Evolutionary Genomics [E-Book] : Statistical and Computational Methods / edited by Maria Anisimova.
Anisimova, Maria, (editor)
2nd edition 2019.
New York, NY : Humana Press, 2019
XVII, 780 pages 189 illustrations, 110 illustrations in color (online resource)
englisch
9781493990740
10.1007/978-1-4939-9074-0
Methods in Molecular Biology ; 1910
Full Text
Table of Contents:
  • Introduction to Genome Biology and Diversity
  • Probability, Statistics, and Computational Science
  • A Not-So-Long Introduction to Computational Molecular Evolution
  • Whole-Genome Alignment
  • Inferring Orthology and Paralogy
  • Transposable Elements and Their Identification
  • Modern Phylogenomics: Building Phylogenetic Trees Using the Multispecies Coalescent Model
  • Genome-Wide Comparative Analysis of Phylogenetic Trees: The Prokaryotic Forest of Life
  • The Methodology Behind Network-Thinking: Graphs to Analyze Microbial Complexity and Evolution
  • Bayesian Molecular Clock Dating Using Genome-Scale Datasets
  • Genome Evolution in Outcrossing vs. Selfing vs. Asexual Species
  • Selection Acting on Genomes
  • Looking for Darwin in Genomic Sequences: Validity and Success Depends on the Relationship between Model and Data
  • Evolution of Viral Genomes: Interplay between Selection, Recombination, and Other Forces
  • Evolution of Protein Domain Architectures
  • New Insights on the Evolution of Genome Content: Population Dynamics of Transposable Elements in Flies and Humans
  • Association Mapping and Disease: Evolutionary Perspectives
  • Ancestral Population Genomics
  • Introduction to the Analysis of Environmental Sequences: Metagenomics with MEGAN
  • Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials
  • Systems Genetics for Evolutionary Studies
  • Analyzing Epigenome Data in Context of Genome Evolution and Human Diseases
  • Semantic Integration and Enrichment of Heterogeneous Biological Databases
  • High-Performance Computing in Bayesian Phylogenetics and Phylodynamics Using BEAGLE
  • Scalable Workflows and Reproducible Data Analysis for Genomics
  • Sharing Programming Resources between Bio* Projects.