Quantitative Biology Center Tübingen (QBiC)
Final Report Abstract
After completion of the DFG project, institutional support by the university and its medical faculty was significantly increased. Together with an increasing share of core facility revenues and other third party funding the center’s operation has been established on a solid funding. The structure of the center was consolidated. A permanent scientific directorship has been implemented and the internal structure bases on three divisions: (1) data management infrastructure, (2) data science and (3) project management and bioinformatics support. The center operates as an umbrella for a total of ten partner labs that primarily provide data generation technology. Upon completion of DFG project, QBiC operates with a total number of 23 employees. The yearly throughput of core facility projects averages to 60. The customer base is balanced between the University of Tübingen, its medical faculty and external customers. We implemented a mechanism to collect structured user feedback for every completed core facility project, which clearly helps to shape the future service level. As a service for all ten partner labs QBiC facilitates the advanced organization, data analysis and software engineering. All bioinformatics workflows that are needed for production operation of the core facility could be successfully automated. The concept of fully automated and reproducible workflows is part of the nf-co.re (https://nf-cor.re) project that we co-invented within an international collaboration. Nf-core has become an internationally renowned project and is being adapted at several other academic sites. As of 2018 QBiC was successful to acquire additional DFG funding for the implementation of one of five national sequencing centers: the NGS competence center Tübingen (NCCT). The QBiC concept is also being used as blueprint at many other academic sites that aim at building core facility structures. Currently we are in the process of extending the technology platform towards medical imaging, microscopy and machine learning.
