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      Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          The population genetic perspective is that the processes shaping genomic variation can be revealed only through simultaneous investigation of sequence polymorphism and divergence within and between closely related species. Here we present a population genetic analysis of Drosophila simulans based on whole-genome shotgun sequencing of multiple inbred lines and comparison of the resulting data to genome assemblies of the closely related species, D. melanogaster and D. yakuba. We discovered previously unknown, large-scale fluctuations of polymorphism and divergence along chromosome arms, and significantly less polymorphism and faster divergence on the X chromosome. We generated a comprehensive list of functional elements in the D. simulans genome influenced by adaptive evolution. Finally, we characterized genomic patterns of base composition for coding and noncoding sequence. These results suggest several new hypotheses regarding the genetic and biological mechanisms controlling polymorphism and divergence across the Drosophila genome, and provide a rich resource for the investigation of adaptive evolution and functional variation in D. simulans.

          Author Summary

          Population genomics, the study of genome-wide patterns of sequence variation within and between closely related species, can provide a comprehensive view of the relative importance of mutation, recombination, natural selection, and genetic drift in evolution. It can also provide fundamental insights into the biological attributes of organisms that are specifically shaped by adaptive evolution. One approach for generating population genomic datasets is to align DNA sequences from whole-genome shotgun projects to a standard reference sequence. We used this approach to carry out whole-genome analysis of polymorphism and divergence in Drosophila simulans, a close relative of the model system, D. melanogaster. We find that polymorphism and divergence fluctuate on a large scale across the genome and that these fluctuations are probably explained by natural selection rather than by variation in mutation rates. Our analysis suggests that adaptive protein evolution is common and is often related to biological processes that may be associated with gene expression, chromosome biology, and reproduction. The approaches presented here will have broad applicability to future analysis of population genomic variation in other systems, including humans.


          Low-coverage genome sequences from multiple Drosophila simulans strains provide the first comprehensive view of polymorphism and divergence in the fruit fly.

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          Most cited references 126

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          Dating of the human-ape splitting by a molecular clock of mitochondrial DNA.

          A new statistical method for estimating divergence dates of species from DNA sequence data by a molecular clock approach is developed. This method takes into account effectively the information contained in a set of DNA sequence data. The molecular clock of mitochondrial DNA (mtDNA) was calibrated by setting the date of divergence between primates and ungulates at the Cretaceous-Tertiary boundary (65 million years ago), when the extinction of dinosaurs occurred. A generalized least-squares method was applied in fitting a model to mtDNA sequence data, and the clock gave dates of 92.3 +/- 11.7, 13.3 +/- 1.5, 10.9 +/- 1.2, 3.7 +/- 0.6, and 2.7 +/- 0.6 million years ago (where the second of each pair of numbers is the standard deviation) for the separation of mouse, gibbon, orangutan, gorilla, and chimpanzee, respectively, from the line leading to humans. Although there is some uncertainty in the clock, this dating may pose a problem for the widely believed hypothesis that the pipedal creature Australopithecus afarensis, which lived some 3.7 million years ago at Laetoli in Tanzania and at Hadar in Ethiopia, was ancestral to man and evolved after the human-ape splitting. Another likelier possibility is that mtDNA was transferred through hybridization between a proto-human and a proto-chimpanzee after the former had developed bipedalism.
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            The Bioperl toolkit: Perl modules for the life sciences.

            The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.
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              Molecular signatures of natural selection.

              There is an increasing interest in detecting genes, or genomic regions, that have been targeted by natural selection. The interest stems from a basic desire to learn more about evolutionary processes in humans and other organisms, and from the realization that inferences regarding selection may provide important functional information. This review provides a nonmathematical description of the issues involved in detecting selection from DNA sequences and SNP data and is intended for readers who are not familiar with population genetic theory. Particular attention is placed on issues relating to the analysis of large-scale genomic data sets.

                Author and article information

                Role: Academic Editor
                PLoS Biol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                November 2007
                6 November 2007
                : 5
                : 11
                [1 ] Department of Evolution and Ecology, University of California Davis, Davis, California, United States of America
                [2 ] Center for Population Biology, University of California Davis, Davis, California, United States of America
                [3 ] Genome Sequencing Center, Washington University School of Medicine, St. Louis, Missouri, United States of America
                [4 ] Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan Authority
                [5 ] Research Center for Biodiversity, Academica Sinica, Taipei, Taiwan Authority
                [6 ] Department of Biology, Indiana University, Bloomington, Indiana, United States of America
                [7 ] School of Informatics, Indiana University, Bloomington, Indiana, United States of America
                [8 ] Department of Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America
                [9 ] Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
                [10 ] Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America
                [11 ] Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America
                [12 ] Department of Mathematics, University of California, Berkeley, California, United States of America
                [13 ] Department of Computer Science, University of California, Berkeley, California, United States of America
                Duke University, United States of America
                Author notes
                * To whom correspondence should be addressed. E-mail: djbegun@ 123456ucdavis.edu (DJB); akholloway@ 123456ucdavis.edu (AKH); chlangley@ 123456ucdavis.edu (CHL)
                07-PLBI-RA-0660R3 plbi-05-11-14
                Copyright: © 2007 Begun et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 26
                Research Article
                Computational Biology
                Evolutionary Biology
                Genetics and Genomics
                Custom metadata
                Begun DJ, Holloway AK, Stevens K, Hillier LW, Poh YP, et al. (2007) Population genomics: whole-genome analysis of polymorphism and divergence in Drosophila simulans. PLoS Biol 5(11): e310. doi: 10.1371/journal.pbio.0050310

                Life sciences


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