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Domestication and Divergence of Saccharomyces cerevisiae Beer Yeasts

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      Summary

      Whereas domestication of livestock, pets, and crops is well documented, it is still unclear to what extent microbes associated with the production of food have also undergone human selection and where the plethora of industrial strains originates from. Here, we present the genomes and phenomes of 157 industrial Saccharomyces cerevisiae yeasts. Our analyses reveal that today’s industrial yeasts can be divided into five sublineages that are genetically and phenotypically separated from wild strains and originate from only a few ancestors through complex patterns of domestication and local divergence. Large-scale phenotyping and genome analysis further show strong industry-specific selection for stress tolerance, sugar utilization, and flavor production, while the sexual cycle and other phenotypes related to survival in nature show decay, particularly in beer yeasts. Together, these results shed light on the origins, evolutionary history, and phenotypic diversity of industrial yeasts and provide a resource for further selection of superior strains.

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      Highlights

      • We sequenced and phenotyped 157 S. cerevisiae yeasts
      • Present-day industrial yeasts originate from only a few domesticated ancestors
      • Beer yeasts show strong genetic and phenotypic hallmarks of domestication
      • Domestication of industrial yeasts predates microbe discovery

      Abstract

      The history and domestication of yeast used for making beer and other types of alcohol are revealed through genomic and phenotypic analyses.

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      Most cited references 73

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      Fast and accurate short read alignment with Burrows–Wheeler transform

      Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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        PLINK: a tool set for whole-genome association and population-based linkage analyses.

        Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Author and article information

            Affiliations
            [1 ]Laboratory for Genetics and Genomics, Centre of Microbial and Plant Genetics (CMPG), KU Leuven, Kasteelpark Arenberg 22, 3001 Leuven, Belgium
            [2 ]Laboratory for Systems Biology, VIB, Bio-Incubator, Gaston Geenslaan 1, 3001 Leuven, Belgium
            [3 ]Department of Plant Systems Biology, VIB, 9052 Gent, Belgium
            [4 ]Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Gent, Belgium
            [5 ]White Labs, 9495 Candida Street, San Diego, CA 92126, USA
            [6 ]Synthetic Genomics, 11149 North Torrey Pines Road, La Jolla, CA 92037, USA
            [7 ]Department of Microbiology and Immunology, Rega Institute, KU Leuven, 3000 Leuven, Belgium
            [8 ]Encinitas Brewing Science, 141 Rodney Avenue, Encinitas, CA 92024, USA
            [9 ]Illumina, 5200 Illumina Way, San Diego, CA 92122, USA
            [10 ]Biological & Popular Culture (BioPop), 2205 Faraday Avenue, Suite E, Carlsbad, CA 92008, USA
            Author notes
            []Corresponding author steven.maere@ 123456psb.vib-ugent.be
            [∗∗ ]Corresponding author kevin.verstrepen@ 123456biw.vib-kuleuven.be
            [11]

            Co-first author

            [12]

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            Contributors
            Journal
            Cell
            Cell
            Cell
            Cell Press
            0092-8674
            1097-4172
            08 September 2016
            08 September 2016
            : 166
            : 6
            : 1397-1410.e16
            27610566
            5018251
            S0092-8674(16)31071-6
            10.1016/j.cell.2016.08.020
            © 2016 The Author(s)

            This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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            Cell biology

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