Bioinformatics Algorithms: Techniques and Applications

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vi CONTENTS 6 A Survey of Seeding for Sequence Alignment 117 Daniel G. Brown 7 The Comparison of Phylogenetic Networks: Algorithms and Complexity 143 Paola Bonizzoni, Gianluca Della Vedova, Riccardo Dondi, and Giancarlo Mauri PART II GENOME AND SEQUENCE ANALYSIS 175 8 Formal Models of Gene Clusters 177 Anne Bergeron, Cedric Chauve, and Yannick Gingras 9 Integer Linear Programming Techniques for Discovering Approximate Gene Clusters 203 Sven Rahmann and Gunnar W. Klau 10 Efficient Combinatorial Algorithms for DNA Sequence Processing 223 Bhaskar DasGupta and Ming-Yang Kao 11 Algorithms for Multiplex PCR Primer Set Selection with Amplification Length Constraints 241 K.M. Konwar, I.I. Măndoiu, A.C. Russell, and A.A. Shvartsman 12 Recent Developments in Alignment and Motif Finding for Sequences and Networks 259 Sing-Hoi Sze PART III MICROARRAY DESIGN AND DATA ANALYSIS 277 13 Algorithms for Oligonucleotide Microarray Layout 279 Sérgio A. De Carvalho Jr. and Sven Rahmann 14 Classification Accuracy Based Microarray Missing Value Imputation 303 Yi Shi, Zhipeng Cai, and Guohui Lin 15 Meta-Analysis of Microarray Data 329 Saumyadipta Pyne, Steve Skiena, and Bruce Futcher
CONTENTS vii PART IV GENETIC VARIATION ANALYSIS 353 16 Phasing Genotypes Using a Hidden Markov Model 355 P. Rastas, M. Koivisto, H. Mannila, and E. Ukkonen 17 Analytical and Algorithmic Methods for Haplotype Frequency Inference: What Do They Tell Us? 373 Steven Hecht Orzack, Daniel Gusfield, Lakshman Subrahmanyan, Laurent Essioux, and Sebastien Lissarrague 18 Optimization Methods for Genotype Data Analysis in Epidemiological Studies 395 Dumitru Brinza, Jingwu He, and Alexander Zelikovsky PART V STRUCTURAL AND SYSTEMS BIOLOGY 417 19 Topological Indices in Combinatorial Chemistry 419 Sergey Bereg 20 Efficient Algorithms for Structural Recall in Databases 439 Hao Wang, Patra Volarath, and Robert W. Harrison 21 Computational Approaches to Predict Protein–Protein and Domain–Domain Interactions 465 Raja Jothi and Teresa M. Przytycka Index 493
Page 2 and 3: BIOINFORMATICS ALGORITHMS
Page 4 and 5: Copyright © 2008 by John Wiley & S
Page 8 and 9: PREFACE Bioinformatics, broadly def
Page 10 and 11: CONTRIBUTORS Sudha Balla, Departmen
Page 12 and 13: CONTRIBUTORS xiii Steven Hecht Orza
Page 14 and 15: 1 EDUCATING BIOLOGISTS IN THE 21ST
Page 16 and 17: EDUCATING BIOLOGISTS IN THE 21ST CE
Page 18 and 19: EDUCATING BIOLOGISTS IN THE 21ST CE
Page 20 and 21: 2 DYNAMIC PROGRAMMING ALGORITHMS FO
Page 22 and 23: SEQUENCE ALIGNMENT: GLOBAL, LOCAL,
Page 24 and 25: ecurrence: SEQUENCE ALIGNMENT: GLOB
Page 30 and 31: DYNAMIC PROGRAMMING ALGORITHMFOR RN
Page 32 and 33: DYNAMIC PROGRAMMING ALGORITHMFOR RN
Page 34 and 35: DYNAMIC PROGRAMMING ALGORITHMS FOR
Page 36 and 37: REFERENCES 25 the flexible structur
Page 38 and 39: REFERENCES 27 32. Gusfield D. Effic
Page 40 and 41: 3 GRAPH THEORETICAL APPROACHES TO D
Page 42 and 43: GRAPH THEORY BACKGROUND 31 beginnin
Page 44 and 45: GRAPH THEORY BACKGROUND 33 FIGURE 3
Page 46 and 47: GRAPH THEORY BACKGROUND 35 chordal
Page 48 and 49: GRAPH THEORY BACKGROUND 37 decompos
Page 50 and 51: RECONSTRUCTING PHYLOGENIES 39 are (
Page 52 and 53: RECONSTRUCTING PHYLOGENIES 41 only
Page 54 and 55: FORMATION OF MULTIPROTEIN COMPLEXES
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3.4.1 Ribosomal Assembly FORMATION
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FORMATION OF MULTIPROTEIN COMPLEXES
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FORMATION OF MULTIPROTEIN COMPLEXES
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ACKNOWLEDGMENTS REFERENCES 51 This
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REFERENCES 53 37. Golumbic MC, Hart
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4 ADVANCES IN HIDDEN MARKOV MODELS
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HIDDEN MARKOV MODELS FOR SEQUENCE A
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ALTERNATIVES TO VITERBI DECODING 63
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Noncoding Coding Intron (a) Without
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also have this same label). We get
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change as follows: GENERALIZED HIDD
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0.00004 0.00002 0.00000 0 20000 400
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HMMS WITH MULTIPLE OUTPUTS OR EXTER
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TRAINING THE PARAMETERS OF AN HMM 8
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CONCLUSION 85 of parameters compare
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REFERENCES 87 4. Altun Y, Tsochanta
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REFERENCES 89 42. Krogh A. Using da
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REFERENCES 91 77. Xu EW, Kearney P,
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94 SORTING- AND FFT-BASED TECHNIQUE
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100 SORTING- AND FFT-BASED TECHNIQU
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6 A SURVEY OF SEEDING FOR SEQUENCE
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ALIGNMENTS 119 6.2.1 Formal Definit
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TRADITIONAL APPROACHES TO HEURISTIC
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MORE CONTEMPORARY SEEDING APPROACHE
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MORE COMPLICATED SEED DESCRIPTIONS
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SOME THEORETICAL ISSUES IN ALIGNMEN
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REFERENCES 141 6. Brown DG. Optimiz
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7 THE COMPARISON OF PHYLOGENETIC NE
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INTRODUCTION 145 known phylogeny re
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BASIC DEFINITIONS 147 The undirecte
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BASIC DEFINITIONS 149 N1 displays N
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A B C A B C SUBTREES AND SUBNETWORK
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SUBTREES AND SUBNETWORKS 153 of x a
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SUBTREES AND SUBNETWORKS 155 1. it
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SUPERTREES AND SUPERNETWORKS 157 By
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SUPERTREES AND SUPERNETWORKS 159 Go
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SUPERTREES AND SUPERNETWORKS 161 Th
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RECONCILIATION OF GENE TREES AND SP
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REFERENCES 173 21. Gòrecki P, Tiur
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8 FORMAL MODELS OF GENE CLUSTERS An
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8.2 GENOME PLASTICITY 8.2.1 Genome
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GENOME PLASTICITY 181 FIGURE 8.2 An
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BASIC CONCEPTS 183 “more or less
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BASIC CONCEPTS 185 of {m, o, s}. On
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MODELS OF GENE CLUSTERS 187 Definit
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4, 2, 3, 1, 11, 10, 9, 8, 7, 6, 5 4
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MODELS OF GENE CLUSTERS 191 FIGURE
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MODELS OF GENE CLUSTERS 193 another
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MODELS OF GENE CLUSTERS 195 of gene
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MODELS OF GENE CLUSTERS 197 The two
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REFERENCES 199 flexibility by bound
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REFERENCES 201 28. Hoberman R, Dura
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9 INTEGER LINEAR PROGRAMMING TECHNI
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BASIC PROBLEM SPECIFICATION 205 a n
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INTEGER LINEAR PROGRAMMING FORMULAT
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INTEGER LINEAR PROGRAMMING FORMULAT
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9.4 EXTENSIONS AND VARIATIONS EXTEN
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i=1 EXTENSIONS AND VARIATIONS 213 H
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9.5 COMPUTATIONAL RESULTS COMPUTATI
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DISCUSSION 217 TABLE 9.2 Cluster Si
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DISCUSSION 219 FIGURE 9.5 Manually
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ACKNOWLEDGMENTS REFERENCES 221 We t
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224 EFFICIENT COMBINATORIAL ALGORIT
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11 ALGORITHMS FOR MULTIPLEX PCR PRI
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INTRODUCTION 243 problem: given a s
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1. p hybridizes at position t of f
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Thus, constraints 11.7 can be repla
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A GREEDY ALGORITHM 249 FIGURE 11.3
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EXPERIMENTAL RESULTS 251 11.5.1 Amp
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#primers/(2x#SNPs) (%) #primers/(2x
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TABLE 11.2 (Continued ) EXPERIMENTA
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REFERENCES 257 p, discard all candi
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12 RECENT DEVELOPMENTS IN ALIGNMENT
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12.2 MULTIPLE SEQUENCE ALIGNMENT 12
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MULTIPLE SEQUENCE ALIGNMENT 263 The
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MOTIF FINDING 265 Marsan and Sagot
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BIOLOGICAL NETWORK ANALYSIS 267 mul
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DISCUSSION 269 an interaction pair
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REFERENCES 271 13. Bucka-Lassen K,
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REFERENCES 273 52. Lee C, Grasso C,
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REFERENCES 275 90. Stormo GD, Hartz
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PART III MICROARRAY DESIGN AND DATA
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280 ALGORITHMS FOR OLIGONUCLEOTIDE
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14 CLASSIFICATION ACCURACY BASED MI
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INTRODUCTION 305 Decomposition (SVD
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METHODS 307 Note that in most of th
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estimated as K� 1 ai,j = aik,j. d
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METHODS 311 [7]. The KNN-classifier
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ROWimpute-KNN ROWimpute-SVM KNNimpu
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Classification accuracies of SRBCT
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Classification accuracies of SRBCT
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REFERENCES 325 From these two plots
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REFERENCES 327 18. Troyanskaya OG,
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330 META-ANALYSIS OF MICROARRAY DAT
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16 PHASING GENOTYPES USING A HIDDEN
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A HIDDEN MARKOV MODEL FOR RECOMBINA
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LEARNING THE HMM FROM UNPHASED GENO
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EXPERIMENTAL RESULTS 365 It is also
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DISCUSSION 367 TABLE 16.1 Phasing A
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GERBIL PHASE fastPHASE 0.4 0.35 0.3
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REFERENCES 371 however, that direct
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17 ANALYTICAL AND ALGORITHMIC METHO
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INTRODUCTION 375 The use of real ha
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follows: X11 = 2N11 + N12 + N21 X21
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METHODS 379 FIGURE 17.1 The likelih
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METHODS 381 TABLE 17.3 Tests for Ha
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METHODS 383 The sixth stochastic al
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RESULTS 385 TABLE 17.4 The Distribu
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RESULTS 387 TABLE 17.6 The Distribu
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DISCUSSION 389 2SNP also produced r
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ACKNOWLEDGMENTS 391 haplotypes need
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REFERENCES 393 16. Hill WG. Estimat
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18 OPTIMIZATION METHODS FOR GENOTYP
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Tag-restricted haplotype n Complete
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INFORMATIVE SNP SELECTION 399 from
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DISEASE ASSOCIATION SEARCH 401 18.2
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DISEASE ASSOCIATION SEARCH 403 18.3
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Below is the formal description of
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RESULTS AND DISCUSSION 407 to decid
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RESULTS AND DISCUSSION 409 � Comp
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RESULTS AND DISCUSSION 411 TABLE 18
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RESULTS AND DISCUSSION 413 nonindex
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REFERENCES 415 20. Lee PH, Shatkay
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19 TOPOLOGICAL INDICES IN COMBINATO
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TOPOLOGICAL INDICES 421 The quantit
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Theorem 19.2 Let T = (V, E) be a tr
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HOSOYA POLYNOMIAL 425 The Laplacian
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H2(G, x) = � {u,v}⊆V INVERSE WI
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HEXAGONAL SYSTEMS 429 hexagonal sys
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C 2 HEXAGONAL SYSTEMS 431 FIGURE 19
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THE WIENER INDEX OF PEPTOIDS 433 Th
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if R ≥ L, then π(Lp) = i; Lp = L
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REFERENCES 437 19. Entringer RC, Me
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20 EFFICIENT ALGORITHMS FOR STRUCTU
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COMPOUND REPRESENTATION 441 FIGURE
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COMPOUND REPRESENTATION 443 breakag
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TABLE 20.1 Bond List of Aspirin Bon
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COMPOUND REPRESENTATION 447 20.2.5
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Initial class value for node A A 3
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CHEMICAL COMPOUND DATABASE 451 In c
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CHEMICAL COMPOUND DATABASE 453 taki
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CHEMICAL COMPOUND DATABASE 455 Othe
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REFERENCES 457 lab may take months
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REFERENCES 459 22. Curco D, Rodrigu
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REFERENCES 461 61. An J, Nakama T,
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REFERENCES 463 101. Shen J. HAD An
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466 COMPUTATIONAL APPROACHES TO PRE
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INDEX 2SNP computer program 383, 38
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degeneracy 101-104, 112 degenerate
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lowest p-value method 484-486 max-g
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pseudoknots 20 p-value 339-343, 347
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