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      SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics

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          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@ 123456rega.kuleuven.be

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          Many-core algorithms for statistical phylogenetics.

          Marc Suchard, Andrew Rambaut (2009)
          Statistical phylogenetics is computationally intensive, resulting in considerable attention meted on techniques for parallelization. Codon-based models allow for independent rates of synonymous and replacement substitutions and have the potential to more adequately model the process of protein-coding sequence evolution with a resulting increase in phylogenetic accuracy. Unfortunately, due to the high number of codon states, computational burden has largely thwarted phylogenetic reconstruction under codon models, particularly at the genomic-scale. Here, we describe novel algorithms and methods for evaluating phylogenies under arbitrary molecular evolutionary models on graphics processing units (GPUs), making use of the large number of processing cores to efficiently parallelize calculations even for large state-size models. We implement the approach in an existing Bayesian framework and apply the algorithms to estimating the phylogeny of 62 complete mitochondrial genomes of carnivores under a 60-state codon model. We see a near 90-fold speed increase over an optimized CPU-based computation and a >140-fold increase over the currently available implementation, making this the first practical use of codon models for phylogenetic inference over whole mitochondrial or microorganism genomes. Source code provided in BEAGLE: Broad-platform Evolutionary Analysis General Likelihood Evaluator, a cross-platform/processor library for phylogenetic likelihood computation (http://beagle-lib.googlecode.com/). We employ a BEAGLE-implementation using the Bayesian phylogenetics framework BEAST (http://beast.bio.ed.ac.uk/).
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            GenGIS: A geospatial information system for genomic data.

            Donovan Parks, Michael Porter, Sylvia Churcher … (2009)
            The increasing availability of genetic sequence data associated with explicit geographic and ecological information is offering new opportunities to study the processes that shape biodiversity. The generation and testing of hypotheses using these data sets requires effective tools for mathematical and visual analysis that can integrate digital maps, ecological data, and large genetic, genomic, or metagenomic data sets. GenGIS is a free and open-source software package that supports the integration of digital map data with genetic sequences and environmental information from multiple sample sites. Essential bioinformatic and statistical tools are integrated into the software, allowing the user a wide range of analysis options for their sequence data. Data visualizations are combined with the cartographic display to yield a clear view of the relationship between geography and genomic diversity, with a particular focus on the hierarchical clustering of sites based on their similarity or phylogenetic proximity. Here we outline the features of GenGIS and demonstrate its application to georeferenced microbial metagenomic, HIV-1, and human mitochondrial DNA data sets.
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              Three roads diverged? Routes to phylogeographic inference.

              Erik W Bloomquist, Marc Suchard, Philippe Lemey (2010)
              Phylogeographic methods facilitate inference of the geographical history of genetic lineages. Recent examples explore human migration and the origins of viral pandemics. There is longstanding disagreement over the use and validity of certain phylogeographic inference methodologies. In this paper, we highlight three distinct frameworks for phylogeographic inference to give a taste of this disagreement. Each of the three approaches presents a different viewpoint on phylogeography, most fundamentally on how we view the relationship between the inferred history of a sample and the history of the population the sample is embedded in. Satisfactory resolution of this relationship between history of the tree and history of the population remains a challenge for all but the most trivial models of phylogeographic processes. Intriguingly, we believe that some recent methods that entirely avoid inference about the history of the population will eventually help to reach a resolution. Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 October 2011
                Publication date (Electronic): 11 September 2011
                Publication date PMC-release: 11 September 2011
                Volume: 27
                Issue: 20
                Pages: 2910-2912
                Affiliations
                1Rega Institute for Medical Research, Clinical and Epidemiological Virology Section, Katholieke Universiteit Leuven, Leuven, Belgium, 2Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK, 3Fogarty International Center, National Institutes of Health, Bethesda, MD, 4Department of Biomathematics, 5Department of Biostatistics and 6Department of Human Genetics, University of California, Los Angeles, USA
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: David Posada

                Article
                Publisher ID: btr481
                DOI: 10.1093/bioinformatics/btr481
                PMC ID: 3187652
                PubMed ID: 21911333
                SO-VID: 88f8c660-0404-49fc-8105-eab5aa38c280
                Copyright © © The Author(s) 2011. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 17 June 2011
                Date revision received : 11 August 2011
                Date accepted : 13 August 2011
                Page count
                Pages: 3
                Categories
                Subject: Applications Note
                Subject: Phylogenetics

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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