Bioinformatics

An exciting compilation that addresses the key issues in biological data management. -Sylvia Spengler, Lawrence Berkeley National Laboratory.

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.

Authors : Su Yun Chung, John C. Wooley, Barbara A. Eckman, Thure Etzold, Howard Harris, Simon Beaulah, Jing Chen, Limsoon Wong, Robert Stevens, Carole Goble, Norman W. Paton, Sean Bechhofer, Gary Ng, Patricia Baker, Andy Brass, Val Tannen, Susan B. Davidson, Scott Harker, Graham J. L. Kemp and Peter M. D. Gray, Laura M. Haas, Barbara A. Eckman, Prasad Kodali, Eileen T. Lin, Julia E. Rice, and Peter M. Schwarz, Victor M. Markowitz, John Campbell, I-Min A. Chen, Anthony Kosky, Krishna Palaniappan, and Thodoros Topaloglou.

Contents

• Introduction
  • Overview
  • Problem and Scope
  • Biological Data Integration
  • Developing a Biological Data Integration System
• Challenges Faced in the Integration of Biological Information
  • The Life Science Discovery Process
  • An Information Integration Environment for Life Science Discovery
  • The Nature of Biological Data
  • Data Sources in Life Science
  • Challenges in Information Integration
• A Practitioner's Guide to Data Management and Data Integration in Bioinformatics
  • Introduction
  • Data Management in Bioinformatics
  • Dimensions Describing the Space of Integration Solutions
  • Use Cases of Integration Solutions
  • Strengths and Weaknesses of the Various Approaches to Integration
  • Tough Problems in Bioinformatics Integration
  • Summary
• Issues to Address While Designing a Biological Information System
  • Legacy
  • A Domain in Constant Evolution
  • Biological Queries
  • Query Processing
  • Visualization
  • Conclusion
• SRS: An Integration Platform for Databanks and Analysis Tools in Bioinformatics
  • Integrating Flat File Databanks
  • Integration of XML Databases
  • Integrating Relational Databases
  • The SRS Query Language
  • Linking Databanks
  • The Object Loader
  • Scientific Analysis Tools
  • Interfaces to SRS
  • Automated Server Maintenance with SRS Prisma
  • Conclusion
• The Kleisli Query System as a Backbone for Bioinformatics Data Integration and Analysis
  • Motivating Example
  • Approach
  • Data Model and Representation
  • Query Capability
  • Warehousing Capability
  • Data Sources
  • Optimizations
  • User Interfaces
  • Other Data Integration Technologies
  • Conclusions
• Complex Query Formulation Over Diverse Information Sources in TAMBIS
  • The Ontology
  • The User Interface
  • The Query Processor
  • Related Work
  • Current and Future Developments in TAMBIS
• The Information Integration System K2
  • Approach
  • Data Model and Languages
  • An Example
  • Internal Language
  • Data Sources
  • Query Optimization
  • User Interfaces
  • Scalability
  • Impact
  • Summary
• P/FDM Mediator for a Bioinformatics Database Federation
  • Approach
  • Analysis
  • Conclusions
• Integration Challenges in Gene Expression Data Management
  • Gene Expression Data Management: Background
  • The GeneExpress System
  • Managing Gene Expression Data: Integration Challenges
  • Integrating Third-Party Gene Expression Data in GeneExpress
  • Summary
• DiscoveryLink
  • Approach
  • Query Processing Overview
  • Ease of Use, Scalability, and Performance
  • Conclusions
• A Model-Based Mediator System for Scientific Data Management
  • Background
  • Scientific Data Integration Across Multiple Worlds: Examples
  • Model-Based Mediation
  • Knowledge Representation for Model-Based Mediation
  • Model-Based Mediator System and Tools
  • Related Work and Conclusion
• Compared Evaluation of Scientific Data Management Systems
  • Performance Model
  • Evaluation Criteria
  • Tradeoffs
  • Summary