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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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