TNT el mejor programa de parsimonia

TNT el mejor programa de parsimonia
TNT el mejor programa de parsimonia

Páginas

Docentes Innovadores

viernes, 12 de octubre de 2012

Taller en Cladística Cuantitativa - España

En esta entrada les traigo la información (PDF) sobre un Workshop en Cladística utilizando TnT:


 

 
Transmitting Science --->


QUANTITATIVE CLADISTICS AND USE OF TNT 
June 3-7, 2013 

The workshop will cover the basics of parsimony analysis and character optimization, tree-searches, diagnosing and summarizing results efficiently, and measuring group supports. The course will be informal, with extensive hands-on exercises which will help students get familiar with the main aspects of phylogenetic analysis using TNT. For each of the units in the workshop, there will be a lecture (one to two hours, depending on the topics), then switching to exercises illustrating the points just seen in the lecture. Switches between "lecture" and "hands-on" mode will be dynamic, depending on how students advance on the exercises. The workshop will make extensive use of TNT. There will also be a demonstration and some practice with GB->TNT, a program to create TNT matrices from GenBank data (in turn, GB->TNT requires installation of some alignment program, ideally Mafft or Muscle and possibly BioEdit to inspect alignments).

jueves, 4 de octubre de 2012

Configuraciones nodo-x-nodo

Hola:

Acá les coloco una animación realizada con GIFAnimator.


























Esta animación se realizó utilizando, uno de los dos árboles más parsimoniosos obtenidos con la matriz de Soto-Vivas et al. (2011)* en TNT. Luego se preparó una matriz de Puntos Anatómicos de Referencia o landmarks, para mapaear las configuraciones de los PAR. Seguidamente se empleó el macro de TNT export_tps.run para exportar las configuraciones hipotéticas de los nodos, y las configuraciones de los terminales (Taxa). Finalmente este archivo TPS se abrió en PAST para utilizar la opción thin-plate spline y graficar las deformaciones entre los nodos seleccionados.


(*) SOTO-VIVAS, Ana, LIRIA, Jonathan, & LUNA, Efraín de. (2011). Morfometría geométrica y filogenia en Rhodniini (Hemiptera, Reduviidae) de Venezuela. Acta zoológica mexicana, 27(1), 87-102.




martes, 2 de octubre de 2012

Nuevo Libro "Systematics: A Course of Lectures" - Wheeler


Acá les dejo la información de este libro:
















Wiley: Systematics: A Course of Lectures

Systematics: A Course of Lectures is designed for use in an advanced undergraduate or introductory graduate level course in systematics and is meant to present core systematic concepts and literature. The book covers topics such as the history of systematic thinking and fundamental concepts in the field including species concepts, homology, and hypothesis testing. Analytical methods are covered in detail with chapters devoted to sequence alignment, optimality criteria, and methods such as distance, parsimony, maximum likelihood and Bayesian approaches. Trees and tree searching, consensus and super-tree methods, support measures, and other relevant topics are each covered in their own sections.


Tabla de contenido:

Preface xv Using these notes xv
Acknowledgments  xvi
List of algorithms xix
I Fundamentals 1
1 History 2
1.1 Aristotle  2
1.2 Theophrastus 3
1.3 Pierre Belon 4
1.4 Carolus Linnaeus 4
1.5 Georges Louis Leclerc, Comte de Buffon  6
1.6 Jean-Baptiste Lamarck 7
1.7 Georges Cuvier  8
1.8 ´Etienne Geoffroy Saint-Hilaire  8
1.9 JohannWolfgang von Goethe 8
1.10 Lorenz Oken 9
1.11 Richard Owen 9
1.12 Charles Darwin  9
1.13 Stammb¨aume  12
1.14 Evolutionary Taxonomy 14
1.15 Phenetics 15
1.16 Phylogenetic Systematics  16
1.16.1 Hennig’s Three Questions 16
1.17 Molecules and Morphology  18
1.18 We are all Cladists 18
1.19 Exercises 19
2 Fundamental Concepts 20
2.1 Characters 20
2.1.1 Classes of Characters and Total Evidence  22
2.1.2 Ontogeny, Tokogeny, and Phylogeny  23
2.1.3 Characters and Character States 23
2.2 Taxa 26
2.3 Graphs, Trees, and Networks 28
2.3.1 Graphs and Trees 30
2.3.2 Enumeration 31
2.3.3 Networks  33
2.3.4 Mono-, Para-, and Polyphyly 33
2.3.5 Splits and Convexity  38
2.3.6 Apomorphy, Plesiomorphy, and Homoplasy  39
2.3.7 Gene Trees and Species Trees 41
2.4 Polarity and Rooting 43
2.4.1 Stratigraphy  43
2.4.2 Ontogeny  43
2.4.3 Outgroups  45
2.5 Optimality 49
2.6 Homology  49
2.7 Exercises  50
3 Species Concepts, Definitions, and Issues 53
3.1 Typological or Taxonomic Species Concept  54
3.2 Biological Species Concept  54
3.2.1 Criticisms of the BSC 55
3.3 Phylogenetic Species Concept(s) 56
3.3.1 Autapomorphic/Monophyletic Species Concept 56
3.3.2 Diagnostic/Phylogenetic Species Concept  58
3.4 Lineage Species Concepts  59
3.4.1 Hennigian Species  59
3.4.2 Evolutionary Species  60
3.4.3 Criticisms of Lineage-Based Species  61
3.5 Species as Individuals or Classes  62
3.6 Monoism and Pluralism  63
3.7 Pattern and Process  63
3.8 Species Nominalism  64
3.9 Do Species Concepts Matter?  65
3.10 Exercises  65
4 Hypothesis Testing and the Philosophy of Science 67
4.1 Forms of Scientific Reasoning 67
4.1.1 The Ancients  67
4.1.2 Ockham’s Razor  68
4.1.3 Modes of Scientific Inference  69
4.1.4 Induction 69
4.1.5 Deduction 69
4.1.6 Abduction 70
4.1.7 Hypothetico-Deduction  71
4.2 Other Philosophical Issues 75
4.2.1 Minimization, Transformation, and Weighting 75
4.3 Quotidian Importance  76
4.4 Exercises  76
5 Computational Concepts 77
5.1 Problems, Algorithms, and Complexity 77
5.1.1 Computer Science Basics  77
5.1.2 Algorithms  79
5.1.3 Asymptotic Notation 79
5.1.4 Complexity  80
5.1.5 Non-Deterministic Complexity  82
5.1.6 Complexity Classes: P and NP  82
5.2 An Example: The Traveling Salesman Problem  84
5.3 Heuristic Solutions  85
5.4 Metricity, and Untrametricity  86
5.5 NP–Complete Problems in Systematics  87
5.6 Exercises 88
6 Statistical and Mathematical Basics 89
6.1 Theory of Statistics  89
6.1.1 Probability  89
6.1.2 Conditional Probability  91
6.1.3 Distributions 92
6.1.4 Statistical Inference  98
6.1.5 Prior and Posterior Distributions  99
6.1.6 Bayes Estimators 100
6.1.7 Maximum Likelihood Estimators  101
6.1.8 Properties of Estimators 101
6.2 Matrix Algebra, Differential Equations, and Markov Models 102
6.2.1 Basics  102
6.2.2 Gaussian Elimination 102
6.2.3 Differential Equations  104
6.2.4 Determining Eigenvalues  105
6.2.5 MarkovMatrices  106
6.3 Exercises  107
II Homology 109
7 Homology 110
7.1 Pre-Evolutionary Concepts110
7.1.1 Aristotle  110
7.1.2 Pierre Belon  110
7.1.3 ´Etienne Geoffroy Saint-Hilaire  111
7.1.4 Richard Owen 112
7.2 Charles Darwin  113
7.3 E. Ray Lankester  114
7.4 Adolf Remane  114
7.5 Four Types of Homology  115
7.5.1 Classical View  115
7.5.2 Evolutionary Taxonomy  115
7.5.3 Phenetic Homology  116
7.5.4 Cladistic Homology  116
7.5.5 Types of Homology  117
7.6 Dynamic and Static Homology  118
7.7 Exercises  120
8 Sequence Alignment 121
8.1 Background  121
8.2 “Informal” Alignment  121
8.3 Sequences  121
8.3.1 Alphabets  122
8.3.2 Transformations  123
8.3.3 Distances  123
8.4 Pairwise StringMatching 123
8.4.1 An Example  127
8.4.2 Reducing Complexity  129
8.4.3 Other Indel Weights  130
8.5 Multiple Sequence Alignment  131
8.5.1 The Tree Alignment Problem  133
8.5.2 Trees and Alignment  133
8.5.3 Exact Solutions 134
8.5.4 Polynomial Time Approximate Schemes  134
8.5.5 Heuristic Multiple Sequence Alignment  134
8.5.6 Implementations  135
8.5.7 Structural Alignment  139
8.6 Exercises 145
III Optimality Criteria 147
9 Optimality CriteriaDistance 148
9.1 Why Distance? 148
9.1.1 Benefits  149
9.1.2 Drawbacks 149
9.2 Distance Functions  150
9.2.1 Metricity  150
9.3 Ultrametric Trees  150
9.4 Additive Trees  152
9.4.1 Farris Transform  153
9.4.2 Buneman Trees  154
9.5 General Distances  156
9.5.1 Phenetic Clustering 157
9.5.2 Percent Standard Deviation 160
9.5.3 Minimizing Length  163
9.6 Comparisons 170
9.7 Exercises  171
10 Optimality CriteriaParsimony 173
10.1 Perfect Phylogeny  174
10.2 Static Homology Characters  174
10.2.1 Additive Characters  175
10.2.2 Non-Additive Characters  179
10.2.3 Matrix Characters  182
10.3 Missing Data  184
10.4 Edge Transformation Assignments  187
10.5 Collapsing Branches  188
10.6 Dynamic Homology  188
10.7 Dynamic and Static Homology  189
10.8 Sequences as Characters 190
10.9 The Tree Alignment Problem on Trees  191
10.9.1 Exact Solutions  191
10.9.2 Heuristic Solutions 191
10.9.3 Lifted Alignments, Fixed-States, and Search-Based Heuristics  193
10.9.4 Iterative Improvement  197
10.10 Performance of Heuristic Solutions 198
10.11 Parameter Sensitivity  198
10.11.1 Sensitivity Analysis  199
10.12 Implied Alignment  199
10.13 Rearrangement  204
10.13.1 Sequence Characters with Moves  204
10.13.2Gene Order Rearrangement 205
10.13.3Median Evaluation  207
10.13.4Combination ofMethods 207
10.14 Horizontal Gene Transfer, Hybridization, and Phylogenetic Networks  209
10.15 Exercises  210
11 Optimality CriteriaLikelihood 213
11.1 Motivation  213
11.1.1 Felsenstein’s Example  213
11.2 Maximum Likelihood and Trees  216
11.2.1 Nuisance Parameters  216
11.3 Types of Likelihood  217
11.3.1 Flavors ofMaximum Relative Likelihood 217
11.4 Static-Homology Characters  218
11.4.1 Models  218
11.4.2 Rate Variation  219
11.4.3 Calculating p(D|T, θ)  221
11.4.4 Links Between Likelihood and Parsimony  222
11.4.5 A Note onMissing Data 224
11.5 Dynamic-Homology Characters  224
11.5.1 Sequence Characters  225
11.5.2 CalculatingML Pairwise Alignment  227
11.5.3 MLMultiple Alignment  230
11.5.4 Maximum Likelihood Tree Alignment Problem 230
11.5.5 Genomic Rearrangement  232
11.5.6 Phylogenetic Networks  234
11.6 Hypothesis Testing  234
11.6.1 Likelihood Ratios  234
11.6.2 Parameters and Fit  236
11.7 Exercises  238
12 Optimality CriteriaPosterior Probability 240
12.1 Bayes in Systematics  240
12.2 Priors  241
12.2.1 Trees  241
12.2.2 Nuisance Parameters  242
12.3 Techniques 246
12.3.1 Markov ChainMonte Carlo  246
12.3.2 Metropolis–Hastings Algorithm 246
12.3.3 Single Component 248
12.3.4 Gibbs Sampler  249
12.3.5 Bayesian MC3 249
12.3.6 Summary of Posterior  250
12.4 Topologies and Clades  252
12.5 Optimality versus Support  254
12.6 Dynamic Homology  254
12.6.1 Hidden Markov Models  255
12.6.2 An Example 256
12.6.3 Three Questions—Three Algorithms  258
12.6.4 HMMAlignment  262
12.6.5 Bayesian Tree Alignment  264
12.6.6 Implementations  264
12.7 Rearrangement  266
12.8 Criticisms of BayesianMethods  267
12.9 Exercises  267
13 Comparison of Optimality Criteria 269
13.1 Distance and CharacterMethods  269
13.2 Epistemology 270
13.2.1 Ockham’s Razor and Popperian Argumentation  271
13.2.2 Parsimony and the Evolutionary Process  272
13.2.3 Induction and Statistical Estimation  272
13.2.4 Hypothesis Testing and Optimality Criteria  272
13.3 Statistical Behavior  273
13.3.1 Probability  273
13.3.2 Consistency  274
13.3.3 Efficiency  281
13.3.4 Robustness  282
13.4 Performance 282
13.4.1 Long-Branch Attraction 283
13.4.2 Congruence  285
13.5 Convergence  285
13.6 CanWe Argue Optimality Criteria? 286
13.7 Exercises 287
IV Trees 289
14 Tree Searching 290
14.1 Exact Solutions  290
14.1.1 Explicit Enumeration 290
14.1.2 Implicit Enumeration—Branch-and-Bound  292
14.2 Heuristic Solutions 294
14.2.1 Local versus Global Optima 294
14.3 Trajectory Search 296
14.3.1 Wagner Algorithm 296
14.3.2 Branch-Swapping Refinement  298
14.3.3 Swapping as Distance 301
14.3.4 Depth-First versus Breadth-First Searching  302
14.4 Randomization  304
14.5 Perturbation  305
14.6 Sectorial Searches and Disc-Covering Methods  309
14.6.1 Sectorial Searches  309
14.6.2 Disc-CoveringMethods  310
14.7 Simulated Annealing  312
14.8 Genetic Algorithm  316
14.9 Synthesis and Stopping 318
14.10 Empirical Examples  319
14.11 Exercises 323
15 Support 324
15.1 ResamplingMeasures 324
15.1.1 Bootstrap  325
15.1.2 Criticisms of the Bootstrap  326
15.1.3 Jackknife  328
15.1.4 Resampling and Dynamic Homology Characters  329
15.2 Optimality-BasedMeasures  329
15.2.1 Parsimony  330
15.2.2 Likelihood 332
15.2.3 Bayesian Posterior Probability  334
15.2.4 Strengths of Optimality-Based Support  335
15.3 Parameter-BasedMeasures 336
15.4 Comparison of Support Measures—Optimal and Average  336
15.5 Which to Choose?  339
15.6 Exercises  339
16 Consensus, Congruence, and Supertrees 341
16.1 Consensus TreeMethods  341
16.1.1 Motivations  341
16.1.2 Adams I and II  341
16.1.3 Gareth Nelson  344
16.1.4 Majority Rule  347
16.1.5 Strict  347
16.1.6 Semi-Strict/Combinable Components  348
16.1.7 Minimally Pruned 348
16.1.8 When to UseWhat?  350
16.2 Supertrees 350
16.2.1 Overview  350
16.2.2 The Impossibility of the Reasonable  350
16.2.3 Graph-BasedMethods 353
16.2.4 Strict Consensus Supertree  355
16.2.5 MR-Based  355
16.2.6 Distance-Based Method  358
16.2.7 Supertrees or Supermatrices?  360
16.3 Exercises  361
V Applications 363
17 Clocks and Rates 364
17.1 The Molecular Clock  364
17.2 Dating  365
17.3 Testing Clocks  365
17.3.1 Langley–Fitch  365
17.3.2 Farris  366
17.3.3 Felsenstein  367
17.4 Relaxed ClockModels  368
17.4.1 Local Clocks  368
17.4.2 Rate Smoothing  368
17.4.3 Bayesian Clock  369
17.5 Implementations  369
17.5.1 r8s  369
17.5.2 MULTIDIVTIME 370
17.5.3 BEAST  370
17.6 Criticisms  370
17.7 Molecular Dates?  373
17.8 Exercises  373
A Mathematical Notation 374
Bibliography 376
Index 415
Color plate section between pp. 76 and 77