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        Statistical Population Genomics

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        Contributor(s)
        Dutheil, Julien Y. (editor)
        Language
        English
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        Abstract
        This open access volume presents state-of-the-art inference methods in population genomics, focusing on data analysis based on rigorous statistical techniques. After introducing general concepts related to the biology of genomes and their evolution, the book covers state-of-the-art methods for the analysis of genomes in populations, including demography inference, population structure analysis and detection of selection, using both model-based inference and simulation procedures. Last but not least, it offers an overview of the current knowledge acquired by applying such methods to a large variety of eukaryotic organisms. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, pointers to the relevant literature, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Statistical Population Genomics aims to promote and ensure successful applications of population genomic methods to an increasing number of model systems and biological questions.
        URI
        http://library.oapen.org/handle/20.500.12657/23339
        Keywords
        Life sciences; Bioinformatics; Genetics
        DOI
        10.1007/978-1-0716-0199-0
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2020
        Series
        Methods in Molecular Biology,
        Classification
        Genetics (non-medical)
        Molecular biology
        Pages
        468
        Rights
        https://creativecommons.org/licenses/by/4.0
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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