Abstract
Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources.
Keywords
- Data Integration
- Global Schema
- Transformation Pathway
- Data Integration System
- Query Processor
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Zamboulis, L. et al. (2006). Data Access and Integration in the ISPIDER Proteomics Grid. In: Leser, U., Naumann, F., Eckman, B. (eds) Data Integration in the Life Sciences. DILS 2006. Lecture Notes in Computer Science(), vol 4075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11799511_3
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DOI: https://doi.org/10.1007/11799511_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36593-8
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