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      Genome-culture coevolution promotes rapid divergence of killer whale ecotypes

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          Abstract

          Analysing population genomic data from killer whale ecotypes, which we estimate have globally radiated within less than 250,000 years, we show that genetic structuring including the segregation of potentially functional alleles is associated with socially inherited ecological niche. Reconstruction of ancestral demographic history revealed bottlenecks during founder events, likely promoting ecological divergence and genetic drift resulting in a wide range of genome-wide differentiation between pairs of allopatric and sympatric ecotypes. Functional enrichment analyses provided evidence for regional genomic divergence associated with habitat, dietary preferences and post-zygotic reproductive isolation. Our findings are consistent with expansion of small founder groups into novel niches by an initial plastic behavioural response, perpetuated by social learning imposing an altered natural selection regime. The study constitutes an important step towards an understanding of the complex interaction between demographic history, culture, ecological adaptation and evolution at the genomic level.

          Abstract

          Killer whales have evolved into specialized ecotypes based on hunting strategies and ecological niches. Here, Andrew Foote and colleagues sequenced the whole genome of individual killer whales representing 5 different ecotypes from North Pacific and Antarctic, and show expansion of small founder groups to adapt to specific ecological niches.

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          Most cited references74

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                31 May 2016
                2016
                : 7
                : 11693
                Affiliations
                [1 ]Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University , Norbyvägen 18D, Uppsala SE-752 36, Sweden
                [2 ]Centre for GeoGenetics, Natural History Museum of Denmark, University of Copenhagen , Øster Volgade 5-7, Copenhagen K 1350, Denmark
                [3 ]Computational and Molecular Population Genetics Laboratory, Institute of Ecology and Evolution, University of Bern , Baltzerstrasse 6, Bern 3012, Switzerland
                [4 ]Department of Genetics, Stanford University , Stanford, California 94305, USA
                [5 ]Cascadia Research , 4th Avenue, Olympia, Washington 98501, USA
                [6 ]Marine Mammal and Turtle Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanographic and Atmospheric Administration , 8901 La Jolla Shores Drive, La Jolla, California 92037, USA
                [7 ]Department of Genetics, Evolution, and Environment, UCL Genetics Institute, University College London , London WC1E 6BT, UK
                [8 ]Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza , Houston, Texas 77030, USA
                [9 ]Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration , 2725 Montlake Boulevard East, Seattle, Washington 98112, USA
                [10 ]Faculty of Mathematics, Physics and Informatics, Comenius University, Mlynska Dolina , Bratislava 84248, Slovakia
                [11 ]National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration , 7600 Sand Point Way NE, Seattle, Washington 98115, USA
                [12 ]Department of Environment and Agriculture, Trace and Environmental DNA Laboratory, Curtin University , Perth, Western Australia 6102, Australia
                [13 ]Department of Evolutionary Biology, Science for Life Laboratory, Evolutionary Biology Centre, Uppsala University , Uppsala 75236, Sweden
                [14 ]Section of Evolutionary Biology, Department of Biology II, Ludwig Maximilian University of Munich , Großhaderner Strasse 2, Planegg-Martinsried 82152, Germany
                Author notes
                [*]

                These authors contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-7507-6494
                Article
                ncomms11693
                10.1038/ncomms11693
                4895049
                27243207
                e7b56b9b-dbb0-4680-a286-6334e30dd83f
                Copyright © 2016, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 19 July 2015
                : 18 April 2016
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