关于演化学习的重要专著。
Evolutionary Genomics Statistical and Computational Methods
Maria Anisimova Editor
Second Edition
This volume is a thoroughly revised second edition of Evolutionary Genomics: Statistical and
Computational Methods published in 2012. Like the first edition, the new volume includes
comprehensive reviews of the most recent and fundamental developments in bioinformatics
methods for evolutionary genomics and related challenges associated with increasing data
size, heterogeneity, and its inherent complexity.
Throughout the volume, prominent authors address the challenge of analyzing and
understanding the dynamics of complex biological systems, and elaborate on some
promising strategies that would bring us closer to the ultimate “holy grail” of biology—
uncovering of the relationships between genotype and phenotype. Consequently, the presented
collection of peer-reviewed articles also represents a synergy between theoretical and
experimental scientists from a range of disciplines, working together towards a common
goal. Once again, the revised volume reiterates the power of taking an evolutionary
approach to study molecular data.
This book is intended for scientists looking for a compact overview of the cutting-edge
statistical and computational methods in evolutionary genomics. The volume may serve as a
comprehensive guide for both graduate and advanced undergraduate students planning to
specialize in genomics and bioinformatics. Equally, the volume should be helpful for
experienced researchers entering genomics from more fundamental disciplines, such as
statistics, computer science, physics, and biology. In other words, the material presented
here should suit both a novice in biology with strong statistics and computational skills and a
molecular biologist with a good grasp of standard mathematical concepts. To cater to
differences in reader backgrounds, Part I is composed of educational primers to help with
fundamental concepts in genome biology (Chapter 1), probability and statistics (Chapter 2),
and molecular evolution (Chapter 3). As these concepts reappear repeatedly throughout the
book, the first three chapters will help the neophyte to stay “afloat”. The exercises and
questions offered at the end of each chapter serve to deepen the understanding of the
material.
Part II of this volume focuses on sequence homology and alignment—from aligning
whole genomes (Chapter 4) to disentangling orthologs, paralogs, and transposable elements
(Chapters 5 and 6). Part III includes chapters on phylogenetic methods to study
genome evolution. Chapter 7 presents multispecies coalescent methods for reconciling
phylogenetic discord between gene and species trees. However, a mathematically convenient
“binary tree” model does not always live up to scrutiny as numerous evolutionary processes
act in reticulate (network-like) fashion, complicating the statistical description of evolutionary
models and increasing computational complexity, often to prohibitive levels. One
simplification is to assume that some molecular sequence units (genes, gene segments)
still evolve in a treelike manner. If so, Chapter 8 describes one practical approach to
meaningfully summarize the binary tree distributions for a set of genomes as a “forest of
trees”. Alternatively network-like phylogenetic relationships can be represented by graphs
(Chapter 9). Dating methods for genome-scale data are discussed in Chapter 10, while
Chapter 11 provides more examples of non-treelike processes in a comparative review of
genome evolution in different breeding systems.