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PathVisio 3: an extendable pathway analysis toolbox.

Martina Kutmon1, Martijn P van Iersel2, Anwesha Bohler3

  • 1Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, The Netherlands; Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.

Plos Computational Biology
|February 24, 2015
PubMed
Summary
This summary is machine-generated.

PathVisio is a pathway editor and analysis software that helps biologists understand biological processes. The new version 3 introduces an extension system for plugins, enhancing its functionality for pathway drawing and data visualization.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Biological pathways are crucial for understanding complex biological processes.
  • PathVisio, a pathway editor and analysis software, has been widely used since its first publication in 2008.
  • It is integrated with WikiPathways, a community-curated pathway database.

Purpose of the Study:

  • To present the third version of PathVisio, highlighting its new features and improvements.
  • To introduce the extension system enabling plugin development for enhanced functionality.
  • To showcase PathVisio's capabilities in pathway drawing, data visualization, and statistical analysis.

Main Methods:

  • PathVisio 3 is implemented in Java, ensuring cross-platform compatibility.
  • It features a core set of tools for pathway creation and analysis.
  • A new extension system allows for the integration of third-party plugins.

Main Results:

  • PathVisio 3 offers enhanced pathway drawing, data visualization, and statistical analysis capabilities.
  • The new extension system facilitates the development and integration of over 15 plugins.
  • The software is freely available, open-source, and has seen widespread adoption with over 5,500 downloads in 2014.

Conclusions:

  • PathVisio 3 is a powerful and versatile tool for biological pathway analysis.
  • The extension system significantly expands its functionality and adaptability.
  • Its open-source nature and community integration promote collaborative research in systems biology.