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      Continent-wide genomic signatures of adaptation to urbanisation in a songbird across Europe

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          Abstract

          Urbanisation is increasing worldwide, and there is now ample evidence of phenotypic changes in wild organisms in response to this novel environment. Yet, the genetic changes and genomic architecture underlying these adaptations are poorly understood. Here, we genotype 192 great tits ( Parus major) from nine European cities, each paired with an adjacent rural site, to address this major knowledge gap in our understanding of wildlife urban adaptation. We find that a combination of polygenic allele frequency shifts and recurrent selective sweeps are associated with the adaptation of great tits to urban environments. While haplotypes under selection are rarely shared across urban populations, selective sweeps occur within the same genes, mostly linked to neural function and development. Collectively, we show that urban adaptation in a widespread songbird occurs through unique and shared selective sweeps in a core-set of behaviour-linked genes.

          Abstract

          The genetic architecture underlying rapid adaptive responses to novel environments are poorly understood. A study of great tits from nine European cities finds that urban adaptation in a widespread songbird occurred through unique and shared selective sweeps in a core-set of behaviour-linked genes.

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                pablo.salmon.saro@gmail.com
                Caroline.Isaksson@biol.lu.se
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 May 2021
                20 May 2021
                2021
                : 12
                : 2983
                Affiliations
                [1 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Department of Biology, , Lund University, ; Lund, Sweden
                [2 ]GRID grid.8756.c, ISNI 0000 0001 2193 314X, Institute of Biodiversity, Animal Health and Comparative Medicine, , University of Glasgow, ; Glasgow, UK
                [3 ]GRID grid.462350.6, Sorbonne Université, UPEC, Paris 7, CNRS, INRA, IRD, Institut d’Écologie et des Sciences de l’Environnement de Paris, iEES Paris, ; F-75005 Paris, France
                [4 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Department of Biology, , Ludwig Maximilians University Munich, ; Munich, Germany
                [5 ]GRID grid.507605.1, ISNI 0000 0001 1958 5537, Museu de Ciències Naturals de Barcelona, ; Barcelona, Spain
                [6 ]GRID grid.418375.c, ISNI 0000 0001 1013 0288, Department of Animal Ecology, , Netherlands Institute of Ecology (NIOO-KNAW), ; Wageningen, Netherlands
                [7 ]GRID grid.4830.f, ISNI 0000 0004 0407 1981, Present Address: GELIFES - Groningen Institute for Evolutionary Life Sciences, University of Groningen, ; Groningen, The Netherlands
                Author information
                http://orcid.org/0000-0001-9718-6611
                http://orcid.org/0000-0001-7635-5447
                http://orcid.org/0000-0001-7474-5345
                http://orcid.org/0000-0003-3320-0861
                http://orcid.org/0000-0003-2063-9955
                http://orcid.org/0000-0002-6648-1463
                http://orcid.org/0000-0001-9955-3892
                http://orcid.org/0000-0002-1456-1939
                http://orcid.org/0000-0002-6889-1386
                Article
                23027
                10.1038/s41467-021-23027-w
                8137928
                34016968
                239bc3ef-7bfb-4d37-a12a-0152f1ed60c0
                © The Author(s) 2021

                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
                : 5 May 2020
                : 1 April 2021
                Funding
                Funded by: Ministry of Economics and Competiveness (CGL-2016-79568-C3-3-P)
                Funded by: FundRef https://doi.org/10.13039/501100004359, Vetenskapsrådet (Swedish Research Council);
                Award ID: C0361301
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                ecology,evolution,molecular biology
                Uncategorized
                ecology, evolution, molecular biology

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