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      The barley pan-genome reveals the hidden legacy of mutation breeding

      research-article
      1 , 1 , 2 , 2 , 2 , 1 , 2 , 2 , 2 , 1 , 3 , 4 , 4 , 4 , 5 , 4 , 4 , 4 , 6 , 7 , 7 , 7 , 1 , 1 , 8 , 9 , 9 , 9 , 7 , 7 , 9 , 10 , 11 , 12 , 13 , 13 , 13 , 14 , 14 , 6 , 14 , 15 , 3 , 1 , 2 , 16 , 2 , 4 , 5 , 17 , , 1 , 18 , , 1 , 19 ,
      Nature
      Nature Publishing Group UK
      Structural variation, Genomics, Plant genetics

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          Abstract

          Genetic diversity is key to crop improvement. Owing to pervasive genomic structural variation, a single reference genome assembly cannot capture the full complement of sequence diversity of a crop species (known as the ‘pan-genome’ 1 ). Multiple high-quality sequence assemblies are an indispensable component of a pan-genome infrastructure. Barley ( Hordeum vulgare L.) is an important cereal crop with a long history of cultivation that is adapted to a wide range of agro-climatic conditions 2 . Here we report the construction of chromosome-scale sequence assemblies for the genotypes of 20 varieties of barley—comprising landraces, cultivars and a wild barley—that were selected as representatives of global barley diversity. We catalogued genomic presence/absence variants and explored the use of structural variants for quantitative genetic analysis through whole-genome shotgun sequencing of 300 gene bank accessions. We discovered abundant large inversion polymorphisms and analysed in detail two inversions that are frequently found in current elite barley germplasm; one is probably the product of mutation breeding and the other is tightly linked to a locus that is involved in the expansion of geographical range. This first-generation barley pan-genome makes previously hidden genetic variation accessible to genetic studies and breeding.

          Abstract

          Chromosome-scale sequence assemblies of 20 diverse varieties of barley are used to construct a first-generation pan-genome, revealing previously hidden genetic variation that can be used by studies aimed at crop improvement

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          Most cited references66

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              Minimap2: pairwise alignment for nucleotide sequences

              Heng Li (2018)
              Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.
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                Author and article information

                Contributors
                C.Li@murdoch.edu.au
                mascher@ipk-gatersleben.de
                stein@ipk-gatersleben.de
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                25 November 2020
                25 November 2020
                2020
                : 588
                : 7837
                : 284-289
                Affiliations
                [1 ]GRID grid.418934.3, ISNI 0000 0001 0943 9907, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, ; Seeland, Germany
                [2 ]GRID grid.4567.0, ISNI 0000 0004 0483 2525, Plant Genome and Systems Biology (PGSB), Helmholtz Center Munich, , German Research Center for Environmental Health, ; Neuherberg, Germany
                [3 ]GRID grid.25152.31, ISNI 0000 0001 2154 235X, Department of Plant Sciences, , University of Saskatchewan, ; Saskatoon, Saskatchewan Canada
                [4 ]GRID grid.1025.6, ISNI 0000 0004 0436 6763, Western Barley Genetics Alliance, State Agricultural Biotechnology Centre, College of Science, Health, Engineering and Education, , Murdoch University, ; Murdoch, Western Australia Australia
                [5 ]GRID grid.493004.a, Agriculture and Food, Department of Primary Industries and Regional Development, ; South Perth, Western Australia Australia
                [6 ]GRID grid.43641.34, ISNI 0000 0001 1014 6626, The James Hutton Institute, ; Dundee, UK
                [7 ]GRID grid.417691.c, ISNI 0000 0004 0408 3720, HudsonAlpha, Institute for Biotechnology, ; Huntsville, AL USA
                [8 ]Montana BioAg Inc, Missoula, MT USA
                [9 ]GRID grid.410727.7, ISNI 0000 0001 0526 1937, Institute of Crop Sciences, , Chinese Academy of Agricultural Sciences (ICS-CAAS), ; Beijing, China
                [10 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, College of Agriculture and Biotechnology, , Zhejiang University, ; Hangzhou, China
                [11 ]GRID grid.7597.c, ISNI 0000000094465255, Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, ; Yokohama, Japan
                [12 ]GRID grid.268441.d, ISNI 0000 0001 1033 6139, Kihara Institute for Biological Research, , Yokohama City University, ; Yokohama, Japan
                [13 ]GRID grid.261356.5, ISNI 0000 0001 1302 4472, Institute of Plant Science and Resources, , Okayama University, ; Kurashiki, Japan
                [14 ]GRID grid.1010.0, ISNI 0000 0004 1936 7304, School of Agriculture, Food and Wine, , University of Adelaide, ; Glen Osmond, South Australia Australia
                [15 ]GRID grid.8241.f, ISNI 0000 0004 0397 2876, School of Life Sciences, , University of Dundee, ; Dundee, UK
                [16 ]GRID grid.6936.a, ISNI 0000000123222966, School of Life Sciences Weihenstephan, , Technical University of Munich, ; Freising, Germany
                [17 ]GRID grid.410654.2, ISNI 0000 0000 8880 6009, Hubei Collaborative Innovation Centre for Grain Industry, , Yangtze University, ; Jingzhou, China
                [18 ]GRID grid.421064.5, ISNI 0000 0004 7470 3956, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, ; Leipzig, Germany
                [19 ]GRID grid.7450.6, ISNI 0000 0001 2364 4210, Center for Integrated Breeding Research (CiBreed), , Georg-August-University Göttingen, ; Göttingen, Germany
                Author information
                http://orcid.org/0000-0003-2951-0541
                http://orcid.org/0000-0003-3125-3695
                http://orcid.org/0000-0001-6550-1648
                http://orcid.org/0000-0002-6757-0943
                http://orcid.org/0000-0002-0574-3976
                http://orcid.org/0000-0002-5543-1911
                http://orcid.org/0000-0002-2166-0716
                http://orcid.org/0000-0003-2663-1006
                http://orcid.org/0000-0002-2400-1732
                http://orcid.org/0000-0002-7943-3997
                http://orcid.org/0000-0002-2556-2478
                http://orcid.org/0000-0001-8062-9172
                http://orcid.org/0000-0001-9970-5382
                http://orcid.org/0000-0002-3868-2380
                http://orcid.org/0000-0001-8818-5203
                http://orcid.org/0000-0001-9494-400X
                http://orcid.org/0000-0003-1045-3065
                http://orcid.org/0000-0002-7536-3856
                http://orcid.org/0000-0001-6113-3518
                http://orcid.org/0000-0001-6484-1077
                http://orcid.org/0000-0003-0701-7035
                http://orcid.org/0000-0002-9653-2700
                http://orcid.org/0000-0001-6373-6013
                http://orcid.org/0000-0003-3011-8731
                Article
                2947
                10.1038/s41586-020-2947-8
                7759462
                33239781
                f03fef14-56d0-4648-85a4-1e611e5ba843
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 April 2020
                : 9 September 2020
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                © The Author(s), under exclusive licence to Springer Nature Limited 2020

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                structural variation,genomics,plant genetics
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                structural variation, genomics, plant genetics

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