Dataset Information

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

ABSTRACT:

Background

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.

Results

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.

Conclusions

openBIS 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.

SUBMITTER: Bauch A

PROVIDER: S-EPMC3275639 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Bauch Angela A Adamczyk Izabela I Buczek Piotr P Elmer Franz-Josef FJ Enimanev Kaloyan K Glyzewski Pawel P Kohler Manuel M Pylak Tomasz T Quandt Andreas A Ramakrishnan Chandrasekhar C Beisel Christian C Malmström Lars L Aebersold Ruedi R Rinn Bernd B

BMC bioinformatics 20111208

<h4>Background</h4>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.<h4>Results</h4>We have developed op ...[more]

PMID: 22151573

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