Bioinformatics and Molecular Evolution 1405106832, 9781405106832, 1444311182, 9781444311181

Table of contents :
Bioinformatics and Molecular Evolution......Page 5
Full Contents......Page 9
Preface......Page 12
1.1 DATA EXPLOSIONS......Page 17
1.2 GENOMICS AND HIGH-THROUGHPUT TECHNIQUES......Page 21
1.3 WHAT IS BIOINFORMATICS?......Page 22
1.4 THE RELATIONSHIP BETWEEN POPULATION GENETICS, MOLECULAR EVOLUTION, AND BIOINFORMATICS......Page 23
SUMMARY......Page 26
2.1 NUCLEIC ACID STRUCTURE......Page 28
2.2 PROTEIN STRUCTURE......Page 30
2.3 THE CENTRAL DOGMA......Page 32
2.4 PHYSICO-CHEMICAL PROPERTIES OF THE AMINO ACIDS AND THEIR IMPORTANCE IN PROTEIN FOLDING......Page 38
BOX 2.1 Polymerase chain reaction (PCR)......Page 39
2.5 VISUALIZATION OF AMINO ACID PROPERTIES USING PRINCIPAL COMPONENT ANALYSIS......Page 41
2.6 CLUSTERING AMINO ACIDS ACCORDING TO THEIR PROPERTIES......Page 44
BOX 2.2 Principal component analysis in more detail......Page 45
SUMMARY......Page 50
3.1 WHAT IS EVOLUTION?......Page 53
3.2 MUTATIONS......Page 55
3.3 SEQUENCE VARIATION WITHIN AND BETWEEN SPECIES......Page 56
3.4 GENEALOGICAL TREES AND COALESCENCE......Page 60
3.5 THE SPREAD OF NEW MUTATIONS......Page 62
3.6 NEUTRAL EVOLUTION AND ADAPTATION......Page 65
BOX 3.1 The influence of selection on the fixation probability......Page 66
BOX 3.2 A deterministic theory for the spread of mutations......Page 67
SUMMARY......Page 70
4.1 MODELS OF NUCLEIC ACID SEQUENCE EVOLUTION......Page 74
BOX 4.1 Solution of the Jukes–Cantor model......Page 77
4.2 THE PAM MODEL OF PROTEIN SEQUENCE EVOLUTION......Page 81
BOX 4.2 PAM distances......Page 86
4.3 LOG-ODDS SCORING MATRICES FOR AMINO ACIDS......Page 87
SUMMARY......Page 92
5.1 WHY BUILD A DATABASE?......Page 97
5.2 DATABASE FILE FORMATS......Page 98
5.3 NUCLEIC ACID SEQUENCE DATABASES......Page 99
5.4 PROTEIN SEQUENCE DATABASES......Page 105
5.5 PROTEIN FAMILY DATABASES......Page 111
5.6 COMPOSITE PROTEIN PATTERN DATABASES......Page 124
5.7 PROTEIN STRUCTURE DATABASES......Page 127
5.8 OTHER TYPES OF BIOLOGICAL DATABASE......Page 129
SUMMARY......Page 131
6.1 WHAT IS AN ALGORITHM?......Page 135
6.2 PAIRWISE SEQUENCE ALIGNMENT – THE PROBLEM......Page 137
6.3 PAIRWISE SEQUENCE ALIGNMENT – DYNAMIC PROGRAMMING METHODS......Page 139
6.4 THE EFFECT OF SCORING PARAMETERS ON THE ALIGNMENT......Page 143
6.5 MULTIPLE SEQUENCE ALIGNMENT......Page 146
SUMMARY......Page 152
7.1 SIMILARITY SEARCH TOOLS......Page 155
7.2 ALIGNMENT STATISTICS (IN THEORY)......Page 163
BOX 7.1 Extreme value distributions......Page 167
BOX 7.2 Derivation of the extreme value distribution in the word-matching example......Page 168
7.3 ALIGNMENT STATISTICS (IN PRACTICE)......Page 169
SUMMARY......Page 171
8.1 UNDERSTANDING PHYLOGENETIC TREES......Page 174
8.2 CHOOSING SEQUENCES......Page 177
8.3 DISTANCE MATRICES AND CLUSTERING METHODS......Page 178
BOX 8.1 Calculation of distances in the neighbor-joining method......Page 183
8.4 BOOTSTRAPPING......Page 185
8.5 TREE OPTIMIZATION CRITERIA AND TREE SEARCH METHODS......Page 187
8.6 THE MAXIMUM-LIKELIHOOD CRITERION......Page 189
BOX 8.2 Calculating the likelihood of the data on a given tree......Page 190
8.7 THE PARSIMONY CRITERION......Page 193
8.8 OTHER METHODS RELATED TO MAXIMUM LIKELIHOOD......Page 195
BOX 8.3 Calculating posterior probabilities......Page 198
SUMMARY......Page 201
9.1 GOING BEYOND PAIRWISE ALIGNMENT METHODS FOR DATABASE SEARCHES......Page 211
9.2 REGULAR EXPRESSIONS......Page 213
9.3 FINGERPRINTS......Page 216
9.4 PROFILES AND PSSMS......Page 221
9.5 BIOLOGICAL APPLICATIONS – G PROTEIN-COUPLED RECEPTORS......Page 224
SUMMARY......Page 232
10.1 USING MACHINE LEARNING FOR PATTERN RECOGNITION IN BIOINFORMATICS......Page 243
10.2 PROBABILISTIC MODELS OF SEQUENCES – BASIC INGREDIENTS......Page 244
BOX 10.1 Dirichlet prior distributions......Page 248
10.3 INTRODUCING HIDDEN MARKOV MODELS......Page 250
BOX 10.2 The Viterbi algorithm......Page 254
BOX 10.3 The forward and backward algorithms......Page 255
10.4 PROFILE HIDDEN MARKOV MODELS......Page 257
10.5 NEURAL NETWORKS......Page 260
BOX 10.4 The back-propagation algorithm......Page 265
10.6 NEURAL NETWORKS AND PROTEIN SECONDARY STRUCTURE PREDICTION......Page 266
SUMMARY......Page 269
11.1 RNA STRUCTURE AND EVOLUTION......Page 273
11.2 FITTING EVOLUTIONARY MODELS TO SEQUENCE DATA......Page 282
11.3 APPLICATIONS OF MOLECULAR PHYLOGENETICS......Page 288
SUMMARY......Page 295
12.1 PROKARYOTIC GENOMES......Page 299
BOX 12.1 Web resources for bacterial genomes......Page 300
12.2 ORGANELLAR GENOMES......Page 314
SUMMARY......Page 325
13.1 ’OMES AND ’OMICS......Page 329
13.2 HOW DO MICROARRAYS WORK?......Page 330
13.3 NORMALIZATION OF MICROARRAY DATA......Page 332
13.4 PATTERNS IN MICROARRAY DATA......Page 335
13.5 PROTEOMICS......Page 341
13.6 INFORMATION MANAGEMENT FOR THE ’OMES......Page 346
BOX 13.1 Examples from the Gene Ontology......Page 351
SUMMARY......Page 353
M.1 EXPONENTIALS AND LOGARITHMS......Page 359
M.3 SUMMATIONS......Page 360
M.5 PERMUTATIONS AND COMBINATIONS......Page 361
M.6 DIFFERENTIATION......Page 362
M.8 DIFFERENTIAL EQUATIONS......Page 363
M.10 NORMAL DISTRIBUTIONS......Page 364
M.11 POISSON DISTRIBUTIONS......Page 366
M.12 CHI-SQUARED DISTRIBUTIONS......Page 367
M.13 GAMMA FUNCTIONS AND GAMMA DISTRIBUTIONS......Page 368
PROBLEMS......Page 369
List of Web addresses......Page 371
Glossary......Page 373
Index......Page 379