Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids

371 Pages·1998·3.65 MB·English

by  Richard Durbin

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Biological sequence analysis
Probabilistic models of proteins and nucleic acids
The face of biology has been changed by the emergence of modern molecular genetics.
Among the most exciting advances are large-scale DNA sequencing efforts such as the
HumanGenomeProjectwhichareproducinganimmenseamountofdata.Theneedto
understandthedataisbecomingevermorepressing.Demandsforsophisticatedanalyses
ofbiologicalsequencesaredrivingforwardthenewly-createdandexplosivelyexpanding
research area of computational molecular biology, or bioinformatics.
Many of the most powerful sequence analysis methods are now based on principles
ofprobabilisticmodelling.Examplesofsuchmethodsincludetheuseofprobabilistically
derived score matrices to determine the significance of sequence alignments, the use of
hidden Markov models as the basis for profile searches to identify distant members
of sequence families, and the inference of phylogenetic trees using maximum likelihood
approaches.
Thisbookprovidesthefirstunified,up-to-date,andtutorial-leveloverviewofsequence
analysismethods,withparticularemphasisonprobabilisticmodelling.Pairwisealignment,
hidden Markov models, multiple alignment, profile searches, RNA secondary structure
analysis, and phylogenetic inference are treated at length.
Written by an interdisciplinary team of authors, the book is accessible to molecular
biologists, computer scientists and mathematicians with no formal knowledge of each
others’fields.Itpresentsthestate-of-the-artinthisimportant,newandrapidlydeveloping
discipline.
Richard Durbinis Headof the InformaticsDivisionattheSangerCentre inCambridge,
England.
SeanEddyisAssistantProfessoratWashingtonUniversity’sSchoolofMedicineandalso
oneofthePrincipleInvestigatorsattheWashingtonUniversityGenomeSequencingCenter.
Anders Krogh is a Research Associate Professor in the Center for Biological Sequence
Analysis at the Technical University of Denmark.
GraemeMitchisonisattheMedicalResearchCouncil’sLaboratoryforMolecularBiologyin
Cambridge, England.

Biological sequence analysis
Probabilistic models of proteins and nucleic acids
Richard Durbin
Sean R. Eddy
Anders Krogh
Graeme Mitchison
CAMBRIDGEUNIVERSITYPRESS
Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo
Cambridge University Press
The Edinburgh Building, Cambridge CB28RU, UK
Published in the United States of America by Cambridge University Press, New York
www.cambridge.org
Information on this title: www.cambridge.org/9780521629713
© Cambridge University Press 1998
This publication is in copyright. 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.
First published in print format 1998
ISBN-13 978-0-511-33708-6 eBook (EBL)
ISBN-10 0-511-33708-6 eBook (EBL)
ISBN-13 978-0-521-62971-3 paperback
ISBN-10 0-521-62971-3 paperback
Cambridge 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.
Contents
Preface pageix
1 Introduction 1
1.1 Sequencesimilarity,homology,andalignment 2
1.2 Overviewofthebook 2
1.3 Probabilitiesandprobabilisticmodels 4
1.4 Furtherreading 10
2 Pairwisealignment 12
2.1 Introduction 12
2.2 Thescoringmodel 13
2.3 Alignmentalgorithms 18
2.4 Dynamicprogrammingwithmorecomplexmodels 29
2.5 Heuristicalignmentalgorithms 33
2.6 Linearspacealignments 35
2.7 Significanceofscores 36
2.8 Derivingscoreparametersfromalignmentdata 42
2.9 Furtherreading 45
3 MarkovchainsandhiddenMarkovmodels 47
3.1 Markovchains 48
3.2 HiddenMarkovmodels 52
3.3 ParameterestimationforHMMs 62
3.4 HMMmodelstructure 69
3.5 MorecomplexMarkovchains 73
3.6 NumericalstabilityofHMMalgorithms 78
3.7 Furtherreading 80
4 PairwisealignmentusingHMMs 81
4.1 PairHMMs 82
4.2 Thefullprobabilityofx and y,summingoverallpaths 88
4.3 Suboptimalalignment 90
4.4 Theposteriorprobabilitythatx isalignedto y 92
i j
4.5 PairHMMsversusFSAsforsearching 96
v
vi Contents
4.6 Furtherreading 99
5 ProfileHMMsforsequencefamilies 101
5.1 Ungappedscorematrices 103
5.2 AddinginsertanddeletestatestoobtainprofileHMMs 103
5.3 DerivingprofileHMMsfrommultiplealignments 106
5.4 SearchingwithprofileHMMs 109
5.5 ProfileHMMvariantsfornon-globalalignments 114
5.6 Moreonestimationofprobabilities 116
5.7 Optimalmodelconstruction 123
5.8 Weightingtrainingsequences 125
5.9 Furtherreading 133
6 Multiplesequencealignmentmethods 135
6.1 Whatamultiplealignmentmeans 136
6.2 Scoringamultiplealignment 138
6.3 Multidimensionaldynamicprogramming 141
6.4 Progressivealignmentmethods 145
6.5 MultiplealignmentbyprofileHMMtraining 150
6.6 Furtherreading 159
7 Buildingphylogenetictrees 161
7.1 Thetreeoflife 161
7.2 Backgroundontrees 163
7.3 Makingatreefrompairwisedistances 166
7.4 Parsimony 174
7.5 Assessingthetrees:thebootstrap 180
7.6 Simultaneousalignmentandphylogeny 181
7.7 Furtherreading 189
7.8 Appendix:proofofneighbour-joiningtheorem 190
8 Probabilisticapproachestophylogeny 193
8.1 Introduction 193
8.2 Probabilisticmodelsofevolution 194
8.3 Calculatingthelikelihoodforungappedalignments 198
8.4 Usingthelikelihoodforinference 206
8.5 Towardsmorerealisticevolutionarymodels 215
8.6 Comparisonofprobabilisticandnon-probabilisticmethods 224
8.7 Furtherreading 232
9 Transformationalgrammars 234
9.1 Transformationalgrammars 235
9.2 Regulargrammars 238
9.3 Context-freegrammars 243
Contents vii
9.4 Context-sensitivegrammars 248
9.5 Stochasticgrammars 250
9.6 Stochasticcontext-freegrammarsforsequencemodelling 253
9.7 Furtherreading 259
10 RNAstructureanalysis 261
10.1 RNA 262
10.2 RNAsecondarystructureprediction 268
10.3 Covariancemodels:SCFG-basedRNAprofiles 278
10.4 Furtherreading 299
11 Backgroundonprobability 300
11.1 Probabilitydistributions 300
11.2 Entropy 306
11.3 Inference 312
11.4 Sampling 315
11.5 Estimationofprobabilitiesfromcounts 320
11.6 TheEMalgorithm 324
Bibliography 327
Authorindex 346
Subjectindex 351