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A DNA-Based Registry for All Animal Species: The Barcode Index Number (BIN) System

1 , * , 1 , 2

PLoS ONE

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      Abstract

      Because many animal species are undescribed, and because the identification of known species is often difficult, interim taxonomic nomenclature has often been used in biodiversity analysis. By assigning individuals to presumptive species, called operational taxonomic units (OTUs), these systems speed investigations into the patterning of biodiversity and enable studies that would otherwise be impossible. Although OTUs have conventionally been separated through their morphological divergence, DNA-based delineations are not only feasible, but have important advantages. OTU designation can be automated, data can be readily archived, and results can be easily compared among investigations. This study exploits these attributes to develop a persistent, species-level taxonomic registry for the animal kingdom based on the analysis of patterns of nucleotide variation in the barcode region of the cytochrome c oxidase I (COI) gene. It begins by examining the correspondence between groups of specimens identified to a species through prior taxonomic work and those inferred from the analysis of COI sequence variation using one new (RESL) and four established (ABGD, CROP, GMYC, jMOTU) algorithms. It subsequently describes the implementation, and structural attributes of the Barcode Index Number (BIN) system. Aside from a pragmatic role in biodiversity assessments, BINs will aid revisionary taxonomy by flagging possible cases of synonymy, and by collating geographical information, descriptive metadata, and images for specimens that are likely to belong to the same species, even if it is undescribed. More than 274,000 BIN web pages are now available, creating a biodiversity resource that is positioned for rapid growth.

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      Most cited references 36

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      The neighbor-joining method: a new method for reconstructing phylogenetic trees.

       N Saitou,  M Nei (1987)
      A new method called the neighbor-joining method is proposed for reconstructing phylogenetic trees from evolutionary distance data. The principle of this method is to find pairs of operational taxonomic units (OTUs [= neighbors]) that minimize the total branch length at each stage of clustering of OTUs starting with a starlike tree. The branch lengths as well as the topology of a parsimonious tree can quickly be obtained by using this method. Using computer simulation, we studied the efficiency of this method in obtaining the correct unrooted tree in comparison with that of five other tree-making methods: the unweighted pair group method of analysis, Farris's method, Sattath and Tversky's method, Li's method, and Tateno et al.'s modified Farris method. The new, neighbor-joining method and Sattath and Tversky's method are shown to be generally better than the other methods.
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        APE: Analyses of Phylogenetics and Evolution in R language.

        Analysis of Phylogenetics and Evolution (APE) is a package written in the R language for use in molecular evolution and phylogenetics. APE provides both utility functions for reading and writing data and manipulating phylogenetic trees, as well as several advanced methods for phylogenetic and evolutionary analysis (e.g. comparative and population genetic methods). APE takes advantage of the many R functions for statistics and graphics, and also provides a flexible framework for developing and implementing further statistical methods for the analysis of evolutionary processes. The program is free and available from the official R package archive at http://cran.r-project.org/src/contrib/PACKAGES.html#ape. APE is licensed under the GNU General Public License.
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          Bayesian Phylogenetics with BEAUti and the BEAST 1.7

          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
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            Author and article information

            Affiliations
            [1 ]Biodiversity Institute of Ontario, University of Guelph, Guelph, Ontario, Canada
            [2 ]Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada
            Consiglio Nazionale delle Ricerche (CNR), Italy
            Author notes

            Competing Interests: The authors' home institution may pursue a patent based on algorithmic aspects of this study. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials nor does it limit the application or use of the described system.

            Conceived and designed the experiments: SR PH. Performed the experiments: SR. Analyzed the data: SR. Contributed reagents/materials/analysis tools: PH SR. Wrote the paper: PH SR.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2013
            8 July 2013
            : 8
            : 7
            23861743
            3704603
            PONE-D-13-04955
            10.1371/journal.pone.0066213
            (Editor)

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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            Pages: 16
            Funding
            This work was enabled by a grant from the Ontario Ministry of Economic Development and Innovation and by an award from the government of Canada to Genome Canada through the Ontario Genomics Institute in support of the International Barcode of Life project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology
            Computational Biology
            Genomics
            Genome Analysis Tools
            Biological Data Management
            Sequence Analysis
            Genomics
            Genome Databases
            Sequence Databases
            Zoology
            Animal Taxonomy
            Computer Science
            Algorithms
            Computer Applications
            Web-Based Applications

            Uncategorized

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