Bioinformatics Algorithms: Techniques and Applications

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pseudoknots 20 p-value 339–343, 347 quadratic assignment problem (QAP) 286 quantitative-structure-activityrelationship (QSAR) 419 quorum 212 radix sort 94, 101, 105–106, 108 r<strong>and</strong>om effects modeling 333–336 RB (rule-based) algorithm 374, 382 RB (rule-based) computer program 382, 384, 385 rearrangements 177 recombination 62 reconciled tree 167, 170, 171 re-embedding algorithm 286, 290–292, 298–300 refinement 145,148, 149, 157, 158 RepeatMasker computer program 231 ribosomal complex assembly 44–45 ring structure 442 RMA normalization 344 RNA interference (RNAi) 95, 104, 112, 113 RNA secondary structure 11,19 RNA secondary structure prediction 20 rooted network 146 rooted triple 149 root mean squared distance (RMSD) 22 row-epitaxial placement 289 RPS-BLAST computer program 478 Russel-Rao coefficient 454 r-window 205, 214 scenario 166, 167, 171 Schultz index 421 segment alignment (SEA) 18 segment chaining 13, 24 separation problem 209 sequence alignment 3, 117–140, 466 sequence-structure motifs 18 sequential re-embedding 292 INDEX 499 shared substructure 440 shortest path 10 short-interference RNA (siRNA) 93, 95, 105, 107, 112, 113, 115 shrinkage 339 Sidak’s correction 341 similarity coding 453 similarity measurement 94, 108, 111 similar subset 440 Simpson coefficient 454 single nucleotide polymorphism (SNP) 241–257, 355–372, 395–415 siRNA patterns 107 sliding-window matching algorithm 288 SMARTS 447 SMD repository 344 SMILES 447 SMIRKS 448 soft conflict 150 SOS algorithm 108 spaced seeds 126–133 speciation 163, 166 species tree 145, 146, 147, 162–171 specific selection problem 107, 108 spliced alignment 16 SP-STAR algorithm 96 st<strong>and</strong>ardized mean difference 334 Stereochemically Extended Morgan algorithm (SEMA) 449 Stouffer’s sum of Z’s 340 strong common interval 188 structural flexibility 24 structure-based sequence alignment 23 structure search 451 subdivision operation 165 suboptimal structures 21 suffix array 94 suffix tree 94, 100, 101, 105, 107, 115 sum of logits 341 sum of logs 340 supernetwork 144, 156, 157, 159, 160, 161 switch distance 365 Szeged index 421
500 INDEX tagging 396 Tanimoto coefficient 453 t-Coffee computer program 468 threading 287, 288 three-dimensional molecular representation 441 thresholded Fisher product 341–342 Tippett’s Minimum p 341 TOP2A, 348 topological index 419 topological restriction 148 toposisomerase II 348 total agreement supertree problem 157 TRANSFAC database 336 transfer arc 165 translocations 181 transmembrane proteins 61 traveling salesman problem (TSP) 287 tree isomorphism 475–477 treelike cluster system 147, 159, 169 tree topology 475, 477 triple repeat identification problem (TRIP) 106 t-statistic 347 two-dimensional molecular representation 441 two-dimensional partitioning 293, 295–296 Ullman algorithm 452 uniformly ordered maximum homeomorphic subtree 156 union graph 165 unphased genotype 356 variance 339 vector seeds 134–135 vertex contraction 148 Viterbi algorithm 58–59 Watson-Crick complement 241, 244 weighted sum of Z’s 341 Wiener index 420 WinBUGS computer program 338 Winer’s sum of t’s 341 WINNOWER algorithm 96 Wiswesser line notation (WLN) 447 wrapping interval 211
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BIOINFORMATICS ALGORITHMS
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Copyright © 2008 by John Wiley & S
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vi CONTENTS 6 A Survey of Seeding f
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PREFACE Bioinformatics, broadly def
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CONTRIBUTORS Sudha Balla, Departmen
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CONTRIBUTORS xiii Steven Hecht Orza
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1 EDUCATING BIOLOGISTS IN THE 21ST
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EDUCATING BIOLOGISTS IN THE 21ST CE
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EDUCATING BIOLOGISTS IN THE 21ST CE
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2 DYNAMIC PROGRAMMING ALGORITHMS FO
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SEQUENCE ALIGNMENT: GLOBAL, LOCAL,
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ecurrence: SEQUENCE ALIGNMENT: GLOB
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DYNAMIC PROGRAMMING ALGORITHMFOR RN
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DYNAMIC PROGRAMMING ALGORITHMFOR RN
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DYNAMIC PROGRAMMING ALGORITHMS FOR
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REFERENCES 25 the flexible structur
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REFERENCES 27 32. Gusfield D. Effic
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3 GRAPH THEORETICAL APPROACHES TO D
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GRAPH THEORY BACKGROUND 31 beginnin
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GRAPH THEORY BACKGROUND 33 FIGURE 3
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GRAPH THEORY BACKGROUND 35 chordal
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GRAPH THEORY BACKGROUND 37 decompos
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RECONSTRUCTING PHYLOGENIES 39 are (
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RECONSTRUCTING PHYLOGENIES 41 only
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FORMATION OF MULTIPROTEIN COMPLEXES
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3.4.1 Ribosomal Assembly FORMATION
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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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Page 464 and 465: 466 COMPUTATIONAL APPROACHES TO PRE
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