Table Of ContentCodon Evolution
Mechanisms and Models
EDITEDBY
Gina M. Cannarozzi
UniversityofBern,Switzerland
Adrian Schneider
UniversityofUtrecht,TheNetherlands
1
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Contents
Foreword ix
NickGoldmanandZihengYang
Preface xi
ListofContributors xiv
PartI:Modellingcodonevolution
1: Background 3
AdrianSchneiderandGinaM.Cannarozzi
1.1 Modelsofmolecularevolution 3
1.2 Markovmodels 3
1.2.1 Markovchains 4
1.2.2 Multiplesubstitutions 4
1.2.3 Continuous-timeprocesses 4
1.2.4 Time-reversibility 5
1.3 Maximum-likelihoodestimation 5
1.3.1 MLexample 5
1.3.2 Posteriorprobabilities 6
1.3.3 Likelihoodofaphylogenetictree 6
1.4 Performanceassessment 7
1.4.1 Likelihood-basedtests 7
1.4.2 Simulations 8
1.4.3 Empiricaltests 8
2: Parametricmodelsofcodonevolution 12
MariaAnisimova
2.1 BasicMarkovmodelsofcodonsubstitution 12
2.1.1 FromDNAsubstitutionmodelstocodonmodels 12
2.1.2 Estimatingcodonfrequencydistribution 14
2.2 Evaluatingselectivepressureattheproteinlevel 15
2.2.1 Theneutraltheoryandthelikelihoodratiotest(LRT)forpositive
selection 15
2.2.2 Modellingvariableselectionpressureovertime 16
2.2.3 Modellingvariableselectionpressureamongsites 19
2.2.4 Predictinglocationsofsitesunderpositiveselection 20
2.2.5 Detectingpositiveselectioninpresenceofrecombination 20
2.2.6 Modellingvariableselectionpressureamongsitesandovertime 22
2.3 Measuringselectiononphysico-chemicalpropertiesofaminoacids 24
iv CONTENTS
2.4 Modellingsite-dependenceincodingsequences 25
2.5 Furtherdevelopmentofparametricmodels 26
3: Empiricalandsemi-empiricalmodelsofcodonevolution 34
AdrianSchneiderandGinaM.Cannarozzi
3.1 Introduction 34
3.2 EmpiricalmodelbySchneideretal.(2005) 34
3.2.1 Methods 35
3.2.2 Resultsanddiscussion 36
3.2.3 Conclusion 37
3.3 CombinedmodelbyDoron-FaigenboimandPupko(2007) 37
3.3.1 Methods 37
3.3.2 Discussion 39
3.4 ModelbyKosioletal.(2007) 39
3.4.1 Methods 40
3.4.2 Discussion 41
3.5 Codontest 42
3.6 Empiricalsearchforthemostimportantparameters 42
3.7 Summary 42
4: MonteCarlocomputationalapproachesinBayesiancodon-substitution
modelling 45
NicolasRodrigueandNicolasLartillot
4.1 Introduction 45
4.2 TheBayesianframework 46
4.3 Site-independentmodelsofcodonsubstitution 47
4.3.1 TheMuseandGaut,andGoldmanandYang-basedmodels 47
4.3.2 PlainMCMC 48
4.3.3 ThermodynamicMCMC 50
4.4 Site-interdependentmodelsofcodonsubstitution 53
4.4.1 TheRobinsonetal.-basedmodels 53
4.4.2 PlainMCMC 54
4.4.3 ThermodynamicMCMC 55
4.5 Otherrecentmodellinginnovationsandoverallrankings 57
4.6 Futuredirections 58
5: Likelihood-basedclustering(LiBaC)forcodonmodels 60
HongGu,KatherineA.Dunn,andJosephP.Bielawski
5.1 Introduction 60
5.2 Theoryforlikelihood-basedclustering(LiBaC) 61
5.3 Detectingpositiveselectioninalarge-scaleanalysisofrealgene
sequences 63
5.4 Objectivecomparisonofmodel-basedclassifications 65
5.5 Simulationstudiesofmodel-basedclassification 67
5.5.1 PerformanceofLiBaCandothermethodsonsimulateddata 67
5.5.2 TradeoffsbetweenprecisionandrecallunderLiBaCareadjustable
bytheposteriorprobabilitycutoff 68
5.6 RecommendationsforusingLiBaC 69
CONTENTS v
6: Detectingandunderstandingnaturalselection 73
MariaAnisimovaandDavidA.Liberles
6.1 Selectivemechanismsoperatingongenesequences 73
6.2 Briefoverviewofstatisticalmethodologiesfordetectingpositiveselection 77
6.2.1 Neutralitytestsbasedonfrequencyspectrum 77
6.2.2 Neutralitytestsbasedonvariabilitywithinandbetweenspecies 77
6.2.3 Poissonrandom-fieldmodels(PRF) 78
6.2.4 Methodsbasedonpopulationdifferentiation 78
6.2.5 Methodsbasedonlinkagedisequilibrium(LD)andhaplotype
structure 79
6.2.6 Methodsbasedondetectingrateshifts 79
6.2.7 Detectingselectionbasedond /d withMarkovcodonmodels 80
N S
6.3 Theutilityandtheinterpretationofthed /d measure 81
N S
6.4 AccountingforindelsandoverlappingORFs 83
6.5 Model-basedapproachesandcommonmisconceptions 84
6.6 Selectionandadaptivetraits 87
6.7 Lessonsfromgenomicstudiesandimplicationsforstudiesof
geneticdisease 88
7: Codonmodelsasavehicleforreconcilingpopulationgeneticswithinter-specific
sequencedata 97
JeffreyL.Thorne,NicolasLartillot,NicolasRodrigue,andSangChulChoi
7.1 Introduction 97
7.2 Theimportanceofphenotype 97
7.3 TheHalpern-Brunoapproach 98
7.3.1 Thebasicidea 99
7.3.2 Populationgeneticinterpretationsthroughretrofits 101
7.3.3 TheRobinsonmodel 101
7.3.4 TheSella–Hirshrefinement 102
7.3.5 The˘parameter 104
7.3.6 Applicationsandpotentialapplications 105
7.4 LimitationsoftheHalpern–Brunoapproach 106
7.4.1 Thestationarityassumption 106
7.4.2 ThelowmutationrateassumptionandtheHill–Robertsoneffect 107
7.5 Futuredirections 108
8: Robustestimationofnaturalselectionusingparametriccodonmodels 111
GavinA.HuttleyandVonBingYap
8.1 Introduction 111
8.2 Context-dependentsubstitutionmodels 112
8.3 Evaluatingpropertiesofdinucleotidemodels 115
8.3.1 Analysisofsimulateddata 115
8.3.2 Analysisofprimateintrons 116
8.4 Evaluatingpropertiesofcodonmodels 117
8.4.1 Analysisofsimulateddata 118
8.4.2 Analysisofprimateintrons 119
8.5 Impactofmodeldefinitionsonstatisticalpower 121
8.6 Conclusion 122
vi CONTENTS
9: Simulationofcodingsequenceevolution 126
MiguelArenasandDavidPosada
9.1 Introduction 126
9.2 Simulationofcodingsequences 126
9.2.1 Forwardsimulations 126
9.2.2 Simulationsofcoalescenthistories 127
9.2.3 Simulationofcodonsubstitutions 127
9.3 Usesofsimulatedcodingdata 128
9.4 Softwareimplementations 130
10: Useofcodonmodelsinmoleculardatingandfunctionalanalysis 133
StevenA.Benner
10.1 Introduction 133
10.2 Thelevelofanalysismostusefulforfunctionalbiology 133
10.3 ImprovingcodonanalysisbeyondtheK /K andd /d ratios 135
a s N S
10.4 HeuristicapproachestoimprovecodonanalysisbeyondtheK /K andd /d
a s N S
ratios 136
10.5 Clocks 138
10.6 CalibratingtheTRExclock 140
10.7 Conclusions 143
11: Thefutureofcodonmodelsinstudiesofmolecularfunction:ancestralreconstructionand
clademodelsoffunctionaldivergence 145
BelindaS.W.Chang,JingjingDu,CameronJ.Weadick,JohannesMüller,ConstanzeBickelmann,
D.DavidYu,andJamesM.Morrow
11.1 Introduction 145
11.2 Ancestralreconstruction 145
11.3 Reconstructingsynonymousevolutioninvertebraterhodopsins 148
11.4 Clademodelsoffunctionaldivergence 152
11.5 TestingforfunctionaldivergenceamongteleostSWS2opsins 155
11.6 Conclusions 158
12: Codonmodelsappliedtothestudyoffungalgenomes 164
GabrielaAguiletaandTatianaGiraud
12.1 Introduction 164
12.2 Fungiaspathogens 164
12.2.1 Adaptiveevolution:characterizingfunctionaldivergenceandassociated
selectivepressurechanges 164
12.2.2 Host–pathogenevolution:detectingarmsracesthroughtheevolutionof
R-genes,avirulencegenes,aswellasfungaleffectorsandelicitors 169
12.2.3 Lifestyle-associatedadaptations:fromsaprophytestopathogens 172
12.3 Fungiassymbionts:selectivepressuretomaintainsymbiosisin
mycorrhizaeandlichens 173
12.4 Evolutionofcodonusageinfungalgenomes 173
12.4.1 Fungiaseukaryoticmodelsofcodonusageevolution 174
12.4.2 Codonmodelsappliedtodetectcodonbiasinfungi:
translationalselection 175
12.4.3 Fungalpreferredcodonuses 176
12.5 Functionalshifts:measuringtheconcomitantvariationinselectivepressure 177
CONTENTS vii
12.6 Adaptiveevolutionofgeneexpression:wiringandre-wiring
regulatorynetworks 177
12.7 Ancestralpolymorphisms:maintainingallelicvariantsforextendedperiods 178
12.8 TheoriginofsexualchromosomesinFungi:reducedselectionefficiencyand
degenerativechangesinpreferredcodonusage 180
12.9 Findinggenesassociatedwithspecializationandspeciation 180
12.10 Conclusion:newusesofcodonmodelsforanalysingfungalgenomes 181
PartII:Codonusagebias
13: Measuringcodonusagebias 189
AlexanderRoth,MariaAnisimova,andGinaM.Cannarozzi
13.1 Introduction 189
13.2 Causesofcodonusagebias 189
13.2.1 Mutationalbiasesaffectingcodonusage 189
13.2.2 Selectionaffectingcodonusage 190
13.3 Applicationsforindicesofcodonusagebias 192
13.4 Previousstudiesofcodonusageindices 192
13.5 Measuresofcodonbias 193
13.5.1 Relativecodonfrequencies 194
13.5.2 Measuresbasedonreference 194
13.5.3 Measuresbasedonthegeometricmean 196
13.5.4 Measuresbasedondeviationfromanexpecteddistribution 199
13.5.5 Measuresbasedoninformationtheory 200
13.5.6 MeasuresfocusingontRNAinteraction 201
13.5.7 Measuresbasedonintrinsicpropertiesofcodonusage 202
13.5.8 Measuresfortotalcodonusageingenomes 205
13.6 Dependenciesofmeasures 206
13.6.1 Dependenceonnucleotidecomposition 206
13.6.2 Dependenceongenelength 207
13.6.3 Dependenceonthedegreeofcodondegeneracy 207
13.6.4 Dependenceontheskewnessofsynonymouscodonusage 208
13.6.5 Dependenceonaminoaciddiscrepancy 208
13.7 Comparisonsusingbiologicaldata 210
13.7.1 Correlationwithtranscriptandproteinlevels 211
13.7.2 Correlationwithrateofproteinsynthesis 211
13.8 Limitationsofcodonusageindices 212
13.9 Conclusions 212
14: Detectionandanalysisofconservationatsynonymoussites 218
NimrodD.RubinsteinandTalPupko
14.1 Introductiontoconservation 218
14.2 Classicalviewregardingsynonymousmutationsasneutral 218
14.3 Conservationduetotranslationaloptimization 219
14.4 ConservationduetomRNAstructure 220
14.5 Conservationduetooverlappinggenes 222
14.6 Conservationtomaintainsplicingsignals 223
14.7 Applicationofcodonmodelstothedetectionofconservedsynonymoussites 223
14.8 Othercis-encodedelementsresponsibleforsynonymousconservation 224
14.9 Concludingremarks 225
viii CONTENTS
15: Distancemeasuresandmachinelearningapproachesforcodonusageanalyses 229
FranSupekandTomislavŠmuc
15.1 Causesofbiasedcodonusage 229
15.2 Methodsforquantifyingcodonbiases 231
15.2.1 Unsupervisedmethods 231
15.2.2 Supervisedmethods 234
15.3 Applicationtobacterialandarchaealgenomes 236
15.3.1 Rationalebehindusingclassifierstocontrolforbackgroundnucleotide
composition 236
15.3.2 Anexampleapplicationofsupervisedmachinelearningincodonusage
analysis 237
15.3.3 Proportionofgenomessubjecttotranslationalselectionandcorrelationswith
genefunctionalcategories 239
15.3.4 Distributionofcodon-optimizedgeneswithinspecificgenefunctional
categoriesandrelationshiptomicrobiallifestyle 240
15.3.5 mRNAexpressionlevelsandcodonpreferencesofgenessubjectto
translationalselection 241
16: Theapplicationofpopulationgeneticsinthestudyofcodonusagebias 245
KaiZeng
16.1 Introduction 245
16.2 Theory 246
16.2.1 Thereversiblemutationmodelandtheinfinitesitesmodel 246
16.2.2 ParameterestimationanddatapreparationundertheRMmodel 247
16.2.3 ParameterestimationanddatapreparationundertheISmodel 249
16.3 Somerecenttheoreticaldevelopments 250
16.3.1 Methodsthattakeaccountoftheeffectsofrecentchangesofpopulationsize 250
16.3.2 Amulti-allelemodelwithreversiblemutation 252
16.3.3 Theeffectsoflinkageonparameterestimation 253
16.4 Conclusion 254
17: Structuralandmolecularfeaturesofnon-standardgeneticcodes 258
MariadoCéuSantosandManuelA.S.Santos
17.1 Overview 258
17.1.1 Geneticcodediversity:mitochondrialandnuclear 258
17.1.2 Neutralandnon-neutralmechanisms 260
17.2 Howarenon-neutralgeneticcodechangesselected? 261
17.2.1 Selenocysteine 261
17.2.2 Pyrrolysine 262
17.2.3 TheCUGcaseinCandidaspp. 264
17.3 Cellularandmolecularconsequencesofnon-neutralgeneticcodealterations 265
17.3.1 Consequencesatproteomelevel 265
17.3.2 Consequencesatgenomelevel 266
17.3.3 Consequencesatphenotypiclevel 267
17.4 Conclusionsandperspectives 268
Index 273
Foreword
The fundamental biological insights necessary to a suite of models existed, the most complex of
inspiremathematicalmodellingofcodonevolution which essentially allowed for any pattern of DNA
in protein-coding genes became available 50 years sequence evolution to be modelled by a suitable
ago,whenCrick,Benner,Barnett,andWatts-Tobin choiceofparameters.
confirmed the triplet nature of the genetic code in Forprotein-codinggenes,codonsarethenatural
1961. Presumably because the sequences that first leveltostudytheevolutionaryprocess,astheyper-
becameavailablewereproteins,thefirstevolution- mitconsiderationofbothmutationprocessesatthe
arymodelsdescribedaminoacidreplacements.In DNA level and natural selection on the protein.
aseriesofpioneeringstudiesstartingin1966,Day- A Markov chain model of codon evolution was
hoffandcolleaguesappliedanexplicitlyevolution- describedasearlyas1975byJorréandCurnow,to
ary approach to summarizing changes in protein predictaminoacidfrequenciesintheprotein.Sadly,
sequencesempirically.ThePAMmatrixhighlights this found no applications and seems largely for-
twomajorfactorsaffectingtheaminoacidreplace- gotten. Increasing interest in using sequence data
mentsthataccumulateoverevolutionarytime:the to study selection led to consideration of codon
mutational distance as determined by the genetic evolution by Miyata and Yasunaga, and by Gojo-
code (that is, amino acids that can be reached by bori in the early 1980s. However, inference under
asinglenucleotidemutationreplaceeachotherfar codon models requires working with a 61-letter
more often than those that are separated by two alphabetofcodons,whichmeansroughlya(61/4)3
or three positional differences), and the physico- or 3500-fold increase in computational time. The
chemical distance (that is, similar amino acids codon-modellingproblemseemedtoobig,tooslow,
replace each other for more often than dissimilar andsimplytoodauntinguntilanotherdecadehad
aminoacids).Thematrixbecameaninstantclassic passed.
andisstillwidelyused,alongsidemorerecentana- By1990,thescenewassetforcodonmodels.That
loguesestimatedfromhugedatabasesorforpartic- year, Schöniger and colleagues demonstrated that
ular groups of organisms or particular proteins or Dayhoff’s approach for proteins could equally be
proteindomains. appliedtocodons.In1994,twopapersauthoredby
In parallel to this modelling based on the Muse and Gaut, and by us, coincidentally appear-
20-letter alphabet of amino acids, modelling of ing on consecutive pages of the journal Molecular
DNA sequence evolution based on the four-letter Biology and Evolution, implemented codon models
nucleotidealphabetwasinitiatedbyJukesandCan- inaphylogeneticframework.Bothtookaparamet-
tor in 1969. Here, however, a parametric or mech- ric approach, describing possible codon changes
anisticapproachwas used.Earlymodelsassumed usingasmallnumberofparameters.Inthecaseof
that all replacements occur at the same rate and ourowncontribution,wehopedafewparameters
only gradually was greater parametric complex- would be enough to capture the major features
ity introduced, inspired by observations such as of amino acid replacements: in particular, the
unequal nucleotide frequencies or unequal tran- mutationalandphysico-chemicaldistancesthatare
sition and transversion rates. By the mid-1980s importantinaffectingtherelativereplacementrates
ix
