Bioinformatics
Managing Scientific Data
Published on 8. September 2003
441 pages
978-0-08-052798-7 (ISBN)
Description
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.* Provides a clear overview of the state-of-the-art in data integration and interoperability in genomics, highlighting a variety of systems and giving insight into the strengths and weaknesses of their different approaches. * Discusses shared vocabulary, design issues, complexity of use cases, and the difficulties of transferring existing data management approaches to bioinformatics systems, which serves to connect computer and life scientists. * Written by the primary contributors of eight reputable bioinformatics systems in academia and industry including: BioKris, TAMBIS, K2, GeneExpress, P/FDM, MBM, SDSC, SRS, and DiscoveryLink.
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09/2003
Morgan Kaufmann
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Content
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.
