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      A physical and genetic map of Cannabis sativa identifies extensive rearrangements at the THC/CBD acid synthase loci

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          Abstract

          Cannabis sativa is widely cultivated for medicinal, food, industrial, and recreational use, but much remains unknown regarding its genetics, including the molecular determinants of cannabinoid content. Here, we describe a combined physical and genetic map derived from a cross between the drug-type strain Purple Kush and the hemp variety “Finola.” The map reveals that cannabinoid biosynthesis genes are generally unlinked but that aromatic prenyltransferase ( AP), which produces the substrate for THCA and CBDA synthases (THCAS and CBDAS), is tightly linked to a known marker for total cannabinoid content. We further identify the gene encoding CBCA synthase ( CBCAS) and characterize its catalytic activity, providing insight into how cannabinoid diversity arises in cannabis. THCAS and CBDAS (which determine the drug vs. hemp chemotype) are contained within large (>250 kb) retrotransposon-rich regions that are highly nonhomologous between drug- and hemp-type alleles and are furthermore embedded within ∼40 Mb of minimally recombining repetitive DNA. The chromosome structures are similar to those in grains such as wheat, with recombination focused in gene-rich, repeat-depleted regions near chromosome ends. The physical and genetic map should facilitate further dissection of genetic and molecular mechanisms in this commercially and medically important plant.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs.

              Genomics has revolutionized biological research, but quality assessment of the resulting assembled sequences is complicated and remains mostly limited to technical measures like N50.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                January 2019
                January 2019
                : 29
                : 1
                : 146-156
                Affiliations
                [1 ]Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada;
                [2 ]Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada;
                [3 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
                [4 ]Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA;
                [5 ]Department of Botany, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada;
                [6 ]CanniMed Therapeutics Incorporated, Saskatoon, Saskatchewan S7K 3J8, Canada;
                [7 ]Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada;
                [8 ]Canadian Institute for Advanced Research, Toronto, Ontario M5G 1M1, Canada;
                [9 ]Anandia Labs, Vancouver, British Columbia V6T 1Z4, Canada
                Author notes
                Author information
                http://orcid.org/0000-0002-8721-4719
                http://orcid.org/0000-0002-1376-6916
                Article
                9509184
                10.1101/gr.242594.118
                6314170
                30409771
                b79c6ba9-5029-4b2e-9432-7bc3fb115344
                © 2019 Laverty et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 6 August 2018
                : 7 November 2018
                Page count
                Pages: 11
                Funding
                Funded by: Canadian Institutes of Health Research , open-funder-registry 10.13039/501100000024;
                Award ID: MOP-126070
                Award ID: FDN-148403
                Funded by: Canadian Institute for Advanced Research (CIFAR) , open-funder-registry 10.13039/100007631;
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Funded by: National Institute of Allergy and Infectious Diseases , open-funder-registry 10.13039/100000060;
                Award ID: R01 AI119145
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