Abstract
In Cytomics, the study of cellular systems at the single cell level, High-Throughput Screening (HTS) techniques have been developed to implement the testing of hundreds to thousands of conditions applied to several or up to millions of cells in a single experiment.
Recent technological developments of imaging systems and robotics have lead to an exponential increase in data volumes generated in HTS-experiments. This is pushing forward the need for a semantically oriented bioinformatics approach capable of storing large volume of linked metadata, handling a diversity of data formats, and querying data in order to extract meaning from the experiments performed.
This paper describes our research in developing CytomicsDB, a modern RDBMS based platform, designed to provide an architecture capable of dealing with the computational requirements involved in high-throughput content. CytomicsDB supports web services and collaborative infrastructure in order to perform further exploration of linked information generated in each experiment.
The objective of this system is to build a semantic layer over the data so as to enable querying metadata and at the same time allowing scientists to integrate new tools and APIs taking care of the image and data analysis. The results will become part of the metadata of the whole HTS experiment and will be available for semantic post analysis.
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Keywords
- Database Schema
- Laboratory Information Management System
- Retrieval Approach
- Image Analysis Process
- Localization Phenotype
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.
References
Bertens, L.M.F., Slob, J., Verbeek, F.: A generic organ based ontology system, applied to vertebrate heart anatomy, development and physiology. J. Integrative Bioinformatics 8(2) (2011)
Cao, L., Yan, K., Winkel, L., de Graauw, M., Verbeek, F.J.: Pattern recogntion in high-content cytomics screens for target discovery - case studies in endocytosis. In: Loog, M., Wessels, L., Reinders, M.J.T., de Ridder, D. (eds.) PRIB 2011. LNCS, vol. 7036, pp. 330–342. Springer, Heidelberg (2011)
Chan, T., Malik, P., Singh, R.: An interactive visualization-based approach for high throughput screening information management in drug discovery. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2006, pp. 5794–5797 (August 2006)
Colmsee, C., Flemming, S., Klapperstuck, M., Lange, M., Scholz, U.: A case study for efficient management of high throughput primary lab data. BMC Research Notes 4(1), 413 (2011)
de Graauw, M., Cao, L., Winkel, L., van Miltenburg, M.H.A.M., LeDévédec, S., Klop, M., Yan, K., Pont, C., Rogkoti, V.-M., Tijsma, A., Chaudhuri, A., Lalai, R., Price, L., Verbeek, F., van de Water, B.: Annexin a2 depletion delays egfr endocytic trafficking via cofilin activation and enhances egfr signaling and metastasis formation. In: Oncogene (2013)
Duin, R.P.W.: Prtools - version 3.0 - a matlab toolbox for pattern recognition. In: Proc. of SPIE, p. 1331 (2000)
Kohl, K., Basler, G., Ludemann, A., Selbig, J., Walther, D.: A plant resource and experiment management system based on the golm plant database as a basic tool for omics research. Plant Methods 4(1), 11 (2008)
Larios, E., Zhang, Y., Yan, K., Di, Z., LeDévédec, S., Groffen, F., Verbeek, F.J.: Automation in cytomics: A modern RDBMS based platform for image analysis and management in high-throughput screening experiments. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds.) HIS 2012. LNCS, vol. 7231, pp. 76–87. Springer, Heidelberg (2012)
Linkert, M., Rueden, C.T., Allan, C., Burel, J., Moore, W., Patterson, A., Loranger, B., Moore, J., Neves, C., MacDonald, D., Tarkowska, A., Sticco, C., Hill, E., Rossner, M., Eliceiri, K.W., Swedlow, J.R.: Metadata matters: access to image data in the real world. The Journal of Cell Biology 189, 1 (2010)
Mayr, L., Fuerst, P.: The future of high-throughput screening. Journal of Biomolecular Screening (2008)
Nix, D., Sera, T.D., Dalley, B., Milash, B., Cundick, R., Quinn, K., Courdy, S.: Next generation tools for genomic data generation, distribution, and visualization. BMC Bioinformatics 11(1), 455 (2010)
Wendl, M., Smith, S., Pohl, C., Dooling, D., Chinwalla, A., Crouse, K., Hepler, T., Leong, S., Carmichael, L., Nhan, M., Oberkfell, B., Mardis, E., Hillier, L., Wilson, R.: Design and implementation of a generalized laboratory data model. BMC Bioinformatics 8(1), 362 (2007)
Yan, K., Verbeek, F.J.: Segmentation for high-throughput image analysis: Watershed masked clustering. In: Margaria, T., Steffen, B. (eds.) ISoLA 2012, Part II. LNCS, vol. 7610, pp. 25–41. Springer, Heidelberg (2012)
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Larios, E., Zhang, Y., Cao, L., Verbeek, F.J. (2014). CytomicsDB: A Metadata-Based Storage and Retrieval Approach for High-Throughput Screening Experiments. In: Comin, M., Käll, L., Marchiori, E., Ngom, A., Rajapakse, J. (eds) Pattern Recognition in Bioinformatics. PRIB 2014. Lecture Notes in Computer Science(), vol 8626. Springer, Cham. https://doi.org/10.1007/978-3-319-09192-1_7
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DOI: https://doi.org/10.1007/978-3-319-09192-1_7
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