1006817.pdf

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...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Γλώσσα:English
Έκδοση: Springer Nature 2020
Διαθέσιμο Online:https://www.springer.com/9781493990740
id oapen-20.500.12657-23338
record_format dspace
spelling oapen-20.500.12657-233382024-03-22T19:23:42Z Evolutionary Genomics Anisimova, Maria Life sciences Bioinformatics Genetics Evolutionary biology thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAJ Evolution thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSA Life sciences: general issues::PSAK Genetics (non-medical) thema EDItEUR::P Mathematics and Science::PS Biology, life sciences::PSD Molecular biology 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. 2020-03-18 13:36:15 2020-04-01T09:12:25Z 2020-04-01T09:12:25Z 2019 book 1006817 http://library.oapen.org/handle/20.500.12657/23338 eng Methods in Molecular Biology application/pdf n/a 1006817.pdf https://www.springer.com/9781493990740 Springer Nature 10.1007/978-1-4939-9074-0 10.1007/978-1-4939-9074-0 6c6992af-b843-4f46-859c-f6e9998e40d5 780 open access
institution OAPEN
collection DSpace
language English
description 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.
title 1006817.pdf
spellingShingle 1006817.pdf
title_short 1006817.pdf
title_full 1006817.pdf
title_fullStr 1006817.pdf
title_full_unstemmed 1006817.pdf
title_sort 1006817.pdf
publisher Springer Nature
publishDate 2020
url https://www.springer.com/9781493990740
_version_ 1799945306234683392