List of Contributors . . XXV
1 The minimum evolution distance-based approach to phylogenetic inference . . 1
1.1 Introduction . . 1
1.2 Tree metrics . . 3
1.3 Edge and tree length estimation . . 11
1.4 The agglomerative approach . . 17
1.5 Iterative topology searching and tree building . . 20
1.6 Statistical consistency . . 25
1.7 Discussion . . 28
Acknowledgements . . 29
2 Likelihood calculation in molecular phylogenetics . . 33
2.1 Introduction . . 33
2.2 Markov models of sequence evolution . . 35
2.3 Likelihood calculation: the basic algorithm . . 40
2.4 Likelihood calculation: improved models . . 42
2.5 Optimizing parameters . . 46
2.6 Consistency of the likelihood approach . . 49
2.7 Likelihood ratio tests . . 55
2.8 Concluding remarks . . 58
Acknowledgements . . 58
3 Bayesian inference in molecular phylogenetics . . 63
3.1 The likelihood function and maximum likelihood estimates . . 63
3.2 The Bayesian paradigm . . 66
3.3 Prior . . 67
3.4 Markov chain Monte Carlo . . 69
3.5 Simple moves and their proposal ratios . . 74
3.6 Monitoring Markov chains and processing output . . 78
3.7 Applications to molecular phylogenetics . . 81
3.8 Conclusions and perspectives . . 85
Acknowledgements . . 86
4 Statistical approach to tests involving phytogenies . . 91
4.1 The statistical approach to phylogenetic inference . . 91
4.2 Hypotheses testing . . 92
4.3 Different types of tests involving phylogenies . . 106
4.4 Non-parametric multivariate hypothesis testing . . 111
4.5 Conclusions: there are many open problems . . 115
Acknowledgements . . 115
5 Mixture models in phylogenetic inference . . 121
5.1 Introduction: models of gene-sequence evolution . . 121
5.2 Mixture models . . 122
5.3 Defining mixture models . . 123
5.4 Digression: Bayesian phylogenetic inference . . 125
5.5 A mixture model combining rate and pattern-heterogeneity . . 127
5.6 Application of the mixture model to inferring the phylogeny of the mammals . . 129
5.7 Results . . 131
5.8 Discussion . . 138
Acknowledgements . . 139
6 Hadamard conjugation: an analytic tool for phylogenetics . . 143
6.1 Introduction . .143
6.2 Hadamard conjugation for two sequences . .144
6.3 Some symmetric models of nucleotide substitution . . 147
6.4 Hadamard conjugation—Neyman model . . 151
6.5 Applications: using the Neyman model . . 162
6.6 Kimura's 3-substitution types model . . 171
6.7 Other applications and perspectives . . 174
7 Phylogenetic networks . . 178
7.1 Introduction . . 178
7.2 Median networks . . 180
7.3 Visual complexity of median networks . . 184
7.4 Consensus networks . . 186
7.5 Treelikeness . . 188
7.6 Deriving phylogenetic networks from distances . . 191
7.7 Neighbour-net . . 195
7.8 Discussion . . 199
Acknowledgements . . 200
8 Reconstructing the duplication history of tandemly repeated sequences . . 205
8.1 Introduction . . 205
8.2 Repeated sequences and duplication model . . 206
8.3 Mathematical model and properties . . 212
8.4 Inferring duplication trees from sequence data . . 221
8.5 Simulation comparison and prospects . . 229
Acknowledgements . . 231
9 Conserved segment statistics and rearrangement 
9.1 Introduction . . 236
9.2 Genetic (recombinational) distance . . 237
9.3 Gene counts . . 238
9.4 The inference problem . . 239
9.5 What can we infer from conserved segments? . . 240
9.6 Rearrangement algorithms . . 243
9.7 Loss of signal . . 244
9.8 From gene order to genomic sequence . . 245
9.9 Between the blocks . . 252
9.10 Conclusions . . 256
Acknowledgements . . 257
10 The inversion distance problem . . 262
10.1 Introduction and biological background . . 262
10.2 Definitions and examples . . 264
10.3 Anatomy of a signed permutation . . 266
10.4 The Hannenhalli-Pevzner duality theorem . . 277
10.5 Algorithms . . 282
10.6 Conclusion . . 287 
Glossary . . 287
11 Genome rearrangements with gene families . . 291
11.1 Introduction . . 291
11.2 The formal representation of the genome . . 293
11.3 Genome rearrangement . . 294
11.4 Multigene families . . 298
11.5 Algorithms and models . . 299
11.6 Genome duplication . . 303
11.7 Duplication of chromosomal segments . . 309
11.8 Conclusion . . 313
12 Reconstructing phylogenies from gene-content and gene-order data . . 321
12.1 Introduction: phylogenies and phylogenetic data . . 321
12.2 Computing with gene-order data . . 330
12.3 Reconstruction from gene-order data . . 337
12.4 Experimentation in phylogeny . . 342
12.5 Conclusion and open problems . . 345
Acknowledgements . . 346
13 Distance-based genome rearrangement phylogeny . . 353
13.1 Introduction . . 353
13.2 Whole genomes and events that change gene orders . . 354
13.3 Distance-based phytogeny reconstruction . . 356
13.4 Empirically Derived Estimator . . 359
13.5 IEBP: "Inverting the expected breakpoint distance" . . 363
13.6 Simulation studies . . 372
13.7 Summary . . 378
Acknowledgements . . 380
14 How much can evolved characters tell us about the tree that generated them? . . 384
14.1 Introduction . . 384
14.2 Preliminaries . . 386
14.3 Information-theoretic bounds: ancestral states and deep divergences . . 388
14.4 Phase transitions in ancestral state and tree reconstruction . . 396
14.5 Processes on an unbounded state space: the random cluster model . . 401
14.6 Large but finite state spaces . . 405
14.7 Concluding comments . . 408
Acknowledgements . . 409
Index . . 413