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      Model selection may not be a mandatory step for phylogeny reconstruction

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      1 , 1 , 2 , 2 , , 1 ,
      Nature Communications
      Nature Publishing Group UK

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          Abstract

          Determining the most suitable model for phylogeny reconstruction constitutes a fundamental step in numerous evolutionary studies. Over the years, various criteria for model selection have been proposed, leading to debate over which criterion is preferable. However, the necessity of this procedure has not been questioned to date. Here, we demonstrate that although incongruency regarding the selected model is frequent over empirical and simulated data, all criteria lead to very similar inferences. When topologies and ancestral sequence reconstruction are the desired output, choosing one criterion over another is not crucial. Moreover, skipping model selection and using instead the most parameter-rich model, GTR+I+G, leads to similar inferences, thus rendering this time-consuming step nonessential, at least under current strategies of model selection.

          Abstract

          Model selection is a time-intensive step of molecular phylogenetic analysis. Here, Abadi, Azouri and colleagues show that all model selection criteria lead to similar inferences, and that for topology and ancestral sequence reconstruction, using the GTR+I+G model is as accurate.

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          Most cited references54

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          Comparison of phylogenetic trees

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            BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data.

            O. Gascuel (1997)
            We propose an improved version of the neighbor-joining (NJ) algorithm of Saitou and Nei. This new algorithm, BIONJ, follows the same agglomerative scheme as NJ, which consists of iteratively picking a pair of taxa, creating a new mode which represents the cluster of these taxa, and reducing the distance matrix by replacing both taxa by this node. Moreover, BIONJ uses a simple first-order model of the variances and covariances of evolutionary distance estimates. This model is well adapted when these estimates are obtained from aligned sequences. At each step it permits the selection, from the class of admissible reductions, of the reduction which minimizes the variance of the new distance matrix. In this way, we obtain better estimates to choose the pair of taxa to be agglomerated during the next steps. Moreover, in comparison with NJ's estimates, these estimates become better and better as the algorithm proceeds. BIONJ retains the good properties of NJ--especially its low run time. Computer simulations have been performed with 12-taxon model trees to determine BIONJ's efficiency. When the substitution rates are low (maximum pairwise divergence approximately 0.1 substitutions per site) or when they are constant among lineages, BIONJ is only slightly better than NJ. When the substitution rates are higher and vary among lineages,BIONJ clearly has better topological accuracy. In the latter case, for the model trees and the conditions of evolution tested, the topological error reduction is on the average around 20%. With highly-varying-rate trees and with high substitution rates (maximum pairwise divergence approximately 1.0 substitutions per site), the error reduction may even rise above 50%, while the probability of finding the correct tree may be augmented by as much as 15%.
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              Codon-substitution models for heterogeneous selection pressure at amino acid sites.

              Comparison of relative fixation rates of synonymous (silent) and nonsynonymous (amino acid-altering) mutations provides a means for understanding the mechanisms of molecular sequence evolution. The nonsynonymous/synonymous rate ratio (omega = d(N)d(S)) is an important indicator of selective pressure at the protein level, with omega = 1 meaning neutral mutations, omega 1 diversifying positive selection. Amino acid sites in a protein are expected to be under different selective pressures and have different underlying omega ratios. We develop models that account for heterogeneous omega ratios among amino acid sites and apply them to phylogenetic analyses of protein-coding DNA sequences. These models are useful for testing for adaptive molecular evolution and identifying amino acid sites under diversifying selection. Ten data sets of genes from nuclear, mitochondrial, and viral genomes are analyzed to estimate the distributions of omega among sites. In all data sets analyzed, the selective pressure indicated by the omega ratio is found to be highly heterogeneous among sites. Previously unsuspected Darwinian selection is detected in several genes in which the average omega ratio across sites is 1. Genes undergoing positive selection include the beta-globin gene from vertebrates, mitochondrial protein-coding genes from hominoids, the hemagglutinin (HA) gene from human influenza virus A, and HIV-1 env, vif, and pol genes. Tests for the presence of positively selected sites and their subsequent identification appear quite robust to the specific distributional form assumed for omega and can be achieved using any of several models we implement. However, we encountered difficulties in estimating the precise distribution of omega among sites from real data sets.
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                Author and article information

                Contributors
                talp@tauex.tau.ac.il
                itaymay@tauex.tau.ac.il
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 February 2019
                25 February 2019
                2019
                : 10
                : 934
                Affiliations
                [1 ]ISNI 0000 0004 1937 0546, GRID grid.12136.37, School of Plant Sciences and Food Security, , Tel Aviv University, Ramat Aviv, ; Tel-Aviv, 69978 Israel
                [2 ]ISNI 0000 0004 1937 0546, GRID grid.12136.37, School of Molecular Cell Biology & Biotechnology, , Tel Aviv University, Ramat Aviv, ; Tel-Aviv, 69978 Israel
                Author information
                http://orcid.org/0000-0002-3932-6310
                http://orcid.org/0000-0002-8460-1502
                Article
                8822
                10.1038/s41467-019-08822-w
                6389923
                30804347
                e2bb58f5-fe21-48db-8a03-1a78b83c82d7
                © The Author(s) 2019

                Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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                : 6 June 2018
                : 29 January 2019
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