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A graphical overview with color-coded alignment scores is provided for the most similar sequences.Description.Estimated genetic distances: the JC69 model Kimura 2-parameters (K80) and F84 genetic distances More complex models 4.10.1 Modeling rate heterogeneity among sites 4.11 Estimating standard errors using Mega4 4.12 The problem of substitution saturation 4.13 Choosing among different evolutionary models 5 Phylogenetic inference based on distance methods TheoryIntroduction Tree-inference methods based on genetic distances 5.2.1 Cluster analysis (UPGMA and WPGMA) 5.2.2 Minimum evolution and neighbor-joining 5.2.3 Other distance methodsEvaluating the reliability of inferred trees 5.3.1 Bootstrap analysis 5.3.2 Jackknifing 5.4 Conclusions5.5 Programs to display and manipulate phylogenetic trees 5.6 Distance-based phylogenetic inference in Phylip 5.7 Inferring a Neighbor-Joining tree for the primates data set 5.7.1 Outgroup rooting 5.8 Inferring a Fitch–Margoliash tree for the mtDNA data set 5.9 Bootstrap analysis using Phylip 5.10 Impact of genetic distances on tree topology: an example using Mega45.11 Other programs 6 Phylogenetic inference using maximum likelihood methods TheoryIntroduction The formal framework 6.2.1 The simple case: maximum-likelihood tree for two sequences 6.2.2 The complex case Computing the probability of an alignment for a fixed tree 6.3.1 Felsenstein’s pruning algorithm Finding a maximum-likelihood tree 6.4.1 Early heuristics 6.4.2 Full-tree rearrangement 6.4.3 DNaml and fastDNAml 6.4.4 PhyML and PhyMl-SPR 6.4.5 Iqpnni 6.4.6 RAxML 6.4.7 Simulated annealing 6.4.8 Genetic algorithms Branch support The quartet puzzling algorithm 6.6.1 Parameter estimation 6.6.2 ML step 6.6.3 Puzzling step 6.6.4 Consensus step Likelihood-mapping analysis181 184 184 185 186 188 189 190 190 191 192 192 193 193 194 194 195 195 196 196 196 196Software packages An illustrative example of an ML tree reconstruction 6.9.1 Reconstructing an ML tree with Iqpnni 6.9.2 Getting a tree with branch support values using quartet puzzling 6.9.3 Likelihood-mapping analysis of the HIV data set 6.10 Conclusions7 Bayesian phylogenetic analysis using MRBAYES TheoryFredrik Ronquist, Paul van der Mark, and John P. Amino acid sequences 3.10 Visualizing alignments and manual editing Practice68 68 70 72 74 74 75 79 80 89 90 90 91 91 92 93 95 96 1003.11 Clustal alignment 3.11.1 File formats and availability 3.11.2 Aligning the primate Trim5α amino acid sequencesComparing alignments using the AltAVisT web tool From protein to nucleotide alignment Editing and viewing multiple alignments Databases of alignmentsSection III: Phylogenetic inference 4 Genetic distances and nucleotide substitution models TheoryKorbinian Strimmer and Arndt von HaeselerIntroduction Observed and expected distances Number of mutations in a given time interval *(optional) Nucleotide substitutions as a homogeneous Markov process 4.4.1 The Jukes and Cantor (JC69) model 4.5 Derivation of Markov Process *(optional) 4.5.1 Inferring the expected distances 4.6 Nucleotide substitution models 4.6.1 Rate heterogeneity among sitesSoftware packages Observed vs. First published in print format 2009 ISBN-13Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate.List of contributors Foreword Preface Section I: Introduction 1 Basic concepts of molecular evolutionGenetic information Population dynamics Evolution and speciation Data used for molecular phylogenetics What is a phylogenetic tree? Methods for inferring phylogenetic trees Is evolution always tree-like?Section II: Data preparation 2 Sequence databases and database searching Theory2.1 Introduction 2.2 Sequence databases 2.2.1 General nucleic acid sequence databases 2.2.2 General protein sequence databases 2.2.3 Specialized sequence databases, reference databases, and genome databases 2.3 Composite databases, database mirroring, and search tools 2.3.1 Entrez v2.3.2 Sequence Retrieval System (SRS) 2.3.3 Some general considerations about database searching by keyword Database searching by sequence similarity 2.4.1 Optimal alignment 2.4.2 Basic Local Alignment Search Tool (Blast) 2.4.3 FastA 2.4.4 Other tools and some general considerations2.5 Database searching using ENTREZ 2.6 Blast 2.7 FastA 3 Multiple sequence alignment TheoryIntroduction The problem of repeats The problem of substitutions The problem of gaps Pairwise sequence alignment 3.5.1 Dot-matrix sequence comparison 3.5.2 Dynamic programming 3.6 Multiple alignment algorithms 3.6.1 Progressive alignment 3.6.2 Consistency-based scoring 3.6.3 Iterative refinement methods 3.6.4 Genetic algorithms 3.6.5 Hidden Markov models 3.6.6 Other algorithms 3.7 Testing multiple alignment methods 3.8 Which program to choose? 3.9 Nucleotide sequences vs. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press.
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