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      Secondary DNA Barcodes (CAM, GAPDH, GS, and RpB2) to Characterize Species Complexes and Strengthen the Powdery Mildew Phylogeny

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      Frontiers in Ecology and Evolution
      Frontiers Media SA

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          Abstract

          Powdery mildews are a group of economically and ecologically important plant pathogens. In the past 25 years the use of ribosomal DNA (rDNA) in the powdery mildews has led to major taxonomic revisions. However, the broad scale use of rDNA has also revealed multiple species complexes that cannot be differentiated based on ITS + LSU data alone. Currently, there are only two powdery mildew taxonomic studies that took a multi-locus approach to resolve a species complex. In the present study, we introduce primers to sequence four additional regions (CAM, GAPDH, GS, and RPB2) that have the potential to improve support values in both broad and fine scale phylogenetic analyses. The primers were applied to a broad set of powdery mildew genera in China and the United States, and phylogenetic analyses included some of the common complexes. In taxa with nearly identical ITS sequences the analyses revealed a great amount of diversity. In total 154 non-rDNA sequences from 11 different powdery mildew genera were deposited in NCBI’s GenBank, laying the foundation for secondary barcode databases for powdery mildews. The combined and single loci phylogenetic trees constructed generally followed the previously defined species/genus concepts for the powdery mildews. Future research can use these primers to conduct in depth phylogenetic, and taxonomic studies to elucidate the evolutionary relationships of species and genera within the powdery mildews.

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          MEGA11: Molecular Evolutionary Genetics Analysis Version 11

          The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
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            CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP.

            The recently-developed statistical method known as the "bootstrap" can be used to place confidence intervals on phylogenies. It involves resampling points from one's own data, with replacement, to create a series of bootstrap samples of the same size as the original data. Each of these is analyzed, and the variation among the resulting estimates taken to indicate the size of the error involved in making estimates from the original data. In the case of phylogenies, it is argued that the proper method of resampling is to keep all of the original species while sampling characters with replacement, under the assumption that the characters have been independently drawn by the systematist and have evolved independently. Majority-rule consensus trees can be used to construct a phylogeny showing all of the inferred monophyletic groups that occurred in a majority of the bootstrap samples. If a group shows up 95% of the time or more, the evidence for it is taken to be statistically significant. Existing computer programs can be used to analyze different bootstrap samples by using weights on the characters, the weight of a character being how many times it was drawn in bootstrap sampling. When all characters are perfectly compatible, as envisioned by Hennig, bootstrap sampling becomes unnecessary; the bootstrap method would show significant evidence for a group if it is defined by three or more characters.
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              Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10

              Abstract The Bayesian Evolutionary Analysis by Sampling Trees (BEAST) software package has become a primary tool for Bayesian phylogenetic and phylodynamic inference from genetic sequence data. BEAST unifies molecular phylogenetic reconstruction with complex discrete and continuous trait evolution, divergence-time dating, and coalescent demographic models in an efficient statistical inference engine using Markov chain Monte Carlo integration. A convenient, cross-platform, graphical user interface allows the flexible construction of complex evolutionary analyses.
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                Author and article information

                Journal
                Frontiers in Ecology and Evolution
                Front. Ecol. Evol.
                Frontiers Media SA
                2296-701X
                June 14 2022
                June 14 2022
                : 10
                Article
                10.3389/fevo.2022.918908
                a9658f50-4839-471a-b8f1-ea8fc3a7bdd8
                © 2022

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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