Ulteriori informazioni
Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies. Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight. Volume 2 begins with phylogenomics and continues with in-depth coverage of natural selection, recombination, and genomic innovation. The remaining chapters treat topics of more recent interest, including population genomics, -omics studies, and computational issues related to the handling of large-scale genomic data. Written in the highly successful Methods in Molecular Biology(TM) series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses.
Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.
Sommario
Tangled Trees: The Challenge of Inferring Species Trees from Coalescent and Non-Coalescent Genes.- Modeling Gene Family Evolution and Reconciling Phylogenetic Discord.- Genome-Wide Comparative Analysis of Phylogenetic Trees: The Prokaryotic Forest of Life.- Philosophy and Evolution: Minding the Gap Between Evolutionary Patterns and Tree-Like Patterns.- Selection on the Protein Coding Genome.- Methods to Detect Selection on Non-Coding DNA.- The Origin and Evolution of New Genes.- Evolution of Protein Domain Architectures.- Estimating Recombination Rates from Genetic Variation in Humans.- Evolution of Viral Genomes: Interplay Between Selection, Recombination, and Other Forces.- Association Mapping and Disease: Evolutionary Perspectives.- Ancestral Population Genomics.- Non-Redundant Representation of Ancestral Recombinations Graphs.- Using Genomic Tools to Study Regulatory Evolution.- Characterization and Evolutionary Analysis of Protein-Protein Interaction Networks.- Statistical Methods in Metabolomics.- Introduction to the Analysis of Environmental Sequences: Metagenomics with MEGAN.- Analyzing Epigenome Data in Context of Genome Evolution and Human Diseases.- Genetical Genomics for Evolutionary Studies.- Genomics Data Resources: Frameworks and Standards.- Sharing Programming Resources Between Bio* Projects through Remote Procedure Call and Native Call Stack Strategies.- Scalable Computing for Evolutionary Genomics.
Riassunto
Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies. Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight. Volume 2 begins with phylogenomics and continues with in-depth coverage of natural selection, recombination, and genomic innovation. The remaining chapters treat topics of more recent interest, including population genomics, -omics studies, and computational issues related to the handling of large-scale genomic data. Written in the highly successful Methods in Molecular Biology™ series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses.
Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.
