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Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational

Daniel L Rubin1, Farhad Shafa, Diane E Oliver

  • 1Department of Genetics, Stanford Medical Informatics, MSOB X-215, Stanford, CA 94305-5479, USA. rubin@smi.stanford.edu

Bioinformatics (Oxford, England)
|August 10, 2002
PubMed
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Choosing the right data model for pharmacogenetics research is crucial. Ontologies are best for evolving data, while relational models excel for stable, fast queries.

Area of Science:

  • Bioinformatics
  • Genomics
  • Pharmacogenetics

Background:

  • The selection of an information model for biological data impacts query capabilities, database performance, and model maintainability.
  • Genetic sequence data in pharmacogenetics is complex and data storage requirements evolve with scientific advancements.

Purpose of the Study:

  • To develop and compare an ontology-based model and a relational model for storing and querying genetic sequence and polymorphism data.
  • To evaluate the models based on query complexity, performance, and adaptability to changing data structures.

Main Methods:

  • Developed a data model for genetic sequence and polymorphism data using XML Schema.
  • Implemented the model as a frame-based ontology and a relational database model.
  • Collected data from two pharmacogenetics resequencing studies and formulated analytical queries.

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Main Results:

  • Ontologies facilitate early-stage development by adapting to rapid schema changes and simplifying query formulation.
  • Relational models provide superior query performance once the information model and query types stabilize.
  • Comparison highlighted the trade-offs between flexibility and speed in data storage models.

Conclusions:

  • Evolving data schemas are beneficial for pharmacogenetics data storage.
  • Ontology models offer flexibility during initial research phases.
  • Relational models are optimal for established, high-performance querying needs.