The Phylogenetic Handbook

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The Phylogenetic HandbookA Practical Approach to PhylogeneticAnalysis and Hypothesis TestingSecond EditionEdited byPhilippe LemeyKatholieke Universiteit Leuven, BelgiumMarco SalemiUniversity of Florida, Gainesville, USAAnne-Mieke VandammeKatholieke Universiteit Leuven, Belgium

Contents. " -r· · ·'. .,.List of cbntriflu.to.rs ,:I· Forewa-r:r ·' 1""''. .Prefa ceSedion 1:IntroductionBasic concepts of molecular evolutionpagexixxxiiiXXV13Anne-Mieke Vandamme1.11.21.31.41.51.61.7Sedion II:Genetic informationPopulation dynamicsEvolution and speciationData used for molecular phylogeneticsWhat is a phylogenetic tree?Methods for inferring phylogenetic treesIs evolution always tree-like?Data preparationSequence databases and database searching3914161923283133TheoryGuy Bottu332.1 Introduction2.2 Sequence databases2.2.1 General nucleic acid sequence databases2.2.2 General protein sequence databases2.2.3 Specialized sequence databases, reference databases, and33353537

viContents2.3.22.3.32.4Sequence Retrieval System (SRS)Some general considerations about database searchingby keywordDatabase searching by sequence similarity2.4.1 Optimal alignment2.4.2 Basic Local Alignment Search Tool (BLAST)2.4.3 FASTA2.4.4 Other tools and some general considerations4344454547so52PracticeMarc Van Ranst and Philippe Lemey552.52.62.7556266Database searching using ENTREZBLASTFASTAMultiple sequence alignment6868TheoryDes Higgins and Philippe Lemey3.13.23.33.43.5IntroductionThe problem of repeatsThe problem of substitutionsThe problem of gapsPairwise sequence alignment3.5.1 Dot-matrix sequence comparison3.5.2 Dynamic programming3.6 Multiple alignment algorithms3.6.1 Progressive alignment3.6.2 Consistency-based scoring3.6.3 Iterative refinement methods3.6.4 Genetic algorithms3.6.5 Hidden Markov models3.6.6 Other algorithms3.7 Testing multiple alignment methods3.8 Which program to choose?3.9 Nucleotide sequences vs. amino acid sequences3.10 Visualizing alignments and manual editingPracticeDes Higgins and Philippe Lemey 1r,,,A,t686870727474757980899090919192939596100

-viiContents3.123.133.143.153.163.17section Ill:alignmentalignmentComparing alignments using the ALTAVJsT web toolFrom protein to nucleotide alignmentEditing and viewing multiple alignmentsDatabases of alignmentsT-(OFFEEMUSCLEPhylogenetic inferenceGenetic distances and nucleotide ubstitution modelsTheory102102103104105106109111111Korbinian Strimmer and Arndt von Haeseler4.14.24.34.44.54.6IntroductionObserved and expected distancesNumber of mutations in a given time interval*( optionaONucleotide substitutions as a homogeneous Markov process4.4.1 The Jukes and Cantor (JC69) modelDerivation of Markov Process *(optional)4.5.1 Inferring the expected distancesNucleotide substitution models4.6.1 Rate heterogeneity among sitesPractice111112113116117118121121123126Marco Salemi4. 74.84.94.10Software packagesObserved vs. estimated genetic distances: the JC69 modelKimura 2-parameters (K80) and F84 genetic distancesMore complex models4.10.1 Modeling rate heterogeneity among sites4.11 Estimating standard errors using MEGA44.12 The problem of substitution saturation4.13 Choosing among different evolutionary models,Phylogenetic inference based on distance methodsTheory126128131132133135137140142142Yves Van de Peer5.15.2IntroductionTree-inference methods based on genetic distances1421441

viiiContents5.35.4Evaluating the reliability of inferred trees5.3.1 Bootstrap analysis5.3.2 JackknifingConclusionsPractice1St15:15C15 16Marco SalemiPrograms to display and manipulate phylogenetic treesDistance-based phylogenetic inference in PHYLIPInferring a Neighbor-Joining tree for the primates data set5.7.1 Outgroup rooting5.8 Inferring a Fitch-Margoliash tree for the mtDNA data set5.9 Bootstrap analysis using PHYLIP5.10 Impact of genetic distances on tree topology: an example using5.55.65.7MEGA45.11 Other programsPhylogenetic inference using maximum likelihood methodsTheory16 :16;16:16 17(17(17418(181181Heiko A. Schmidt and Arndt von Haeseler6.16.26.36.4IntroductionThe formal framework6.2.1 The simple case: maximum-likelihood tree fortwo sequences6.2.2 The complex caseComputing the probability of an alignment for a fixed tree6.3.1 Felsenstein's pruning algorithmFinding a maximum-likelihood tree6.4.1 Early heuristics6.4.2 Full-tree rearrangement6.4.3 DNAMl and FASTDNAML6.4.4 PHYML and PHYML-SPR6.4.5 IQPNNI6.4.6 RAxML6.4.7 Simulated annealing6.4.8 Genetic algorithms6.56.6Branch supportThe quartet puzzling algorithm6.6.1 Parameter 93194194195195

ixContentsPracticeHeiko A. Schmidt and Arndt von Haeseler199Software packagesAn illustrative example of an ML tree reconstruction6.9.1Reconstructing an ML tree with IQPNNI6.9.2Getting a tree with branch support values usingquartet puzzling6.9.3Likelihood-mapping analysis of the HIV data set6.10 Conclusions1991991996.86.9Bayesian phylogenetic analysis using MRBAvEsTheory203207207210210Fredrik Ronquist, Paul van der Mark, and John P. Bayesian phylogenetic inferenceMarkov chain Monte Carlo samplingBurn-in, mixing and convergenceMetropolis couplingSummarizing the resultsAn introduction to phylogenetic modelsBayesian model choice and model averagingPrior probability edrik Ronquist, Paul van der Mark, and John P. Huelsenbeck2377.10 Introduction to MRBAYES7.10.1 Acquiring and installing the program7.10.2 Getting started7.10.3 Changing the size of the MRBAYES window7.10.4 Getting help7.11 A simple analysis7.11.1 Quick start version7.11.2 Getting data into MRBAYES7.11.3 Specifying a model7.11.4 Setting the priors7.11.5 Checking the model7.11.6 Setting up the analysis7.11.7 Running the analysis7.11.8 When to stop the analysis237237238238239240240241242244247248252254

xContents7.12 Analyzing a partitioned data set7.12.1 Getting mixed data into MRBAYES7.12.2 Dividing the data into partitions7.12.3 Specifying a partitioned model7.12.4 Running the analysis7.12.5 Some practical advicetl Phylogeny inference based on parsimony and other methodsUSing PAUP*267TheoryDavid L. Swofford and Jack 265IntroductionParsimony analysis- backgroundParsimony analysis- methodology8.3.1 Calculating the length of a given tree under the parsimonycriterionSearching for optimal trees8.4.1 Exact methods8.4.2 Approximate methods270273277282PracticeDavid L. Swofford and Jack Sullivan2898.58.68.78.8292293300303Analyzing data with PAUP* through the command-line interfaceBasic parsimony analysis and tree-searchingAnalysis using distance methodsAnalysis using maximum likelihood methodsPhylogenetic analysis using protein sequences313TheoryFred R. tionProtein evolution9.2 .1 Why analyze protein sequences?9.2.2 The genetic code and codon bias9.2.3 Look-back time9.2.4 Nature of sequence divergence in proteins (the PAM unit)9.2.5 Introns and non-coding DNA9.2.6 Choosing DNA or protein?

--."'ContentsPractice332Fred R. Opperdoes and Philippe Lemey9.4A phylogenetic analysis of the Leishmania! glyceraldehyde3-phosphate dehydrogenase gene carried out via theInternetA phylogenetic analysis of trypanosomatid glyceraldehyde3-phosphate dehydrogenase protein sequences using Bayesianinference9.5section IV:Testing models and treesSelecting models of evolutionTheory332337343345345David Posada10.110.210.3Models of evolution and phylogeny reconstructionModel fitHierarchical likelihood ratio tests (hLRTs)10.3.1 Potential problems with the hLRTs10.4 Information criteria10.5 Bayesian approaches10.6 Performance-based selection10.7 Model selection uncertainty10.8 Model id Posada10.9 The model selection procedure10.10 MODELTEST10.11 PROTTEST10.12 Selecting the best-fit model in the example data sets10.12.1 Vertebrate mtDNA10.12.2 HIV-1 envelope gene10.12.3 G3PDH proteinMolecular clock analysisTheoryPhilippe Lemey and David Posada355355358359359360361362362

xiiContents11.311.4ll.511.6·K'ZLikelihood ratio test of the global molecular clockDated tipsRelaxing the molecular clockDiscussion and future directions365367369371PracticePhilippe Lemey and David Posada37311.711.811 .9373375377Molecular clock analysis using PAMLAnalysis of the primate sequencesAnalysis of the viral sequencesTesting tree topologies381TheoryHeiko A. 38838939039039212.512.612.7IntroductionSome definitions for distributions and testingLikelihood ratio tests for nested modelsHow to get the distribution oflikelihood ratios12.4.1 Non-parametric bootstrap12.4.2 Parametric bootstrapTesting tree topologies12.5.1 Tree tests- a general structure12.5.2 The original Kishino-Hasegawa (KH) test12.5.3 One-sided Kishino-Hasegawa test12.5.4 Shimodaira-Hasegawa (SH) test12.5.5 Weighted test variants12.5.6 The approximately unbiased test12.5. 7 Swofford-Olsen-Waddell-Hillis (SOWH)testConfidence sets based on likelihood weightsConclusions393394395PracticeHeiko A. Schmidt39712.812.9397397Software packagesTesting a set of trees with TREE-PUZZLE and CONSEL12.9.1 Testing and obtaining site-likelihood with398TREE-PUULE12.9.2 Testing withCONSEL401

.contentsXIII::.--section V:Molecular adaptationNatural selection and adaptation of molecular sequences405407Oliver G. Pybus and Beth Shapiro13.113.213.3111Basic conceptsThe molecular footprint of selection13.2.1 Summary statistic methods13.2.2 dNids methods13.2.3 Codon volatilityConclusionEstimating selection pressures on alignments of coding sequences407412413415417418419TheorySergei L. Kosakovsky Pond, Art F. Y. Poon, and Simon D. W. .714.814.9IntroductionPrerequisitesCodon substitution modelsSimulated data: how and why?Statistical estimation procedures14.5.1 Distance-based approaches14.5.2 Maximum likelihood approaches14.5.3 Estimating dS and dN14.5.4 Correcting for nucleotide substitution biases14.5.5 Bayesian approachesEstimating branch-by-branch variation in rates14.6.1 Local vs. global model14.6.2 Specifying branches a priori14.6.3 Data-driven branch selectionEstimating site-by-site variation in rates14.7.1 Random effects likelihood (REL)14.7.2 Fixed effects likelihood (FEL)14.7.3 Counting methods14.7 .4 Which method to use?14.7 .5 The importance of synonymous rate variationComparing rates at a site in different branchesDiscussion and further directionsPracticeSergei L. Kosakovsky Pond, Art F. Y. Poon, and Simon D. W. Frost452

xivContents14.10.3 MEGA14.10.4 HYPHY14.10.5 DATAMONKEY14.11 Influenza A as a case study14.12 Prerequisites14.12.1 Getting acquainted with HYPHY14.12.2 Importing alignments and trees14.12.3 Previewing sequences in HYPHY14.12.4 Previewing trees in HYPHY14.12.5 Making an alignment14.12.6 Estimating a tree14.12.7 Estimating nucleotide biases14.12.8 Detecting recombination14.13 Estimating global rates14.13.1 Fitting a global model in the HYPHY GUI14.13.2 Fitting a global model with a HYPHYbatch file14.14 Estimating branch-by-branch variation in rates14.14.1 Fitting a local codon model in HYPHY14.14.2 Interclade variation in substitution rates14.14.3 Comparing internal and terminal branches14.15 Estimating site-by-site variation in rates14.15.1 Preliminary analysis set-up14.15.2 Estimating ,13/a14.15.3 Single-likelihood ancestor counting (SLAC)14.15.4 Fixed effects likelihood (FEL)14.15.5 REL methods in HvPHY14.16 Estimating gene-by-gene variation in rates14.16.1 Comparing selection in different populations14.16.2 Comparing selection between different14.1714.1814.1914.20Section VI:15genesAutomating choices for HYPHY analysesSimulationsSummary of standard analysesDiscussionRecombinationIntroduction to recombination detectionPhilippe Lemev and David 488488490491493

XVcontents:--15 .3 Linkage disequilibrium, substitution patterns, andevolutionary inferenceJ5.4 Evolutionary implications of recombination1.5.5 Impact on phylogenetic analyses15.6 Recombination analysi as a multifaceted discipline15.6.1 Detecting recombination15.6.2 Recombinant identification and breakpoint detection15.6.3 Recombination rate15.7 Overview of recombination detection tools15.8 Performance of recombination detection toolsDetecting and characterizing individual recombination events495496498506506507507509517519519TheoryMika Salminen and Darren Martin16.116.216.316.4IntroductionRequirements for detecting recombinationTheoretical basis for recombination detection methodsIdentifying and characterizing actual recombination eventsPractice519520523530532Mika Salminen and Darren Martin16.516.6Existing tools for recombination analysisAnalyzing example sequences to detect and characterize individualrecombination events16.6.1 Exercise 1: Working with SIMPLOT16.6.2 Exercise 2: Mapping recombination with SJMPLOT16.6.3 Exercise 3: Using the "groups" feature of SIMPLOT16.6.4 Exercise 4: Setting up RDP3 to do an exploratoryanalysis16.6.5 Exercise 5: Doing a simple exploratory analysis ili 16.6.6 Exercise 6: Using RDP3 to refine a recombinationhypothesisSection VII:Population geneticsThe coalescent: population genetic inference using genealogies5325335335365375385W546549551Allen Rodrigo17.1Tntroduction551

xviContents17.4 The mutation clock17.5 Demographic history and the coalescent17.6 Coalescent-based inference17.7 The serial coalescent17.8 Advanced topicsBayesian evolutionary analysis by sampling trees555556558559561564TheoryAlexei J. Drummond and Andrew groundBayesian MCMC for genealogy-based population genetics18.2.1 Implementation18.2.2 Input format18.2.3 Output and results18.2.4 Computational performanceResults and discussion18.3.1 Substitution models and rate models among sites18.3.2 Rate models among branches, divergence time estimation,and time-stamped data18.3.3 Tree priors18.3.4 Multiple data partitions and linking and unlinkingparameters18.3.5 Definitions and units of the standard parametersand variables18.3.6 Model comparison18.3. 7 ConclusionsPractice570571572572572575576Alexei J. Drummond and Andrew 3The BEAST software packageRunning BEAUnLoading the NEXUS fileSetting the dates of the taxa18.7.1 Translating the data in amino acid sequencesSetting the evolutionary modelSetting up the operatorsSetting the MCMC optionsRunning BEASTAnalyzing the BEAST outputSummarizing the trees576576577577579579580581582583co ,-

--------- -------------- --.l(VIIcontents::.----LAMARC:--Estimating population genetic parameters592from molecular dataTheoryMary K. Kuhner59219.1 Introduction19.2 Basis of the Metropolis-Hastings MCMC sampler19.2.1 Bayesian vs.likelihood sampling19.2.2 Random sample19.2.3 Stability19.2.4 No other forces19.2.5 Evolutionary model19.2.6 Large population relative to sample19.2.7 Adequate run time592593595595596596596597597PracticeMary K. Kuhner59819.3 The LAMARC software package19.3.1 FLUCTUATE (COALESCE)19.3.2 MIGRATE-N19.3.3 RECOMBINE19.3.4 lAMARC19.4 Startingvalues19.5 Space and time19.6 Sample size considerations19.7 Virus-specific issues19.7.1 Multiple loci19.7.2 Rapid growth rates19.7.3 Sequential samples19.8 An exercise with LAMARC19.8.1 Converting data using the LAMARC file converter19.8.2 Estimating the population parameters19.8.3 Analyzing the output19.9 604605607611Section VIII: Additional topicsAssessing substitution saturation withTheoryll'uh"" Vi;,613DAMBE615615

-xviiiContents20.3 Xia's method: its problem, limitation, and implementationin DAMBE621Practice624Xuhua Xia and Philippe Lemey20.420.520.6Working with the VertebrateMtCOI.FAS fileWorking with the InvertebrateEF1a.FAS fileWorking with the SIY.FAS file·' ., Split networks. A tool for exploring complex evolutionaryrelationships in molecular dataTheory624628629631631Vincent Moulton and Katharina T. Huber21.121.221.321.4Understanding evolutionary relationships through networksAn introduction to split decomposition theory21.2.1 The Buneman tree21.2.2 Split decompositionFrom weakly compatible splits to networksAlternative ways to compute split networks21.4.1 NeighborNet21.4.2 Median networks21.4.3 Consensus networks and 2Vincent Moulton and Katharina T. Huber21.521.621.7The SPLITSTREE program21.5.1 Introduction21.5.2 Downloading SPLITSTREEUsing SPUTSTREE on the mtDNA data set21.6.1 Getting started21.6.2 The fit index21.6.3 Laying out split networks21.6.4 Recomputing split networks21.6.5 Computing trees21.6.6 Computing different networks21.6.7 Bootstrapping21.6.8 PrintingUsing SPUTSTREE on other data sets642642642642643643645645646646646647648CI:A

The Phylogenetic Handbook A Practical Approach to Phylogenetic Analysis and Hypothesis Testing Second Edition Edited by Philippe Lemey Katholieke Universiteit Leuven, Belgium Marco Salemi University of Florida, Gainesville, USA Anne-Mieke Vandamme Katholieke Universiteit Leuven, Belgium -

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