SAP AG (NYSE: SAP) and Technische Universität München (TUM) today announced ProteomicsDB, a data base that stores protein and peptide identifications from mass spectrometry-based experiments. The proteomic data resulted from the identification of proteins mapping to over 18,000 human genes. This represents 90 percent coverage of the human proteome. Data stored and analyzed within ProteomicsDB can be used in basic and biomedical research for discovering therapeutic targets, developing new drugs and enhanced diagnosis methods.
As personalized medicine is on the rise, the healthcare field is discovering the opportunities of “big data” analysis. The result of a joint project between the TUM Chair of Proteomics and Bioanalytics, SAP and the SAP Innovation Center, ProteomicsDB, is a major step forward in human proteomics. It currently contains more than11,000 datasets from human cancer cell lines, tissues and body fluids and enables real-time analysis of this highly dimensional data and creates instant value by allowing to test analytical hypothesis.
ProteomicsDB will be available free of charge. The database will be a valuable asset for researchers in the field of life sciences as well as for the pharmaceutical and biotechnology industry. Insights from analyzing the inherent datasets can be used in biomedical research and for example in developing new drugs that operate in a more targeted way without adversely influencing other cellular processes, helping to reduce side effects.
“The vast amounts of molecular data generated in biomedical research increasingly challenge the ability of scientists to see ‘the forest for the trees’, said Prof. Dr. Bernhard Kuester of TUM. ProteomicsDB is a significant step ahead in our research aiming at a better understanding of human disease and more informed future treatments. The software helps us and others to store, integrate and analyze experimental data in real time, allowing us to study more complex biological systems at greater depth than previously possible.”
Link to ProteomicsDB: https://www.proteomicsdb.org