The phylogenetic handbook : a practical approach to DNA and protein phylogeny / edited by Marco Salemi and Anne-Mieke Vandamme.

Format:

Book

Contributor:

Salemi, Marco, 1968-

Vandamme, Anne-Mieke, 1960-

Language:

English

Subjects (All):

DNA--Analysis--Handbooks, manuals, etc.

DNA.

Proteins--Analysis--Handbooks, manuals, etc.

Proteins.

Cladistic analysis--Handbooks, manuals, etc.

Cladistic analysis.

Proteins--Analysis.

DNA--Analysis.

Genre:

Handbooks and manuals.

Physical Description:

xxiv, 406 pages : illustrations (some color) ; 26 cm

Place of Publication:

Cambridge, UK ; New York : Cambridge University Press, 2003.

Summary:

The Phylogentic Handbook is a broad introduction to the theory and practice of nucleotide and amino-acid phylogenetic analysis. As a unique feature of this book, each chapter contains an extensive practical section, in which step-by-step exercises on real data sets introduce the reader to the most widely used phylogeny software, including CLUSTAL, PHYLIP, PAUP*, DAMBE, TREE-PUZZLE, TREECON, SplitsTree, TreeView, MEGA2, PAML, and SimPlot. Chapters 1 through 10 provide a strong background in basic topics such as the use of sequence databases, alignment algorithms, tree-building methods, estimation of genetic distances, and testing models of evolution. Additional chapters briefly survey special topics in evolution; for example, modeling evolution with networks, studying recombination, testing for positive selection, and methods in population genetics. The book will be an invaluable resource for advanced-level undergraduate and graduate students, as well as for professionals working in the fields of molecular biology and evolution.

Contents:

1 Basic concepts of molecular evolution / Anne-Mieke Vandamme 1

1.1 Genetic information 1

1.2 Population dynamics 6

1.3 Data used for molecular phylogenetic analysis 10

1.4 What is a phylogenetic tree? 14

1.5 Methods to infer phylogenetic trees 17

1.6 Is evolution always tree-like? 21

2 Sequence databases 24

Theory / Guy Bottu, Marc Van Ranst 24

2.1 General nucleic acid sequence databases 24

2.2 General protein sequence databases 26

2.3 Nonredundant sequence databases 27

2.4 Specialized sequence databases 28

2.5 Databases with aligned protein sequences 29

2.6 Database documentation search 30

2.6.1 Text-string searching 30

2.6.2 Searching by index 30

2.7 ENTREZ database 32

2.8 Sequence similarity searching: BLAST 33

Practice / Marco Salemi 37

2.9 File formats 37

2.10 Three example data sets 40

2.10.1 Preparing input files: HIV/SIV example data set 41

3 Multiple alignment 45

Theory / Des Higgins 45

3.2 The problem of repeats 46

3.3 The problem of substitutions 47

3.4 The problem of gaps 50

3.5 Testing multiple-alignment methods 51

3.6 Multiple-alignment algorithms 52

3.6.1 Dot-matrix sequence comparison 52

3.6.2 Dynamic programming 54

3.6.3 Genetic algorithms 55

3.6.4 Other algorithms 55

3.7 Progressive alignment 55

3.7.1 Clustal 57

3.7.2 T-Coffee 58

3.8 Hidden Markov models 58

3.9 Nucleotide sequences versus amino-acid sequences 59

Practice / Des Higgins, Marco Salemi 61

3.10 Searching for homologous sequences with BioEdit 61

3.11 File formats for Clustal 63

3.12 Access to ClustalW and ClustalX 64

3.13 Aligning the HIV/SIV sequences with ClustalX 64

3.14 Aligning nucleotide sequences in a coding region with DAMBE 66

3.15 Adding sequences to preexisting alignments 67

3.16 Editing and viewing multiple alignments 68

3.17 Databases of alignments 69

4 Nucleotide substitution models 72

Theory / Korbinian Strimmer, Arndt von Haeseler 72

4.2 Observed and expected distances 73

4.3 Number of mutations in a given time interval *(optional) 74

4.4 Nucleotide substitutions as a homogeneous Markov process 77

4.4.1 The Jukes and Cantor (JC69) model 79

4.5 Derivation of Markov process *(optional) 80

4.5.1 Inferring the expected distances 83

4.6 Nucleotide substitution models 83

4.6.1 Rate heterogeneity over sites 85

Practice: The PHYLIP and TREE-PUZZLE software packages / Marco Salemi 88

4.7 Software packages 88

4.8 Jukes and Cantor (JC69) genetic distances 90

4.9 Kimura 2-parameters (K80) and F84 genetic distances 91

4.10 More complex models 92

4.10.1 Modeling rate heterogeneity over sites 93

4.11 The problem of substitution saturation 95

4.12 Choosing among different evolutionary models 97

5 Phylogeny inference based on distance methods 101

Theory / Yves Van de Peer 101

5.2 Tree-inferring methods based on genetic distances 103

5.2.1 Cluster analysis (UPGMA and WPGMA) 103

5.2.2 Minimum evolution and neighbor-joining 107

5.2.3 Other distance methods 113

5.3 Evaluating the reliability of inferred trees 115

5.3.1 Bootstrap analysis 115

5.3.2 Jackknifing 118

Practice / Marco Salemi 120

5.5 The TreeView program 120

5.6 Procedure to estimate distance-based phylogenetic trees with PHYLIP 120

5.7 Inferring an NJ tree for the mtDNA data set 121

5.8 Inferring a Fitch-Margoliash tree for the mtDNA data set 125

5.9 Inferring an NJ tree for the HIV-1 data set 125

5.10 Bootstrap analysis with PHYLIP 126

5.11 Other programs 133

6 Phylogeny inference based on maximum-likelihood methods with TREE-PUZZLE 137

Theory / Arndt von Haeseler, Korbinian Strimmer 137

6.2 The formal framework 140

6.2.1 The simple case: Maximum-likelihood tree for two sequences 140

6.2.2 The complex case 141

6.3 Computing the probability of an alignment for a fixed tree 142

6.3.1 Felsenstein's pruning algorithm 144

6.4 Finding a maximum-likelihood tree 145

6.4.1 The quartet-puzzling algorithm 146

6.5 Estimating the model parameters with maximum likelihood 149

6.6 Likelihood-mapping analysis 150

Practice / Arndt von Haeseler, Korbinian Strimmer 153

6.7 Software packages 153

6.8 An illustrative example of quartet-puzzling tree reconstruction 153

6.9 Likelihood-mapping analysis of the HIV data set 156

7 Phylogeny inference based on parsimony and other methods using PAUP 160

Theory / David L. Swofford, Jack Sullivan 160

7.2 Parsimony analysis - background 161

7.3 Parsimony analysis - methodology 163

7.3.1 Calculating the length of a given tree under the parsimony criterion 163

7.4 Searching for optimal trees 166

7.4.1 Exact methods 171

7.4.2 Approximate methods 175

Practice / David L. Swofford, Jack Sullivan 182

7.5 Analyzing data with PAUP* through the command-line interface 182

7.6 Basic parsimony analysis and tree-searching 186

7.7 Analysis using distance methods 193

7.8 Analysis using maximum-likelihood methods 196

8 Phylogenetic analysis using protein sequences 207

Theory / Fred R. Opperdoes 207

8.2 Why protein sequences? 209

8.2.1 The genetic code 210

8.2.2 Codon bias 210

8.2.3 Long time horizon 210

8.2.4 Phylogenetic noise reduction 211

8.2.5 Introns and noncoding DNA 211

8.2.6 Multigene families and post-transcriptional editing 212

8.3 Measurement of sequence divergence in proteins: The PAM 213

8.4 Alignment of protein sequences 215

8.4.1 Sequence retrieval and multiple-sequence alignment 219

8.4.2 Secondary-structure-based alignment 219

8.4.3 Prodom, Pfam, and Blocks databases 220

8.4.4 Manual adjustment of a protein alignment 220

8.5 Tree-building methods for protein phylogeny 221

8.6 Some good advice 224

Practice / Fred R. Opperdoes 226

8.7 A phylogenetic analysis of the Leismanial GPD gene carried out via the Internet 226

8.8 A comparison of the trypanosomatid phylogeny from nucleotide and protein sequences 230

8.9 Implementing different evolutionary models with DAMBE and TREE-PUZZLE 233

9 Analysis of nucleotide sequences using TREECON 236

Theory / Yves Van de Peer 236

9.2 TREECON, distance trees, and among-site rate variation 236

9.2.1 Taking into account among-site rate variation: An example 241

Practice / Yves Van de Peer 246

9.4 The TREECON software package 246

9.5 Implementation 246

9.6 Substitution rate calibration 251

10 Selecting models of evolution 256

Theory / David Posada 256

10.1 Models of evolution and phylogeny reconstruction 256

10.2 The relevance of models of evolution 257

10.3 Selecting models of evolution 257

10.4 The likelihood ratio test 258

10.4.1 LRTs and parametric bootstrapping 259

10.4.2 Hierarchical LRTs 260

10.4.3 Dynamical LRTs 261

10.5 Information criteria 263

10.5.1 AIC 264

10.5.2 BIC 264

10.6 Fit of a single model to the data 264

10.7 Testing the molecular clock hypothesis 265

10.7.1 The relative rate test 266

10.7.2 LRT of the global molecular clock 267

Practice / David Posada 270

10.8 The model-selection procedure 270

10.9 The program MODELTEST 273

10.10 Implementing the LRT of the molecular clock using PAUP* 275

10.11 Selecting the best-fit model in the example data sets 276

10.11.1 Vertebrate mtDNA 277

10.11.2 HIV envelope gene 278

10.11.3 G3PDH protein 279

11 Analysis of coding sequences 283

Theory / Yoshiyuki Suzuki, Takashi Gojobori 283

11.2 Mutation fraction methods 285

11.2.1 Method of Nei and Gojobori (NG86 method) 285

11.2.2 Method of Zhang et al. (ZRN98 method) 287

11.2.3 Method of Ina (I95 method) 288

11.3 Degenerate site methods 290

11.3.1 Method of Li et al.

(LWL85 method) 291

11.3.2 Method of Pamilo and Bianchi, and Li (PBL93 method) 294

11.4 Codon model methods 294

11.4.1 Method of Muse (M96 method) 295

11.4.2 Method of Yang and Nielsen (YN98 method) 296

11.5 Methods for estimating d[subscript S] and d[subscript N] at single codon sites 296

11.5.1 Method of Suzuki and Gojobori (SG99 method) 297

11.6 Test of neutrality for two sequences 298

11.6.1 Z test 298

11.6.2 Likelihood ratio test (LRT) 298

11.6.3 Window analysis 299

11.7 Test of neutrality at single codon sites 299

11.7.1 Method of Nielsen and Yang (1998) (NY98 method) 300

11.7.2 SG99 method 300

Practice / Yoshiyuki Suzuki, Takashi Gojobori 302

11.8 Software for analyzing coding sequences 302

11.9 Estimation of d[subscript S] and d[subscript N] in an HCV data set 302

11.9.1 Estimation of d[subscript S] and d[subscript N] with NG86, ZRN98, LWL85, and PBL93 methods (MEGA2) 303

11.9.2 Estimation of d[subscript S] and d[subscript N] with YN98 method (PAML) 304

11.9.3 Comparing different estimates of d[subscript S] and d[subscript N] 305

11.10 An example of window analysis 306

11.11 Detection of positive selection at single amino acid sites 307

12 SplitsTree: A network-based tool for exploring evolutionary relationships in molecular data 312

Theory / Vincent Moulton 312

12.1 Exploring evolutionary relationships through networks 312

12.2 An introduction to split-decomposition theory 314

12.2.1 The Buneman tree 315

12.2.2 Split decomposition 316

12.3 From weakly compatible splits to networks 318

Practice / Vincent Moulton 320

12.4 The SplitsTree program 320

12.5 Using SplitsTree on the mtDNA data set 320

12.6 Using SplitsTree on the HIV-1 data set 324

13 Tetrapod phylogeny and data exploration using DAMBE 329

Theory / Xuhua Xia, Zheng Xie 329

13.1 The phylogenetic problem and the sequence data 329

13.2 Results of routine phylogenetic analyses without data exploration 330

13.3 Distance-based statistical test of alternative phylogenetic trees (optional) 332

13.4 Likelihood-based statistical tests of alternative phylogenetic trees 333

13.5 Data exploration 335

13.5.1 Nucleotide frequencies 335

13.5.2 Substitution saturation and the rate heterogeneity over sites 337

13.5.3 The pattern of nucleotide substitution 338

13.5.4 Insertion and deletion as phylogenetic characters 339

Practice / Xuhua Xia, Zheng Xie 342

13.6 Data exploration with DAMBE 342

13.6.1 Nucleotide frequencies 342

13.6.2 Basic phylogenetic reconstruction 342

13.6.3 Rate heterogeneity over sites estimated through reconstruction of ancestral sequences 343

13.6.4 Empirical substitution pattern 344

13.6.5 Testing alternative phylogenetic hypotheses with the distance-based method 344

13.6.6 Testing alternative phylogenetic hypotheses with the likelihood-based method 345

14 Detecting recombination in viral sequences 348

Theory / Mika Salminen 348

14.1 Introduction and theoretical background to exploring recombination in viral sequences 348

14.2 Requirements for detecting recombination 349

14.3 Theoretical basis for methods to detect recombination 351

14.4 Examples of viral recombination 360

Practice / Mika Salminen 362

14.5 Existing tools for analysis of recombination 362

14.6 Analyzing example sequences to visualize recombination 364

14.6.1 Exercise 1: Working with Simplot 364

14.6.2 Exercise 2: Mapping recombination with Simplot 368

14.6.3 Exercise 3: Using the "groups" feature of Simplot 369

14.6.4 Exercise 4: Using SplitsTree to visualize recombination 373

15 Lamarc: Estimating population genetic parameters from molecular data 378

Theory / Mary K. Kuhner 378

15.2 Basis of the Metropolis-Hastings MCMC sampler 379

15.2.1 Random sample 381

15.2.2 Stability 381

15.2.3 No other forces 381

15.2.4 Evolutionary model 381

15.2.5 Large population relative to sample 382

15.2.6 Adequate run time 382

Practice / Mary K. Kuhner 384

15.3 The LAMARC software package 384

15.3.1 Fluctuate (Coalesce) 384

15.3.2 Migrate 384

15.3.3 Recombine 385

15.3.4 Lamarc 386

15.4 Starting values 386

15.5 Space and time 387

15.6 Sample size considerations 387

15.7 Virus-specific issues 388

15.7.1 Multiple loci 388

15.7.2 Rapid growth rates 388

15.7.3 Sequential samples 389

15.8 An exercise with LAMARC 389

15.8.1 Exercise using FLUCTUATE 390

15.8.2 Exercise using RECOMBINE 395.

Notes:

Includes bibliographical references and index.

ISBN:

052180390X

OCLC:

50155249

Online:

Publisher description