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      Comparative genomics of the wheat fungal pathogen Pyrenophora tritici-repentis reveals chromosomal variations and genome plasticity

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          Abstract

          Background

          Pyrenophora tritici-repentis (Ptr) is a necrotrophic fungal pathogen that causes the major wheat disease, tan spot. We set out to provide essential genomics-based resources in order to better understand the pathogenicity mechanisms of this important pathogen.

          Results

          Here, we present eight new Ptr isolate genomes, assembled and annotated; representing races 1, 2 and 5, and a new race. We report a high quality Ptr reference genome, sequenced by PacBio technology with Illumina paired-end data support and optical mapping. An estimated 98% of the genome coverage was mapped to 10 chromosomal groups, using a two-enzyme hybrid approach. The final reference genome was 40.9 Mb and contained a total of 13,797 annotated genes, supported by transcriptomic and proteogenomics data sets.

          Conclusions

          Whole genome comparative analysis revealed major chromosomal segmental rearrangements and fusions, highlighting intraspecific genome plasticity in this species. Furthermore, the Ptr race classification was not supported at the whole genome level, as phylogenetic analysis did not cluster the ToxA producing isolates. This expansion of available Ptr genomics resources will directly facilitate research aimed at controlling tan spot disease.

          Electronic supplementary material

          The online version of this article (10.1186/s12864-018-4680-3) contains supplementary material, which is available to authorized users.

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          Most cited references69

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          LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons

          Background Transposable elements are abundant in eukaryotic genomes and it is believed that they have a significant impact on the evolution of gene and chromosome structure. While there are several completed eukaryotic genome projects, there are only few high quality genome wide annotations of transposable elements. Therefore, there is a considerable demand for computational identification of transposable elements. LTR retrotransposons, an important subclass of transposable elements, are well suited for computational identification, as they contain long terminal repeats (LTRs). Results We have developed a software tool LTRharvest for the de novo detection of full length LTR retrotransposons in large sequence sets. LTRharvest efficiently delivers high quality annotations based on known LTR transposon features like length, distance, and sequence motifs. A quality validation of LTRharvest against a gold standard annotation for Saccharomyces cerevisae and Drosophila melanogaster shows a sensitivity of up to 90% and 97% and specificity of 100% and 72%, respectively. This is comparable or slightly better than annotations for previous software tools. The main advantage of LTRharvest over previous tools is (a) its ability to efficiently handle large datasets from finished or unfinished genome projects, (b) its flexibility in incorporating known sequence features into the prediction, and (c) its availability as an open source software. Conclusion LTRharvest is an efficient software tool delivering high quality annotation of LTR retrotransposons. It can, for example, process the largest human chromosome in approx. 8 minutes on a Linux PC with 4 GB of memory. Its flexibility and small space and run-time requirements makes LTRharvest a very competitive candidate for future LTR retrotransposon annotation projects. Moreover, the structured design and implementation and the availability as open source provides an excellent base for incorporating novel concepts to further improve prediction of LTR retrotransposons.
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            Using RepeatMasker to identify repetitive elements in genomic sequences.

            The RepeatMasker program is used for identifying repetitive elements in nucleotide sequences for further detailed analyses. Users can run RepeatMasker remotely via a Web site, or, for larger input sequences, the program and its dependent programs may be downloaded and run locally on Unix/Linux computers. The protocols in this chapter detail how to use RepeatMasker both remotely and locally to extract repetitive elements data and mask these repetitive elements in nucleotide sequences.
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              SMURF: Genomic mapping of fungal secondary metabolite clusters.

              Fungi produce an impressive array of secondary metabolites (SMs) including mycotoxins, antibiotics and pharmaceuticals. The genes responsible for their biosynthesis, export, and transcriptional regulation are often found in contiguous gene clusters. To facilitate annotation of these clusters in sequenced fungal genomes, we developed the web-based software SMURF (www.jcvi.org/smurf/) to systematically predict clustered SM genes based on their genomic context and domain content. We applied SMURF to catalog putative clusters in 27 publicly available fungal genomes. Comparison with genetically characterized clusters from six fungal species showed that SMURF accurately recovered all clusters and detected additional potential clusters. Subsequent comparative analysis revealed the striking biosynthetic capacity and variability of the fungal SM pathways and the correlation between unicellularity and the absence of SMs. Further genetics studies are needed to experimentally confirm these clusters. 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Paula.Moolhuijzen@curtin.edu.au
                Paotheen.See@curtin.edu.au
                james.hane@curtin.edu.au
                gongjun.shi@ndsu.edu
                zhh.liu@ndsu.edu
                Richard.Oliver@curtin.edu.au
                Caroline.Moffat@curtin.edu.au
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                23 April 2018
                23 April 2018
                2018
                : 19
                : 279
                Affiliations
                [1 ]ISNI 0000 0004 0375 4078, GRID grid.1032.0, Centre for Crop Disease and Management, Department of Environment and Agriculture, , Curtin University, ; Bentley, Western Australia Australia
                [2 ]ISNI 0000 0001 2293 4611, GRID grid.261055.5, Department of Plant Pathology, , North Dakota State University, ; Fargo, ND USA
                Author information
                http://orcid.org/0000-0002-3502-7612
                Article
                4680
                10.1186/s12864-018-4680-3
                5913888
                29685100
                a8fdc4c4-681c-4cdb-bd83-df97a56c38c9
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

                History
                : 23 October 2017
                : 16 April 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000980, Grains Research and Development Corporation;
                Award ID: COR00023
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2018

                Genetics
                wheat,fungal pathogen,comparative genomics,toxa,race
                Genetics
                wheat, fungal pathogen, comparative genomics, toxa, race

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