Evolutionary biology broadly covers phylogeny or the phylogenetic tree, representing the relationships among individuals or groups of organisms. Specifically, computational phylogenetics deals with the implementation of computational methods and algorithms for phylogeny estimation. This book introduces design concepts and methods for phylogeny estimation.
The book is divided into two parts: “Basic Techniques” (seven chapters) and “Molecular Phylogenetics” (four chapters). Part 1 introduces phylogenetic estimation, trees, constructing trees from true subtrees, constructing trees from qualitative characters, distance-based tree estimation methods, consensus and agreement trees, and supertrees. Chapter 1 includes the Cavender-Farris-Neyman (CFN) model and CFN tree estimation, and provides methods such as unweighted pair-group method with arithmetic mean (UPGMA), maximum parsimony, maximum likelihood (ML), and Markov chain Monte Carlo (MCMC) for phylogeny estimation used in practice.
Chapter 2 discusses various trees, namely rooted, unrooted, binary, strict consensus, estimated, homomorphic subtrees, and some special trees. Chapter 3 uses Aho, Sagiv, Szymanski, and Ullman’s algorithm to describe the construction of trees from true subtrees and qualitative characters, and chapter 4 covers maximum parsimony, compatible characters, and maximum compatibility. Chapter 5 explains UPGMA, the four-point method, the naive quartet method, the Buneman tree, neighbor joining, minimum evolution, and safety radius as distance-based tree estimation methods. Chapter 6 is on consensus and agreement trees and clustering sets of trees, and chapter 7, “Supertrees,” includes compatibility, asymmetric median, Robinson-Foulds, and quartet-based supertrees; matrix representation with parsimony and likelihood; the strict consensus merger; and the SuperFine method.
Part 2 consists of four chapters (8 through 11): “Statistical Gene Tree Estimation Methods,” “Multiple Sequence Alignment,” “Phylogenomics: Constructing Species Phylogenies from Multi-Locus Data,” and “Designing Methods for Large-Scale Phylogeny Estimation.” Chapter 8 presents three site evolution models: nucleotide site evolution, deoxyribonucleic acid (DNA) sequence evolution, and amino acid site evolution. It further explains ML implementation, Bayesian phylogenetics, sample complexity, heterotachy, and the no common mechanism model. Chapter 9 describes optimization problems for multiple sequence alignment and sequence alignment methods such as seed, aligning, and progressive.
Chapter 10 constructs species phylogenies from multilocus data and discusses a multi-species coalescent model (MSC). It also covers species tree estimation using summary, co-estimation, and site-based methods, as well as species tree estimation under duplication and loss models via gene family evolution, orthology detection, gene tree parsimony, and statistical methods. In addition, the author discusses phylogenetic networks covering reticulate evolution, evolutionary networks, and data display networks. Chapter 11 focuses on large-scale phylogeny estimation, including distance-based methods, subtree assembly-based methods, heuristics for NP-hard optimization problems, and Bayesian MCMC methods. It also discusses distance-based disk-covering methods (DCMs), tree-based DCMs, DACTAL (a general-purpose DCM), and triangulated graphs.
Each chapter ends with three sections: “Further Reading,” “Review Questions,” and “Homework Problems,” making this book an interesting read for doctoral students, researchers, and professionals working in the area of computational phylogeny estimation. The book’s main highlights are its four appendices, extensive references, and short index. Appendix C, “Guidelines for Writing Papers About Computational Methods,” makes this book worth reading.
