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      Genome sequence of Gossypium herbaceum and genome updates of Gossypium arboreum and Gossypium hirsutum provide insights into cotton A-genome evolution

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          Abstract

          Upon assembling the first Gossypium herbaceum (A 1) genome and substantially improving the existing Gossypium arboreum (A 2) and Gossypium hirsutum ((AD) 1) genomes, we showed that all existing A-genomes may have originated from a common ancestor, referred to here as A 0, which was more phylogenetically related to A 1 than A 2. Further, allotetraploid formation was shown to have preceded the speciation of A 1 and A 2. Both A-genomes evolved independently, with no ancestor–progeny relationship. Gaussian probability density function analysis indicates that several long-terminal-repeat bursts that occurred from 5.7 million years ago to less than 0.61 million years ago contributed compellingly to A-genome size expansion, speciation and evolution. Abundant species-specific structural variations in genic regions changed the expression of many important genes, which may have led to fiber cell improvement in (AD) 1. Our findings resolve existing controversial concepts surrounding A-genome origins and provide valuable genomic resources for cotton genetic improvement.

          Abstract

          Assembly of the first Gossypium herbaceum genome and improved Gossypium arboreum and Gossypium hirsutum genomes provide insights into the phylogenetic relationships and origin history of cotton A-genomes.

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          PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R

          Bastian Pfeifer, Ulrich Wittelsbürger, Sebastián Ramos-Onsins … (2014)
          Although many computer programs can perform population genetics calculations, they are typically limited in the analyses and data input formats they offer; few applications can process the large data sets produced by whole-genome resequencing projects. Furthermore, there is no coherent framework for the easy integration of new statistics into existing pipelines, hindering the development and application of new population genetics and genomics approaches. Here, we present PopGenome, a population genomics package for the R software environment (a de facto standard for statistical analyses). PopGenome can efficiently process genome-scale data as well as large sets of individual loci. It reads DNA alignments and single-nucleotide polymorphism (SNP) data sets in most common formats, including those used by the HapMap, 1000 human genomes, and 1001 Arabidopsis genomes projects. PopGenome also reads associated annotation files in GFF format, enabling users to easily define regions or classify SNPs based on their annotation; all analyses can also be applied to sliding windows. PopGenome offers a wide range of diverse population genetics analyses, including neutrality tests as well as statistics for population differentiation, linkage disequilibrium, and recombination. PopGenome is linked to Hudson’s MS and Ewing’s MSMS programs to assess statistical significance based on coalescent simulations. PopGenome’s integration in R facilitates effortless and reproducible downstream analyses as well as the production of publication-quality graphics. Developers can easily incorporate new analyses methods into the PopGenome framework. PopGenome and R are freely available from CRAN (http://cran.r-project.org/) for all major operating systems under the GNU General Public License.
          Article 0 comments Cited 435 times – based on 0 reviews     Review now
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            The genome of Theobroma cacao.

            Xavier Argout, Jérôme Salse, Jean-Marc Aury … (2011)
            We sequenced and assembled the draft genome of Theobroma cacao, an economically important tropical-fruit tree crop that is the source of chocolate. This assembly corresponds to 76% of the estimated genome size and contains almost all previously described genes, with 82% of these genes anchored on the 10 T. cacao chromosomes. Analysis of this sequence information highlighted specific expansion of some gene families during evolution, for example, flavonoid-related genes. It also provides a major source of candidate genes for T. cacao improvement. Based on the inferred paleohistory of the T. cacao genome, we propose an evolutionary scenario whereby the ten T. cacao chromosomes were shaped from an ancestor through eleven chromosome fusions.
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              Bayesian inference of ancient human demography from individual genome sequences

              Ilan Gronau, Melissa Hubisz, Brad Gulko … (2011)
              Besides their value for biomedicine, individual genome sequences are a rich source of information about human evolution. Here we describe an effort to estimate key evolutionary parameters from sequences for six individuals from diverse human populations. We use a Bayesian, coalescent-based approach to extract information about ancestral population sizes, divergence times, and migration rates from inferred genealogies at many neutrally evolving loci from across the genome. We introduce new methods for accommodating gene flow between populations and integrating over possible phasings of diploid genotypes. We also describe a custom pipeline for genotype inference to mitigate biases from heterogeneous sequencing technologies and coverage levels. Our analysis indicates that the San of Southern Africa diverged from other human populations 108–157 thousand years ago (kya), that Eurasians diverged from an ancestral African population 38–64 kya, and that the effective population size of the ancestors of all modern humans was ~9,000.
              Article 0 comments Cited 273 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                John Z. Yu:
                ORCID: http://orcid.org/0000-0002-7417-3102
                john.yu@usda.gov
                Yuxian Zhu:
                ORCID: http://orcid.org/0000-0003-2371-5933
                zhuyx@whu.edu.cn
                Journal
                Journal ID (nlm-ta): Nat Genet
                Journal ID (iso-abbrev): Nat. Genet
                Title: Nature Genetics
                Publisher: Nature Publishing Group US (New York )
                ISSN (Print): 1061-4036
                ISSN (Electronic): 1546-1718
                Publication date (Electronic): 13 April 2020
                Publication date PMC-release: 13 April 2020
                Publication date (Print): 2020
                Volume: 52
                Issue: 5
                Pages: 516-524
                Affiliations
                [1 ]ISNI 0000 0001 2331 6153, GRID grid.49470.3e, Institute for Advanced Studies, , Wuhan University, ; Wuhan, China
                [2 ]ISNI 0000 0001 2256 9319, GRID grid.11135.37, State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, , Peking University, ; Beijing, China
                [3 ]ISNI 0000 0001 2331 6153, GRID grid.49470.3e, College of Life Sciences, , Wuhan University, ; Wuhan, China
                [4 ]ISNI 0000 0001 0946 3608, GRID grid.463419.d, Crop Germplasm Research Unit, Southern Plains Agricultural Research Center, United States Department of Agriculture-Agricultural Research Service (USDA-ARS), ; College Station, TX USA
                [5 ]ISNI 0000 0001 2034 1839, GRID grid.21155.32, BGI Genomics, BGI-Shenzhen, ; Shenzhen, China
                [6 ]GRID grid.459813.2, Nextomics Biosciences Institute, ; Wuhan, China
                Author information
                http://orcid.org/0000-0003-0426-9691
                http://orcid.org/0000-0001-5910-1568
                http://orcid.org/0000-0002-7417-3102
                http://orcid.org/0000-0003-2371-5933
                Article
                Publisher ID: 607
                DOI: 10.1038/s41588-020-0607-4
                PMC ID: 7203013
                PubMed ID: 32284579
                SO-VID: c75e50be-9b09-49f0-8075-48bdb60ae7a3
                Copyright © © The Author(s) 2020
                License:

                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
                Date received : 24 September 2019
                Date accepted : 4 March 2020
                Funding
                Funded by: The Natural Science Foundation of China (31690090, 31690091)
                Funded by: The United States Department of Agriculture, Agricultural Research Service (USDA-ARS project 3091-21000-044-00D)
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s), under exclusive licence to Springer Nature America, Inc. 2020

                ScienceOpen disciplines: Genetics
                Keywords: sequencing,plant sciences,genomics
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: sequencing, plant sciences, genomics

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