xie, Dong and Rambaut, Andrew},
	Date-Added = {2015-11-24 09:33:25 +0000},
	Date-Modified = {2015-11-24 09:33:25 +0000},
	Doi = {10.1093/molbev/mss075},
	Journal = {Mol Biol Evol},
	Journal-Full = {Molecular biology and evolution},
	Month = {Mar},
	Pmid = {22367748},
	Pst = {aheadofprint},
	Title = {Bayesian phylogenetics with BEAUti and the BEAST 1.7},
	Year = {2012},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/molbev/mss075}}

@article{Bostock:2011aa,
	Abstract = {Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations.},
	Author = {Bostock, M. and Ogievetsky, V. and Heer, J.},
	Date-Added = {2015-11-23 14:09:41 +0000},
	Date-Modified = {2015-11-23 14:10:50 +0000},
	Doi = {10.1109/TVCG.2011.185},
	Issn = {1077-2626},
	Journal = {Visualization and Computer Graphics, IEEE Transactions on},
	Keywords = {Web sites;computer animation;data visualisation;document handling;user interfaces;Web visualization;animation;data-driven documents;document elements;dynamic transforms;native representation;representation-transparent approach;representational transparency;scene graph;standard document object model;toolkit-specific abstraction;Cascading style sheets;Data visualization;Debugging;Image color analysis;Information analysis;2D graphics.;Information visualization;toolkits;user interfaces},
	Month = {{Dec}},
	Number = {12},
	Pages = {2301-2309},
	Title = {D3; Data-Driven Documents},
	Volume = {17},
	Year = {2011},
	Bdsk-Url-1 = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064996}}

@article{bielejec11,
	Abstract = {SUMMARY: SPREAD is a user-friendly, cross-platform
		  application to analyze and visualize Bayesian
		  phylogeographic reconstructions incorporating
		  spatial-temporal diffusion. The software maps phylogenies
		  annotated with both discrete and continuous spatial
		  information and can export high-dimensional posterior
		  summaries to keyhole markup language (KML) for animation of
		  the spatial diffusion through time in virtual globe
		  software. In addition, SPREAD implements Bayes factor
		  calculation to evaluate the support for hypotheses of
		  historical diffusion among pairs of discrete locations
		  based on Bayesian stochastic search variable selection
		  estimates. SPREAD takes advantage of multicore
		  architectures to process large joint posterior
		  distributions of phylogenies and their spatial diffusion
		  and produces visualizations as compelling and interpretable
		  statistical summaries for the different spatial
		  projections. AVAILABILITY: SPREAD is licensed under the GNU
		  Lesser GPL and its source code is freely available as a
		  GitHub repository: https://github.com/phylogeography/SPREAD
		  CONTACT: filip.bielejec@rega.kuleuven.be.},
	Author = {Bielejec, Filip and Rambaut, Andrew and Suchard, Marc A and Lemey, Philippe},
	Date-Added = {2015-11-23 13:52:46 +0000},
	Date-Modified = {2015-11-23 13:52:46 +0000},
	Doi = {10.1093/bioinformatics/btr481},
	Journal = {Bioinformatics},
	Journal-Full = {Bioinformatics (Oxford, England)},
	Mesh = {Bayes Theorem; Biological Evolution; Computer Graphics; Phylogeny; Phylogeography; Software},
	Month = {Oct},
	Number = {20},
	Pages = {2910-2},
	Pmc = {PMC3187652},
	Pmid = {21911333},
	Pst = {ppublish},
	Title = {SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics},
	Volume = {27},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1093/bioinformatics/btr481}}