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Network structures and algorithms in Bioconductor.

Vincent J Carey1, Jeff Gentry, Elizabeth Whalen

  • 1Channing Laboratory, Brigham and Women's Hospital 75 Francis Street, Boston, MA 02115, USA. stvjc@channing.harvard.edu

Bioinformatics (Oxford, England)
|August 7, 2004
PubMed
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This paper reviews Bioconductor tools for analyzing biological networks. It integrates open-source visualization and algorithm resources for genomics and computational biology research.

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Bioconductor provides essential bioinformatics tools.
  • Network structures are crucial in biological research.
  • Existing tools facilitate the analysis of complex biological data.

Purpose of the Study:

  • To review central concepts and implementations of network analysis tools in Bioconductor.
  • To highlight the integration of open-source resources for network visualization and algorithms.
  • To support the analysis of graphical structures in genomics and computational biology.

Main Methods:

  • Review of Bioconductor packages (graph, Rgraphviz, RBGL).
  • Integration of AT&T Graphviz for network visualization.
  • Utilization of Boost for network algorithms.

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Main Results:

  • Central concepts and implementations for network analysis in Bioconductor are presented.
  • Interfaces to external open-source resources (Graphviz, Boost) have been developed.
  • These tools support the analysis of graphical structures in biological data.

Conclusions:

  • Bioconductor offers a robust environment for network analysis in bioinformatics.
  • The integration of visualization and algorithmic tools enhances biological network research.
  • These developments aid researchers in genomics and computational biology.