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BiNA: A Visual Analytics Tool for Biological Network Data

Author

Listed:
  • Andreas Gerasch
  • Daniel Faber
  • Jan Küntzer
  • Peter Niermann
  • Oliver Kohlbacher
  • Hans-Peter Lenhof
  • Michael Kaufmann

Abstract

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

Suggested Citation

  • Andreas Gerasch & Daniel Faber & Jan Küntzer & Peter Niermann & Oliver Kohlbacher & Hans-Peter Lenhof & Michael Kaufmann, 2014. "BiNA: A Visual Analytics Tool for Biological Network Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
  • Handle: RePEc:plo:pone00:0087397
    DOI: 10.1371/journal.pone.0087397
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    Cited by:

    1. Rong Zhang & Zhao Ren & Wei Chen, 2018. "SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-14, August.

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