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      Genomic signatures of strawberry domestication and diversification

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      The Plant Cell
      Oxford University Press

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          Abstract

          Cultivated strawberry ( Fragaria × ananassa) has a brief history of less than 300 yr, beginning with the hybridization of octoploids Fragaria chiloensis and Fragaria virginiana. Here we explored the genomic signatures of early domestication and subsequent diversification for different climates using whole-genome sequences of 289 wild, heirloom, and modern varieties from two major breeding programs in the United States. Four nonadmixed wild octoploid populations were identified, with recurrent introgression among the sympatric populations. The proportion of F. virginiana ancestry increased by 20% in modern varieties over initial hybrids, and the proportion of F. chiloensis subsp. pacifica rose from 0% to 3.4%. Effective population size rapidly declined during early breeding. Meanwhile, divergent selection for distinct environments reshaped wild allelic origins in 21 out of 28 chromosomes. Overlapping divergent selective sweeps in natural and domesticated populations revealed 16 convergent genomic signatures that may be important for climatic adaptation. Despite 20 breeding cycles since initial hybridization, more than half of loci underlying yield and fruit size are still not under artificial selection. These insights add clarity to the domestication and breeding history of what is now the most widely cultivated fruit in the world.

          Abstract

          Whole-genome sequences of 289 octoploid strawberry samples were used to explore the genomic signatures of domestication and subsequent diversification in different climates.

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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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            IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

            Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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              ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

              High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .
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                Author and article information

                Contributors
                Journal
                Plant Cell
                Plant Cell
                plcell
                The Plant Cell
                Oxford University Press (US )
                1040-4651
                1532-298X
                May 2024
                19 December 2023
                19 December 2023
                : 36
                : 5
                : 1622-1636
                Affiliations
                Horticultural Sciences Department, University of Florida, IFAS Gulf Coast Research and Education Center , Wimauma, FL 33597, USA
                Horticultural Sciences Department, University of Florida, IFAS Gulf Coast Research and Education Center , Wimauma, FL 33597, USA
                Author notes
                Author for correspondence: vwhitaker@ 123456ufl.edu

                The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors ( https://academic.oup.com/plcell/pages/General-Instructions) is: Vance M. Whitaker ( vwhitaker@ 123456ufl.edu ).

                Conflict of interest statement. None declared.

                Author information
                https://orcid.org/0000-0002-8965-7898
                https://orcid.org/0000-0002-2172-3019
                Article
                koad314
                10.1093/plcell/koad314
                11062436
                38113879
                238826f1-5890-4ca6-a942-90f232d210cb
                © The Author(s) 2023. Published by Oxford University Press on behalf of American Society of Plant Biologists.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 12 September 2023
                : 25 November 2023
                : 28 December 2023
                Page count
                Pages: 15
                Categories
                Large-Scale Biology
                AcademicSubjects/SCI01270
                AcademicSubjects/SCI01280
                AcademicSubjects/SCI02286
                AcademicSubjects/SCI02287
                AcademicSubjects/SCI02288

                Plant science & Botany
                Plant science & Botany

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