A semantic-based metadata validation for an automated high-throughput screening workflow: Case study in cytomicsDB

High-Throughput Screening (HTS) techniques are typically used to identify potential drug candidates. These type of experiments require invest in large amount of resources. The appropriate data management of HTS experiments has become a key challenge in order to succeed in the target validation. Current developments in imaging systems has to cope with computational requirements due to the significant increment of volumes of data. However, no special care has been taken to ensure the consistency, integrity and reliability of the data managed in HTS experiments. The appropriate validation of the data used in an HTS experiment has turned to be a key success factor in the target validation, thus a mandatory process to be included in the HTS workflow. This paper describes our research in the validation process as performed in CytomicsDB. This system is a modern RDBMS-based platform, designed to provide an architecture capable of dealing with the strict validation requirements during each stage of the HTS workflow. Furthermore, CytomicsDB has a flexible architecture which support easy access to external repositories in order to validate experiments data.