Abstract
BackgroundModern 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.ResultsWe 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.ConclusionsopenBIS is currently being used by several SystemsX.ch 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.
Highlights
Modern data generation techniques used in distributed systems biology research projects often create datasets of enormous size and diversity
Applications openBIS is a framework for information systems that can be adapted for individual use cases
This is done by developing extensions to the base system and by configuring the system including the extensions to the operating environment
Summary
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. Systems biology is a recent approach to life sciences that poses unprecedented computational challenges [1,2,3,4]. These challenges are rooted in the way systems biology projects are approached and are the following. Large and complex datasets measuring different properties of the system studied are acquired and need to be analyzed and included in theoretical models. Data analysts and mathematical modellers need to get access
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