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      Enemy or ally: a genomic approach to elucidate the lifestyle of Phyllosticta citrichinaensis


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          Members of the fungal genus Phyllosticta can colonize a variety of plant hosts, including several Citrus species such as Citrus sinensis (orange), Citrus limon (lemon), and Citrus maxima (pomelo). Some Phyllosticta species have the capacity to cause disease, such as Citrus Black Spot, while others have only been observed as endophytes. Thus far, genomic differences underlying lifestyle adaptations of Phyllosticta species have not yet been studied. Furthermore, the lifestyle of Phyllosticta citrichinaensis is ambiguous, as it has been described as a weak pathogen but Koch’s postulates may not have been established and the presence of this species was never reported to cause any crop or economic losses. Here, we examined the genomic differences between pathogenic and endophytic Phyllosticta spp. colonizing Citrus and specifically aimed to elucidate the lifestyle of Phyllosticta citrichinaensis. We found several genomic differences between species of different lifestyles, including groups of genes that were only present in pathogens or endophytes. We also observed that species, based on their carbohydrate active enzymes, group independent of their phylogenetic association, and this clustering correlated with trophy prediction. Phyllosticta citrichinaensis shows an intermediate lifestyle, sharing genomic and phenotypic attributes of both pathogens and endophytes. We thus present the first genomic comparison of multiple citrus-colonizing pathogens and endophytes of the genus Phyllosticta, and therefore provide the basis for further comparative studies into the lifestyle adaptations within this genus.

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          Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

          Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.
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            QUAST: quality assessment tool for genome assemblies.

            Limitations of genome sequencing techniques have led to dozens of assembly algorithms, none of which is perfect. A number of methods for comparing assemblers have been developed, but none is yet a recognized benchmark. Further, most existing methods for comparing assemblies are only applicable to new assemblies of finished genomes; the problem of evaluating assemblies of previously unsequenced species has not been adequately considered. Here, we present QUAST-a quality assessment tool for evaluating and comparing genome assemblies. This tool improves on leading assembly comparison software with new ideas and quality metrics. QUAST can evaluate assemblies both with a reference genome, as well as without a reference. QUAST produces many reports, summary tables and plots to help scientists in their research and in their publications. In this study, we used QUAST to compare several genome assemblers on three datasets. QUAST tables and plots for all of them are available in the Supplementary Material, and interactive versions of these reports are on the QUAST website. http://bioinf.spbau.ru/quast . Supplementary data are available at Bioinformatics online.
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              OrthoFinder: phylogenetic orthology inference for comparative genomics

              Here, we present a major advance of the OrthoFinder method. This extends OrthoFinder’s high accuracy orthogroup inference to provide phylogenetic inference of orthologs, rooted gene trees, gene duplication events, the rooted species tree, and comparative genomics statistics. Each output is benchmarked on appropriate real or simulated datasets, and where comparable methods exist, OrthoFinder is equivalent to or outperforms these methods. Furthermore, OrthoFinder is the most accurate ortholog inference method on the Quest for Orthologs benchmark test. Finally, OrthoFinder’s comprehensive phylogenetic analysis is achieved with equivalent speed and scalability to the fastest, score-based heuristic methods. OrthoFinder is available at https://github.com/davidemms/OrthoFinder.

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press
                May 2022
                21 March 2022
                21 March 2022
                : 12
                : 5
                : jkac061
                [1 ] Evolutionary Phytopathology, Westerdijk Fungal Biodiversity Institute , Utrecht 3584 CT, The Netherlands
                [2 ] Department of Plant Sciences, Laboratory of Phytopathology, Wageningen University and Research , Wageningen 6708 PB, The Netherlands
                [3 ] US Department of Energy Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley , CA 94720, USA
                [4 ] Department of Biology, Institut National de la Recherche Agronomique, UMR INRA-Université de Lorraine “Interaction Arbres/Microorganismes” , Champenoux F-54280, France
                [5 ] Department of Plant and Microbial Biology, University of California Berkeley , Berkeley, CA 94720, USA
                [6 ] Theoretical Biology & Bioinformatics, Utrecht University , Utrecht 3584 CH, The Netherlands
                Author notes
                Corresponding author: Evolutionary Phytopathology, Westerdijk Fungal Biodiversity Institute, Uppsalalaan 8, 3584 CT Utrecht, The Netherlands. Email: v.buijs@ 123456wi.knaw.nl ; Corresponding author: Theoretical Biology & Bioinformatics, Utrecht University, Utrecht 3584 CH, The Netherlands. Email: m.f.seidl@ 123456uu.nl
                Author information
                © The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 02 March 2022
                : 24 November 2021
                : 11 April 2022
                Page count
                Pages: 13
                Funded by: Dutch Applied Science division (TTW) of NWO and the Technology Program of the Ministry of Infrastructure and Water Management;
                Award ID: 15807
                Funded by: Research Programme I&W Biotechnology and Safety;
                Funded by: U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, is supported by the Office of Science of the U.S. Department of Energy;
                Award ID: DE-AC02-05CH11231

                fungal plant pathogens,genomics,cazymes,lifestyle adaptations,citrus,endophyte,pathogen
                fungal plant pathogens, genomics, cazymes, lifestyle adaptations, citrus, endophyte, pathogen


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