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 per- formed. 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.

Additional Metadata
Keywords CytomicsDB, MonetDB, HTS
THEME Information (theme 2)
Publisher Springer
Series Lecture notes in bioinformatics
Conference IAPR International Conference on Pattern Recognition in Bioinformatics
Citation
Larios, E, Zhang, Y, Cao, L, & Verbeek, F.J. (2014). CytomicsDB: A Metadata-Based Storage and Retrieval Approach for High-Throughput Screening Experiments. In Lecture notes in bioinformatics. Springer.