Abstract Motivation: The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change. Results: We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model. We implemented this model as an ontology in a frame-based representation and as a relational model in a database system. We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data. We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model. Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize. Availability: Our ontology and relational models are available at http://smi-web.stanford.edu/projects/helix/pubs/ismb02/. Contact: [email protected]@[email protected] Keywords: ontologies; relational databases; schema; data models; pharmacogenomics.

The blackboard model is a versatile framework for implementing expert systems. In recent years the speed of blackboard access has been improved but the execution cycle, which manages knowledge source activation and execution, still needs substantial improvement. A method of condensing knowledge sources is presented, and it is shown how to efficiently activate and execute a class of blackboard application systems using the condensed representation. It is shown that the time complexity of the execution cycle of a condensed system is faster than that of the conventional approach by the ratio of the time required for blackboard retrievals to the time required for vector element retrievals. In practice, this ratio is approximately four order of magnitude.>