10.3 Life Sciences Are Turning to XML to Model Their Information

It has been said that the difficulties in dealing with bioinformatics data come more from its idiosyncrasies than its quantity, and there certainly is no lack of quantity of information (Achard et al. 2001). Biological data are complex to model, and because of this complexity, our models must have the flexibility to grow. The complexity comes in part from the large variety of data types and their many interrelationships. In addition, new data types emerge regularly, and these new types modify our perception of the old types.

François Rechenmann confirms this synopsis by stating, "It is not so much the volume of data which characterizes biology, as it is the data's diversity and heterogeneity" (Rechenmann 2000). He wraps up his editorial with the statement, "The crucial role of computer science is now unanimously recognized for the management and the analysis of biological data; through knowledge modeling, it could be brought to play a role in life sciences similar to the role mathematics plays in physical sciences."

Since the earliest origins of scientific thought, there have been efforts to collect, organize (i.e., categorize), and analyze data. A major alteration in scientific thought occurred with the publication in 1735 of Systema Naturae by Carolus Linnaeus. While the desire to organize data for eventual analysis predated this publication, the Linnaean classification system transformed the world of biology. The establishment of a common system for the identification of living organisms opened countless avenues to analyze the information gathered. This allowed vast increases in the overall knowledge base for biological science.

Interesting to note is that the Linnaean system, like XML, uses a hierarchical structure. The reason the Linnaean system was and XML is revolutionary is that they are flexible structures that support new information as it is discovered. Just as the Linnaean system of nomenclature allowed researchers to communicate in exact terms regarding specific organisms, XML, with its similar hierarchical nature, will allow researchers from widely diverse backgrounds to communicate information that leads to knowledge development.

Information is the combination of data and context. If someone gave you the sequence of letters "seat" without any context, you have no way of knowing to what he or she was referring. He or she could be referring to an uncomfortable seat or to a short amino acid sequence contained within the aspartokinase I protein in E. coli. The point is that data without context is incomplete. It is only when we combine data with its context that we have information. A system that locks context and only allows data to be modified, which is typical of many traditional data models, is unsuitable for a knowledge management system. Ideally, a knowledge management system will handle context (metadata) as freely and fluidly as it handles data (Direen et al. 2001).

XML is well suited for knowledge management systems in that it pairs data with its context via the hierarchical tag structure. XML is the first widely adopted standard for expressing information as opposed to just data. XML is also rapidly becoming an accepted information-modeling language for bioinformatics. A number of groups are developing standard XML markup languages in this area, including

• BIOML (BIOpolymer Markup Language): BIOML is used to describe experimental information about proteins, genes, and other biopolymers. A BIOML document will describe a physical object (e.g., a particular protein) in such a way that all known experimental information about that object can be associated with the object in a logical and meaningful way. The information is nested at different levels of complexity and fits with the tree-leaf structure inherent in XML. BIOML is a product of core working groups at Proteometrics, LLC and Proteometrics Canada, Ltd. More information is available at http://www.bioml.com/BIOML/.

• BSML (Bioinformatic Sequence Markup Language): The National Human Genome Research Institute (NHGRI) funded the development of BSML in 1997 as a public domain standard for the bioinformatics community. Among the early goals for BSML was to create a data representation model for sequences and their annotations. This model would enable linking the behavior of the display object to the sequences, annotations, and links it represents. The linking capability of BSML has paralleled the evolution of storage, analysis, and linking of biological data on computer networks. Sequence-related phenomena from the biomolecular level to the complete genome can be described using BSML. This flexibility provides a needed medium for genomics research. LabBook, Inc. (http://www.labbook.com/) is the author and owner of the copyrights for BSML.

• PSDML: The Protein Information Resource (PIR) database is a partnership between the National Biomedical Research Foundation at Georgetown University Medical Center, the Munich Information Center for Protein Sequences, and the Japan International Protein Information Database. The PIR is an annotated, public-domain sequence database that allows sequence similarity and text searching. The Protein Sequence Database Markup Language (PSDML) is used to store protein information in the PIR database. More information is available at http://pir.georgetown.edu/.

• GAME: Genome Annotation Markup Elements can be utilized to represent features or annotations about specific regions of a sequence. These annotations may be differentiated using GAME. Examples of this are features generated by a sequence analysis program and those generated by a professional lab worker. Facilitation of the exchange of genomic annotations between researchers, genome centers, and model organism databases will allow each to specify the conclusions they have drawn from their analysis and then share these descriptions in XML with each other. The first widely used version of GAME was created at the BDGP (Berkeley Drosophila Genome Project) by Suzanna Lewis and Erwin Frise. More information is available at http://www.bioxml.org/Projects/game/game0.1.html.

• SBML (Systems Biology Markup Language): The Systems Biology Work-bench (SBW) project at the California Institute of Technology seeks to provide for the sharing of models and resources between simulation and analysis tools for systems biology. One of the two approaches that are being pursued to attain the goal has been the incremental development of the Systems Biology Markup Language (SBML). SBML is an XML-based representation of biochemical network models. More information is available at http://www.cds.caltech.edu/erato/index.html.

• CellML: CellML is an XML-based markup language whose purpose is to store and exchange computer-based biological models. CellML has primarily been developed by Physiome Sciences, Inc., in Princeton, New Jersey, and the Bioengineering Institute at the University of Auckland. CellML allows the sharing of models, even if they are using different model-building software and the reuse of components between models. This capability accelerates the model-building process. More information is available at http://www.cellml.org/.

• MAGE-ML: Microarray Gene Expression Markup Language has been automatically derived from Microarray Gene Expression Object Model (MAGE OM). MAGE-ML is based on XML and is designed to describe and communicate information about microarray-based experiments. The information can describe microarray designs, manufacturing information, experiment setup and execution information, gene expression data, and data analysis results. The Object Management Group (OMG) was primarily involved in the creation and distribution of MAGE-OM standards through the Gene Expression Request For Proposal (RFP). MAGE-ML replaced the MAML (Microarray Markup Language) as of February 2002. More information is available at http://www.mged.org/Workgroups/MAGE/introduction.html.

These are a few of the XML markup languages being defined for the bioinformatics arena. Hank Simon (Simon 2001) provides a more complete list, and Paul Gordon has a Web site devoted to XML for molecular biology (http://www.visualgenomics.ca/gordonp/xml/).

The unprecedented degree of flexibility and extensibility of XML in terms of its ability to capture information is what makes it ideal for knowledge management and for use in bioinformatics.

Top Title TOC Preface P.1 What Is XML? P.2 XML Concepts P.3 XML-Related Technologies P.4 XML Data Management P.5 How This Book Is Organized P.6 Who Should Read This Book P.7 Resources Acknowledgments Chapter 8 Chapter 12 Chapter 13 Chapter 14 Chapter 19 Part I: What Is XML? Chapter 1. Information Modeling with XML 1.1 Introduction 1.2 XML as an Information Domain 1.3 How XML Expresses Information 1.4 Patterns in XML 1.5 Common XML Information-Modeling Pitfalls 1.6 A Very Simple Way to Design XML 1.7 Conclusion Part II: Native XML Databases Chapter 2. Tamino-Software AG's Native XML Server 2.1 Introduction 2.2 Tamino Architecture and APIs 2.3 XML Storage 2.4 Querying XML 2.5 Tools 2.6 Full Database Functionality 2.7 Conclusion Chapter 3. eXist Native XML Database 3.1 Introduction 3.2 Features 3.3 System Architecture Overview 3.4 Getting Started 3.5 Query Language Extensions 3.6 Application Development 3.7 Technical Background 3.8 Conclusion Chapter 4. Embedded XML Databases 4.1 Introduction 4.2 A Primer on Embedded Databases 4.3 Embedded XML Databases 4.4 Building Applications for Embedded XML Databases 4.5 Conclusion Part III: XML and Relational Databases Chapter 5. IBM XML-Enabled Data Management Product Architecture & Technology 5.1 Introduction 5.2 Product and Technology Offering Summaries 5.3 Current Architecture and Technology 5.4 Future Architecture and Technology 5.5 Conclusion Notices Chapter 6. Supporting XML in Oracle9i 6.1 Introduction 6.2 Storing XML as CLOB 6.3 XMLType 6.4 Using XSU for Fine-Grained Storage 6.5 Building XML Documents from Relational Data 6.6 Web Access to the Database 6.7 Special Oracle Features 6.8 Conclusion Chapter 7. XML Support in Microsoft SQL Server 2000 7.1 Introduction 7.2 XML and Relational Data 7.3 XML Access to SQL Server 7.4 Serializing SQL Query Results into XML 7.5 Providing Relational Views over XML 7.6 SQLXML Templates 7.7 Providing XML Views over Relational Data 7.8 Conclusion Chapter 8. A Generic Architecture for Storing XML Documents in a Relational Database 8.1 Introduction 8.2 System Architecture 8.3 The Data Model 8.4 Creating the Database 8.5 Connecting to the Repository 8.6 Uploading XML Documents 8.7 Querying the Repository 8.8 Further Enhancements 8.9 Conclusion Chapter 9. An Object-Relational Approach to Building a High-Performance XML Repository 9.1 Introduction 9.2 Overview of XML Use-Case Scenario 9.3 High-Level System Architecture 9.4 Detailed Design Descriptions 9.5 Conclusion Part IV: Applications of XML Chapter 10. Knowledge Management in Bioinformatics 10.1 Introduction 10.2 A Brief Molecular Biology Background 10.3 Life Sciences Are Turning to XML to Model Their Information 10.4 A Genetic Information Model 10.5 NeoCore XMS* 10.6 Integration of BLAST into NeoCore XMS Conclusion Chapter 11. Case Studies of XML Used with IBM DB2 Universal Database 11.1 Introduction 11.2 Case Study 1: "Our Most Valued Customers Come First" 11.3 Case Study 2: "Improve Cash Flow" 11.4 Conclusion Notices Chapter 12. The Design and Implementation of an Engineering Data Management System Using XML and J2EE 12.1 Introduction 12.2 Background and Requirements 12.3 Overview 12.4 Design Choices 12.5 Future Directions 12.6 Conclusion Chapter 13. Geographical Data Interchange Using XML-Enabled Technology within the GIDB System 13.1 Introduction 13.2 GIDB METOC Data Integration 13.3 GIDB Web Map Service Implementation 13.4 GIDB GML Import and Export 13.5 Conclusion Chapter 14. Space Wide Web by Adapters in Distributed Systems Configuration from Reusable Components 14.1 Introduction 14.2 Advanced Concept Description: The Research Problem 14.3 Integration of Components with Architecture 14.4 Example 14.5 Future Generation NASA Institute for Advanced Concepts, Space Wide Web Research, and Boundaries 14.6 Advanced Concept Development 14.7 Conclusion Chapter 15. XML as a Unifying Framework for Inductive Databases 15.1 Introduction 15.2 Past Work 15.3 The Proposed Data Model: XDM 15.4 Benefits of XDM 15.5 Toward Flexible and Open Systems 15.6 Related Work 15.7 Conclusion Chapter 16. Designing and Managing an XML Warehouse 16.1 Introduction 16.2 Architecture 16.3 Data Warehouse Specification 16.4 Managing the Metadata 16.5 Storage and Management of the Data Warehouse 16.6 DAWAX: A Graphic Tool for the Specification and Management of a Data Warehouse 16.7 Related Work 16.8 Conclusion Part V: Performance and Benchmarks Chapter 17. XML Management System Benchmarks 17.1 Introduction 17.2 Benchmark Specification 17.3 Benchmark Data Set 17.4 Existing Benchmarks for XML 17.5 Conclusion Chapter 18. The Michigan Benchmark: A Micro-Benchmark for XML Query Performance Diagnostics 18.1 Introduction 18.2 Related Work 18.3 Benchmark Data Set 18.4 Benchmark Queries 18.5 Using the Benchmark 18.6 Conclusion Chapter 19. A Comparison of Database Approaches for Storing XML Documents 19.1 Introduction 19.2 Data Models for XML Documents 19.3 Databases for Storing XML Documents 19.4 Benchmarking Specification 19.5 Test Results 19.6 Related Work 19.7 Summary Chapter 20. Performance Analysis between an XML-Enabled Database and a Native XML Database 20.1 Introduction 20.2 Related Work 20.3 Methodology 20.4 Database Design 20.5 Discussion 20.6 Experiment Result 20.7 Conclusion Chapter 21. Conclusion References Contributors Editors Chapter 1: Information Modeling with XML Chapter 2: Tamino-Software AG's Native XML Server Chapter 3: eXist Native XML Database Chapter 4: Embedded XML Databases Chapter 5: IBM XML-Enabled Data Management Product Architecture and Technology Chapter 6: Supporting XML in Oracle9i Chapter 7: XML Support in Microsoft SQL Server 2000 Chapter 8: A Generic Architecture for Storing XML Documents in a Relational Database Chapter 9: An Object-Relational Approach to Building a High-Performance XML Repository Chapter 10: Knowledge Management in Bioinformatics Chapter 11: Case Studies of XML Used with IBM DB2 Universal Database Chapter 12: The Design and Implementation of an Engineering Data Management System Using XML and J2EE Chapter 13: Geographical Data Interchange Using XML-Enabled Technology within the GIDB System Chapter 14: Space Wide Web by Adapters in Distributed Systems Configuration from Reusable Components Chapter 15: XML as a Unifying Framework for Inductive Databases Chapter 16: Designing and Managing an XML Warehouse Chapter 17: XML Management System Benchmarks Chapter 18: The Michigan Benchmark: A Micro-Benchmark for XML Query Performance Diagnostics Chapter 19: A Comparison of Database Approaches for Storing XML Documents Chapter 20: Performance Analysis between an XML-Enabled Database and a Native XML Database Remember the name: eTutorials.org Copyright eTutorials.org 2008-2023. 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