Managing Scientific DataEdited by
- Zoé Lacroix, Arizona State University, USA
- Terence Critchlow, Lawrence Livermore National Laboratory, Livermore, CA, USA
Life science data integration and interoperability is one of the most challenging problems facing bioinformatics today. In the current age of the life sciences, investigators have to interpret many types of information from a variety of sources: lab instruments, public databases, gene expression profiles, raw sequence traces, single nucleotide polymorphisms, chemical screening data, proteomic data, putative metabolic pathway models, and many others. Unfortunately, scientists are not currently able to easily identify and access this information because of the variety of semantics, interfaces, and data formats used by the underlying data sources. Bioinformatics: Managing Scientific Data tackles this challenge head-on by discussing the current approaches and variety of systems available to help bioinformaticians with this increasingly complex issue. The heart of the book lies in the collaboration efforts of eight distinct bioinformatics teams that describe their own unique approaches to data integration and interoperability. Each system receives its own chapter where the lead contributors provide precious insight into the specific problems being addressed by the system, why the particular architecture was chosen, and details on the system's strengths and weaknesses. In closing, the editors provide important criteria for evaluating these systems that bioinformatics professionals will find valuable.
Bioinformaticians involved in data management (development, design, management, etc) at corporations and research companies. CS and life science students in bioinformatics programs.
Hardbound, 441 Pages
Published: September 2003
Imprint: Morgan Kaufmann
An exciting compilation that addresses the key issues in biological data management. -Sylvia Spengler, Lawrence Berkeley National Laboratory
- 1 Introduction Zoe Lacroix and Terence Critchlow1.1 Overview 1.2 Problem and Scope 1.3 Biological Data Integration 1.4 Developing a Biological Data Integration System 1.4.1 Specifications 1.4.2 Translating Specifications into a Technical Approach 1.4.3 Development Process 1.4.4 Evaluation of the System References 2 Challenges Faced in the Integration of BiologicalInformation Su Yun Chung and John C. Wooley2.1 The Life Science Discovery Process 2.2 An Information Integration Environment for Life Science Discovery 2.3 The Nature of Biological Data 2.3.1 Diversity 2.3.2 Variability 2.4 Data Sources in Life Science 2.4.1 Biological Databases Are Autonomous 2.4.2 Biological Databases Are Heterogeneous in Data Formats 2.4.3 Biological Data Sources Are Dynamic 2.4.4 Computational Analysis Tools Require SpecificInput/Output Formats and Broad Domain Knowledge 2.5 Challenges in Information Integration 2.5.1 Data Integration 2.5.2 Meta-Data Specification 2.5.3 Data Provenance and Data Accuracy 2.5.4 Ontology 2.5.5 Web Presentations Conclusion References 3 A Practitioner's Guide to Data Management and DataIntegration in Bioinformatics Barbara A. Eckman3.1 Introduction 3.2 Data Management in Bioinformatics 3.2.1 Data Management Basics 3.2.2 Two Popular Data Management Strategiesand Their Limitations 3.2.3 Traditional Database Management 3.3 Dimensions Describing the Space of Integration Solutions 3.3.1 A Motivating Use Case for Integration 3.3.2 Browsing vs. Querying 3.3.3 Syntactic vs. Semantic Integration 3.3.4 Warehouse vs. Federation 3.3.5 Declarative vs. Procedural Access 3.3.6 Generic vs. Hard-Coded 3.3.7 Relational vs. Non-Relational Data Model 3.4 Use Cases of Integration Solutions 3.4.1 Browsing-Driven Solutions 3.4.2 Data Warehousing Solutions 3.4.3 Federated Database Systems Approach 3.4.4 Semantic Data Integration 3.5 Strengths and Weaknesses of the Various Approaches to Integration 3.5.1 Browsing and Querying: Strengths and Weaknesses 3.5.2 Warehousing and Federation: Strengths and Weaknesses 3.5.3 Procedural Code and Declarative Query Language:Strengths and Weaknesses 3.5.4 Generic and Hard-Coded Approaches:Strengths and Weaknesses 3.5.5 Relational and Non-Relational Data Models: Strengthsand Weaknesses 3.5.6 Conclusion: A Hybrid Approach to Integration Is Ideal 3.6 Tough Problems in Bioinformatics Integration 3.6.1 Semantic Query Planning Over Web Data Sources 3.6.2 Schema Management 3.7 Summary Acknowledgments References 4 Issues to Address While Designing a BiologicalInformation System Zoe Lacroix4.1 Legacy 4.1.1 Biological Data 4.1.2 Biological Tools and Workflows 4.2 A Domain in Constant Evolution 4.2.1 Traditional Database Management and Changes 4.2.2 Data Fusion 4.2.3 Fully Structured vs. Semi-Structured 4.2.4 Scientific Object Identity 4.2.5 Concepts and Ontologies 4.3 Biological Queries 4.3.1 Searching and Mining 4.3.2 Browsing 4.3.3 Semantics of Queries 4.3.4 Tool-Driven vs. Data-Driven Integration 4.4 Query Processing 4.4.1 Biological Resources 4.4.2 Query Planning 4.4.3 Query Optimization 4.5 Visualization 4.5.1 Multimedia Data 4.5.2 Browsing Scientific Objects 4.6 Conclusion Acknowledgments References 5 SRS: An Integration Platform for Databanksand Analysis Tools in Bioinformatics Thure Etzold, Howard Harris, and Simon Beaulah5.1 Integrating Flat File Databanks 5.1.1 The SRS Token Server 5.1.2 Subentry Libraries 5.2 Integration of XML Databases 5.2.1 What Makes XML Unique? 5.2.2 How Are XML Databanks Integrated into SRS? 5.2.3 Overview of XML Support Features 5.2.4 How Does SRS Meet the Challenges of XML? 5.3 Integrating Relational Databases 5.3.1 Whole Schema Integration 5.3.2 Capturing the Relational Schema 5.3.3 Selecting a Hub Table 5.3.4 Generation of SQL 5.3.5 Restricting Access to Parts of the Schema 5.3.6 Query Performance to Relational Databases 5.3.7 Viewing Entries from a Relational Databank 5.3.8 Summary 5.4 The SRS Query Language 5.4.1 SRS Fields 5.5 Linking Databanks 5.5.1 Constructing Links 5.5.2 The Link Operators 5.6 The Object Loader 5.6.1 Creating Complex and Nested Objects 5.6.2 Support for Loading from XML Databanks 5.6.3 Using Links to Create Composite Structures 5.6.4 Exporting Objects to XML 5.7 Scientific Analysis Tools 5.7.1 Processing of Input and Output 5.7.2 Batch Queues 5.8 Interfaces to SRS 5.8.1 The Web Interface 5.8.2 SRS Objects 5.8.3 SOAP and Web Services 5.9 Automated Server Maintenance with SRS Prisma 5.10 Conclusion References 6 The Kleisli Query System as a Backbone forBioinformatics Data Integration and Analysis Jing Chen, Su Yun Chung, and Limsoon Wong6.1 Motivating Example 6.2 Approach 6.3 Data Model and Representation 6.4 Query Capability 6.5 Warehousing Capability 6.6 Data Sources 6.7 Optimizations 6.7.1 Monadic Optimizations 6.7.2 Context-Sensitive Optimizations 6.7.3 Relational Optimizations 6.8 User Interfaces 6.8.1 Programming Language Interface 6.8.2 Graphical Interface 6.9 Other Data Integration Technologies 6.9.1 SRS 6.9.2 DiscoveryLink6.9.3 Object-Protocol Model (OPM) 6.10 Conclusions References 7 Complex Query Formulation Over DiverseInformation Sources in TAMBIS Robert Stevens, Carole Goble, Norman W. Paton,Sean Bechhofer, Gary Ng, Patricia Baker, and Andy Brass7.1 The Ontology 7.2 The User Interface 7.2.1 Exploring the Ontology 7.2.2 Constructing Queries 7.2.3 The Role of Reasoning in Query Formulation 7.3 The Query Processor 7.3.1 The Sources and Services Model 7.3.2 The Query Planner 7.3.3 The Wrappers 7.4 Related Work x Contents7.4.1 Information Integration in Bioinformatics 7.4.2 Knowledge Based Information Integration 7.4.3 Biological Ontologies 7.5 Current and Future Developments in TAMBIS 7.5.1 Summary Acknowledgments References 8 The Information Integration System K2 Val Tannen, Susan B. Davidson, and Scott Harker8.1 Approach 8.2 Data Model and Languages 8.3 An Example 8.4 Internal Language 8.5 Data Sources 8.6 Query Optimization 8.7 User Interfaces 8.8 Scalability 8.9 Impact 8.10 Summary Acknowledgments References 9 P/FDM Mediator for a Bioinformatics DatabaseFederation Graham J. L. Kemp and Peter M. D. Gray9.1 Approach 9.1.1 Alternative Architectures for Integrating Databases 9.1.2 The Functional Data Model 9.1.3 Schemas in the Federation 9.1.4 Mediator Architecture 9.1.5 Example 9.1.6 Query Capabilities 9.1.7 Data Sources 9.2 Analysis 9.2.1 Optimization 9.2.2 User Interfaces 9.2.3 Scalability 9.3 Conclusions Acknowledgment References 10 Integration Challenges in Gene Expression DataManagement Victor M. Markowitz, John Campbell, I-Min A. Chen,Anthony Kosky, Krishna Palaniappan,and Thodoros Topaloglou10.1 Gene Expression Data Management: Background 10.1.1 Gene Expression Data Spaces 10.1.2 Standards: Benefits and Limitations 10.2 The GeneExpress System 10.2.1 GeneExpress System Components 10.2.2 GeneExpress Deployment and Update Issues 10.3 Managing Gene Expression Data: Integration Challenges 10.3.1 Gene Expression Data: Array Versions 10.3.2 Gene Expression Data: Algorithms and Normalization 10.3.3 Gene Expression Data: Variability 10.3.4 Sample Data 10.3.5 Gene Annotations 10.4 Integrating Third-Party Gene Expression Data in GeneExpress 10.4.1 Data Exchange Formats 10.4.2 Structural Data Transformation Issues 10.4.3 Semantic Data Mapping Issues 10.4.4 Data Loading Issues 10.4.5 Update Issues 10.5 Summary Acknowledgments Trademarks References 11 DiscoveryLink Laura M. Haas, Barbara A. Eckman, Prasad Kodali,Eileen T. Lin, Julia E. Rice, and Peter M. Schwarz11.1 Approach 11.1.1 Architecture 11.1.2 Registration 11.2 Query Processing Overview 11.2.1 Query Optimization 11.2.2 An Example 11.2.3 Determining Costs 11.3 Ease of Use, Scalability, and Performance 11.4 Conclusions References 12 A Model-Based Mediator System for Scientific DataManagement Bertram Ludascher, Amarnath Gupta,and Maryann E. Martone12.1 Background 12.2 Scientific Data Integration Across Multiple Worlds: Examplesand Challenges from the Neurosciences 12.2.1 From Terminology and Static Knowledgeto Process Context 12.3 Model-Based Mediation 12.3.1 Model-Based Mediation: The Protagonists 12.3.2 Conceptual Models and Registrationof Sources at the Mediator 12.3.3 Interplay Between Mediator and Sources 12.4 Knowledge Representation for Model-Based Mediation 12.4.1 Domain Maps 12.4.2 Process Maps 12.5 Model-Based Mediator System and Tools 12.5.1 The KIND Mediator Prototype 12.5.2 The Cell-Centered Database and SMART Atlas:Retrieval and Navigation ThroughMulti-Scale Data 12.6 Related Work and Conclusion 12.6.1 Related Work 12.6.2 Summary: Model-Based Mediationand Reason-Able Meta-Data Acknowledgments References 13 Compared Evaluation of Scientific DataManagement Systems Zoe Lacroix and Terence Critchlow13.1 Performance Model 13.1.1 Evaluation Matrix 13.1.2 Cost Model 13.1.3 Benchmarks 13.1.4 User Survey 13.2 Evaluation Criteria 13.2.1 The Implementation Perspective13.2.2 The User Perspective 13.3 Tradeoffs 13.3.1 Materialized vs. Non-Materialized 13.3.2 Data Distribution and Heterogeneity 13.3.3 Semi-Structured Data vs. Fully Structured Data 13.3.4 Text Retrieval 13.3.5 Integrating Applications 13.4 Summary References Concluding Remarks Summary Looking Toward the Future Appendix: Biological Resources Glossary System Information SRS Kleisli TAMBIS K2 P/FDM Mediator GeneExpress DiscoveryLink KIND Index