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      The western painted turtle genome, a model for the evolution of extreme physiological adaptations in a slowly evolving lineage

      research-article
      1 , 2 , , 3 , , 4 , 5 , 6 , 7 ,   8 , 9 , 10 , 8 , 11 , 12 , 13 , 14 , 12 , 15 , 8 , 16 , 17 , 18 , 19 , 20 , 21 , 19 , 3 , 22 , 3 , 21 , 23 , 3 , 3 , 24 , 25 , 26 , 27 , 3 , 16 , 8 , 8 , 3 , 25 , 20 , 25 , 8 , 28 , 19 , 3 , 12 , 20 , 25 , 8 , 29 , 30 , 9 , 24 , 8 , 29 , 8 , 29 , 1 , 2 , 11 , 3 , 31 , 12 , 3 , 3 , 3
      Genome Biology
      BioMed Central
      Amniote phylogeny, anoxia tolerance, chelonian, freeze tolerance, genomics, longevity, phylogenomics, physiology, turtle, evolutionary rates

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          Abstract

          Background

          We describe the genome of the western painted turtle, Chrysemys picta bellii, one of the most widespread, abundant, and well-studied turtles. We place the genome into a comparative evolutionary context, and focus on genomic features associated with tooth loss, immune function, longevity, sex differentiation and determination, and the species' physiological capacities to withstand extreme anoxia and tissue freezing.

          Results

          Our phylogenetic analyses confirm that turtles are the sister group to living archosaurs, and demonstrate an extraordinarily slow rate of sequence evolution in the painted turtle. The ability of the painted turtle to withstand complete anoxia and partial freezing appears to be associated with common vertebrate gene networks, and we identify candidate genes for future functional analyses. Tooth loss shares a common pattern of pseudogenization and degradation of tooth-specific genes with birds, although the rate of accumulation of mutations is much slower in the painted turtle. Genes associated with sex differentiation generally reflect phylogeny rather than convergence in sex determination functionality. Among gene families that demonstrate exceptional expansions or show signatures of strong natural selection, immune function and musculoskeletal patterning genes are consistently over-represented.

          Conclusions

          Our comparative genomic analyses indicate that common vertebrate regulatory networks, some of which have analogs in human diseases, are often involved in the western painted turtle's extraordinary physiological capacities. As these regulatory pathways are analyzed at the functional level, the painted turtle may offer important insights into the management of a number of human health disorders.

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          Most cited references54

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          Mobile elements: drivers of genome evolution.

          Mobile elements within genomes have driven genome evolution in diverse ways. Particularly in plants and mammals, retrotransposons have accumulated to constitute a large fraction of the genome and have shaped both genes and the entire genome. Although the host can often control their numbers, massive expansions of retrotransposons have been tolerated during evolution. Now mobile elements are becoming useful tools for learning more about genome evolution and gene function.
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            Human-mouse alignments with BLASTZ.

            The Mouse Genome Analysis Consortium aligned the human and mouse genome sequences for a variety of purposes, using alignment programs that suited the various needs. For investigating issues regarding genome evolution, a particularly sensitive method was needed to permit alignment of a large proportion of the neutrally evolving regions. We selected a program called BLASTZ, an independent implementation of the Gapped BLAST algorithm specifically designed for aligning two long genomic sequences. BLASTZ was subsequently modified, both to attain efficiency adequate for aligning entire mammalian genomes and to increase its sensitivity. This work describes BLASTZ, its modifications, the hardware environment on which we run it, and several empirical studies to validate its results.
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              Identification of novel transcripts in annotated genomes using RNA-Seq.

              We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2013
                28 March 2013
                28 March 2014
                : 14
                : 3
                : R28
                Affiliations
                [1 ]Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095-1606, USA
                [2 ]La Kretz Center for California Conservation Science, Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA 90095-1496, USA
                [3 ]The Genome Institute, Washington University School of Medicine, Campus Box 8501, 4444 Forest Park Avenue, St Louis, MO 63108, USA
                [4 ]Department of Biology, Saint Louis University, St Louis, MO 63103, USA
                [5 ]College of Charleston Biology Department and Grice Marine Laboratory, Charleston, SC 29424, USA
                [6 ]Medical University of South Carolina College of Graduate Studies and Center for Marine Biomedicine and Environmental Sciences, Charleston, SC 29412, USA
                [7 ]Department of Biology, University of Hawaii at Manoa, Honolulu, HI 96822, USA
                [8 ]Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
                [9 ]Faculty of Dentistry, Life Sciences Institute University of British Columbia, Vancouver BC, Canada
                [10 ]Benaroya Research Institute at Virginia Mason, Seattle, WA 98101 USA
                [11 ]Department of Biology and Institute of Biochemistry, Carleton University, Ottawa, ON, Canada K1S 5B6, Canada
                [12 ]School of Biological Sciences, Illinois State University, Normal, IL 61790, USA
                [13 ]Department of Biological Sciences, Life Sciences Building, University of South Alabama, Mobile, AL 36688-0002, USA
                [14 ]Research Computing, Harvard Medical School, Boston, MA 02115, USA
                [15 ]Department of Biology, University of Florida, Gainesville, FL 32611 USA
                [16 ]Gladstone Institute of Cardiovascular Disease, San Francisco, CA 94158, USA
                [17 ]Cardiovascular Research Institute and Department of Pediatrics, University of California, San Francisco, San Francisco, CA 94158, USA
                [18 ]Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada M5S 3G5, Canada
                [19 ]Department of Cell Biology, Duke University Medical Center, Durham, NC 27710, USA
                [20 ]Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA
                [21 ]Department of Biology, University of Texas at Arlington, Arlington, TX 76019, USA
                [22 ]Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
                [23 ]Museum of Comparative Zoology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
                [24 ]Baskin School of Engineering University of California, Santa Cruz Santa Cruz, CA 95064, USA
                [25 ]MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, Henry Wellcome Building of Gene Function, University of Oxford, Oxford, OX13PT, UK
                [26 ]Cancer Research Program, Rajiv Gandhi Centre for Biotechnology, Poojapura, Thycaud P.O, Thiruvananthapuram, Kerala 695014, India
                [27 ]FUDECI, Fundación para el Desarrollo de las Ciencias Físicas, Matemáticas y Naturales. Av, Universidad, Bolsa a San Francisco, Palacio de Las Academias, Caracas, Venezuela
                [28 ]Biology Department, Duke University, Durham, NC 27708, US
                [29 ]Bioinformatics and Computational Biology Laboratory, Iowa State University, Ames, IA 50011, USA
                [30 ]Center for Biomolecular Science and Engineering, School of Engineering, University of California Santa Cruz (UCSC), Santa Cruz, CA 95064, USA
                [31 ]Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynska Dolina, Bratislava 84248, Slovakia
                Article
                gb-2013-14-3-r28
                10.1186/gb-2013-14-3-r28
                4054807
                23537068
                5450cd0d-a330-4248-a983-2b39045b2fde
                Copyright © 2013 Shaffer et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 October 2012
                : 15 March 2013
                : 28 March 2013
                Categories
                Research

                Genetics
                amniote phylogeny,anoxia tolerance,chelonian,freeze tolerance,genomics,longevity,phylogenomics,physiology,turtle,evolutionary rates

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