Contents -- Foreword -- Preface -- List of Contributors -- SECTION I GENES AND GENOMES -- Chapter 1 Methods for Discovery and Characterization of DNA Sequence Motifs Philipp Bucher -- 1. Introduction -- 1.1. Motif Discovery from a Biological Perspective -- 1.2. DNA Motifs from a Physical Perspective -- 2. Motif Discovery in a Nutshell -- 3. Overview of the Methods -- 3.1. Objective Functions -- 3.2. Scanning the Search Space -- 3.2.1. Finding the best consensus sequence -- 3.2.2. Optimizing a base probability matrix -- 3.2.3. Optimizing the motif annotation -- 3.2.4. Finding multiple motifs -- 3.2.5. Estimating the significance of a newly discovered motif -- 4. Bottlenecks and Limitations -- 4.1. Benchmarking Procedures for Motif Discovery -- 4.2. Why is Protein Domain Discovery Easier? -- 4.3. Reasons for the Limited Success of DNA Motif Discovery -- 5. Locally Overrepresented Sequence Motifs -- 5.1. Modification of the Problem Statements -- 5.2. Search Algorithms for Locally Overrepresented Sequence Motifs -- 6. Conclusions and Perspectives -- References -- Chapter 2 Comparative Genome Analysis Robert M. Waterhouse, Evgenia V. Kriventseva and Evgeny M. Zdobnov -- 1. Introduction -- 2. Gene Annotation -- 3. Protein Families -- 4. Orthologs and Paralogs -- 5. Genome Architecture -- 6. RNA Genes and Conserved Noncoding Sequences -- 7. Perspectives -- References -- Chapter 3 From Modules to Models: Advanced Analysis Methods for Large-Scale Data Sven Bergmann -- 1. Introduction -- 1.1. The Modular Concept -- 1.2. Regulatory Patterns are Context-Specific -- 1.3. Coclassification of Genes and Conditions -- 1.4. Complexity of the Output and Visualization -- 1.5. From Modules to Models -- 1.6. Data Integration -- 2. Modular Algorithms -- 2.1. Signature Algorithm -- 2.2. Comparative Analysis -- 2.3. Differential Clustering Algorithm.

2.4. Ping-Pong Algorithm -- 3. Module Analysis -- 3.1. Module Annotation -- 3.2. Module Visualization -- 4. Outlook -- References -- Chapter 4 Integrated Analysis of Gene Expression Profiling Studies - Examples in Breast Cancer Pratyaksha Wirapati, Darlene R. Goldstein and Mauro Delorenzi -- 1. Introduction -- 2. Combining Information -- 3. Individual Patient Data (IPD) -- 4. SwissBrod: Swiss Breast Oncology Database -- 4.1. Difficulties with Public Data Sources -- 4.2. Aim of SwissBrod -- 4.3. SwissBrod Data Curation -- 5. Spectrum of Possible Analyses -- 5.1. Pooling Raw Data -- 5.2. Pooling Adjusted Data -- 5.3. Combining Parameter Estimates -- 5.4. Combining Test Statistics -- 5.5. Combining p-values -- 5.6. Combining Statistic Ranks -- 5.7. Combining Decisions -- 6. Data Integration Methodology -- 6.1. Data Acquisition -- 6.2. Data Cleaning -- 6.3. Gene Matching -- 6.4. Outcome Modeling -- 6.5. Z-Transform for Combining Test Statistics -- 6.6. Multiple Testing -- 7. Breast Cancer Examples -- 7.1. Example I: Breast Cancer Survival -- 7.2. Example II: Breast Cancer Gene Signatures -- 7.2.1. Prototype-based coexpression module analysis -- 7.2.2. Model for identifying coexpression modules -- 7.2.3. Coexpression patterns -- 8. Conclusion -- Acknowledgments -- References -- Chapter 5 Computational Biology of Small Regulatory RNAs Mihaela Zavolan and Lukasz Jaskiewicz -- 1. Introduction -- 2. Identification of Small Regulatory RNAs -- 3. Classes of Small Regulatory RNAs -- 3.1. miRNAs -- 3.2 . Piwi-Interacting RNAs -- 4. Biogenesis of Small Regulatory RNAs -- 4.1. miRNA Biogenesis -- 4.2. Biogenesis of piRNAs -- 5. Function of Small Regulatory RNAs -- 6. MicroRNA Gene Prediction -- 6.1. What Does a miRNA Precursor Look Like? -- 6.1.1. Stable hairpin precursors -- 6.1.2. Structural constraints -- 6.1.3 . Position-dependent selection strength.

6.1.4. Sequence composition -- 6.1.5 . miRNA gene prediction methods -- 7. MicroRNA Target Prediction -- 7.1. What Does a miRNA Target Site Look Like? -- 7.2. The miRNA Seed Region -- 7.3. Structural Determinants -- 7.4. Other Determinants -- 7.5. miRNA Target Prediction Methods -- 8. Conclusions -- References -- SECTION II PROTEINS AND PROTEOMES -- Chapter 6 UniProtKB/Swiss-Prot Manual and Automated Annotation of Complete Proteomes: The Dictyostelium discoideum Case Study Amos Bairoch and Lydie Lane -- 1. Introduction -- 1.1. What is UniProtKB? -- 1.2. What is Dictyostelium discoideum? -- 1.3. The Dictyostelium discoideum Proteome at DictyBase -- 1.4. The Dictyostelium Annotation Project at UniProt -- 2. Annotation Pipeline of the Dictyostelium discoideumProteome in Uniprotkb -- 2.1. Creating a Complete Proteome Set Across Swiss-Prot and TrEMBL -- 2.2. UniProtKB/Swiss-Prot Annotation -- 2.2.1. Sequence annotation -- 2.2.2. Sequence feature annotation -- 2.2.3. Nomenclature annotation -- 2.2.4. Functional annotation -- 2.3. Why are DictyBase and UniProtkb Complementary? -- 3. Conclusion: Status and Perspective -- 3.1. Status of Dictyostelium Annotation in Swiss-Prot -- 3.2. Future of Dictyostelium Annotation in Swiss-Prot -- 3.3 How may the Annotation of Dictyostelium Help to Annotate other Eukaryotic Proteomes? -- Acknowledgments -- References -- Chapter 7 Analytical Bioinformatics for Proteomics Patricia M. Palagi and Frédérique Lisacek -- 1. Introduction -- 2. Proteome Imaging -- 2.1. 2-DE Gel Imaging -- 2.2. LC/MS Imaging for Label-Free Quantitation -- 3. Protein Identification and Characterization with MS Data -- 3.1. PMF -- 3.2. PFF -- 3.3. Identification Platforms - SwissPIT -- 4. Proteomics Knowledge Integration and Databases -- 4.1. Standards for High-Throughput Data -- 4.2. Integrative Proteomics Data -- 4.2.1. Proteomics servers.

4.2.2. Proteomics repositories -- 4.2.3. Proteomics integrated resources -- 5. Conclusion -- Acknowledgments -- References -- Chapter 8 Protein-Protein Interaction Networks: Assembly and Analysis Christian von Mering -- 1. Introduction -- 2. Experimental Protein Interaction Data -- 3. Networks That Include Indirect Associations -- 4. Clustering, Modules, and Motifs -- 5. Interpreting Network Topology -- 6. Online Resources -- References -- Chapter 9 Protein Structure Modeling and Docking at the Swiss Institute of Bioinformatics Torsten Schwede and Manuel C. Peitsch -- 1. Introduction -- 2. Protein Structure Prediction with SWISS-MODEL - Methods and Tools -- 2.1. SWISS-MODEL Pipeline -- 2.2. SWISS-MODEL Template Library -- 2.3. SWISS-MODEL Server and Workspace -- 2.4. SWISS-MODEL Repository -- 2.5. SWISS-MODEL and DeepView - Swiss-PdbViewer -- 3. Large-Scale Protein Structure Prediction and Structural Genomics -- 4. Protein Model Quality -- 4.1. Correctness and Accuracy -- 4.1.1. Model correctness -- 4.1.2. Model accuracy -- 4.2. Limitations of Comparative Protein Modeling -- 4.2.1. Template availability and structural diversity -- 4.2.2. Unstructured proteins -- 4.2.3. Membrane proteins -- 4.3. Model Quality Evaluation -- 5. Applications of Protein Models -- 5.1. Functional Analysis of Proteins -- 5.1.1. Studying the impact of mutations and SNPs on protein function -- 5.1.2. Planning site-directed mutagenesis experiments -- 5.2. Molecular Replacement -- 5.3. Protein Design -- 5.4. Docking -- 6. Protein Model Portal -- 7. Future Outlook -- References -- Chapter 10 Molecular Modeling of Proteins: From Simulations to Drug Design Applications Vincent Zoete, Michel Cuendet, Ute F. Röhrig, Aurélien Grosdidier and Olivier Michielin -- 1. Introduction -- 2. Molecular Force Fields -- 2.1. Statistical Mechanics Connection -- 2.2. The CHARMM Force Field.

3. Molecular Dynamics Simulations -- 3.1. Integration of the Equation of Motion -- 3.2. Thermodynamic Ensembles -- 4. Free Energy Calculations -- 4.1. Exact Statistical Mechanics Methods for Free Energy Differences -- 4.1.1. Free energy perturbation -- 4.1.2. Thermodynamic integration -- 4.2. Relative Free Energy Differences from Thermodynamic Cycles -- 4.3. Endpoint Methods -- 5. Examples of Applications -- 5.1. Protein Design -- 5.2. Drug Design -- References -- SECTION III PHYLOGENETICS AND EVOLUTIONARY BIOINFORMATICS -- Chapter 11 An Introduction to Phylogenetics and Its Molecular Aspects Gabriel Jîvasattha Bittar and Bernhard Pascal Sonderegger -- 1. Introduction -- 2. Homology and Homoplasy: Look-alikes are Not Necessarily Closely Related -- 2.1. Characters and Their States -- 2.2. Homology - A Phylogenetic Hypothesis -- 2.3. Ancestral or Derived - Qualifying the State of a Character -- 2.4. Homoplasy - Pitfall in Phylogenetics -- 3. Molecular Phylogenetics -- 3.1. Gene Duplication vs. Speciation, Paralogy vs. Orthology -- 3.2. Sequence Alignment - A Homology Hypothesis -- 3.3. Evolutionary Time -- 3.3.1. Evolutionary distance and the course of time -- 3.3.2. Time and time again: paleontology and molecular evolution -- 3.3.3. Micromutations and the molecular clock -- 4. Tree Reconstitution -- 4.1. The Tree Graph Model - Transmission of Phylogenetic Information -- 4.2. Numerical Taxonomic Phenetics (NTP) -- 4.2.1. The neighbor joining algorithm -- 4.2.2. A common NTP artefact -- 4.3. Cladistic Maximum Parsimony (CMP) Methods -- 4.3.1. Symplesiomorphy, synapomorphy, and autapomorphy -- 4.3.2. Cladistic maximum parsimony (CMP) and character compatibility (CC) methods -- 4.3.3. CMP common artefacts -- 4.4. Probabilistic Methods -- 4.5. Searching for an Optimal Tree in a Large, Populated Space -- 4.5.1. The number of possible phylogenetic trees.

4.5.2. The branch-and-bound algorithm.