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

Angela Bauch1, Izabela Adamczyk, Piotr Buczek

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

BMC Bioinformatics
|December 14, 2011
PubMed
Summary

Managing large biological datasets requires a robust information system. We developed openBIS, an open-source framework for scalable data management and integration in systems biology research.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Modern distributed systems biology research generates massive and diverse datasets.
  • Effective management of large quantitative biological data is crucial for extracting maximum biological insights.
  • Integration with data analysis pipelines and computational tools is a key requirement for biological information systems.

Purpose of the Study:

  • To develop a user-friendly, scalable, and powerful information system framework for biological research data.
  • To facilitate data collection, integration, sharing, and publication within biological experiments.
  • To enable seamless connection of biological data to processing pipelines.

Main Methods:

  • Development of openBIS, an open-source software framework.
  • Customization of the framework for diverse data types and experimental technologies.
  • Implementation of features for data and metadata management.

Main Results:

  • openBIS provides a scalable and extensible framework for biological information systems.
  • The framework supports user-friendly data collection, integration, sharing, and publication.
  • openBIS can be customized for various data types and technologies, including mass spectrometry, High Content Screening, and Next Generation Sequencing.

Conclusions:

  • openBIS is actively used in SystemsX.ch and EU projects.
  • Its versatility, ease of deployment, scalability, flexibility, and extensibility make it valuable for systems biology research.
  • The framework effectively handles large-scale biological data and diverse data types.