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      phangorn: phylogenetic analysis in R

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      Bioinformatics
      Oxford University Press

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          Abstract

          Summary: phangorn is a package for phylogenetic reconstruction and analysis in the R language. Previously it was only possible to estimate phylogenetic trees with distance methods in R. phangorn, now offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses.

          Availability: phangorn can be obtained through the CRAN homepage http://cran.r-project.org/web/packages/phangorn/index.html. phangorn is licensed under GPL 2.

          Contact: klaus.kschliep@ 123456snv.jussieu.fr

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references9

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          R: A Language and Environment for Statistical Computing.

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            The Parsimony Ratchet, a New Method for Rapid Parsimony Analysis

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              A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data.

              We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate-variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.
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                Author and article information

                Journal
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                15 February 2011
                17 December 2010
                17 December 2010
                : 27
                : 4
                : 592-593
                Affiliations
                UMR CNRS 7138 Systématique, Adaptation, Evolution, Université Pierre et Marie Curie, Muséum National d'Histoire Naturelle, Paris, France
                Author notes

                Associate Editor: David Posada

                Article
                btq706
                10.1093/bioinformatics/btq706
                3035803
                21169378
                1a68220d-bd6b-4837-b2f8-d63395e86f90
                © The Author(s) 2010. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 September 2010
                : 26 November 2010
                : 14 December 2010
                Categories
                Applications Note
                Phylogenetics

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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