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      A roadmap for global synthesis of the plant tree of life

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          A DNA barcode for land plants.

          DNA barcoding involves sequencing a standard region of DNA as a tool for species identification. However, there has been no agreement on which region(s) should be used for barcoding land plants. To provide a community recommendation on a standard plant barcode, we have compared the performance of 7 leading candidate plastid DNA regions (atpF-atpH spacer, matK gene, rbcL gene, rpoB gene, rpoC1 gene, psbK-psbI spacer, and trnH-psbA spacer). Based on assessments of recoverability, sequence quality, and levels of species discrimination, we recommend the 2-locus combination of rbcL+matK as the plant barcode. This core 2-locus barcode will provide a universal framework for the routine use of DNA sequence data to identify specimens and contribute toward the discovery of overlooked species of land plants.
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            Is Open Access

            ASTRAL: genome-scale coalescent-based species tree estimation

            Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions. Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees. Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral. Contact: warnow@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              From algae to angiosperms–inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes

              Background Next-generation sequencing has provided a wealth of plastid genome sequence data from an increasingly diverse set of green plants (Viridiplantae). Although these data have helped resolve the phylogeny of numerous clades (e.g., green algae, angiosperms, and gymnosperms), their utility for inferring relationships across all green plants is uncertain. Viridiplantae originated 700-1500 million years ago and may comprise as many as 500,000 species. This clade represents a major source of photosynthetic carbon and contains an immense diversity of life forms, including some of the smallest and largest eukaryotes. Here we explore the limits and challenges of inferring a comprehensive green plant phylogeny from available complete or nearly complete plastid genome sequence data. Results We assembled protein-coding sequence data for 78 genes from 360 diverse green plant taxa with complete or nearly complete plastid genome sequences available from GenBank. Phylogenetic analyses of the plastid data recovered well-supported backbone relationships and strong support for relationships that were not observed in previous analyses of major subclades within Viridiplantae. However, there also is evidence of systematic error in some analyses. In several instances we obtained strongly supported but conflicting topologies from analyses of nucleotides versus amino acid characters, and the considerable variation in GC content among lineages and within single genomes affected the phylogenetic placement of several taxa. Conclusions Analyses of the plastid sequence data recovered a strongly supported framework of relationships for green plants. This framework includes: i) the placement of Zygnematophyceace as sister to land plants (Embryophyta), ii) a clade of extant gymnosperms (Acrogymnospermae) with cycads + Ginkgo sister to remaining extant gymnosperms and with gnetophytes (Gnetophyta) sister to non-Pinaceae conifers (Gnecup trees), and iii) within the monilophyte clade (Monilophyta), Equisetales + Psilotales are sister to Marattiales + leptosporangiate ferns. Our analyses also highlight the challenges of using plastid genome sequences in deep-level phylogenomic analyses, and we provide suggestions for future analyses that will likely incorporate plastid genome sequence data for thousands of species. We particularly emphasize the importance of exploring the effects of different partitioning and character coding strategies.
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                Author and article information

                Journal
                American Journal of Botany
                Am J Bot
                Wiley
                00029122
                March 2018
                March 2018
                March 30 2018
                : 105
                : 3
                : 614-622
                Affiliations
                [1 ]Royal Botanic Gardens; Kew TW9 3AE Richmond Surrey UK
                [2 ]Department of Bioscience; Aarhus University; Ny Munkegade 116 8000 Aarhus C Denmark
                [3 ]Gothenburg Global Biodiversity Centre; Box 461 405 30 Gothenburg Sweden
                [4 ]Department of Biological and Environmental Sciences; University of Gothenburg; Box 461 405 30 Gothenburg Sweden
                [5 ]Gothenburg Botanical Garden; Carl Skottsbergs Gata 22B SE-413 19 Gothenburg Sweden
                [6 ]Department of Biology; University of Florida; Florida 32611 USA
                [7 ]Department of Ecology and Evolutionary Biology; University of Arizona; Tucson AZ 85721 USA
                [8 ]The Santa Fe Institute; Santa Fe NM 87501 USA
                [9 ]Scientific Computing Group; Heidelberg Institute for Theoretical Studies; 69118 Heidelberg Germany
                [10 ]Department of Electrical and Computer Engineering; University of California; San Diego San Diego CA 92093 USA
                [11 ]Yale-NUS College; 16 College Avenue West Singapore 138527 Republic of Singapore
                [12 ]Center for Macroecology, Evolution and Climate; University of Copenhagen; Universitetsparken 15 DK-2100 Copenhagen O Denmark
                [13 ]Imperial College London; Silwood Park, Buckhurst Road Ascot Berkshire SL5 7PY UK
                [14 ]Department of Biology; Santa Clara University; Santa Clara CA 95053 USA
                [15 ]Department of Ecology and Evolutionary Biology; University of Michigan; Ann Arbor MI 48109 USA
                [16 ]Institute for Theoretical Informatics; Karlsruhe Institute of Technology; 76128 Karlsruhe Germany
                [17 ]Naturalis Biodiversity Center; P.O. Box 9517 2300RA Leiden The Netherlands
                [18 ]Institute of Biology Leiden; P.O. Box 9505 2300RA Leiden The Netherlands
                [19 ]Department of Computer Science; University of Illinois at Urbana-Champaign; Urbana IL 61801 USA
                Article
                10.1002/ajb2.1041
                29603138
                57ac666a-4812-4e07-8e4f-a1bb80f1cf7c
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

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