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      Transposable element-assisted evolution and adaptation to host plant within the Leptosphaeria maculans- Leptosphaeria biglobosa species complex of fungal pathogens

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          Abstract

          Background

          Many plant-pathogenic fungi have a tendency towards genome size expansion, mostly driven by increasing content of transposable elements (TEs). Through comparative and evolutionary genomics, five members of the Leptosphaeria maculans- Leptosphaeria biglobosa species complex (class Dothideomycetes, order Pleosporales), having different host ranges and pathogenic abilities towards cruciferous plants, were studied to infer the role of TEs on genome shaping, speciation, and on the rise of better adapted pathogens.

          Results

          L. maculans ‘brassicae’, the most damaging species on oilseed rape, is the only member of the species complex to have a TE-invaded genome (32.5%) compared to the other members genomes (<4%). These TEs had an impact at the structural level by creating large TE-rich regions and are suspected to have been instrumental in chromosomal rearrangements possibly leading to speciation. TEs, associated with species-specific genes involved in disease process, also possibly had an incidence on evolution of pathogenicity by promoting translocations of effector genes to highly dynamic regions and thus tuning the regulation of effector gene expression in planta.

          Conclusions

          Invasion of L. maculans ‘brassicae’ genome by TEs followed by bursts of TE activity allowed this species to evolve and to better adapt to its host, making this genome species a peculiarity within its own species complex as well as in the Pleosporales lineage.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1471-2164-15-891) contains supplementary material, which is available to authorized users.

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          Most cited references 115

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            Clustal W and Clustal X version 2.0.

            The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems. The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/
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              TopHat: discovering splice junctions with RNA-Seq

              Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                jonathan.grandaubert@gmail.com
                rohan.lowe@unimelb.edu.au
                jessica.soyer@versailles.inra.fr
                schoch2@ncbi.nlm.nih.gov
                apvdw2@unimelb.edu.au
                fudal@versailles.inra.fr
                robberts@ncbi.nlm.nih.gov
                nicolas.lapalu@versailles.inra.fr
                Matthew.Links@agr.gc.ca
                benedicte.ollivier@versailles.inra.fr
                juliette.linglin@versailles.inra.fr
                vbarbe@genoscope.cns.fr
                mangenot@genoscope.cns.fr
                cruaud@genoscope.cns.fr
                Hossein.Borhan@agr.gc.ca
                bhowlett@unimelb.edu.au
                mhb@versailles.inra.fr
                rouxel@versailles.inra.fr
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                12 October 2014
                12 October 2014
                2014
                : 15
                : 1
                Affiliations
                [ ]INRA-Bioger, UR1290, Avenue Lucien Brétignières, BP 01, 78850 Thiverval-Grignon, France
                [ ]School of Botany, The University of Melbourne, Melbourne, Victoria 3010 Australia
                [ ]NIH/NLM/NCBI, 45 Center Drive, MSC 6510, Bethesda, Maryland 20892-6510 USA
                [ ]INRA-Bioger, URGI Route de St Cyr, 78026 Versailles Cedex, France
                [ ]Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada, Saskatoon Research Centre, 107 Science Place, Saskatoon, SK S7N OX2 Canada
                [ ]Department of Computer, University of Saskatchewan, 110 Science Place, Saskatoon, SK S7N 5C9 Canada
                [ ]GENOSCOPE, Centre National de Séquençage, Institut de Génomique CEA/DSV, 2, rue Gaston Crémieux, CP 5706, 91057 Evry Cedex, France
                Article
                6595
                10.1186/1471-2164-15-891
                4210507
                25306241
                © Grandaubert et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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                Research Article
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                © The Author(s) 2014

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