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        Evolutionary Genomics

        Statistical and Computational Methods

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        Contributor(s)
        Anisimova, Maria (editor)
        Language
        English
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        Abstract
        This open access book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sections on sequence homology and alignment, phylogenetic methods to study genome evolution, methodologies for evaluating selective pressures on genomic sequences as well as genomic evolution in light of protein domain architecture and transposable elements, population genomics and other omics, and discussions of current bottlenecks in handling and analyzing genomic data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that lead to the best results. Authoritative and comprehensive, Evolutionary Genomics: Statistical and Computational Methods, Second Edition aims to serve both novices in biology with strong statistics and computational skills, and molecular biologists with a good grasp of standard mathematical concepts, in moving this important field of study forward.
        URI
        http://library.oapen.org/handle/20.500.12657/23338
        Keywords
        Life sciences; Bioinformatics; Genetics; Evolutionary biology
        DOI
        10.1007/978-1-4939-9074-0
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2019
        Series
        Methods in Molecular Biology,
        Classification
        Evolution
        Genetics (non-medical)
        Molecular biology
        Pages
        780
        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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