Open Access Highly Accessed Open Badges Software

openBIS: a flexible framework for managing and analyzing complex data in biology research

Angela Bauch1, Izabela Adamczyk1, Piotr Buczek1, Franz-Josef Elmer12, Kaloyan Enimanev12, Pawel Glyzewski12, Manuel Kohler12, Tomasz Pylak12, Andreas Quandt4, Chandrasekhar Ramakrishnan12, Christian Beisel3, Lars Malmström4, Ruedi Aebersold45 and Bernd Rinn12*

Author Affiliations

1 Department of Biosystems Science and Engineering, Center for Information Sciences and Databases, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland

2 Swiss Institute of Bioinformatics (SIB), Switzerland

3 Department of Biosystems Science and Engineering, Quantitative Genomics Facility, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland

4 Department of Biology, Institute of Molecular Systems Biology, Swiss Federal Institute of Technology (ETH) Zurich, Switzerland

5 Faculty of Science, University of Zurich, Switzerland

For all author emails, please log on.

BMC Bioinformatics 2011, 12:468  doi:10.1186/1471-2105-12-468

Published: 8 December 2011



Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity. We argue that in order to overcome the challenge of managing those large quantitative datasets and maximise the biological information extracted from them, a sound information system is required. Ease of integration with data analysis pipelines and other computational tools is a key requirement for it.


We have developed openBIS, an open source software framework for constructing user-friendly, scalable and powerful information systems for data and metadata acquired in biological experiments. openBIS enables users to collect, integrate, share, publish data and to connect to data processing pipelines. This framework can be extended and has been customized for different data types acquired by a range of technologies.


openBIS is currently being used by several and EU projects applying mass spectrometric measurements of metabolites and proteins, High Content Screening, or Next Generation Sequencing technologies. The attributes that make it interesting to a large research community involved in systems biology projects include versatility, simplicity in deployment, scalability to very large data, flexibility to handle any biological data type and extensibility to the needs of any research domain.