Data Management of Protein Interaction Networks [NOOK Book]

Overview

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 ...
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Data Management of Protein Interaction Networks

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Overview

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.
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Editorial Reviews

From the Publisher

“The material is suitable for researchers, practitioners, and graduate students in bioinformatics, molecular biology, biomedicine, and biotechnology.” (Book News, 1 April 2012)

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Product Details

  • ISBN-13: 9781118103739
  • Publisher: Wiley
  • Publication date: 2/3/2012
  • Series: Wiley Series in Bioinformatics , #17
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 300
  • File size: 5 MB

Meet the Author

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.

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Table of Contents

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

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