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Computational Molecular Evolution
출판사 : Oxford University Press
저 자 : Yang
ISBN : 9780198567028
발행일 : 2006-11
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 376
판매가격 : 59,000원
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   Computational Molecular Evolution 목차
Modelling Molecular Evolution 1
Models of nucleotide substitution 3
Introduction 3
Markov models of nucleotide substitution and distance estimation 4
The JC69 model 4
The K80 model 10
HKY85, F84, TN93, etc. 11
The transition/transversion rate ratio 17
Variable substitution rates across sites 18
Maximum likelihood estimation 22
The JC69 model 22
The K80 model 25
Profile and integrated likelihood methods 27
Markov chains and distance estimation under general models 30
General theory 30
The general time-reversible (GTR) model 33
Discussions 37
Distance estimation under different substitution models 37
Limitations of pairwise comparison 37
Exercises 38
Models of amino acid and codon substitution 40
Introduction 40
Models of amino acid replacement 40
Empirical models 40
Mechanistic models 43
Among-site heterogeneity 44
Estimation of distance between two protein sequences 45
The Poisson model 45
Empirical models 46
Gamma distances 46
Example: distance between cat and rabbit p53 genes 47
Models of codon substitution 48
Estimation of synonymous and nonsynonymous substitution rates 49
Counting methods 50
Maximum likelihood method 58
Comparison of methods 61
Interpretation and a plethora of distances 62
Numerical calculation of the transition-probability matrix 68
Exercises 70
Phylogeny Reconstruction 71
Phylogeny reconstruction: overview 73
Tree concepts 73
Terminology 73
Topological distance between trees 77
Consensus trees 79
Gene trees and species trees 80
Classification of tree-reconstruction methods 81
Exhaustive and heuristic tree search 82
Exhaustive tree search 82
Heuristic tree search 83
Branch swapping 84
Local peaks in the tree space 87
Stochastic tree search 89
Distance methods 89
Least-squares method 90
Neighbour-joining method 92
Maximum parsimony 93
Brief history 93
Counting the minimum number of changes given the tree 94
Weighted parsimony and transversion parsimony 95
Long-branch attraction 98
Assumptions of parsimony 99
Maximum likelihood methods 100
Introduction 100
Likelihood calculation on tree 100
Data, model, tree, and likelihood 100
The pruning algorithm 102
Time reversibility, the root of the tree and the molecular clock 106
Missing data and alignment gaps 107
An numerical example: phylogeny of apes 108
Likelihood calculation under more complex models 109
Models of variable rates among sites 110
Models for combined analysis of multiple data sets 116
Nonhomogeneous and nonstationary models 118
Amino acid and codon models 119
Reconstruction of ancestral states 119
Overview 119
Empirical and hierarchical Bayes reconstruction 121
Discrete morphological characters 124
Systematic biases in ancestral reconstruction 126
Numerical algorithms for maximum likelihood estimation 128
Univariate optimization 129
Multivariate optimization 131
Optimization on a fixed tree 134
Multiple local peaks on the likelihood surface for a fixed tree 135
Search for the maximum likelihood tree 136
Approximations to likelihood 137
Model selection and robustness 137
LRT, AIC, and BIC 137
Model adequacy and robustness 142
Exercises 144
Bayesian methods 145
The Bayesian paradigm 145
Overview 145
Bayes's theorem 146
Classical versus Bayesian statistics 151
Prior 158
Markov chain Monte Carlo 159
Monte Carlo integration 160
Metropolis-Hastings algorithm 161
Single-component Metropolis-Hastings algorithm 164
Gibbs sampler 166
Metropolis-coupled MCMC (MCMCMC or MC[superscript 3]) 166
Simple moves and their proposal ratios 167
Sliding window using the uniform proposal 168
Sliding window using normal proposal 168
Sliding window using the multivariate normal proposal 169
Proportional shrinking and expanding 170
Monitoring Markov chains and processing output 171
Validating and diagnosing MCMC algorithms 171
Potential scale reduction statistic 173
Processing output 174
Bayesian phylogenetics 174
Brief history 174
General framework 175
Summarizing MCMC output 175
Bayesian versus likelihood 177
A numerical example: phylogeny of apes 180
MCMC algorithms under the coalescent model 181
Overview 181
Estimation of [theta] 181
Exercises 184
Comparison of methods and tests on trees 185
Statistical performance of tree-reconstruction methods 186
Criteria 186
Performance 188
Likelihood 190
Contrast with conventional parameter estimation 190
Consistency 191
Efficiency 192
Robustness 196
Parsimony 198
Equivalence with misbehaved likelihood models 198
Equivalence with well-behaved likelihood models 201
Assumptions and Justifications 204
Testing hypotheses concerning trees 206
Bootstrap 207
Interior branch test 210
Kishino Hasegawa test and modifications 211
Indexes used in parsimony analysis 213
Example: phylogeny of apes 214
Appendix: Tuffley and Steel's likelihood analysis of one character 215
Advanced Topics 221
Molecular clock and estimation of species divergence times 223
Overview 223
Tests of the molecular clock 225
Relative-rate tests 225
Likelihood ratio test 226
Limitations of the clock tests 227
Index of dispersion 228
Likelihood estimation of divergence times 228
Global-clock model 228
Local-clock models 230
Heuristic rate-smoothing methods 231
Dating primate divergences 233
Uncertainties in fossils 235
Bayesian estimation of divergence times 245
General framework 245
Calculation of the likelihood 246
Prior on rates 247
Uncertainties in fossils and prior on divergence times 248
Application to primate and mammalian divergences 252
Perspectives 257
Neutral and adaptive protein evolution 259
Introduction 259
The neutral theory and tests of neutrality 260
The neutral and nearly neutral theory 260
Tajima's D statistic 262
Fu and Li's D and Fay and Wu's H statistics 264
McDonald-Kreitman test and estimation of selective strength 265
Hudson-Kreitman-Aquade test 267
Lineages undergoing adaptive evolution 268
Heuristic methods 268
Likelihood method 269
Amino acid sites undergoing adaptive evolution 271
Three strategies 271
Likelihood ratio test of positive selection under random-sites models 273
Identification of sites under positive selection 276
Positive selection in the human major histocompatability (MHC) locus 276
Adaptive evolution affecting particular sites and lineages 279
Branch-site test of positive selection 279
Other similar models 281
Adaptive evolution in angiosperm phytochromes 282
Assumptions, limitations, and comparisons 284
Limitations of current methods 284
Comparison between tests of neutrality and tests based on d[subscript N] and d[subscript S] 286
Adaptively evolving genes 286
Simulating molecular evolution 293
Introduction 293
Random number generator 294
Generation of continuous random variables 295
Generation of discrete random variables 296
Discrete uniform distribution 296
Binomial distribution 297
General discrete distribution 297
Multinomial distribution 298
The composition method for mixture distributions 298
The alias method for sampling from a discrete distribution 299
Simulating molecular evolution 302
Simulating sequences on a fixed tree 302
Generating random trees 305
Exercises 306
Perspectives 308
Theoretical issues in phylogeny reconstruction 308
Computational issues in analysis of large and heterogeneous data sets 309
Genome rearrangement data 309
Comparative genomics 310
Appendices 311
Functions of random variables 311
The delta technique 313
Phylogenetics software 316
References 319
Index 353
   도서 상세설명   

The field of molecular evolution has experienced explosive growth in recent years due to the rapid accumulation of genetic sequence data, continuous improvements to computer hardware and software, and the development of sophisticated analytical methods. The increasing availability of large genomic data sets requires powerful statistical methods to analyze and interpret them, generating both computational and conceptual challenges for the field.
Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The book emphasizes essential concepts rather than mathematical proofs. It includes detailed derivations and implementation details, as well as numerous illustrations, worked examples, and exercises. It will be of relevance and use to students and professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics and computer science. Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology.

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