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      The genome of the seagrass Zostera marina reveals angiosperm adaptation to the sea.

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          Abstract

          Seagrasses colonized the sea on at least three independent occasions to form the basis of one of the most productive and widespread coastal ecosystems on the planet. Here we report the genome of Zostera marina (L.), the first, to our knowledge, marine angiosperm to be fully sequenced. This reveals unique insights into the genomic losses and gains involved in achieving the structural and physiological adaptations required for its marine lifestyle, arguably the most severe habitat shift ever accomplished by flowering plants. Key angiosperm innovations that were lost include the entire repertoire of stomatal genes, genes involved in the synthesis of terpenoids and ethylene signalling, and genes for ultraviolet protection and phytochromes for far-red sensing. Seagrasses have also regained functions enabling them to adjust to full salinity. Their cell walls contain all of the polysaccharides typical of land plants, but also contain polyanionic, low-methylated pectins and sulfated galactans, a feature shared with the cell walls of all macroalgae and that is important for ion homoeostasis, nutrient uptake and O2/CO2 exchange through leaf epidermal cells. The Z. marina genome resource will markedly advance a wide range of functional ecological studies from adaptation of marine ecosystems under climate warming, to unravelling the mechanisms of osmoregulation under high salinities that may further inform our understanding of the evolution of salt tolerance in crop plants.

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          Most cited references40

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          tRNAscan-SE: A Program for Improved Detection of Transfer RNA Genes in Genomic Sequence

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            Seagrass ecosystems as a globally significant carbon stock

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              Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

              We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.
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                Author and article information

                Journal
                Nature
                Nature
                1476-4687
                0028-0836
                Feb 18 2016
                : 530
                : 7590
                Affiliations
                [1 ] Groningen Institute of Evolutionary Life Sciences (GELIFES), University of Groningen, PO Box 11103, 9700 CC Groningen, The Netherlands.
                [2 ] Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 927, B-9052 Ghent, Belgium.
                [3 ] GEOMAR Helmholtz Centre for Ocean Research-Kiel, Evolutionary Ecology, Düsternbrooker Weg 20, D-24105 Kiel, Germany.
                [4 ] Sorbonne Université, UPMC Univ Paris 06, CNRS, UMR 8227, Integrative Biology of Marine Models, Station Biologique de Roscoff, CS 90074, F-29688, Roscoff cedex, France.
                [5 ] Stazione Zoologica Anton Dohrn, Villa Comunale, 80121 Naples, Italy.
                [6 ] Dipartimento di Scienze Agrarie e Ambientali, University of Udine, Via delle Scienze 206, 33100 Udine, Italy.
                [7 ] INRA, UR1164 URGI-Research Unit in Genomics-Info, INRA de Versailles-Grignon, Route de Saint-Cyr, Versailles 78026, France.
                [8 ] Institute for Evolution and Biodiversity, Westfälische Wilhelms-University of Münster, Hüfferstrasse 1, D-48149 Münster, Germany.
                [9 ] Institute for Computer Science, Heinrich Heine University, D-40255 Duesseldorf, Germany.
                [10 ] Department of Biological and Environmental Sciences, Bioinformatics Infrastructure for Life Sciences (BILS), University of Gothenburg, Medicinaregatan 18A, 40530 Gothenburg, Sweden.
                [11 ] Department of Energy Joint Genome Institute, 2800 Mitchell Dr., #100, Walnut Creek, California 94598, USA.
                [12 ] Environmental and Marine Biology, Faculty of Science and Engineering, Åbo Akademi University, Artillerigatan 6, FI-20520 Turku/Åbo, Finland.
                [13 ] HudsonAlpha Institute for Biotechnology, 601 Genome Way NW, Huntsville, Alabama 35806, USA.
                [14 ] Marine Ecology Group, Nord University, Postbox 1490, 8049 Bodø, Norway.
                [15 ] Amplicon Express, 2345 NE Hopkins Ct., Pullman, Washington 99163, USA.
                [16 ] School of Marine Science and Policy, Department of Plant and Soil Sciences, Delaware Biotechnology Institute, University of Delaware, 15-Innovation Way, Newark, Delaware 19711, USA.
                [17 ] Marine Ecology and Evolution, Centre for Marine Sciences (CCMAR), University of Algarve, 8005-139 Faro, Portugal.
                [18 ] King Abdullah University of Science and Technology (KAUST), Red Sea Research Center (RSRC), Thuwal 23955-6900, Saudi Arabia.
                [19 ] University of Kiel, Faculty of Mathematics and Natural Sciences, Christian-Albrechts-Platz 4, 24118 Kiel, Germany.
                [20 ] Genomics Research Institute, University of Pretoria, Hatfield Campus, Pretoria 0028, South Africa.
                [21 ] Bioinformatics Institute Ghent, Ghent University, Ghent B-9000, Belgium.
                Article
                nature16548
                10.1038/nature16548
                26814964
                fb6986d2-fe18-447e-b5e1-7403c87097c9
                History

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