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      Using Robinson-Foulds supertrees in divide-and-conquer phylogeny estimation

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          Abstract

          One of the Grand Challenges in Science is the construction of the Tree of Life, an evolutionary tree containing several million species, spanning all life on earth. However, the construction of the Tree of Life is enormously computationally challenging, as all the current most accurate methods are either heuristics for NP-hard optimization problems or Bayesian MCMC methods that sample from tree space. One of the most promising approaches for improving scalability and accuracy for phylogeny estimation uses divide-and-conquer: a set of species is divided into overlapping subsets, trees are constructed on the subsets, and then merged together using a “supertree method”. Here, we present Exact-RFS-2, the first polynomial-time algorithm to find an optimal supertree of two trees, using the Robinson-Foulds Supertree (RFS) criterion (a major approach in supertree estimation that is related to maximum likelihood supertrees), and we prove that finding the RFS of three input trees is NP-hard. Exact-RFS-2 is available in open source form on Github at https://github.com/yuxilin51/GreedyRFS.

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          Most cited references38

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          RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

          Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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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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              Comparison of phylogenetic trees

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                Author and article information

                Contributors
                yuxilin51@gmail.com
                thienle@mit.edu
                sac2@illinois.edu
                ekmolloy@umd.edu
                warnow@illinois.edu
                Journal
                Algorithms Mol Biol
                Algorithms Mol Biol
                Algorithms for Molecular Biology : AMB
                BioMed Central (London )
                1748-7188
                28 June 2021
                28 June 2021
                2021
                : 16
                : 12
                Affiliations
                [1 ]Amazon AWS, Seattle, USA
                [2 ]GRID grid.116068.8, ISNI 0000 0001 2341 2786, Department of EECS, , Massachusetts Institute of Technology, ; Cambridge, USA
                [3 ]GRID grid.35403.31, ISNI 0000 0004 1936 9991, Computer Science Department, , University of Illinois at Urbana-Champaign, ; Urbana, USA
                Author information
                http://orcid.org/0000-0001-7717-3514
                Article
                189
                10.1186/s13015-021-00189-2
                8240396
                34183037
                e754f557-14d8-4f82-bfb7-904390751411
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 25 January 2021
                : 5 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: 1535977
                Award ID: 1513629
                Award ID: 1458652
                Award ID: DGE-1144245
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Molecular biology
                supertrees,divide-and-conquer,phylogeny estimation
                Molecular biology
                supertrees, divide-and-conquer, phylogeny estimation

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