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      The draft genome of a socially polymorphic halictid bee, Lasioglossum albipes

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          Abstract

          Background

          Taxa that harbor natural phenotypic variation are ideal for ecological genomic approaches aimed at understanding how the interplay between genetic and environmental factors can lead to the evolution of complex traits. Lasioglossum albipes is a polymorphic halictid bee that expresses variation in social behavior among populations, and common-garden experiments have suggested that this variation is likely to have a genetic component.

          Results

          We present the L. albipes genome assembly to characterize the genetic and ecological factors associated with the evolution of social behavior. The de novo assembly is comparable to other published social insect genomes, with an N50 scaffold length of 602 kb. Gene families unique to L. albipes are associated with integrin-mediated signaling and DNA-binding domains, and several appear to be expanded in this species, including the glutathione-s-transferases and the inositol monophosphatases. L. albipes has an intact DNA methylation system, and in silico analyses suggest that methylation occurs primarily in exons. Comparisons to other insect genomes indicate that genes associated with metabolism and nucleotide binding undergo accelerated evolution in the halictid lineage. Whole-genome resequencing data from one solitary and one social L. albipes female identify six genes that appear to be rapidly diverging between social forms, including a putative odorant receptor and a cuticular protein.

          Conclusions

          L. albipes represents a novel genetic model system for understanding the evolution of social behavior. It represents the first published genome sequence of a primitively social insect, thereby facilitating comparative genomic studies across the Hymenoptera as a whole.

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          Most cited references 41

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          The genomic basis of adaptive evolution in threespine sticklebacks

          Summary Marine stickleback fish have colonized and adapted to innumerable streams and lakes formed since the last ice age, providing an exceptional opportunity to characterize genomic mechanisms underlying repeated ecological adaptation in nature. Here we develop a high quality reference genome assembly for threespine sticklebacks. By sequencing the genomes of 20 additional individuals from a global set of marine and freshwater populations, we identify a genome-wide set of loci that are consistently associated with marine-freshwater divergence. Our results suggest that reuse of globally-shared standing genetic variation, including chromosomal inversions, plays an important role in repeated evolution of distinct marine and freshwater sticklebacks, and in the maintenance of divergent ecotypes during early stages of reproductive isolation. Both coding and regulatory changes occur in the set of loci underlying marine-freshwater evolution, with regulatory changes likely predominating in this classic example of repeated adaptive evolution in nature.
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            Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

            High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.
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              PHYML Online—a web server for fast maximum likelihood-based phylogenetic inference

              PHYML Online is a web interface to PHYML, a software that implements a fast and accurate heuristic for estimating maximum likelihood phylogenies from DNA and protein sequences. This tool provides the user with a number of options, e.g. nonparametric bootstrap and estimation of various evolutionary parameters, in order to perform comprehensive phylogenetic analyses on large datasets in reasonable computing time. The server and its documentation are available at .
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                Author and article information

                Contributors
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2013
                20 December 2013
                : 14
                : 12
                : R142
                Affiliations
                [1 ]Department of Organismic and Evolutionary Biology, Museum of Comparative Zoology, Harvard University, 26 Oxford St, Cambridge, MA 02138, USA
                [2 ]China National GeneBank, BGI-Shenzhen, Shenzen 518083, China
                [3 ]Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen, Øster Voldgade 5-7, Copenhagen 1350, Denmark
                [4 ]School of Biology, Georgia Institute of Technology, Atlanta, GA 30332, USA
                [5 ]Department of Molecular and Cellular Biology, Howard Hughes Medical Institute, Harvard University, 26 Oxford St, Cambridge, MA 02138, USA
                [6 ]Centre for Social Evolution, Department of Biology, University of Copenhagen, Universitetsparken 15, Copenhagen DK-2100, Denmark
                [7 ]State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
                [8 ]School of Biological Sciences, University of East Anglia, Norwich Research Park, Norwich, Norfolk NR47TJ, UK
                Article
                gb-2013-14-12-r142
                10.1186/gb-2013-14-12-r142
                4062844
                24359881
                Copyright © 2013 Kocher et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research

                Genetics

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