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ndexr-an R package to interface with the network data exchange.

Florian Auer1, Zaynab Hammoud1, Alexandr Ishkin2

  • 1Department of Medical Statistics, University Medical Center Göttingen, Göttingen 37099, Germany.

Bioinformatics (Oxford, England)
|November 1, 2017
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Summary
This summary is machine-generated.

This study introduces ndexr, an R package for seamless biological network data exchange. It enables programmatic access to the Network Data Exchange (NDEx) commons for improved bioinformatic analysis and integration.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Interoperability challenges hinder seamless exchange of biological network data.
  • Lack of standardized platforms limits integration of pathway databases and analysis methods.
  • The Network Data Exchange (NDEx) commons aims to facilitate user-centered sharing and publication of biological networks.

Purpose of the Study:

  • To present a software package for programmatic interaction with NDEx servers from within the R environment.
  • To enable users to search, retrieve, modify, and upload biological networks to NDEx.
  • To facilitate the integration of biological networks into bioinformatic workflows.

Main Methods:

  • Development of an R package named ndexr.
  • Programmatic connection and interfacing with NDEx servers using R.
  • Conversion of retrieved networks into igraph-compatible objects for analysis within R.
  • Modification and extension of biological networks within R.
  • Uploading modified networks back to NDEx servers.

Main Results:

  • The ndexr package provides programmatic access to the NDEx network repository.
  • Users can search and retrieve networks, converting them into igraph objects for R-based analysis.
  • Networks can be modified and extended within R and subsequently uploaded back to NDEx.
  • This facilitates a more integrated workflow for biological network analysis.

Conclusions:

  • The ndexr package enhances interoperability for biological network data exchange.
  • It empowers bioinformaticians to leverage NDEx resources directly within R.
  • Facilitates seamless integration of network data as prior knowledge and documentation of results.