Abstract. In cytomics bookkeeping of the data generated during lab experiments is crucial. The current approach in cytomics is to conduct High-Throughput Screening (HTS) experiments so that cells can be tested under many different experimental conditions. Given the large amount of different conditions and the readout of the conditions through images, it is clear that the HTS approach requires a proper data management system to reduce the time needed for experiments and the chance of man-made errors. As different types of data exist, the experimental conditions need to be linked to the images produced by the HTS experiments with their metadata and the results of further analysis. Moreover, HTS experiments never stand by themselves, as more experiments are lined up, the amount of data and computations needed to analyze these increases rapidly. To that end cytomic experiments call for automated and systematic solutions that provide convenient and robust features for scientists to manage and analyze their data. In this paper, we propose a platform for managing and analyzing HTS images resulting from cytomics screens taking the automated HTS workflow as a starting point. This platform seamlessly integrates the whole HTS workflow into a single system. The platform relies on a modern relational database system to store user data and process user requests, while providing a convenient web interface to end-users. By implementing this platform, the overall workload of HTS experiments, from experiment design to data analysis, is reduced significantly. Additionally, the platform provides the potential for data integration to accomplish genotype-to-phenotype modeling studies.

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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