Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Evaluation

244 Pages·2016·2.68 MB·English

by  Ken Nguyen

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MULTIPLE BIOLOGICAL
SEQUENCE ALIGNMENT
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WileySerieson
Bioinformatics:ComputationalTechniquesandEngineering
Acompletelistofthetitlesinthisseriesappearsattheendofthisvolume.
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MULTIPLE BIOLOGICAL
SEQUENCE ALIGNMENT
Scoring Functions, Algorithms
and Applications
KENNGUYEN
XUANGUO
YIPAN
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Copyright©2016byJohnWiley&Sons,Inc.Allrightsreserved
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LibraryofCongressCataloging-in-PublicationData:
Names:Nguyen,Ken,1975-author.|Guo,Xuan,1987-author.|Pan,Yi,1960-
author.
Title:Multiplebiologicalsequencealignment:scoringfunctions,algorithms
andapplications/KenNguyen,XuanGuo,YiPan.
Description:Hoboken,NewJersey:JohnWiley&Sons,2016.|Includes
bibliographicalreferencesandindex.
Identifiers:LCCN2016004186|ISBN9781118229040(cloth)|ISBN9781119273752
(epub)
Subjects:LCSH:Sequencealignment(Bioinformatics)
Classification:LCCQH441.N482016|DDC572.8–dc23LCrecordavailableat
http://lccn.loc.gov/2016004186
CoverimagecourtesyofGettyImages/OktalStudio
Typesetin10/12ptTimesLTStdbySPiGlobal,Chennai,India
PrintedintheUnitedStatesofAmerica
10987654321
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CONTENTS
Preface xi
(cid:2) 1 Introduction 1 (cid:2)
1.1 Motivation, 2
1.2 TheOrganizationofthisBook, 2
1.3 SequenceFundamentals, 3
1.3.1 Protein, 5
1.3.2 DNA/RNA, 6
1.3.3 SequenceFormats, 6
1.3.4 Motifs, 7
1.3.5 SequenceDatabases, 9
2 Protein/DNA/RNAPairwiseSequenceAlignment 11
2.1 SequenceAlignmentFundamentals, 12
2.2 Dot-PlotMatrix, 12
2.3 DynamicProgramming, 14
2.3.1 Needleman–Wunsch’sAlgorithm, 15
2.3.2 Example, 16
2.3.3 Smith–Waterman’sAlgorithm, 17
2.3.4 AffineGapPenalty, 19
2.4 WordMethod, 19
2.4.1 Example, 20
2.5 SearchingSequenceDatabases, 21
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vi CONTENTS
2.5.1 FASTA, 21
2.5.2 BLAST, 21
3 QuantifyingSequenceAlignments 25
3.1 EvolutionandMeasuringEvolution, 25
3.1.1 JukesandCantor’sModel, 26
3.1.2 MeasuringRelatedness, 28
3.2 SubstitutionMatricesandScoringMatrices, 28
3.2.1 IdentityScores, 28
3.2.2 Substitution/MutationScores, 29
3.3 GAPS, 32
3.3.1 SequenceDistances, 35
3.3.2 Example, 35
3.4 ScoringMultipleSequenceAlignments, 36
3.4.1 Sum-of-PairScore, 36
3.5 CircularSumScore, 38
3.6 ConservationScoreSchemes, 39
3.6.1 WuandKabat’sMethod, 39
3.6.2 Jores’sMethod, 39
3.6.3 LocklessandRanganathan’sMethod, 40
3.7 DiversityScoringSchemes, 40
(cid:2) 3.7.1 Background, 41 (cid:2)
3.7.2 Methods, 41
3.8 StereochemicalPropertyMethods, 42
3.8.1 Valdar’sMethod, 43
3.9 HierarchicalExpectedMatchingProbabilityScoringMetric(HEP), 44
3.9.1 BuildinganAACCHScoringTree, 44
3.9.2 TheScoringMetric, 46
3.9.3 ProofofScoringMetricCorrectness, 47
3.9.4 Examples, 48
3.9.5 ScoringMetricandSequenceWeightingFactor, 49
3.9.6 EvaluationDataSets, 50
3.9.7 EvaluationResults, 52
4 SequenceClustering 59
4.1 UnweightedPairGroupMethodwithArithmeticMean – UPGMA, 60
4.2 Neighborhood-JoiningMethod – NJ, 61
4.3 OverlappingSequenceClustering, 65
5 MultipleSequencesAlignmentAlgorithms 69
5.1 DynamicProgramming, 70
5.1.1 DCA, 70
5.2 ProgressiveAlignment, 71
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CONTENTS vii
5.2.1 ClustalFamily, 73
5.2.2 PIMA:Pattern-InducedMultisequenceAlignment, 73
5.2.3 PRIME:Profile-BasedRandomizedIterationMethod, 74
5.2.4 DIAlign, 75
5.3 ConsistencyandProbabilisticMSA, 76
5.3.1 POA:PartialOrderGraphAlignment, 76
5.3.2 PSAlign, 77
5.3.3 ProbCons:ProbabilisticConsistency-BasedMultipleSequence
Alignment, 78
5.3.4 T-Coffee:Tree-BasedConsistencyObjectiveFunctionfor
AlignmentEvaluation, 79
5.3.5 MAFFT:MSABasedonFastFourierTransform, 80
5.3.6 AVID, 81
5.3.7 EulerianPathMSA, 81
5.4 GeneticAlgorithms, 82
5.4.1 SAGA:SequenceAlignmentbyGeneticAlgorithm, 83
5.4.2 GAandSelf-OrganizingNeuralNetworks, 84
5.4.3 FAlign, 85
5.5 NewDevelopmentinMultipleSequenceAlignmentAlgorithms, 85
5.5.1 KB-MSA:Knowledge-BasedMultipleSequence
Alignment, 85
5.5.2 PADT:ProgressiveMultipleSequenceAlignmentBasedon
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DynamicWeightedTree, 94
5.6 TestDataandAlignmentMethods, 97
5.7 Results, 98
5.7.1 MeasuringAlignmentQuality, 98
5.7.2 RT-OSMResults, 98
6 PhylogenyinMultipleSequenceAlignments 103
6.1 TheTreeofLife, 103
6.2 PhylogenyConstruction, 105
6.2.1 DistanceMethods, 106
6.2.2 Character-BasedMethods, 107
6.2.3 MaximumLikelihoodMethods, 109
6.2.4 Bootstrapping, 110
6.2.5 SubtreePruningandRe-grafting, 111
6.3 InferringPhylogenyfromMultipleSequenceAlignments, 112
7 MultipleSequenceAlignmentonHigh-PerformanceComputing
Models 113
7.1 ParallelSystems, 113
7.1.1 Multiprocessor, 113
7.1.2 Vector, 114
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viii CONTENTS
7.1.3 GPU, 114
7.1.4 FPGA, 114
7.1.5 ReconfigurableMesh, 114
7.2 ExitingParallelMultipleSequenceAlignment, 114
7.3 Reconfigurable-MeshComputingModels – (R-Mesh), 116
7.4 PairwiseDynamicProgrammingAlgorithms, 118
7.4.1 R-MeshMaxSwitches, 118
7.4.2 R-MeshAdder/Subtractor, 118
7.4.3 Constant-TimeDynamicProgrammingonR-Mesh, 120
7.4.4 AffineGapCost, 123
7.4.5 R-MeshOn/OffSwitches, 124
7.4.6 DynamicProgrammingBacktrackingonR-Mesh, 125
7.5 ProgressiveMultipleSequenceAlignmentONR-Mesh, 126
7.5.1 HierarchicalClusteringonR-Mesh, 127
7.5.2 ConstantRun-TimeSum-of-PairScoringMethod, 128
7.5.3 ParallelProgressiveMSAAlgorithmandItsComplexity
Analysis, 129
8 SequenceAnalysisServices 133
8.1 EMBL-EBI:EuropeanBioinformaticsInstitute, 133
8.2 NCBI:NationalCenterforBiotechnologyInformation, 135
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8.3 GenomeNetandDataBankofJapan, 136
8.4 OtherSequenceAnalysisandAlignmentWebServers, 137
8.5 SeqAna:MultipleSequenceAlignmentwithQualityRanking, 138
8.6 PairwiseSequenceAlignmentandOtherAnalysisTools, 140
8.7 ToolEvaluation, 142
9 MultipleSequenceforNext-GenerationSequences 145
9.1 Introduction, 145
9.2 OverviewofNextGenerationSequenceAlignmentAlgorithms, 147
9.2.1 AlignmentAlgorithmsBasedonSeedingandHashTables, 147
9.2.2 AlignmentAlgorithmsBasedonSuffixTries, 151
9.3 Next-GenerationSequencingTools, 154
10 MultipleSequenceAlignmentforVariationsDetection 161
10.1 Introduction, 161
10.2 GeneticVariants, 163
10.3 VariationDetectionMethodsBasedonMSA, 165
10.4 EvaluationMethodology, 172
10.4.1 PerformanceMetrics, 172
10.4.2 SimulatedSequenceData, 174
10.4.3 RealSequenceData, 175
10.5 ConclusionandFutureWork, 176
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