Abstract
Scientific laboratories are rich in data management challenges. This paper describes an end-to-end information management infrastructure for a high throughput proteomics industrial laboratory. A unique feature of the platform is a data and applications integration framework that is employed for the integration of heterogeneous data, applications and processes across the entire laboratory production workflow. We also define a reference architecture for implementing similar solutions organized according to the laboratory data lifecycle phases. Each phase is modeled by a set of workflows integrating programs and databases in sequences of steps and associated communication and data transfers. We discuss the issues associated with each phase, and describe how these issues were approached in the proteomics implementation.
The proteomics experience section of this paper draws from the following manuscript: An End-to-End Bioinformatics Platform for High Throughput Proteomics. T. Topaloglou, M. Dharsee, M. Li, R.M. Ewing, Y.V. Bukhman, P. Chu, P. Economopoulos, S. Huynh, D. Lee, A. Pasculescu, A.-M. Salter, H. Wang.
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Topaloglou, T. (2006). Managing Data in High Throughput Laboratories: An Experience Report from Proteomics. In: Embley, D.W., Olivé, A., Ram, S. (eds) Conceptual Modeling - ER 2006. ER 2006. Lecture Notes in Computer Science, vol 4215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11901181_46
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DOI: https://doi.org/10.1007/11901181_46
Publisher Name: Springer, Berlin, Heidelberg
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