Data Management of Protein Interaction Networks
Wiley Series in Bioinformatics, Band 17 1. Aufl.
|Verlag:||Wiley-IEEE Computer Society Press|
Current PPI databases do not offer sophisticated querying interfaces and especially do not integrate existing information about proteins. Current algorithms for PIN analysis use only topological information, while emerging approaches attempt to exploit the biological knowledge related to proteins and kinds of interaction, e.g. protein function, localization, structure, described in Gene Ontology or PDB. The book discusses technologies, standards and databases for, respectively, generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs. Moreover, some case studies and applications of PINs are also discussed.
LIST OF FIGURES xiii LIST OF TABLES xix FOREWORD xxi PREFACE xxiii ACKNOWLEDGMENTS xxix INTRODUCTION xxxi ACRONYMS xxxiii 1 INTERACTOMICS 1 1.1 Interactomics and Omics Sciences / 1 1.2 Genomics and Proteomics / 4 1.3 Representation and Management of Protein Interaction Data / 5 1.4 Analysis of Protein Interaction Networks / 5 1.5 Visualization of Protein Interaction Networks / 6 1.6 Models for Biological Networks / 7 1.7 Flow of Information in Interactomics / 8 1.8 Applications of Interactomics in Biology and Medicine / 10 1.9 Summary / 11 2 TECHNOLOGIES FOR DISCOVERING PROTEIN INTERACTIONS 13 2.1 Introduction / 13 2.2 Techniques Investigating Physical Interactions / 14 2.3 Technologies Investigating Kinetic Dynamics / 17 2.4 Summary / 18 3 GRAPH THEORY AND APPLICATIONS 21 3.1 Introduction / 21 3.2 Graph Data Structures / 22 3.3 Graph-Based Problems and Algorithms / 28 3.4 Summary / 31 4 PROTEIN-TO-PROTEIN INTERACTION DATA 33 4.1 Introduction / 33 4.2 HUPO PSI-MI / 34 4.3 Summary / 41 5 PROTEIN-TO-PROTEIN INTERACTION DATABASES 43 5.1 Introduction / 43 5.2 Databases of Experimentally Determined Interactions / 45 5.3 Databases of Predicted Interactions / 55 5.4 Metadatabases: Integration of PPI Databases / 62 5.5 Summary / 70 6 MODELS FOR PROTEIN INTERACTION NETWORKS 71 6.1 Introduction / 71 6.2 Random Graph Model / 72 6.3 Scale-Free Model / 73 6.4 Geometric Random Graph Model / 73 6.5 Stickiness Index (STICKY) Model / 74 6.6 Degree-Weighted Model / 74 6.7 Network Scoring Models / 75 6.8 Summary / 76 7 ALGORITHMS ANALYZING FEATURES OF PROTEIN INTERACTION NETWORKS 79 7.1 Introduction / 79 7.2 Analysis of Protein Interaction Networks through Centrality Measures / 80 7.3 Extraction of Network Motifs / 81 7.4 Individuation of Protein Complexes / 88 7.5 Summary / 99 8 ALGORITHMS COMPARING PROTEIN INTERACTION NETWORKS 101 8.1 Introduction / 101 8.2 Local Alignment Algorithms / 104 8.3 Global Alignment Algorithms / 109 8.4 Summary / 111 9 ONTOLOGY-BASED ANALYSIS OF PROTEIN INTERACTION NETWORKS 113 9.1 Definition of Ontology / 113 9.2 Languages for Modeling Ontologies / 115 9.3 Biomedical Ontologies / 116 9.4 Ontology-Based Analysis of Protein Interaction Data / 117 9.5 Semantic Similarity Measures of Proteins / 120 9.6 The Gene Ontology Annotation Database (GOA) / 122 9.7 FussiMeg and ProteinOn / 123 9.8 Summary / 123 10 VISUALIZATION OF PROTEIN INTERACTION NETWORKS 125 10.1 Introduction / 125 10.2 Cytoscape / 126 10.3 CytoMCL / 127 10.4 NAViGaTOR / 128 10.5 BioLayout Express3D / 130 10.6 Medusa / 130 10.7 ProViz / 131 10.8 Ondex / 132 10.9 PIVOT / 132 10.10 Pajek / 133 10.11 Graphviz / 134 10.12 GraphCrunch / 134 10.13 VisANT / 135 10.14 PIANA / 136 10.15 Osprey / 136 10.16 cPATH / 137 10.17 PATIKA / 138 10.18 Summary / 139 11 CASE STUDIES IN BIOLOGY AND BIOINFORMATICS 141 11.1 Analysis of an Interaction Network from Proteomic Data / 141 11.2 Experimental Comparison of Two Interaction Networks / 143 11.3 Ontology-Based Management of PIN (OntoPIN) / 145 11.4 Ontology-Based Prediction of Protein Complexes / 149 12 FUTURE TRENDS 151 REFERENCES 157 INDEX 177
“The material is suitable for researchers, practitioners, and graduate students in bioinformatics, molecular biology, biomedicine, and biotechnology.” (Book News, 1 April 2012)
MARIO CANNATARO, PhD, is Associate Professor of Computer Engineering at the Magna Græcia University of Catanzaro. His research explores bioinformatics, computational proteomics and genomics, medical informatics, grid and parallel computing, and adaptive web systems. Dr. Cannataro has published three books and more than 150 papers in international journals and conference proceedings. PIETRO HIRAM GUZZI, PhD, is Assistant Professor of Computer Engineering at the Magna Græcia University of Catanzaro. His research focuses on the analysis of protein interaction networks and the use of biological knowledge encoded in ontologies for modeling, querying, and analyzing protein interaction networks.
Interactomics: a complete survey from data generation to knowledge extraction With the increasing use of high-throughput experimental assays, more and more protein interaction databases are becoming available. As a result, computational analysis of protein-to-protein interaction (PPI) data and networks, now known as interactomics, has become an essential tool to determine functionally associated proteins. From wet lab technologies to data management to knowledge extraction, this timely book guides readers through the new science of interactomics, giving them the tools needed to: Generate and store PPI data Analyze PPI data and networks Develop useful applications The authors have organized the book according to the workflow of interactomics, beginning with data generation and ending with knowledge extraction. Readers will discover how to make full use of all the databases, tools, and techniques currently available for exploiting interactomics data. They'll learn a broad range of approaches for the management and analysis of protein interaction data, including topological-, database-, data mining-, and ontology-based methods. The fundamental principles underlying each of these methods are presented in detail, alongside their advantages and disadvantages. Throughout the book, case studies enable readers to discover how interactomics enables researchers to generate, represent, store, analyze, and manage PPI data and networks. Moreover, the authors discuss new and emerging applications developed from interactomics research. Data Management of Protein Interaction Networks is recommended for all bioinformaticians and protein researchers who want to take full advantage of interactomics software tools and methods in order to enhance their knowledge of biological processes.
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