Bioinformatics and Molecular Evolution -- Full Contents -- Preface -- 1 Introduction: The revolution in biological information -- 1.1 DATA EXPLOSIONS -- 1.2 GENOMICS AND HIGH-THROUGHPUT TECHNIQUES -- 1.3 WHAT IS BIOINFORMATICS? -- 1.4 THE RELATIONSHIP BETWEEN POPULATION GENETICS, MOLECULAR EVOLUTION, AND BIOINFORMATICS -- SUMMARY -- 2 Nucleic acids, proteins, and amino acids -- 2.1 NUCLEIC ACID STRUCTURE -- 2.2 PROTEIN STRUCTURE -- 2.3 THE CENTRAL DOGMA -- 2.4 PHYSICO-CHEMICAL PROPERTIES OF THE AMINO ACIDS AND THEIR IMPORTANCE IN PROTEIN FOLDING -- BOX 2.1 Polymerase chain reaction (PCR) -- 2.5 VISUALIZATION OF AMINO ACID PROPERTIES USING PRINCIPAL COMPONENT ANALYSIS -- 2.6 CLUSTERING AMINO ACIDS ACCORDING TO THEIR PROPERTIES -- BOX 2.2 Principal component analysis in more detail -- SUMMARY -- 3 Molecular evolution and population genetics -- 3.1 WHAT IS EVOLUTION? -- 3.2 MUTATIONS -- 3.3 SEQUENCE VARIATION WITHIN AND BETWEEN SPECIES -- 3.4 GENEALOGICAL TREES AND COALESCENCE -- 3.5 THE SPREAD OF NEW MUTATIONS -- 3.6 NEUTRAL EVOLUTION AND ADAPTATION -- BOX 3.1 The influence of selection on the fixation probability -- BOX 3.2 A deterministic theory for the spread of mutations -- SUMMARY -- 4 Models of sequence evolution -- 4.1 MODELS OF NUCLEIC ACID SEQUENCE EVOLUTION -- BOX 4.1 Solution of the Jukes-Cantor model -- 4.2 THE PAM MODEL OF PROTEIN SEQUENCE EVOLUTION -- BOX 4.2 PAM distances -- 4.3 LOG-ODDS SCORING MATRICES FOR AMINO ACIDS -- SUMMARY -- 5 Information resources for genes and proteins -- 5.1 WHY BUILD A DATABASE? -- 5.2 DATABASE FILE FORMATS -- 5.3 NUCLEIC ACID SEQUENCE DATABASES -- 5.4 PROTEIN SEQUENCE DATABASES -- 5.5 PROTEIN FAMILY DATABASES -- 5.6 COMPOSITE PROTEIN PATTERN DATABASES -- 5.7 PROTEIN STRUCTURE DATABASES -- 5.8 OTHER TYPES OF BIOLOGICAL DATABASE -- SUMMARY -- 6 Sequence alignment algorithms -- 6.1 WHAT IS AN ALGORITHM?.

6.2 PAIRWISE SEQUENCE ALIGNMENT - THE PROBLEM -- 6.3 PAIRWISE SEQUENCE ALIGNMENT - DYNAMIC PROGRAMMING METHODS -- 6.4 THE EFFECT OF SCORING PARAMETERS ON THE ALIGNMENT -- 6.5 MULTIPLE SEQUENCE ALIGNMENT -- SUMMARY -- 7 Searching sequence databases -- 7.1 SIMILARITY SEARCH TOOLS -- 7.2 ALIGNMENT STATISTICS (IN THEORY) -- BOX 7.1 Extreme value distributions -- BOX 7.2 Derivation of the extreme value distribution in the word-matching example -- 7.3 ALIGNMENT STATISTICS (IN PRACTICE) -- SUMMARY -- 8 Phylogenetic methods -- 8.1 UNDERSTANDING PHYLOGENETIC TREES -- 8.2 CHOOSING SEQUENCES -- 8.3 DISTANCE MATRICES AND CLUSTERING METHODS -- BOX 8.1 Calculation of distances in the neighbor-joining method -- 8.4 BOOTSTRAPPING -- 8.5 TREE OPTIMIZATION CRITERIA AND TREE SEARCH METHODS -- 8.6 THE MAXIMUM-LIKELIHOOD CRITERION -- BOX 8.2 Calculating the likelihood of the data on a given tree -- 8.7 THE PARSIMONY CRITERION -- 8.8 OTHER METHODS RELATED TO MAXIMUM LIKELIHOOD -- BOX 8.3 Calculating posterior probabilities -- SUMMARY -- 9 Patterns in protein families -- 9.1 GOING BEYOND PAIRWISE ALIGNMENT METHODS FOR DATABASE SEARCHES -- 9.2 REGULAR EXPRESSIONS -- 9.3 FINGERPRINTS -- 9.4 PROFILES AND PSSMS -- 9.5 BIOLOGICAL APPLICATIONS - G PROTEIN-COUPLED RECEPTORS -- SUMMARY -- 10 Probabilistic methods and machine learning -- 10.1 USING MACHINE LEARNING FOR PATTERN RECOGNITION IN BIOINFORMATICS -- 10.2 PROBABILISTIC MODELS OF SEQUENCES - BASIC INGREDIENTS -- BOX 10.1 Dirichlet prior distributions -- 10.3 INTRODUCING HIDDEN MARKOV MODELS -- BOX 10.2 The Viterbi algorithm -- BOX 10.3 The forward and backward algorithms -- 10.4 PROFILE HIDDEN MARKOV MODELS -- 10.5 NEURAL NETWORKS -- BOX 10.4 The back-propagation algorithm -- 10.6 NEURAL NETWORKS AND PROTEIN SECONDARY STRUCTURE PREDICTION -- SUMMARY -- 11 Further topics in molecular evolution and phylogenetics.

11.1 RNA STRUCTURE AND EVOLUTION -- 11.2 FITTING EVOLUTIONARY MODELS TO SEQUENCE DATA -- 11.3 APPLICATIONS OF MOLECULAR PHYLOGENETICS -- SUMMARY -- 12 Genome evolution -- 12.1 PROKARYOTIC GENOMES -- BOX 12.1 Web resources for bacterial genomes -- 12.2 ORGANELLAR GENOMES -- SUMMARY -- 13 DNA Microarrays and the 'omes -- 13.1 'OMES AND 'OMICS -- 13.2 HOW DO MICROARRAYS WORK? -- 13.3 NORMALIZATION OF MICROARRAY DATA -- 13.4 PATTERNS IN MICROARRAY DATA -- 13.5 PROTEOMICS -- 13.6 INFORMATION MANAGEMENT FOR THE 'OMES -- BOX 13.1 Examples from the Gene Ontology -- SUMMARY -- Mathematical appendix -- M.1 EXPONENTIALS AND LOGARITHMS -- M.2 FACTORIALS -- M.3 SUMMATIONS -- M.4 PRODUCTS -- M.5 PERMUTATIONS AND COMBINATIONS -- M.6 DIFFERENTIATION -- M.7 INTEGRATION -- M.8 DIFFERENTIAL EQUATIONS -- M.9 BINOMIAL DISTRIBUTIONS -- M.10 NORMAL DISTRIBUTIONS -- M.11 POISSON DISTRIBUTIONS -- M.12 CHI-SQUARED DISTRIBUTIONS -- M.13 GAMMA FUNCTIONS AND GAMMA DISTRIBUTIONS -- PROBLEMS -- List of Web addresses -- Glossary -- Index.