Adaptable data management for systems biology investigations. Export

@article{citeulike:4140451, abstract = {BACKGROUND: Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage. RESULTS: The adaptable data management system discussed is designed to support the seamless mining and analysis of biological experiment data that is commonly used in systems biology (e.g. ChIP-chip, gene expression, proteomics, imaging, flow cytometry). We use different content graphs to represent different views upon the data. These views are designed for different roles: equipment specific views are used to gather instrumentation information; data processing oriented views are provided to enable the rapid development of analysis applications; and research project specific views are used to organize information for individual research experiments. This management system allows for both the rapid introduction of new types of information and the evolution of the knowledge it represents. CONCLUSION: Data management is an important aspect of any research enterprise. It is the foundation on which most applications are built, and must be easily extended to serve new functionality for new scientific areas. We have found that adopting a three-tier architecture for data management, built around distributed standardized content repositories, allows us to rapidly develop new applications to support a diverse user community.}, author = {Boyle, John and Rovira, Hector and Cavnor, Chris and Burdick, David and Killcoyne, Sarah and Shmulevich, Ilya}, citeulike-article-id = {4140451}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1471-2105-10-79}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19265554}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19265554}, doi = {10.1186/1471-2105-10-79}, issn = {1471-2105}, journal = {BMC bioinformatics}, keywords = {data\_representation, software}, number = {1}, pages = {79+}, posted-at = {2009-03-13 11:12:20}, priority = {5}, title = {Adaptable data management for systems biology investigations.}, url = {http://dx.doi.org/10.1186/1471-2105-10-79}, volume = {10}, year = {2009} }

TY - JOUR ID - citeulike:4140451 L3 - citeulike-article-id:4140451 N2 - BACKGROUND: Within research each experiment is different, the focus changes and the data is generated from a continually evolving barrage of technologies. There is a continual introduction of new techniques whose usage ranges from in-house protocols through to high-throughput instrumentation. To support these requirements data management systems are needed that can be rapidly built and readily adapted for new usage. RESULTS: The adaptable data management system discussed is designed to support the seamless mining and analysis of biological experiment data that is commonly used in systems biology (e.g. ChIP-chip, gene expression, proteomics, imaging, flow cytometry). We use different content graphs to represent different views upon the data. These views are designed for different roles: equipment specific views are used to gather instrumentation information; data processing oriented views are provided to enable the rapid development of analysis applications; and research project specific views are used to organize information for individual research experiments. This management system allows for both the rapid introduction of new types of information and the evolution of the knowledge it represents. CONCLUSION: Data management is an important aspect of any research enterprise. It is the foundation on which most applications are built, and must be easily extended to serve new functionality for new scientific areas. We have found that adopting a three-tier architecture for data management, built around distributed standardized content repositories, allows us to rapidly develop new applications to support a diverse user community. IS - 1 JF - BMC bioinformatics SN - 1471-2105 TI - Adaptable data management for systems biology investigations. VL - 10 SP - 79 KW - data_representation KW - software AU - Boyle, John AU - Rovira, Hector AU - Cavnor, Chris AU - Burdick, David AU - Killcoyne, Sarah AU - Shmulevich, Ilya PY - 2009/// UR - http://dx.doi.org/10.1186/1471-2105-10-79 ER -