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1024 can be brought down to earth, and in phylogenetics that ...

1024 can be brought down to earth, and in phylogenetics that ...

2007 BOOK REVIEWS 1025

2007 BOOK REVIEWS 1025 Phylogeny Reconstruction (1996), Ziheng Yang does not attempt to completely cover the field of phylogenetics. Instead, he adopts the point of view that molecular evolutionary analyses should be formulated as a problem of statistical inference. This is a long-standing viewpoint (Cavalli-Sforza and Edwards, 1967) but one that has been neglected among most texts. The book is divided into three parts, namely, Modeling Molecular Evolution, Phylogeny Reconstruction, and Advanced Topics. The first impression when reading the book is the high level of detail and discussions. Topics are described in a logical chain from simple to complex where the increase in complexity is motivated by biological facts. Concepts and notations are used consistently throughout the book. This consistency enhances readability, and is an advantage compared to edited books where the notation often changes from one chapter to the next. Modeling Molecular Evolution comprises two chapters: Models of Nucleotide Substitution and Models of Amino Acid and Codon Substitution. Both chapters form the foundation for the rest of the book. Starting with the traditional problem of estimating the evolutionary distance between two aligned homologous sequences, the first chapter demonstrates clearly how biological evidence needs to be included in the modeling process. The argumentation is, as usual, that we set out with the simplest Jukes-Cantor model (Jukes and Cantor, 1969) and then include more and more biological realism, to end up with the general time-reversible model (Tavaré 1986), including among-site rate heterogeneity. The next addition of biologically realistic features, the nonindependence of sites, is unfortunately not explained, although suitable models abound that deal with this new development (Schöniger and von Haeseler, 1994; Pollock et al., 1999). Chapter two applies Markov processes to model amino acid substitutions. Ziheng Yang distinguishes between empirical and mechanistic rate matrices. One example of an empirical model is the well-known Dayhoff substitution matrix (Dayhoff et al., 1978), whereas as a prime example of mechanistic models Yang features the codon model (Yang et al., 1998). The chapter closes with a description of the numerical calculation of a transition probability matrix. The second part of the book deals with phylogenetic reconstruction. In the third chapter the terminology for trees is presented, including the problem of searching through tree space. Then the nonstatistical tree reconstruction approaches (distance methods, maximum parsimony) are briefly summarized. Readers who want to learn more about these methods will need to consult other texts (Swofford et al., 1996; Salemi and Vandamme, 2003; Felsenstein, 2004). Chapter four delves into maximum likelihood analysis to infer both phylogenies and model parameters of the substitution process. The “pruning” algorithm to calculate the likelihood for fixed trees under different models is described thoroughly. Particularly valuable is the section “Numerical Algorithms for Maximum Likelihood Estimation”. This section provides some advice on how to boost the efficiency of maximum likelihood–based software. The numerical aspect of (likelihood) computation is often neglected in other textbooks, but it plays an important role when it comes to the implementation of methods. Bayesian analysis is another philosophy in statistical data analysis. Although the basic theory has been established for centuries, its application to phylogenetic analyses has been studied for only a decade (Rannala and Yang, 1996; Mau and Newton 1997). The two main obstacles are the computational burden and the subjective knowledge about the prior distribution of parameters. The first has been relieved by the availability of powerful computers and applying Markov chain Monte Carlo approximations. However, the second still remains an unavoidable criticism from opponents. Chapter five discusses criticisms of both the maximum likelihood and Bayesian schools of thought. It proceeds through Bayesian theorems, Markov chain Monte Carlo approximations, and their specifications in calculating the posterior probability of phylogenies under different models. Biologists might struggle with formula 5.40 for calculating the posterior probability of a tree, but the formula can be rewritten in a more elegant manner (Huelsenbeck and Ronquist, 2001). The last chapter of part two is a collection of reviews on those methodologies that compare phylogenetic reconstruction approaches and assess the goodness of reconstructed trees. Because different phylogenetic reconstruction methods are based on different philosophies, comparing results is a tricky business. Thus, it is not surprising that the controversy about the “best” method cannot be solved. However, this chapter provides basic and well-established statistical concepts used to validate methods, e.g., “consistency, efficiency and robustness.” Besides modeling molecular evolution processes and constructing phylogenies, computational molecular evolution obviously comprises additional crucial topics. The advanced part of the book describes a selection of these topics. The four chapters focus on Molecular Clock and Estimation of Species Divergence Time, Neutral and Adaptive Protein Evolution, Simulating of Molecular Evolution, and finally Perspectives. Known to be embarrassing confusion for many people, the branch lengths of a tree reflect the expected number of substitutions but not the divergence time. To infer the divergence time (or the age of ancestors), fossil calibrations must be incorporated. Combining both molecular data and as many fossil calibrations as possible to estimate the divergence time is crucial to actually date splitting times. Chapter seven escorts the reader from traditional approaches, based on the unrealistic assumption of a global molecular clock, to more sophisticated maximum likelihood approaches and Bayesian analysis. This chapter is definitely interesting and clearly shows that the entire field is still in a developmental stage. Thus, any novice in the field may get some inspiration for further research. Downloaded from http://sysbio.oxfordjournals.org/ by guest on April 4, 2013

1026 SYSTEMATIC BIOLOGY VOL. 56 Notwithstanding that the Darwinian theory of selection is dominant in evolution studies, neutral theory proposes a different view of evolution (Kimura, 1968). It suggests that the adaptation of genes does not depend on the advantage of fitness, but rather is determined by random fixation of mutations. Chapter eight, Neutral and Adaptive Protein Evolution, lucidly presents the neutral theory and computational approaches to test the neutrality of amino acid substitutions. Researchers interested in the evolution of proteins will find the section Amino Acids Sites Undergoing Adaptive Evolution a useful summary of updated strategies to detect amino acid sites under positive selection. Chapter nine presents technical materials to simulate molecular evolution. Because we lack the luxury of knowing the true alignments and the true phylogenetic trees (in most cases), simulations appear to be one appropriate way to validate approaches to phylogenetic inference. Indeed, the more realistically that simulations are designed, the more reliably conclusions can be drawn. The last chapter discusses some perspectives within molecular evolution, such as exploring heterogeneous datasets, investigating genome-rearrangement data, or genome comparisons. It points students and younger researchers towards potentially active areas in the foreseeable future. In summary, Ziheng Yang has presented a very interesting and readable book that highlights aspects of computational molecular evolution that are not mentioned in other contributions to the field. It also points out open problems that provide ample space for future research. The book covers essential topics in a logical order to progressively educate the reader. It describes topics in a high level of detail with appropriate biological motivations and is full of valuable discussions. The book is highly recommended to graduate computer scientists, mathematicians, and biologists. However, every novice in the field should be aware that a large degree of mathematical, statistical, and biological knowledge is necessary to follow the full argumentation in the book. Although Ziheng Yang provides a sometimes very personal view of computational molecular evolution, the book is a valuable source of thought-provoking aspects of the field. It also discusses some very interesting but not well-known statistical papers. Thus, the book clearly shows that one should consult, every now and then, the wealth of publications in statistics and probability theory. Le Sy Vinh, Invertebrate Zoology Division, American Museum of Natural History, Central Park West at 79th street, 10024 New York, New York, USA; E-mail: vle@amnh.org Arndt von Haeseler, Center for Integrative Bioinformatics Vienna, Max F Perutz Laboratories, University of Vienna, Medical University of Vienna, University of Veterinary Medicine Vienna, Dr. Bohr Gasse 9/6, A1030 Vienna, Austria; E-mail: arndt.von.haeseler@univie.ac.at REFERENCES Cavalli-Sforza, L. L., and A. W. F. Edwards. 1967. Phylogenetic analysis: Models and estimation procedures. Evolution 21:550–570. Dayhoff, M. O., R. M. Schwartz, and B. C. Orcutt. 1978. A model for evolutionary change in proteins. Pages 345–352 in Atlas of protein sequence and structure, volume 5, supplement 3 (M. O. Dayhoff, ed.). National Biomedical Research Foundation, Washington, DC. Felsenstein, J. 2004. Inferring phylogenies. Sinauer Associates, Sunderland, Massachusetts. Huelsenbeck, J. P., and F. Ronquist. 2001. MrBayes: Bayesian inference of phylogenetic trees. Bioinformatics 17:754–755. Jukes, T. H., and C. R. Cantor. 1969. Evolution of protein molecules. Pages 21–132 in Mammalian protein metabolism (H. N. Munro, ed.). Academic Press, New York. Kimura, M. 1968. Evolutionary rate at the molecular level. Nature 271:624–626. Mau, B., and M. Newton. 1997. Phylogenetic inference for binary data on dendrograms using Markov chain Monte Carlo. J. Comput. Graph. Stat. 6:122–131. Pollock, D. D., W. R. Taylor, and N. Goldman 1999. Coevolving protein residues: Maximum likelihood identification and relationship to structure. J. Mol. Biol. 287:187–198. Rannala, B., and Z. Yang. 1996. Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference. J. Mol. Evol. 43:304–311. Salemi, M., and A.-M. Vandamme (eds.). 2003. The phylogenetic handbook: a practical approach to DNA and protein phylogeny. Cambridge University Press, Cambridge, UK. Schöniger, M., and A. von Haeseler. 1994. A stochastic model for the evolution of autocorrelated DNA sequences. Mol. Phylogenet. Evol. 3:240–247. Swofford, D. L., G. J. Olsen, P. J. Waddell, and D. M. Hillis. 1996. Phylogenetic inference. Pages 407–514 in Molecular systematics, 2nd edition (D. M. Hillis, C. Moritz, and B. K. Mable, eds.). Sinauer Associates, Sunderland, Massachusetts. Tavaré, S. 1986. Some probabilistic and statistical sequences. Lect. Math. Life Sci. 17:57–86. Yang, Z., R. Nielsen, and M. Hasegawa. 1998. Models of amino acid substitution and applications to mitochondrial protein evolution. Mol. Biol. Evol. 15:1600–1611. Downloaded from http://sysbio.oxfordjournals.org/ by guest on April 4, 2013

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