Evolutionary Genomics Statistical and Computational Methods /

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. A...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Anisimova, Maria (Editor)
Format: Electronic eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Humana, 2019.
Edition:2nd ed. 2019.
Series:Methods in Molecular Biology, 1910
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Online Access:Link to Metadata
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Summary: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.
Physical Description:XVII, 780 p. 189 illus., 110 illus. in color. online resource.
ISBN:9781493990740
ISSN:1940-6029 ;
DOI:10.1007/978-1-4939-9074-0
Access:Open Access