The Sauer (Web Site) and Zamboni (Web Site) Labs at the Institute of Molecular Systems Biology pursue a strongly data-driven research approach largely based on mass spectrometry and fluorescent microscopy, which aims at collecting and quantifying precise information on the state and activity of metabolic networks. The groups have been actively developing comprehensive measurement and analysis pipelines for both high-throughput screening and highly quantitative measurements for validation or targeted analyses. The labs run several hundreds of experiments and >100’000 mass spectrometry runs per year. SIS supports the two labs with data management services.
The biggest needs in data management are the annotation of experiments and samples by the scientists, an organized storage of original data, results and other kind of files, a searchable index of metadata, and an efficient supply of data to further analysis. SIS developed a data management solution based on the openBIS platform which fulfils these needs and is used productively since 2010. Since then, functional enhancements have been made as needed.
The labs use two types of metabolomics measurements as their main tools of investigation, both based on mass spectrometry measurement technologies. The first one is a quantitative and targeted, the second one a high-throughput, non-targeted method. Data from both types of investigations are automatically extracted from the result files and uploaded to a relational database at the time of ingestion into the data management system, where they are readily available to the down-stream analysis workflows. To date, it hosts > 500’000 mass spectrometry runs and all key metadata. Additional data from complementary analyses (e.g. transcriptomics, proteomics, seqencing) are stored as binaries in parallel to the metabolomics data. As space in the relational database is limited (fast SSD), we have implemented a sophisticated automated archiving process for the database which ensures that there is always enough space available for new experiments. Archived files can be restored by users within seconds.