Distributed Query Processing on the Grid
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Growing adaptation of computer science in Bioinfomatics
ISICT '04 Proceedings of the 2004 international symposium on Information and communication technologies
BioStar models of clinical and genomic data for biomedical data warehouse design
International Journal of Bioinformatics Research and Applications
State of the nation in data integration for bioinformatics
Journal of Biomedical Informatics
Linking Biological Databases Semantically for Knowledge Discovery
ER '08 Proceedings of the ER 2008 Workshops (CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM) on Advances in Conceptual Modeling: Challenges and Opportunities
Towards bounding sequential patterns
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mediator-Based architecture for integrated access to biological databases
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
Integrating and warehousing liver gene expression data and related biomedical resources in GEDAW
DILS'05 Proceedings of the Second international conference on Data Integration in the Life Sciences
BioStar+: a data warehouse schema for integrating clinical and genomic data from HIV patients
ACM SIGBioinformatics Record
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Effective analysis of genome sequences and associated functional data requires access to many different kinds of biological information. For example, when analysing gene expression data, it may be useful to have access to the sequences upstream of the genes, or to the cellular location of their protein products. Such information is currently stored in different formats at different sites in a way that does not readily allow integrated analyses. The Genome Information Management System (GIMS) is an object database that integrates genome sequence data with functional data on the transcriptome and on protein-protein interactions in a single data warehouse. We have used GIMS to store the Saccharomyces cerevisiae (yeast) genome and to demonstrate how the integrated storage of diverse kinds of genomic data can be beneficial for analysing data using context-rich queries and analyses. GIMS allows data to be stored in a way that reflects the underlying mechanisms in the organism, and permits complex questions to be asked of the data. This paper provides an overview of the GIMS system and describes some analyses that illustrate its use for analysing functional data sets for S. cerevisiae.