| | Contents | |
| | | |
| |
| | Volume 1 | |
| | Preface | XXV |
| | List of Contributors | XXIX |
| Part 1 | Introduction | 1 |
| 1 | Bioinformatics - From Genomes to Therapies
Thomas Lengauer | 1 |
| 1 | Introduction | 1 |
| 2 | The Molecular Basis of Disease | 1 |
| 3 | The Molecular Approach to Curing Diseases | 6 |
| 4 | Finding Protein Targets | 8 |
| 4.1 | Genomics versus Proteomics | 10 |
| 4.2 | Extent of Information Available on the Genes/Proteins | 11 |
| 5 | Developing Drugs | 12 |
| 6 | Optimizing Therapies | 14 |
| 7 | Organization of the Book | 15 |
| | References | 23 |
| Part 2 | Sequencing Genomes | 25 |
| 2 | Bioinformatics Support for Genome-Sequencing Projects
Knut Reinert and Daniel Huson | 25 |
| 1 | Introduction | 25 |
| 2 | Assembly Strategies for Large Genomes | 25 |
| 2.1 | Introduction | 25 |
| 2.2 | Properties of the Data | 29 |
| 2.2.1 | Reads, Mate-pairs and Quality Values | 29 |
| 2.2.2 | Physical Maps | 30 |
| 2.3 | Assembly Strategies | 31 |
| 3 | Algorithmic Problems and their Treatment | 33 |
| 3.1 | Overlap Comparison of all Reads | 34 |
| 3.2 | Contig Phase: Layout of Reads | 37 |
| 3.3 | Error Correction and Resolving Repeats | 40 |
| 3.4 | Layout of Contigs | 42 |
| 3.5 | Computation of the Consensus Sequences | 45 |
| 4 | Examples of Existing Assemblers | 47 |
| 4.1 | The Celera Assembler | 47 |
| 4.2 | The Gig Assembler | 48 |
| 4.3 | The ARACHNE Assembler | 48 |
| 4.4 | The JAZZ Assembler | 49 |
| 4.5 | The RePS Assembler | 49 |
| 4.6 | The Barnacle Assembler | 49 |
| 4.7 | The PCAP Assembler | 50 |
| 4.8 | The Phusion Assembler | 50 |
| 4.9 | The Atlas Assembler | 51 |
| 4.10 | Other Assemblers | 52 |
| 5 | Conclusion | 52 |
| | References | 53 |
| Part 3 | Sequence Analysis | 57 |
| 3 | Sequence Alignment and Sequence Database Search
Martin Vingron | 57 |
| 1 | Introduction | 57 |
| 2 | Pairwise Sequence Comparison | 58 |
| 2.1 | Dot plots | 58 |
| 2.2 | Sequence Alignment | 60 |
| 3 | Database Searching I: Single-sequence Heuristic Algorithms | 65 |
| 4 | Alignment and Search Statistics | 68 |
| 5 | Multiple Sequence Alignment | 71 |
| 6 | Multiple Alignments, HMMs and Database Searching II | 74 |
| 7 | Protein Families and Protein Domains | 78 |
| 8 | Conclusions | 79 |
| | References | 79 |
| 4 | Phylogeny Reconstruction
Ingo Ebersberger, Arndt von Haeseler and Heiko A. Schmidt | 83 |
| 1 | Introduction | 83 |
| 1.1 | Reconstructing a Tree from its Leaves | 84 |
| 1.2 | Phylogenetic Relationships of Taxa and their Characters | 85 |
| 1.2.1 | The Problem of Character Inconsistencies | 86 |
| 1.2.2 | Finding the Appropriate Character Set | 87 |
| 2 | Modeling DNA Sequence Evolution | 88 |
| 2.1 | Nucleotide Substitution Models | 90 |
| 2.2 | Modeling Rate Heterogeneity | 90 |
| 2.3 | Codon Models | 91 |
| 3 | Tracing the Evolutionary Signal | 92 |
| 3.1 | The Parsimony Principle of Evolution | 93 |
| 3.1.1 | Generalized Parsimony | 94 |
| 3.1.2 | Multiple/Parallel Hits | 95 |
| 3.2 | Distance-based Methods | 95 |
| 3.2.1 | UPGMA | 95 |
| 3.2.2 | Neighbors-relation Methods | 96 |
| 3.2.3 | Neighbor-joining Method | 97 |
| 3.2.4 | Least-squares Methods | 98 |
| 3.3 | The Criterion of Likelihood | 98 |
| 3.4 | Calculating the Likelihood of a Tree | 99 |
| 3.5 | Bayesian Statistics in Phylogenetic Analysis | 99 |
| 3.6 | Rooting Trees/Molecular Clock | 101 |
| 3.6.1 | Outgroup Rooting | 101 |
| 3.6.2 | Midpoint Rooting and Molecular Clock | 102 |
| 4 | Finding the Optimal Tree | 103 |
| 4.1 | Exhaustive Search Methods | 103 |
| 4.2 | Heuristic Search Methods | 104 |
| 4.2.1 | Hill Climbing and the Problem of Local Optimization | 105 |
| 4.2.2 | Modeling Tree Quality | 108 |
| 4.2.3 | Heuristics for Large Datasets | 108 |
| 5 | The Advent of Phylogenomics | 109 |
| 5.1 | Multilocus Datasets | 109 |
| 5.2 | Combining Incomplete Multilocus Datasets: Supertrees and their Methods | 112 |
| 5.2.1 | Agreement Supertrees | 112 |
| 5.2.2 | Optimization Supertrees | 114 |
| 5.2.3 | The Supertrees/Consensus versus Total Evidence Debate | 115 |
| 5.2.4 | Medium-level Combination | 115 |
| 6 | Phylogenetic Network Methods | 116 |
| 6.1 | From Trees to Split Networks | 116 |
| 6.1.1 | Split Systems and their Visualization | 116 |
| 6.1.2 | Constructing Split Systems from Trees | 118 |
| 6.1.3 | Constructing Split Systems from Sequence Data | 118 |
| 6.2 | Reconstructing Reticulate Evolution and Further Analyses | 119 |
| | References | 121 |
| 5 | Finding Protein-coding Genes
David C. Kulp | 129 |
| 1 | Introduction | 129 |
| 2 | Basic DNA Terminology | 129 |
| 3 | Detecting Coding Sequences | 131 |
| 3.1 | Reading Frames | 132 |
| 3.2 | Coding Potential | 132 |
| 4 | Gene Contents | 135 |
| 5 | Gene Signals | 137 |
| 5.1 | Splice Sites | 137 |
| 5.2 | Translation Initiation | 140 |
| 5.3 | Translation and Transcription Termination | 140 |
| 6 | Integrating Gene Features | 141 |
| 6.1 | Combining Local Features | 141 |
| 6.2 | Dynamic Programming | 142 |
| 6.3 | Gene Grammars | 143 |
| 7 | Performance Comparisons | 145 |
| 8 | Using Homology | 147 |
| 8.1 | cDNA Clustering and Alignments | 147 |
| 8.2 | Orthologous DNA | 150 |
| 8.3 | Protein Homology | 152 |
| 8.4 | Integrative Methods | 153 |
| 9 | Pitfalls: Pseudogenes, Splice Variants and the Cruel Biological Reality | 153 |
| 10 | Further Reading | 154 |
| | References | 155 |
| 6 | Analyzing Regulatory Regions in Genomes
Thomas Werner | 159 |
| 1 | General Features of Regulatory Regions in Eukaryotic Genomes | 159 |
| 1.1 | General Functions of Regulatory Regions | 159 |
| 1.2 | Most Important Elements in Regulatory Regions | 160 |
| 1.3 | TFBSs | 160 |
| 1.4 | Sequence Features | 161 |
| 1.5 | Structural Elements | 161 |
| 1.6 | Organizational Principles of Regulatory Regions | 162 |
| 1.6.1 | Overall Structure of Pol II Promoters | 162 |
| 1.6.2 | TFBS in Promoters | 162 |
| 1.6.3 | Module Properties of the Core Promoter | 163 |
| 1.7 | Bioinformatics Models for the Analysis and Detection of Regulatory Regions | 168 |
| 1.8 | Statistical Models | 168 |
| 1.8.1 | Mixed Models | 168 |
| 1.8.2 | Organizational Models | 169 |
| 2 | Methods for Element Detection | 169 |
| 2.1 | Detection of TFBSs | 169 |
| 2.2 | Detection of Novel TFBS Motifs | 171 |
| 2.3 | Detection of Structural Elements | 172 |
| 2.4 | Assessment of Other Elements | 172 |
| 3 | Analysis of Regulatory Regions | 173 |
| 3.1 | Comparative Sequence Analysis | 173 |
| 3.2 | Training Set Selection | 173 |
| 3.3 | Statistical and Biological Significance | 174 |
| 3.4 | Context Dependency | 174 |
| 4 | Methods for Detection of Regulatory Regions | 175 |
| 4.1 | Scaffold/Matrix Attachment Regions (S/MARs) | 176 |
| 4.2 | Enhancers/Silencers | 177 |
| 4.3 | Promoters | 177 |
| 4.4 | Programs for Recognition of Regulatory Sequences | 177 |
| 4.4.1 | Programs Based on Statistical Models (General Promoter Prediction) | 178 |
| 4.4.2 | Programs Utilizing Mixed Models | 179 |
| 4.4.3 | Programs Based on Specific Promoter Recognition | 179 |
| 4.4.4 | Early Attempts at Promoter Prediction | 181 |
| 5 | Annotation of Large Genomic Sequences | 182 |
| 5.1 | Balance between Sensitivity and Specificity | 182 |
| 5.2 | Genes – Transcripts – Promoters | 183 |
| 5.3 | Sources for Finding Alternative Transcripts and Promoters | 185 |
| 5.4 | Comparative Genomics of Promoters | 185 |
| 6 | Genome-wide Analysis of Transcription Control | 186 |
| 6.1 | Context-specific Transcripts and Pathways | 187 |
| 6.2 | Consequences for Microarray Analysis | 187 |
| 7 | Conclusions | 189 |
| | References | 190 |
| 7 | Finding Repeats in Genome Sequences
Brian J. Haas and Steven L. Salzberg | 197 |
| 1 | Introduction | 197 |
| 2 | Algorithms and Tools for Mining Repeats | 199 |
| 2.1 | Finding Intra- and Inter-sequence Repeats as Pairwise Alignments | 200 |
| 2.2 | Miropeats (alias Printrepeats) | 201 |
| 2.3 | REPuter | 202 |
| 2.4 | RepeatFinder | 206 |
| 2.5 | RECON | 207 |
| 2.6 | PILER | 209 |
| 2.7 | RepeatScout | 212 |
| 3 | Tandem Repeats | 215 |
| 3.1 | TRF | 216 |
| 3.2 | STRING (Search for Tandem Repeats IN Genomes) | 218 |
| 3.3 | MREPS | 219 |
| 4 | Repeats and Genome Assembly Algorithms | 220 |
| 4.1 | Repeat Management in the Celera Assembler and other Assemblers | 221 |
| 4.2 | Repeat Identfication by k-mer Counts | 221 |
| 4.3 | Repeat Identfication by Depth of Coverage (Arrival Rates) | 222 |
| 4.4 | Repeat Identfication by Conflicting Links | 223 |
| 4.5 | Repeat Placement: Rocks and Stones | 223 |
| 4.6 | Repeat Placement: Surrogates | 223 |
| 4.7 | Repeat Resolution in Euler | 224 |
| 5 | Untangling the Mosaic Nature of Repeats (The A-Bruijn Graph) | 225 |
| 6 | Repeat Annotation in Genomes | 227 |
| | References | 230 |
| 8 | Analyzing Genome Rearrangements
Guillaume Bourque | 235 |
| 1 | Introduction | 235 |
| 2 | Basic Concepts | 236 |
| 2.1 | Genome Representation | 236 |
| 2.1.1 | Circular, Linear and Multichromosomal Genomes | 237 |
| 2.1.2 | Unsigned Genomes | 238 |
| 2.1.3 | Unequal Gene Content | 238 |
| 2.1.4 | Homology Markers | 238 |
| 2.2 | Types of Genome Rearrangements | 239 |
| 3 | Distance between Two Genomes | 240 |
| 3.1 | Breakpoint Distance | 240 |
| 3.2 | Rearrangement Distance | 241 |
| 3.2.1 | HP Theory | 242 |
| 3.3 | Conservation Distance | 244 |
| 3.3.1 | Common Intervals | 244 |
| 3.3.2 | Conserved Intervals | 245 |
| 4 | Genome Rearrangement Phylogenies | 245 |
| 4.1 | Distance-based Methods | 246 |
| 4.2 | Maximum Parsimony Methods | 247 |
| 4.3 | Maximum Likelihood Methods | 248 |
| 5 | Recent Applications | 249 |
| 5.1 | Rearrangements in Large Genomes | 249 |
| 5.2 | Genomes Rearrrangements and Cancer | 252 |
| 6 | Conclusion | 253 |
| 6.1 | Challenges | 253 |
| 6.2 | Promising New Approaches | 255 |
| | References | 256 |
| Part 4 | Molecular Structure Prediction | 261 |
| 9 | Predicting Simplified Features of Protein Structure
Dariusz Przybylski and Burkhard Rost | 261 |
| 1 | Introduction | 261 |
| 1.1 | Protein Structures are Determined Much Slower than Sequences | 261 |
| 1.2 | Reliable and Comprehensive Computations of 3-D Structures are not yet Possible | 261 |
| 1.3 | Predictions of Simplified Aspects of 3-D Structure are often very Successful | 262 |
| 2 | Secondary Structure Prediction | 262 |
| 2.1 | Assignment of Secondary from 3-D Structure | 262 |
| 2.1.1 | Regular Secondary Structure Formation is Mostly a Local Process | 262 |
| 2.1.2 | Secondary Structures can be Somehow Flexible | 263 |
| 2.1.3 | Automatic Assignments of Secondary Structure | 263 |
| 2.1.4 | Reduction to Three Secondary Structure States | 264 |
| 2.2 | Measuring Performance | 265 |
| 2.2.1 | Performance has Many Aspects Relating to Many Different Measures | 265 |
| 2.2.2 | Per-residue Percentage Accuracy: QK | 266 |
| 2.2.3 | Per-residue Confusion between Regular Elements: BAD | 266 |
| 2.2.4 | Per-segment Prediction Accuracy: SOV | 266 |
| 2.3 | Comparing Different Methods | 267 |
| 2.3.1 | Generic Problems | 267 |
| 2.3.2 | Numbers can often not be Compared between Two Different Publications | 267 |
| 2.3.3 | Appropriate Comparisons of Methods Require Large, “Blind” Data Sets | 268 |
| 2.4 | History | 269 |
| 2.4.1 | First Generation: Single-residue Statistics | 269 |
| 2.4.2 | Second Generation: Segment Statistics | 269 |
| 2.4.3 | Third Generation: Evolutionary Information | 269 |
| 2.4.4 | Recent Improvements of Third-generation Methods | 271 |
| 2.4.5 | Meta-predictors Improve Somehow | 272 |
| 2.5 | State-of-the-art Performance | 272 |
| 2.5.1 | Average Predictions Have Good Quality | 272 |
| 2.5.2 | Prediction Accuracy Varies among Proteins | 273 |
| 2.5.3 | Reliability of Prediction Correlates with Accuracy | 273 |
| 2.5.4 | UnderstandableWhy Certain Proteins Predicted Poorly? | 274 |
| 2.6 | Applications | 274 |
| 2.6.1 | Better Database Searches | 274 |
| 2.6.2 | One-dimensional Predictions Assist in the Prediction of Higher-dimensional Structure | 275 |
| 2.6.3 | Predicted Secondary Structure Helps Annotating Function | 275 |
| 2.6.4 | Secondary Structure-based Classifications in the Context of Genome Analysis | 276 |
| 2.6.5 | Regions Likely to Undergo Structural Change Predicted Successfully | 276 |
| 2.7 | Things to Remember when using Predictions | 277 |
| 2.7.1 | Special Classes of Proteins | 277 |
| 2.7.2 | Better Alignments Yield Better Predictions | 277 |
| 2.8 | Resources | 277 |
| 2.8.1 | Internet Services are Widely Available | 277 |
| 2.8.2 | Interactive Services | 277 |
| 2.8.3 | Servers | 278 |
| 3 | Transmembrane Regions | 278 |
| 3.1 | Transmembrane Proteins are an Extremely Important Class of Proteins | 278 |
| 3.2 | Prediction Methods | 279 |
| 3.3 | Performance | 279 |
| 3.4 | Servers | 280 |
| 4 | Solvent Accessibility | 280 |
| 4.1 | Solvent Accessibility Somehow Distinguishes Structurally Important from Functionally Important | 280 |
| 4.2 | Measuring Solvent Accessibility | 280 |
| 4.3 | Best Methods Combine Evolutionary Information with Machine Learning | 281 |
| 4.4 | Performance | 282 |
| 4.5 | Servers | 282 |
| 5 | Inter-residue Contacts | 282 |
| 5.1 | Two-dimensional Predictions may be a Step Toward 3-D Structures | 282 |
| 5.2 | Measuring Performance | 282 |
| 5.3 | Prediction Methods | 283 |
| 5.4 | Performance and Applications | 283 |
| 5.5 | Servers | 283 |
| 6 | Flexible and Intrinsically Disordered Regions | 284 |
| 6.1 | Local Mobility, Rigidity and Disorder all are Features that Relate to Function | 284 |
| 6.2 | Measuring Flexibility and Disorder | 284 |
| 6.3 | Prediction Methods | 284 |
| 6.4 | Servers | 285 |
| 7 | Protein Domains | 285 |
| 7.1 | Independent Folding Units | 285 |
| 7.2 | Prediction Methods | 285 |
| 7.3 | Servers | 286 |
| | References | 286 |
| 10 | Homology Modeling in Biology and Medicine
Roland L. Dunbrack, Jr. | 297 |
| 1 | Introduction | 297 |
| 1.1 | The Concept of Homology Modeling | 297 |
| 1.2 | How do Homologous Protein Arise? | 298 |
| 1.3 | The Purposes of Homology Modeling | 299 |
| 1.4 | The Effect of the Genome Projects | 301 |
| 2 | Input Data | 303 |
| 3 | Methods | 307 |
| 3.1 | Modeling at Different Levels of Complexity | 307 |
| 3.2 | Side-chain Modeling | 309 |
| 3.2.1 | Input Information | 309 |
| 3.2.2 | Rotamers and Rotamer Libraries | 311 |
| 3.2.3 | Side-chain Prediction Methods | 312 |
| 3.2.4 | Available Programs for Side-chain Prediction | 317 |
| 3.3 | Loop Modeling | 317 |
| 3.3.1 | Input Information | 317 |
| 3.3.2 | Loop Conformational Analysis | 318 |
| 3.3.3 | Loop Prediction Methods | 320 |
| 3.3.4 | Available Programs | 321 |
| 3.4 | Methods for Complete Modeling | 322 |
| 3.4.1 | MODELLER | 322 |
| 3.4.2 | MolIDE: A Graphical User Interface for Modeling | 323 |
| 3.4.3 | RAMP and PROTINFO | 323 |
| 3.4.4 | SWISS-MODEL | 323 |
| 4 | Results | 324 |
| 4.1 | Range of Targets | 324 |
| 4.2 | Example: Protein Kinase STK11/LKB1 | 324 |
| 4.3 | The Importance of Protein Interactions | 331 |
| 5 | Strengths and Limitations | 334 |
| 6 | Validation | 335 |
| 6.1 | The CASP Meeting | 336 |
| 6.2 | Protein Health | 336 |
| | References | 337 |
| 11 | Protein Fold Recognition Based on Distant Homologs
Ingolf Sommer | 351 |
| 1 | Introduction | 351 |
| 2 | Overview of Template-based Modeling | 352 |
| 2.1 | Key Steps in Template-based Modeling | 352 |
| 2.1.1 | Identifying Templates | 352 |
| 2.1.2 | Assessing Signficance | 353 |
| 2.1.3 | Model Building | 353 |
| 2.1.4 | Evaluation | 354 |
| 2.2 | Template Databases | 354 |
| 3 | Sequence-based Methods for Identifying Templates | 356 |
| 3.1 | Sequence–Sequence Comparison Methods | 356 |
| 3.2 | Frequency Profile Methods | 357 |
| 3.2.1 | Definition of a Frequency Profile and PSSM | 357 |
| 3.2.2 | Generating Frequency Profiles | 359 |
| 3.2.3 | Scoring Frequency Profiles | 360 |
| 3.2.4 | Scoring Profiles Against Sequences | 360 |
| 3.2.5 | Scoring Profiles against Profiles | 361 |
| 3.3 | Hidden Markov Models (HMMs) | 363 |
| 3.3.1 | Definition | 363 |
| 3.3.2 | Profile HMM Technology | 364 |
| 3.3.3 | HMMs in Fold Recognition | 365 |
| 3.3.4 | HMM–HMMComparisons | 365 |
| 3.4 | Support Vector Machines (SVMs) | 365 |
| 3.4.1 | Definition | 365 |
| 3.4.2 | Various Kernels | 366 |
| 3.4.3 | Experimental Assessment | 366 |
| 4 | Structure-basedMethods for Identifying Templates | 367 |
| 4.1 | Boltzmann’s Principle and Knowledge-based Potentials | 368 |
| 4.2 | Threading Using Pair-interaction Potentials | 369 |
| 4.3 | Threading using Frozen Approximation Algorithms | 371 |
| 5 | Hybrid Methods and Recent Developments | 372 |
| 5.1 | Using Different Sources of Information | 372 |
| 5.1.1 | Incorporating Secondary Structure Prediction into Frequency Profiles and HMMs | 372 |
| 5.1.2 | Intrinsically Disordered Regions in Proteins | 373 |
| 5.1.3 | Incorporating 3-D Structure into Frequency Profiles | 374 |
| 5.2 | Combining Information | 374 |
| 5.3 | Meta-servers | 375 |
| 6 | Assessment of Models | 376 |
| 6.1 | Estimating Signficance of Sequence Hits | 376 |
| 6.2 | Scoring 3-D Model Quality: Model Quality Assessment Programs (MQAPs) | 377 |
| 6.3 | Evaluation of Protein Structure Prediction: Critical Assessment of Techniques for Protein Structure Prediction | 378 |
| 7 | Programs and Web Resources | 379 |
| | References | 380 |
| 12 | De Novo Structure Prediction: Methods and Applications
Richard Bonneau | 389 |
| 1 | Introduction | 389 |
| 1.1 | Scope of this Review and Definition of De Novo Structure Prediction | 389 |
| 1.2 | The Role of Structure Prediction in Biology | 390 |
| 1.3 | De novo Structure Prediction in a Genome Annotation Context, Synergy with Other Methods | 391 |
| 2 | Core Features of Current Methods of De Novo Structure Prediction | 393 |
| 2.1 | Rosetta De Novo | 393 |
| 2.2 | Evaluation of Structure Predictions | 396 |
| 2.3 | Domain Prediction is Key | 399 |
| 2.4 | Local Structure Prediction and Reduced Complexity Models are Central to Current De Novo Methods | 403 |
| 2.5 | Clustering as a Heuristic Approach to Approximating Entropic Determinants of Protein Folding | 405 |
| 2.6 | Balancing Resolution with Sampling, Prospects for Improved Accuracy and Atomic Detail | 406 |
| 3 | Applying Structure Prediction: De Novo Structure Prediction in a Systems Biology Context | 408 |
| 3.1 | Structure Prediction as a Road to Function | 408 |
| 3.2 | Initial Application of De Novo Structure Prediction | 409 |
| 3.3 | Application on Genome-wide Scale and Examples of Data Integration | 410 |
| 3.4 | Scaling-up De Novo Structure Prediction: Rosetta on the World Community Grid | 412 |
| 4 | Future Directions | 412 |
| 4.1 | Structure Prediction and Systems Biology: Data Integration | 412 |
| 4.2 | Need for Improved Accuracy and Extending the Reach of De Novo Methods | 413 |
| | References | 413 |
| 13 | Structural Genomics
Philip E. Bourne and Adam Godzik | 419 |
| 1 | Overview | 419 |
| 1.1 | What is Structural Genomics? | 419 |
| 1.2 | What are the Motivators? | 419 |
| 1.2.1 | Fold Coverage as a Motivator | 420 |
| 1.2.2 | Structural Coverage of an Organism as a Motivator | 424 |
| 1.2.3 | Structure Coverage of Central Metabolism Pathways as a Motivator | 424 |
| 1.2.4 | Disease as a Motivator | 425 |
| 1.3 | How Does Structural Genomics Relate to Conventional Structural Biology? | 425 |
| 2 | Methodology | 427 |
| 2.1 | Target Selection | 427 |
| 2.2 | Crystallomics | 428 |
| 2.3 | Data Collection | 429 |
| 2.4 | Structure Solution | 430 |
| 2.5 | Structure Refinement | 431 |
| 2.6 | PDB Deposition | 431 |
| 2.7 | Functional Annotation | 432 |
| 2.7.1 | Biological Multimeric State | 432 |
| 2.7.2 | Active-site Determination | 432 |
| 2.8 | Publishing | 433 |
| 3 | Results – Number and Characteristics of Structures Determined | 434 |
| 4 | Discussion | 435 |
| 4.1 | Follow-up Studies | 435 |
| 4.2 | Examples of Functional Discoveries | 436 |
| 5 | The Future | 436 |
| | References | 436 |
| 14 | RNA Secondary Structures
Ivo L. Hofacker and Peter F. Stadler | 439 |
| 1 | Secondary Structure Graphs | 439 |
| 1.1 | Introduction | 439 |
| 1.2 | Secondary Structure Graphs | 440 |
| 1.3 | Mountain Plots and Dot Plots | 443 |
| 1.4 | Trees and Forests | 443 |
| 1.5 | Notes | 444 |
| 2 | Loop-based Energy Model | 444 |
| 2.1 | Loop Decomposition | 444 |
| 2.2 | Energy Parameters | 445 |
| 2.3 | Notes | 447 |
| 3 | The Problem of RNA Folding | 447 |
| 3.1 | Counting Structures and Maximizing Base Pairs | 447 |
| 3.2 | Backtracing | 449 |
| 3.3 | Energy Minimization in the Loop-based Energy Model | 450 |
| 3.4 | RNA Hybridization | 453 |
| 3.5 | Pseudoknotted Structures | 454 |
| 3.6 | Notes | 454 |
| 4 | Conserved Structures, Consensus Structures and RNA Gene Finding | 456 |
| 4.1 | The Phylogenetic Method | 456 |
| 4.2 | Conserved Structures | 457 |
| 4.3 | Consensus Structures | 459 |
| 4.4 | RNA Gene Finding | 460 |
| 4.5 | Notes | 463 |
| 5 | Grammars for RNA Structures | 463 |
| 5.1 | Context-free Grammars (CFGs) and RNA Secondary Structures | 463 |
| 5.2 | Cocke–Younger–Kasami (CYK) Algorithm | 465 |
| 5.3 | Inside and Outside Algorithms | 465 |
| 5.4 | Parameter Estimation | 466 |
| 5.5 | Algebraic Dynamic Programming | 466 |
| 5.6 | Notes | 467 |
| 6 | Comparison of Secondary Structures | 468 |
| 6.1 | String-based Alignments | 469 |
| 6.2 | Tree Editing | 469 |
| 6.3 | Tree Alignments | 472 |
| 6.4 | The Sankoff Algorithm and Variants | 475 |
| 6.5 | Multiple Alignments | 475 |
| 6.6 | Notes | 476 |
| 7 | Kinetic Folding | 476 |
| 7.1 | Folding Energy Landscapes | 476 |
| 7.2 | Kinetic Folding Algorithms | 477 |
| 7.3 | Approximate Folding Trajectories and Barrier Trees | 478 |
| 7.4 | RNA Switches | 480 |
| 7.5 | Notes | 481 |
| 8 | Concluding Remarks | 481 |
| | References | 482 |
| 15 | RNA Tertiary Structure Prediction
François Major and Philippe Thibault | 491 |
| 1 | Introduction | 491 |
| 2 | Annotation | 493 |
| 2.1 | Nucleotide Conformations | 494 |
| 2.2 | Nucleotide Interactions | 501 |
| 2.2.1 | Base Stacking | 502 |
| 2.2.2 | Base Pairing | 505 |
| 2.2.3 | Isosteric Base Pairs | 508 |
| 3 | Motif Discovery | 508 |
| 3.1 | RNA Motifs | 509 |
| 3.1.1 | Classical Examples | 509 |
| 3.2 | Catalytic Motifs | 513 |
| 3.3 | Transport and Localization | 519 |
| 4 | Modeling | 521 |
| 4.1 | The CSP | 522 |
| 4.2 | MC-Sym | 524 |
| 4.2.1 | Backbone Optimization | 527 |
| 4.2.2 | Probabilistic Backtracking | 529 |
| 4.2.3 | “Divide and Conquer” | 529 |
| 4.3 | MC-Sym atWork | 530 |
| 4.3.1 | Modeling a Yeast tRNA-Phe Stem–Loop | 532 |
| 4.3.2 | Modeling a Pseudoknot | 533 |
| 4.3.3 | Cycles of Interactions | 535 |
| 5 | Perspectives | 535 |
| | References | 536 |
| | Volume 2 | |
| Part 5 | Analysis of Molecular Interactions | 541 |
| 16 | Docking and Scoring for Structure-based Drug Design
Matthias Rarey, Jörg Degen and Ingo Reulecke | 541 |
| 1 | Introduction | 541 |
| 1.1 | A Taxonomy of Docking Problems | 543 |
| 1.2 | Application Scenarios in Structure-based Molecular Design | 544 |
| 2 | Scoring Protein–Ligand Complexes | 546 |
| 2.1 | Modeling Protein–Ligand Interactions | 546 |
| 2.2 | Scoring Functions based on Force Fields | 548 |
| 2.3 | Empirical Scoring | 550 |
| 2.4 | Knowledge-based Scoring | 551 |
| 2.5 | Evaluation | 551 |
| 3 | Methods for Protein–Ligand Docking | 552 |
| 3.1 | Rigid-body Docking Algorithms | 552 |
| 3.1.1 | Approaches based on Clique Search | 553 |
| 3.1.2 | Geometric Hashing | 554 |
| 3.1.3 | Pose Clustering | 555 |
| 3.1.4 | Fast Shape Comparison | 557 |
| 3.2 | Flexible Ligand-docking Algorithms | 558 |
| 3.2.1 | Conformation Ensembles | 558 |
| 3.2.2 | Flexible Docking based on Fragmentation | 559 |
| 3.2.2.1 | “Place & Join” Algorithms | 559 |
| 3.2.2.2 | Incremental Construction Algorithms | 560 |
| 3.2.3 | Genetic Algorithms and Evolutionary Programming | 563 |
| 3.2.4 | Distance Geometry | 565 |
| 3.2.5 | Random Search | 565 |
| 3.3 | Docking by Simulation | 566 |
| 3.3.1 | Simulated Annealing | 566 |
| 3.3.2 | MD Simulations | 567 |
| 3.3.3 | MC Algorithms | 568 |
| 3.3.4 | Hybrid Methods | 570 |
| 4 | Structure-based Virtual Screening | 570 |
| 4.1 | Considering Pharmacophoric Constraints | 571 |
| 4.2 | Docking of Combinatorial Libraries | 571 |
| 4.3 | Database Approaches | 573 |
| 5 | From Molecules to Fragment Spaces: Structure-based De Novo Design | 574 |
| 5.1 | Modeling Fragment Spaces | 575 |
| 5.2 | De Novo Design Algorithms | 575 |
| 5.2.1 | Rigid-body Algorithms | 576 |
| 5.2.2 | Simulation Methods | 576 |
| 5.2.3 | “Place & Join” Algorithms | 577 |
| 5.2.4 | Sequential Growth Algorithms | 578 |
| 5.2.5 | Genetic Algorithms and Evolutionary Programming | 579 |
| 5.3 | Synthetic Accessibility | 580 |
| 5.3.1 | Fragment Selection | 580 |
| 5.3.2 | Virtual Synthesis | 581 |
| 5.3.3 | Compound Analysis | 581 |
| 6 | Structure-based Drug Design atWork: Validation Studies and Applications | 582 |
| 7 | Concluding Remarks | 583 |
| | References | 584 |
| 17 | Modeling Protein–Protein and Protein–DNA Docking
Andreas Hildebrandt, Oliver Kohlbacher and Hans-Peter Lenhof | 601 |
| 1 | Introduction | 601 |
| 2 | Protein–Protein Interactions | 603 |
| 2.1 | Basic Concepts of Docking | 603 |
| 2.2 | Rigid Body Docking | 606 |
| 2.2.1 | Correlation Techniques | 606 |
| 2.2.2 | Graph-based Structure Generation Methods | 610 |
| 2.2.3 | Slice Decomposition and Polygon Descriptors | 612 |
| 2.2.4 | Critical Surface Points and Geometric Hashing | 614 |
| 2.2.5 | Other Approaches | 615 |
| 2.3 | Realizing Protein Flexibility | 615 |
| 2.3.1 | Side Chain Placement | 617 |
| 2.3.1.1 | Dead End Elimination | 618 |
| 2.3.1.2 | “Branch & Bound” and the A* Algorithm | 619 |
| 2.3.1.3 | Integer Linear Programming | 621 |
| 2.3.2 | Hinge-bending | 624 |
| 2.3.3 | Biased Probability Monte Carlo (BPMC) Conformational Search | 626 |
| 2.4 | Scoring Functions | 627 |
| 2.4.1 | Empirical Potentials | 628 |
| 2.4.2 | Knowledge-based Potentials | 630 |
| 2.5 | Data-driven Docking | 632 |
| 2.5.1 | Experimental Techniques | 632 |
| 2.5.2 | Algorithmic Approaches | 633 |
| 2.6 | Assessment of Docking Predictions | 634 |
| 3 | Protein–DNA Interactions | 638 |
| 3.1 | Peculiarities of Protein–DNA Binding | 638 |
| 3.2 | Algorithmic Techniques | 639 |
| 3.2.1 | Correlation Techniques | 639 |
| 3.2.2 | Monte Carlo Techniques | 640 |
| 3.3 | Scoring Functions | 641 |
| 4 | Conclusion | 642 |
| | References | 644 |
| 18 | Lead Identfication by Virtual Screening
Andreas Kämper, Didier Rognan and Thomas Lengauer | 651 |
| 1 | Introduction | 651 |
| 1.1 | Screening Techniques | 652 |
| 1.2 | Drug Discovery Process | 653 |
| 1.3 | Compound Collections | 654 |
| 2 | Filtering and Preparation of Ligands | 655 |
| 2.1 | Library Preprocessing | 656 |
| 2.2 | Bioavailability | 658 |
| 2.3 | Drug-likeness | 659 |
| 2.4 | Molecular Diversity | 660 |
| 3 | Ligand-based VS | 662 |
| 3.1 | Descriptor-based Similarity Measures | 664 |
| 3.2 | Bit String Descriptors | 665 |
| 3.3 | Feature Trees | 666 |
| 3.4 | Molecular Superimposition Approaches | 667 |
| 3.5 | Pharmacophore Searches | 669 |
| 3.6 | QSARs | 670 |
| 3.7 | Other Techniques | 672 |
| 4 | Postprocessing of Hitlists | 672 |
| 4.1 | Data Mining | 673 |
| 4.2 | Analysis of the Protein–Ligand Interface | 674 |
| 4.3 | Consensus Techniques | 675 |
| 4.4 | Visualization | 676 |
| 5 | Critical Evaluation of Structure-based VS | 677 |
| 5.1 | Influence of Parameter Settings | 677 |
| 5.1.1 | Which Library? | 677 |
| 5.1.2 | Which Ligand Conformation(s)? | 678 |
| 5.1.3 | Which Protein Coordinates? | 678 |
| 5.1.4 | Which Docking Tool? | 678 |
| 5.1.5 | Which Scoring Function? | 679 |
| 5.1.6 | Which Postprocessing? | 680 |
| 5.2 | Recent Success Stories | 681 |
| 5.2.1 | Some Privileged Targets | 681 |
| 5.2.2 | First-in-class Compounds | 684 |
| 5.2.3 | Fragment Screening | 685 |
| 5.2.4 | Lead Optimization | 686 |
| 5.2.5 | Homology Models as VS Targets | 686 |
| 5.3 | Concluding Remarks | 687 |
| 6 | Critical Evaluation of Ligand-based VS | 687 |
| 6.1 | Influence of Parameter Settings | 687 |
| 6.2 | Recent Success Stories | 688 |
| 6.3 | Comparison of Structure- and Ligand-based Techniques | 691 |
| 6.4 | Concluding Remarks | 692 |
| | References | 693 |
| 19 | Efficient Strategies for Lead Optimization by Simultaneously Addressing Affinity, Selectivity and Pharmacokinetic Parameters
Karl-Heinz Baringhaus and Hans Matter | 705 |
| 1 | Introduction | 705 |
| 2 | The Origin of Lead Structures | 708 |
| 3 | Optimization for Affinity and Selectivity | 711 |
| 3.1 | Lead Optimization as a Challenge in Drug Discovery | 711 |
| 3.2 | Use and Limitation of Structure-based Design Approaches | 712 |
| 3.3 | Integration of Ligand- and Structure-based Design Concepts | 713 |
| 3.4 | The Selectivity Challenge from the Ligand’s Perspective | 716 |
| 3.5 | Selectivity Approaches Considering Binding Site Topologies | 717 |
| 4 | Addressing Pharmacokinetic Problems | 721 |
| 4.1 | Prediction of Physicochemical Properties | 721 |
| 4.2 | Prediction of ADME Properties | 722 |
| 4.3 | Prediction of Toxicity | 724 |
| 4.4 | Physicochemical and ADMET Property-based Design | 724 |
| 5 | ADME/AntitargetModels for Lead Optimization | 724 |
| 5.1 | Global ADME Models for Intestinal Absorption and Protein Binding | 724 |
| 5.2 | Selected Examples to Address ADME/Toxicology Antitargets | 728 |
| 6 | Integrated Approach | 732 |
| 6.1 | Strategy and Risk Assessment | 732 |
| 6.2 | Integration | 734 |
| 6.3 | Literature and Aventis Examples on Aspects of Multidimensional Optimization | 735 |
| 7 | Conclusions | 742 |
| | References | 743 |
| Part 6 | Molecular Networks | 755 |
| 20 | Modeling and Simulating Metabolic Networks
Stefan Schuster and David Fell | 755 |
| 1 | Introduction | 755 |
| 2 | Fundamentals | 756 |
| 2.1 | Motivation | 756 |
| 2.2 | Stoichiometry | 757 |
| 2.3 | Balance Equations | 759 |
| 2.4 | Enzyme Kinetics | 760 |
| 3 | Network Analysis | 762 |
| 3.1 | Conservation Relations | 762 |
| 3.2 | Stationary States and Stability Analysis | 764 |
| 3.3 | Constraints on Steady-state Fluxes | 766 |
| 3.4 | Defining Component Pathways of a Network | 769 |
| 3.5 | Examples of Elementary-modes Analysis | 771 |
| 3.6 | Extreme Pathways | 777 |
| 3.7 | Optimization of Molar Yields and Flux Balance Analysis (FBA) | 778 |
| 3.8 | Analyzing the Robustness of Metabolism | 781 |
| 4 | Dynamic Simulation | 782 |
| 4.1 | How is a Dynamic Model Constructed? | 782 |
| 4.2 | Metabolic Databases | 788 |
| 4.3 | Example: Red Blood Cell Metabolism | 790 |
| 4.4 | Oscillations | 792 |
| 4.5 | Whole-cell Modeling | 794 |
| 5 | Conclusions | 797 |
| | References | 798 |
| 21 | Inferring Gene Regulatory Networks
Michael Q. Zhang | 807 |
| 1 | Introduction | 807 |
| 2 | Gene Regulation at the Transcriptional Level | 808 |
| 2.1 | Finding TFBSs and Motifs | 809 |
| 2.2 | Identifying Target Genes | 809 |
| 2.3 | Discovering Novel Motifs and Target Genes | 810 |
| 2.4 | Inferring GRN Modules and Integrating Diverse Types of Data | 812 |
| 3 | Gene Regulation at the Level of RNA Processing | 813 |
| 3.1 | Identfication of Splicing Enhancers and Silencers | 814 |
| 3.2 | Splicing Microarrays | 814 |
| 4 | Gene Regulation at the Translational Level | 815 |
| 5 | Gene Regulation by Small ncRNAs | 816 |
| 6 | GRNs in Development and Evolution | 817 |
| | References | 819 |
| 22 | Modeling Cell Signaling Networks
Anthony Hasseldine, Azi Lipshtat, Ravi Iyengar and Avi Ma’ayan | 829 |
| 1 | Introduction | 829 |
| 1.1 | Components and Cascades | 829 |
| 1.2 | From Pathways to Networks | 832 |
| 1.2.1 | Interactions between Signaling Pathways | 832 |
| 1.2.2 | Implications of Network Topology | 834 |
| 1.2.3 | Network Motifs | 835 |
| 2 | Types of Models and the Information they can Yield | 839 |
| 2.1 | Boolean Networks and Bayesian Networks Modeling Approaches | 839 |
| 2.2 | Quantitative Dynamics Modeling | 841 |
| 2.2.1 | Deterministic Models | 843 |
| 2.2.2 | Stochastic Models | 846 |
| 2.2.3 | Hybrid Models | 849 |
| 3 | Identifying Parameters/Data Sets for Modeling | 850 |
| 3.1 | Functionally Relevant Connections | 850 |
| 3.2 | Qualitative Relationships | 850 |
| 3.3 | Quantitative Specifications | 851 |
| 4 | Model Validation | 853 |
| 4.1 | Parameter Variation and Sensitivity Analysis | 853 |
| 4.2 | Constraints and Predictions | 854 |
| 5 | Perspective | 855 |
| | References | 858 |
| 23 | Dynamics of Virus–Host Cell Interaction
Udo Reichl and Yury Sidorenko | 861 |
| 1 | Introduction | 861 |
| 2 | Viral Infection of Cells | 863 |
| 2.1 | Viral Infection of Prokaryotic Cells | 864 |
| 2.2 | Viral Infection of Eukaryotic Cells | 866 |
| 3 | Mathematical Models of Virus Dynamics | 868 |
| 3.1 | Unstructured Models of Virus Dynamics | 869 |
| 3.2 | Structured Models of Virus Dynamics | 871 |
| 4 | Influenza Virus as an Example for Virus–Host Cell Interaction | 872 |
| 4.1 | The Influenza A Virus Life Cycle | 873 |
| 4.2 | Mathematical Model of the Influenza A Virus Life Cycle | 877 |
| 4.3 | Influenza A Virus Growth Dynamics | 880 |
| 4.4 | Discussion and Outlook | 886 |
| 5 | Conclusions | 887 |
| | References | 892 |
| Part 7 | Analysis of Expression Data | 899 |
| 24 | DNA Microarray Technology and Applications – An Overview
John Quackenbush | 899 |
| 1 | Introduction to DNA Microarrays | 899 |
| 2 | Microarrays and Clinical Applications | 899 |
| 3 | Microarray Data Collection, Transformation and Representation | 902 |
| 4 | Identifying Patterns of Expression | 905 |
| 5 | Class Discovery | 906 |
| 5.1 | Hierarchical Clustering | 906 |
| 5.2 | k-means Clustering | 907 |
| 5.3 | Other Unsupervised Approaches | 911 |
| 6 | Classification | 911 |
| 6.1 | kNN Classification | 912 |
| 7 | Validation | 914 |
| 8 | Sample Selection and Classification | 915 |
| 9 | Limitations and Success of Classification | 915 |
| 10 | Data Reporting and Comparisons | 916 |
| 11 | Meta-analysis | 919 |
| 12 | The Path Forward | 922 |
| | References | 923 |
| 25 | Low-level Analysis of Microarray Experiments
Wolfgang Huber, Anja von Heydebreck and Martin Vingron | 929 |
| 1 | Introduction | 929 |
| 1.1 | Microarray technology | 929 |
| 1.2 | Prerequisites | 930 |
| 1.3 | Preprocessing | 931 |
| 2 | Visualization and Exploration of the Raw Data | 932 |
| 2.1 | Image Analysis | 932 |
| 2.2 | Dynamic Range and Spatial Effects | 933 |
| 2.3 | Scatterplot | 934 |
| 2.4 | Batch Effects | 938 |
| 2.5 | Along Chromosome Plots | 941 |
| 2.6 | Sensitivity and Specificity of Probes | 942 |
| 3 | Error Models | 943 |
| 3.1 | Motivation | 943 |
| 3.1.1 | Obtaining Optimal Estimates | 943 |
| 3.1.2 | Biological Inference | 944 |
| 3.1.3 | Quality Control | 944 |
| 3.2 | The Additive–Multiplicative Error Model | 944 |
| 3.2.1 | Induction from Data | 944 |
| 3.2.2 | A Theoretical Deduction | 946 |
| 4 | Normalization | 947 |
| 5 | Detection of Differentially Expressed Genes | 949 |
| 5.1 | Stepwise versus Integrated Approaches | 949 |
| 5.2 | Measures of Differential Eexpression: The Variance Bias Trade-off | 950 |
| 5.3 | Identifying Differentially Expressed Genes from Replicated Measurements | 951 |
| 6 | Software | 953 |
| | References | 954 |
| 26 | Classification of Patients
Claudio Lottaz, Dennis Kostka and Rainer Spang | 957 |
| 1 | Introduction | 957 |
| 2 | Molecular Diagnosis | 958 |
| 2.1 | Problem Statement | 958 |
| 2.1.1 | Notation | 959 |
| 2.1.2 | Loss and Risk | 960 |
| 2.1.3 | Bayes Classifier and Bayes Error | 960 |
| 2.1.4 | Minimal Empirical Risk and Maximum Likelihood | 961 |
| 2.1.5 | Regularized Risk and Priors | 961 |
| 2.2 | Supervised Classification | 963 |
| 2.2.1 | Discriminant Analysis and Feature Selection | 964 |
| 2.2.2 | Penalized Logistic Regression | 965 |
| 2.2.3 | Support Vector Classification | 966 |
| 2.2.4 | Bagging | 967 |
| 2.2.5 | Boosting | 968 |
| 2.3 | Gene Selection | 968 |
| 2.3.1 | Filter Approaches | 969 |
| 2.3.2 | Wrapper Approaches | 969 |
| 2.4 | Adaptive Model Selection and Validation | 970 |
| 2.4.1 | Adaptive Model Selection | 970 |
| 2.4.1.1 | Bias-variance Trade-off | 970 |
| 2.4.1.2 | Choosing a Trade-off via the Hold Out | 971 |
| 2.4.1.3 | Using Data More Efficiently via Cross-Validation | 972 |
| 2.4.2 | Validation of the Predictive Performance of a Molecular Signature | 972 |
| 2.4.2.1 | Estimating Error Rates | 973 |
| 2.4.2.2 | Selection Bias and Nested Loop Cross-validation | 974 |
| 2.5 | Discussion | 975 |
| 3 | Finding Molecular Disease Entities | 975 |
| 3.1 | Clustering | 976 |
| 3.1.1 | Clustering Algorithms | 976 |
| 3.1.2 | The Problem of Distances | 977 |
| 3.2 | Searching for Partitionings | 978 |
| 3.2.1 | Overlapping Partitionings | 978 |
| 3.2.2 | Search and Find | 978 |
| 3.2.3 | ISIS – Identifying Splits with Clear Separation | 978 |
| 3.2.4 | Overabundance of Differential Genes | 980 |
| 3.2.5 | Best-fitting Gaussian Model | 980 |
| 3.3 | Biclustering | 980 |
| 3.4 | Semisupervised Methods | 981 |
| 3.4.1 | Molecular Symptoms | 981 |
| 3.4.2 | Survival-driven Class-finding | 981 |
| 3.4.3 | Towards Survival Prediction | 983 |
| 3.5 | Validating Unsupervised Analysis | 983 |
| 3.5.1 | Statistical Signficance | 983 |
| 3.5.2 | Stability | 983 |
| 3.5.3 | Detect Consensus by Subsampling | 984 |
| 3.5.4 | Adding Simulated Noise | 984 |
| 3.5.5 | Over-represented Pathways | 984 |
| 4 | Conclusions | 985 |
| | References | 986 |
| 27 | Classification of Genes
Jörg Rahnenführer and Thomas Lengauer | 993 |
| 1 | Introduction | 993 |
| 2 | Overview of Gene Classification Tasks | 994 |
| 2.1 | Grouping Genes without Additional Information | 995 |
| 2.2 | Functional Predictions | 995 |
| 3 | Grouping Genes on the Basis of Expression Data | 996 |
| 3.1 | Cluster Analysis | 996 |
| 3.1.1 | Similarity Measures | 996 |
| 3.1.2 | Hierarchical Clustering Algorithms | 997 |
| 3.1.3 | Partitioning Clustering Algorithms | 999 |
| 3.1.4 | Model-based Clustering | 1001 |
| 3.1.5 | Biclustering Algorithms | 1002 |
| 3.2 | Heuristic Gene Grouping of Expression Data | 1003 |
| 3.2.1 | CLICK Algorithm | 1003 |
| 3.2.2 | CAST | 1004 |
| 3.2.3 | Gene Shaving | 1004 |
| 4 | Predicting Gene Function from Expression Data | 1005 |
| 4.1 | Classification methods | 1006 |
| 4.1.1 | Support Vector Machines (SVMs) | 1006 |
| 4.1.2 | Rule-based Models | 1006 |
| 4.2 | Supplementing Expression Data with Additional Biological Information | 1007 |
| 4.2.1 | Adding Sequence Data | 1009 |
| 4.2.2 | Adding Gene Ontology Data | 1009 |
| 4.2.3 | Integrating Pathway Information | 1011 |
| 4.2.4 | Combination of Multiple Data Types | 1012 |
| 5 | Evaluation | 1014 |
| 5.1 | Assessing the Biological Relevance of Gene Groups | 1015 |
| 5.1.1 | Validation of Clustering Results | 1015 |
| 5.1.2 | Estimating the Number of Clusters in a Data Set | 1016 |
| 5.2 | Assessing Function Prediction Accuracy | 1017 |
| 6 | Conclusions | 1017 |
| | References | 1018 |
| 28 | Proteomics: Beyond cDNA
Patricia M. Palagi, Yannick Brunner, Jean-Charles Sanchez and Ron D. Appel | 1023 |
| 1 | Introduction and Principles | 1023 |
| 2 | Proteomics Analytical Methods | 1026 |
| 2.1 | Electrophoresis Gels | 1026 |
| 2.2 | LC | 1028 |
| 2.3 | MS | 1030 |
| 2.4 | Protein Chips | 1033 |
| 3 | Computer Analysis of Proteomics Images | 1034 |
| 3.1 | Analysis of 2-DE Gels | 1034 |
| 3.1.1 | Data Analysis and Validation | 1035 |
| 3.1.2 | Annotation and Databases | 1038 |
| 3.2 | Analysis of LC-MS Images | 1038 |
| 4 | Identfication and Characterization of Proteins after Separation | 1039 |
| 4.1 | Identfication with MS | 1041 |
| 4.2 | Characterization with MS | 1046 |
| 5 | Proteome Databases | 1047 |
| 5.1 | Protein Sequence Databases | 1048 |
| 5.2 | 2-DE Gel Databases | 1049 |
| 5.3 | Mass Spectra Repositories | 1051 |
| 5.4 | PTM Databases | 1051 |
| 5.5 | General Considerations on Databases | 1053 |
| 6 | Conclusion | 1053 |
| | References | 1054 |
| | Volume 3 | |
| Part 8 | Protein Function Prediction | 1061 |
| 29 | Ontologies for Molecular Biology
Chris Wroe and Robert Stevens | 1061 |
| 1 | Introduction | 1061 |
| 2 | Ontologies and their Components | 1063 |
| 2.1 | Ontology Representation | 1065 |
| 3 | Ontologies in the Real World | 1067 |
| 3.1 | Ontology Tools | 1068 |
| 3.2 | Bio-ontology Communities | 1069 |
| 3.3 | Incremental Development of Ontologies | 1071 |
| 3.4 | Ontology Features to Manage Database Content | 1072 |
| 3.4.1 | A Controlled Vocabulary with Human Readable Definitions | 1072 |
| 3.4.1.1 | Gene Ontology | 1072 |
| 3.4.1.2 | MGED Ontology | 1073 |
| 3.4.2 | A Structured Controlled Vocabulary | 1075 |
| 3.4.3 | A Subsumption Hierarchy | 1075 |
| 3.4.4 | Multiple Hierarchies | 1076 |
| 3.4.5 | Formal Definition of Concepts | 1077 |
| 3.5 | Ontology Features to Manage Data Schemata | 1080 |
| 3.5.1 | TAMBIS | 1081 |
| 3.6 | Ontologies for Prediction and Simulation | 1082 |
| 3.6.1 | EcoCyc | 1082 |
| 3.7 | The Physiome Project | 1083 |
| 4 | Summary | 1083 |
| | References | 1085 |
| 30 | Inferring Protein Function from Sequence
Douglas Lee Brutlag | 1087 |
| 1 | Introduction | 1087 |
| 2 | Sequence-based Motif Representations | 1090 |
| 2.1 | Consensus Sequences as Regular Expressions | 1090 |
| 2.2 | Accuracy and Precision of Motifs | 1091 |
| 2.3 | Position-specific Scoring Matrix (PSSM) Motifs | 1094 |
| 2.4 | Dirichlet-mixture Prior Probabilities and Pseudocounts | 1094 |
| 2.5 | Sensitivity and Specificity of PSSM Motifs | 1096 |
| 2.6 | HMMs | 1098 |
| 2.7 | Network Models | 1099 |
| 2.8 | Neural Networks | 1101 |
| 3 | Descriptions of Several Useful Motif Databases | 1101 |
| 3.1 | The Prosite Database | 1101 |
| 3.2 | The Blocks Databases | 1104 |
| 3.3 | The PRINTS Database | 1105 |
| 3.4 | The eBLOCKs Database | 1106 |
| 3.5 | The eMOTIF Database | 1107 |
| 3.6 | The eMATRIX Database | 1108 |
| 3.7 | HMM Databases | 1109 |
| 3.8 | The InterPro Database | 1110 |
| 3.9 | Supervised versus Unsupervised Learning of Motifs | 1111 |
| 4 | Summary and Conclusions | 1112 |
| | References | 1113 |
| 31 | Analyzing Protein Interaction Networks
Johannes Goll and Peter Uetz | 1121 |
| 1 | Introduction | 1121 |
| 2 | Experimental Methods and Interaction Data | 1122 |
| 3 | Validation of Experimental Protein–Protein Interaction Data | 1125 |
| 3.1 | Crystal Structures as Benchmarks | 1126 |
| 3.2 | Overlap with Protein Complex Data | 1126 |
| 3.3 | Correlation with Expression Data | 1126 |
| 3.4 | Functional Annotation | 1127 |
| 3.5 | Localization | 1127 |
| 3.6 | Paralogous Proteins and Evolutionary Rate | 1127 |
| 3.7 | Other Approaches | 1128 |
| 3.8 | Combined Approaches | 1128 |
| 3.9 | Comparison of Specific Data Sets | 1129 |
| 3.9.1 | Comparison of Tandem Affinity Purfication (TAP) and High-throughput MS (HMS) complex purfication data | 1129 |
| 3.9.2 | Comparison between Y2H and MS data sets | 1131 |
| 3.9.3 | Comparison of Spoke versus Matrix Models | 1131 |
| 4 | Predicting Protein–Protein Interactions | 1133 |
| 4.1 | Predictions Based on Genomic Context | 1138 |
| 4.1.1 | The Rosetta Stone Method | 1138 |
| 4.1.2 | Gene Neighborhood | 1139 |
| 4.1.3 | Phylogenetic Profiles | 1139 |
| 4.1.4 | Similarity of Phylogenetic Trees (SPT) | 1140 |
| 4.1.5 | In Silico Two-hybrid (I2H) | 1140 |
| 4.2 | Predictions Based on Known 3-D Structures | 1140 |
| 4.3 | Predicting Interaction Domains | 1140 |
| 4.4 | Predicting Homologous Interactions: Interologs | 1141 |
| 4.5 | Predictions based on Literature Mining | 1143 |
| 4.6 | Validation of Predicted Protein–Protein Interactions | 1144 |
| 5 | Representing Protein–Protein Interactions as Graphs | 1145 |
| 5.1 | Graph Terminology | 1145 |
| 5.2 | Network Models | 1148 |
| 5.3 | Random Networks | 1149 |
| 5.4 | Small-world Networks | 1149 |
| 5.5 | Scale-free Networks | 1150 |
| 5.6 | Connectivity Distributions of Protein–Protein Interaction Networks | 1151 |
| 5.7 | Error Tolerance and Attack Vulnerability | 1151 |
| 5.8 | Modules and Motifs in Networks | 1152 |
| 5.9 | Comparing Protein Interaction Networks: Pathblast | 1152 |
| 6 | Integrating Multiple Protein–Protein Interaction Evidence | 1154 |
| 6.1 | Protein Interactions and Gene Expression Data | 1157 |
| 6.2 | Integration for Predicting Protein Function | 1157 |
| 7 | Predicting Protein Functions from Protein Networks | 1157 |
| 8 | Evolution of Protein–Protein Interactions | 1158 |
| 8.1 | The Network Level | 1159 |
| 8.1.1 | The Rates of Interaction Loss and Gain | 1159 |
| 8.2 | Sequence and Interaction Divergence in Proteins | 1160 |
| 8.2.1 | Protein Evolution Rate and Protein–Protein Interactions | 1161 |
| 8.2.2 | Phylogenetic Relationships between Families of Interacting Proteins | 1162 |
| 8.3 | Structural Aspects of Conserved Interactions | 1165 |
| 9 | Databases and Other Information Sources | 1165 |
| 10 | Analysis and Visualization Tools | 1166 |
| 11 | Outlook/Perspectives | 1166 |
| | References | 1171 |
| 32 | Inferring Protein Function from Genomic Context
Christian von Mering | 1179 |
| 1 | Introduction | 1179 |
| 1.1 | Genomic Context – Genomes, Genes and Gene Arrangements | 1179 |
| 1.2 | Genome Comparisons Reveal Protein–Protein Associations | 1180 |
| 1.3 | Prerequisites for Genomic Context Analysis | 1181 |
| 1.4 | How Specific are the Inferred Functions? | 1182 |
| 2 | Gene Neighborhood | 1183 |
| 2.1 | Conserved Neighborhood versus Simple Synteny | 1183 |
| 2.2 | Operons and “Über-Operons” | 1185 |
| 2.3 | Divergently Transcribed Gene Pairs | 1187 |
| 2.4 | Gene Neighborhood in Eukaryotes | 1189 |
| 3 | Gene Fusion | 1190 |
| 3.1 | Gene Fusions and Gene Fissions | 1190 |
| 3.2 | Functional Implications | 1191 |
| 3.3 | Gene Fusions versus Domain Analysis | 1193 |
| 4 | Gene Co-occurrence | 1194 |
| 4.1 | Phylogenetic Profiles | 1194 |
| 4.2 | Discrete versus Continuous Profiles | 1196 |
| 4.3 | Profile Distance Measures | 1197 |
| 4.4 | Tree-based Methods | 1198 |
| 4.5 | Anti-correlated Profiles | 1199 |
| 5 | Outlook | 1200 |
| 5.1 | Methods based on Sequence Evolution | 1200 |
| 5.2 | Web-based Implementations of Genomic Context Tools | 1202 |
| 5.3 | Scoring and Integration | 1204 |
| 5.4 | Genome Sequencing Strategies: Impact on Genomic Context Analysis | 1205 |
| 5.5 | Environmental Context | 1207 |
| | References | 1208 |
| 33 | Inferring Protein Function from Protein Structure
Francisco S. Domingues and Thomas Lengauer | 1211 |
| 1 | Introduction | 1211 |
| 1.1 | Different Levels of Protein Function | 1212 |
| 1.2 | Structural Models | 1212 |
| 1.3 | Homology and Function | 1213 |
| 1.4 | Structure and Function | 1214 |
| 1.5 | Why Predict Function from Structure | 1216 |
| 1.6 | The Challenges of Automatic Prediction of Function from Structure | 1217 |
| 1.7 | Structure of the Chapter | 1217 |
| 2 | Localization of Functional Sites | 1218 |
| 2.1 | Supersites | 1218 |
| 2.2 | Electrostatics | 1218 |
| 2.3 | Surface Geometry | 1218 |
| 2.4 | Structure and Evolutionary Information | 1219 |
| 2.4.1 | Evolutionary Trace (ET) | 1219 |
| 2.4.2 | ConSurf | 1220 |
| 2.4.3 | Residue Conservation and Structural Information | 1221 |
| 2.5 | Network Centrality | 1222 |
| 2.6 | Combined Approaches | 1223 |
| 2.6.1 | Catalytic Sites in Enzymes | 1223 |
| 2.6.2 | Protein–protein Interactions | 1223 |
| 3 | Characterization of Molecular Function | 1224 |
| 3.1 | General Principles | 1224 |
| 3.1.1 | Homology versus Nonhomology | 1224 |
| 3.1.2 | Uncertainty and Flexibility in the Structural Models | 1225 |
| 3.1.3 | Functional Descriptors, Comparison and Scoring | 1226 |
| 3.2 | Descriptors based on Atom Coordinates | 1227 |
| 3.2.1 | ASSAM | 1227 |
| 3.2.2 | SPASM | 1228 |
| 3.2.3 | PINTS | 1228 |
| 3.2.4 | SuMo | 1229 |
| 3.2.5 | TESS and Jess | 1230 |
| 3.3 | Descriptors based on Chemical Environment and Surface | 1232 |
| 3.3.1 | FEATURE | 1232 |
| 3.3.2 | CavBase and SiteEngine | 1233 |
| 3.3.3 | eF-site | 1234 |
| 3.3.4 | pvSOAR | 1234 |
| 3.3.5 | Enzyme Classifier | 1236 |
| 3.3.6 | 3D Shape Descriptors | 1236 |
| 3.4 | Databases of Functional Sites | 1237 |
| 3.4.1 | Relibase | 1237 |
| 3.4.2 | MSDsite | 1238 |
| 3.4.3 | CSA | 1238 |
| 3.4.4 | SURFACE | 1238 |
| 3.4.5 | Databases of Structural Motifs | 1239 |
| 3.4.6 | Protein–protein Binding Sites | 1239 |
| 4 | Integration Efforts | 1239 |
| 5 | Resources for Structural Characterization | 1241 |
| 5.1 | Available Tools and Databases | 1241 |
| 5.2 | Characterizing a Protein | 1242 |
| 6 | Current Applications | 1243 |
| 7 | Future Perspectives | 1244 |
| | References | 1245 |
| 34 | Mining Information on Protein Function from Text
Martin Krallinger and Alfonso Valencia | 1253 |
| 1 | Introduction | 1253 |
| 2 | Information Types of Protein Function Descriptions | 1255 |
| 3 | Literature Databases in Biomedicine | 1256 |
| 4 | NLP | 1258 |
| 4.1 | Grammatical Features | 1258 |
| 4.2 | Morphological Features | 1259 |
| 4.3 | Syntactic Features | 1259 |
| 4.4 | Semantic Features | 1260 |
| 4.5 | Contextual Features | 1261 |
| 5 | Main NLP Tasks | 1261 |
| 5.1 | IR | 1261 |
| 5.2 | IE | 1265 |
| 5.3 | QA | 1266 |
| 5.4 | NLG | 1268 |
| 6 | Difficulties when Processing Biological Texts | 1268 |
| 7 | Strategies of Extracting Functional Information from Text | 1271 |
| 7.1 | NER and Protein Tagging | 1271 |
| 7.2 | Associating Proteins with Biological Features from Databases and Ontologies | 1274 |
| 7.3 | Mining Interactions and Relations from Text | 1278 |
| 7.4 | Discovering Information Associated with Groups of Proteins | 1281 |
| 7.5 | Other Applications | 1282 |
| 8 | Evaluation of Text Mining Strategies | 1283 |
| 9 | Resources for Text Mining | 1285 |
| 9.1 | Literature Databases | 1286 |
| 9.2 | Annotated Text Corpora | 1286 |
| 9.3 | Generic NLP Tools | 1286 |
| 9.4 | Dictionaries and Ontologies | 1288 |
| 9.5 | Biomedical Domain NLP Systems | 1289 |
| 10 | Concluding Remarks | 1289 |
| | References | 1291 |
| 35 | Integrating Information for Protein Function Prediction
William Stafford Noble and Asa Ben-Hur | 1297 |
| 1 | Introduction | 1297 |
| 2 | Vector-space Integration | 1298 |
| 3 | Classifier Integration | 1301 |
| 4 | Kernel Methods | 1302 |
| 5 | Learning Functional Relationships | 1304 |
| 6 | Learning Function from Networks of Pairwise Relationships | 1307 |
| 7 | Discussion | 1311 |
| | References | 1311 |
| 36 | The Molecular Basis of Predicting Druggability
Bissan Al-Lazikani, Anna Gaulton, Gaia Paolini, Jerry Lanfear, John Overington and Andrew Hopkins | 1315 |
| 1 | Introduction | 1315 |
| 2 | Chemical Properties of Drugs, Leads and Tools | 1316 |
| 3 | Molecular Recognition is the Basis for Druggability | 1316 |
| 4 | Estimating the Size of the Druggable Genome | 1319 |
| 4.1 | Initial Estimates | 1320 |
| 4.2 | Hopkins and Groom’s Method | 1320 |
| 4.3 | Orth and Coworkers Update | 2004 |
| 4.4 | Russ and Lampel’s Update | 2005 |
| 5 | Homology-based Analysis of Drug Targets | 1322 |
| 6 | Feature-based Druggability Prediction | 1327 |
| 7 | Structure-based Druggability Analysis of Protein Data Base (PDB) Structures | 1327 |
| 8 | How Many Drug Targets are Accessible to Protein Therapeutics? | 1329 |
| 9 | Conclusions | 1331 |
| | References | 1333 |
| Part 9 | Comparative Genomics and Evolution of Genomes | 1335 |
| 37 | Comparative Genomics
Martin S. Taylor and Richard R. Copley | 1335 |
| 1 | Introduction | 1335 |
| 2 | The Genomic Landscape | 1336 |
| 3 | Concepts | 1339 |
| 4 | Practicalities | 1343 |
| 4.1 | Available Genomic Sequences | 1343 |
| 4.2 | Defining and Obtaining Genomic Sequences | 1345 |
| 5 | Technology | 1347 |
| 5.1 | Alignments | 1347 |
| 5.1.1 | Local Genomic Alignments | 1349 |
| 5.1.2 | Global Genomic Alignments | 1350 |
| 5.1.3 | Multiple Sequence Alignments | 1351 |
| 5.1.4 | Assessing the Quality of Genomic Alignment Tools | 1353 |
| 5.1.5 | Using Whole-genome Alignments | 1354 |
| 5.2 | Visualizing Genomic Alignments | 1355 |
| 5.3 | Detecting Selection | 1357 |
| 6 | Applications | 1361 |
| 6.1 | How Much of the Human Genome is Constrained? | 1362 |
| 6.2 | Ultra-conserved Regions | 1363 |
| 6.3 | Specific Locus Studies | 1364 |
| 7 | Challenges and Future Directions | 1367 |
| 8 | Conclusion | 1368 |
| | References | 1368 |
| 38 | Association Studies of Complex Diseases
Momiao Xiong and Li Jin | 1375 |
| 1 | Introduction | 1375 |
| 2 | Linkage Disequilibrium (LD), Haplotype and Association Studies | 1378 |
| 2.1 | Concepts of LD | 1378 |
| 2.2 | Measures of LD | 1379 |
| 2.2.1 | LD Coefficient D | 1379 |
| 2.2.2 | Normalized Measure of LD D’ | 1379 |
| 2.2.3 | Correlation Coefficient r | 1380 |
| 2.2.4 | Composite Measure of LD | 1380 |
| 2.2.5 | The Relationship between the Measure of LD and Physical Distance | 1380 |
| 2.3 | SNPs and Haplotype Blocks in the Human Genome | 1381 |
| 2.3.1 | SNPs | 1381 |
| 2.3.2 | Tagging SNPs | 1381 |
| 2.3.3 | Haplotype Block Model | 1381 |
| 2.3.4 | Definitions of Haplotype Block | 1383 |
| 2.3.4.1 | Definition of Haplotype Blocks based on Pairwise LD | 1384 |
| 2.3.4.2 | Definition of Haplotype Blocks based on Haplotype Diversity | 1384 |
| 2.3.4.3 | Definition of Haplotype Blocks based on both Pairwise LD and Haplotype Diversity | 1384 |
| 2.3.5 | Haplotype Reconstruction | 1385 |
| 2.3.5.1 | Clark’s Algorithm | 1385 |
| 2.3.5.2 | Expectation Maximization (EM) Algorithm | 1386 |
| 2.3.5.3 | Bayesian and Coalescence-based Methods | 1386 |
| 2.3.6 | Measure of Haplotype Block LD | 1387 |
| 3 | A General Framework for Population-based Association Studies | 1387 |
| 3.1 | Motivation | 1387 |
| 3.2 | The Traditional  2 | Test Statistic |
| 3.3 | Test Statistics | 1391 |
| 3.4 | Null Distribution of the Nonlinear Statistics | 1392 |
| 3.5 | Power of the Nonlinear Test Statistics and the Standard 2 | Test |
| | Statistic | 1393 |
| 4 | Similarity-based Statistics for Association Studies | 1400 |
| 4.1 | Similarity Measures | 1400 |
| 4.1.1 | Matching Measure | 1402 |
| 4.1.2 | Counting Measure | 1403 |
| 4.1.3 | Length Measure | 1403 |
| 4.2 | Test Statistics | 1403 |
| 5 | Generalized T2 Test Statistic | 1404 |
| 5.1 | Test Statistic | 1405 |
| 5.2 | Nonlinear T2 test | 1406 |
| 6 | Family-based Association Studies | 1406 |
| 6.1 | TDT at a Single Locus with Two Alleles | 1407 |
| 6.2 | TDT at a Single Locus with Multiple Alleles or at Multiple Loci with Phase-known Haplotypes | 1407 |
| 6.3 | Sib-TDT | 1409 |
| 6.3.1 | Comparison of Genotype Frequencies | 1409 |
| 6.3.2 | Comparison of Allele Frequencies | 1410 |
| 7 | Nonlinear Transmission/Disequilibrium Test | 1410 |
| 7.1 | General Procedures for the Construction of the Nonlinear TDT | 1412 |
| 7.1.1 | A Single Locus with Two Alleles | 1412 |
| 7.1.2 | A Single Locus with Multiple Alleles or Multiple Loci with Phase-known Haplotypes | 1413 |
| 7.2 | Power of the N\ nonlinear TDT | 1414 |
| 7.3 | Real Examples | 1415 |
| 8 | Perspective of Genome-wide Association Studies | 1416 |
| | References | 1417 |
| 39 | Pharmacogenetics/Pharmacogenomics
Xing Jian Lou, Russ B. Altman and Teri E. Klein | 1427 |
| 1 | Introduction | 1427 |
| 2 | An Overview of Pharmacogenetics and Pharmacogenomics | 1427 |
| 2.1 | Background of Pharmacogenetics and Pharmacogenomics | 1428 |
| 2.2 | Influence of Pharmacogenetics and Pharmacogenomics on Drug Development and Therapy | 1429 |
| 3 | Biomedical Informatics Resources Relevant to Pharmacogenomics | 1430 |
| 4 | Building the PharmGKB | 1433 |
| 4.1 | Establishing a Repository of Pharmacogenetics and Pharmacogenomics Information | 1435 |
| 4.1.1 | The Data Model | 1435 |
| 4.1.2 | Primary Data | 1436 |
| 4.1.3 | Data from Literature | 1437 |
| 4.1.4 | Linking to other Data Resources | 1438 |
| 4.2 | Turning Data into Knowledge | 1439 |
| 4.2.1 | Categorizing Data | 1440 |
| 4.2.1.1 | Genotype | 1440 |
| 4.2.1.2 | Clinical Outcome | 1441 |
| 4.2.1.3 | Pharmacodynamics and Drug Responses | 1441 |
| 4.2.1.4 | Pharmacokinetics | 1441 |
| 4.2.1.5 | Molecular and Cellular Functional Assays | 1442 |
| 4.2.2 | Establishing Genotype–Phenotype Correlation | 1442 |
| 4.2.3 | Using Pathways to Summarize Current Pharmacogenetics and Pharmacogenomics Knowledge | 1443 |
| 4.3 | Providing Easy Access of Knowledge for the Research Community | 1443 |
| 4.3.1 | Querying System | 1445 |
| 4.3.2 | Visualization and Browsing | 1445 |
| 4.3.3 | Privacy Protection | 1447 |
| 4.3.4 | Data Exchange Strategy | 1449 |
| 5 | Analytic Tools for Pharmacogenomics | 1449 |
| 6 | Future Perspectives on Informatics for Pharmacogenetics/Pharmacogenomics | 1451 |
| | References | 1452 |
| 40 | Evolution of Drug Resistance in HIV
Niko Beerenwinkel, Kirsten Roomp and Martin Däumer | 1457 |
| 1 | Introduction | 1457 |
| 2 | Biomedical Background | 1458 |
| 2.1 | Biology of HIV | 1458 |
| 2.1.1 | Epidemiology of HIV/AIDS | 1458 |
| 2.1.2 | Structure, Genome and Replication Cycle | 1459 |
| 2.1.3 | Basic Immunology and Course of Infection | 1461 |
| 2.2 | Antiretroviral Therapy | 1462 |
| 2.2.1 | Antiretroviral Drugs | 1462 |
| 2.2.2 | Drug Resistance | 1464 |
| 2.3 | Resistance Testing | 1464 |
| 2.3.1 | Genotypic Resistance Testing | 1465 |
| 2.3.2 | Phenotypic Resistance Testing | 1465 |
| 3 | Prediction of Phenotypic Resistance from Genotypes | 1466 |
| 3.1 | Drug Resistance Data | 1466 |
| 3.2 | Methods of Phenotype Prediction | 1467 |
| 3.3 | Comparisons | 1468 |
| 4 | Development of Resistance-associated Mutations | 1470 |
| 4.1 | Viral Evolution | 1470 |
| 4.2 | Learning Mutational Pathways | 1472 |
| 4.3 | Genetic Barrier | 1473 |
| 4.4 | Transitions between Sequence Clusters | 1475 |
| 5 | Selecting Optimal Combination Therapies | 1476 |
| 5.1 | Clinical Databases | 1477 |
| 5.2 | Simple Scoring Functions | 1477 |
| 5.3 | Look-ahead Techniques | 1478 |
| 5.4 | Rules-based Approaches | 1479 |
| 6 | Host Genetic Profiles and Viral Evolution | 1480 |
| 6.1 | Immunobiological Background | 1480 |
| 6.1.1 | HLA Genes | 1480 |
| 6.1.2 | Chemokine Receptors | 1482 |
| 6.2 | Epitope Prediction | 1483 |
| 6.2.1 | Problem Definition | 1483 |
| 6.2.2 | Methods | 1484 |
| 6.3 | Analysis of Escape Mutations | 1485 |
| 7 | Conclusions | 1488 |
| 8 | Webresources | 1488 |
| 8.1 | Los Alamos HIV Databases (http://www.hiv.lanl.gov) | 1488 |
| 8.2 | Stanford HIV Drug Resistance Database (http://hivdb.stanford.edu) | 1488 |
| 8.3 | Geno2pheno (http://www.geno2pheno.org) | 1489 |
| 8.4 | IMGT/HLA Databases (http://www.ebfiac.uk/imgt/hla) | 1489 |
| | References | 1489 |
| 41 | Analyzing the Evolution of Infectious Bacteria
Dawn Field, Edward J. Feil, Gareth Wilson and Paul Swift | 1497 |
| 1 | Introduction | 1497 |
| 1.1 | Introduction to Molecular Evolutionary Theory | 1498 |
| 1.2 | The Quantity and Quality of Data Available | 1501 |
| 1.3 | A Practical Overview of Online Resources | 1502 |
| 2 | Identfication and Study of Determinants of Virulence and Pathogenicity | 1504 |
| 2.1 | Homology-based Detection | 1506 |
| 2.2 | Pattern-based Detection | 1506 |
| 2.3 | Comparative Genomic Methods of Detection | 1507 |
| 2.4 | Taxonomically Restricted Genes (TRGs) and Orphans | 1508 |
| 3 | Putting Isolates of Infectious Bacteria into a Phylogenetic Framework | 1509 |
| 4 | Mixing of Genetic Material among Bacteria | 1512 |
| 4.1 | The Importance of Phage and Plasmids | 1513 |
| 5 | Coevolution of Infectious Bacteria with Their Hosts | 1516 |
| 5.1 | Reconstructing Metabolic Pathways | 1516 |
| 5.2 | The Genetic Arms Race between Pathogen and Host | 1517 |
| 6 | Conclusions | 1518 |
| | References | 1520 |
| Part 10 | Basic Bioinformatics Technologies | 1525 |
| 42 | Integrating Biological Databases
Zoé Lacroix, Bertram Ludäscher and Robert Stevens | 1525 |
| 1 | Biological Resources | 1525 |
| 2 | Data Modeling | 1527 |
| 2.1 | Conceptual Model | 1528 |
| 2.1.1 | ER | 1528 |
| 2.1.2 | Unfied Modeling Language | 1530 |
| 2.2 | “Flat” Data Models | 1532 |
| 2.3 | Tree-structured Representations | 1533 |
| 2.4 | Graph Representations | 1534 |
| 2.5 | Multi-dimensional Data Model | 1536 |
| 3 | Data Integration | 1537 |
| 3.1 | Scientfic View of Data | 1537 |
| 3.2 | DataWarehouse | 1540 |
| 3.3 | Link-driven Federations | 1541 |
| 3.4 | Mediations | 1541 |
| 4 | Integrating Applications and Data | 1542 |
| 4.1 | Middleware | 1543 |
| 4.2 | CORBA | 1544 |
| 4.3 | Web Services | 1545 |
| 4.4 | P2P | 1546 |
| 4.5 | Grid | 1547 |
| 5 | Semantic Integration | 1547 |
| 5.1 | Identifying Objects | 1549 |
| 5.2 | Representing Metadata | 1550 |
| 5.3 | Ontologies and Data Integration | 1552 |
| 5.3.1 | Example | 1553 |
| 5.3.2 | From Information to Reasoning | 1554 |
| 5.3.3 | Biological Ontologies | 1555 |
| 5.3.4 | Ontologies and Data Integration | 1556 |
| 5.4 | Semantic Web | 1557 |
| 6 | Scientfic Workflows | 1558 |
| 6.1 | Example: Promoter IdentficationWorkflow (PIW) | 1559 |
| 6.2 | Scientfic Workflow Requirements and Desiderata | 1561 |
| 6.3 | Semantic Extensions and ScientficWorkflow Design | 1565 |
| 7 | Conclusion | 1567 |
| | References | 1567 |
| 43 | Visualization of Biological Data
Harry Hochheiser, Kevin W. Eliceiri and Ilya G. Goldberg | 1573 |
| 1 | Introduction | 1573 |
| 2 | Microscopy Image Visualization | 1574 |
| 2.1 | Fluorescence Microscopy Techniques Applicable to HCS Screening | 1574 |
| 2.1.1 | Spectral Imaging | 1575 |
| 2.1.2 | Lifetime Imaging | 1575 |
| 2.1.3 | Fluorescence Resonant Energy Transfer (FRET) | 1576 |
| 2.1.4 | Optical Sectioning | 1577 |
| 2.1.5 | MP Imaging | 1578 |
| 2.1.6 | Second Harmonic Imaging | 1579 |
| 2.2 | Functional Genomics | 1580 |
| 2.2.1 | RNAi | 1580 |
| 2.2.2 | Chemical Compound Libraries | 1581 |
| 2.3 | Tools for Scientist-driven Analysis Development and Deployment | 1582 |
| 2.3.1 | ImageJ | 1582 |
| 2.3.2 | VisBio | 1583 |
| 3 | Biological Information Visualization | 1585 |
| 3.1 | Genome and Sequence Data | 1586 |
| 3.2 | Gene Expression Data | 1594 |
| 3.3 | Proteomics | 1601 |
| 3.4 | Interaction Networks and Pathways | 1601 |
| 3.5 | Phylogenies and Taxonomies | 1605 |
| 3.6 | Phenotypes and Lineages | 1607 |
| 3.7 | Visualization of the Scientfic Process | 1608 |
| 4 | Image Informatics | 1608 |
| 4.1 | Data and Information Management | 1611 |
| 4.2 | Image Analysis | 1611 |
| 4.3 | Analysis Workflows | 1613 |
| 4.4 | Provenance | 1613 |
| 4.5 | Federation | 1614 |
| 4.6 | Visualization and User Tools | 1614 |
| 5 | Conclusion: Research Questions and Challenges | 1614 |
| | References | 1616 |
| 44 | Using Distributed Data and Tools in Bioinformatics Applications
Robert Stevens, Phillip Lord and Duncan Hull | 1627 |
| 1 | Introduction to Distributed Resources | 1627 |
| 2 | Heterogeneiety in Bioinformatics Resources | 1629 |
| 3 | Type Systems in Bioinformatics | 1631 |
| 4 | Plumbing Bioinformatics Resources | 1634 |
| 4.1 | CORBA | 1635 |
| 4.2 | XML in Bioinformatics | 1638 |
| 4.3 | Web Services | 1640 |
| 5 | Case Studies in Distributed Bioinformatics | 1642 |
| 5.1 | ISYS | 1642 |
| 5.2 | BioMOBY | 1643 |
| 5.2.1 | MOBY-S | 1643 |
| 5.2.2 | S-MOBY | 1644 |
| 5.3 | The Grid Future – the myGrid Project | 1644 |
| 5.4 | The myGrid Project | 1645 |
| 6 | Discussion | 1647 |
| | References | 1649 |
| Part 11 | Outlook | 1651 |
| 45 | Future Trends
Thomas Lengauer | 1651 |
| 1 | Introduction | 1651 |
| 2 | Building Blocks – Post-translational Modfication of Proteins | 1653 |
| 3 | Regulation – Synthesis and Degradation Pipeline of RNA and Proteins | 1655 |
| 4 | Regulation–RNAi | 1656 |
| 5 | Regulation – Tiling Arrays, ChIP-on-chip and array-CGH | 1657 |
| 6 | Regulation – Epigenetics | 1659 |
| 7 | Protein Function – Alternative Splicing | 1663 |
| 8 | Interaction Networks – Immunoinformatics | 1665 |
| 9 | Cell Engineering – Synthetic Biology | 1670 |
| 9.1 | Genetic Engineering | 1671 |
| 9.2 | Protein Engineering | 1672 |
| 9.3 | Genetic Networks | 1672 |
| 10 | Imaging | 1673 |
| 10.1 | Obtaining Pictures of Cellular Structures | 1673 |
| 10.2 | Movies of Cellular Processes | 1675 |
| 10.3 | Organism Development | 1676 |
| 11 | Modeling Organs | 1676 |
| 12 | Outlook | 1677 |
| | References | 1678 |
| | Index | 1687 |
| | Name Index | 1727 |
