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Biology 2014, 3(1), 85-100; doi:10.3390/biology3010085

R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms

University Medical Center Göttingen, Department of Medical Statistics, Humboldtallee 32, D-37073 Göttingen, Germany
Author to whom correspondence should be addressed.
Received: 7 November 2013 / Revised: 29 November 2013 / Accepted: 31 January 2014 / Published: 7 February 2014
(This article belongs to the Special Issue Developments in Bioinformatic Algorithms)
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Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools. View Full-Text
Keywords: Pathway data; data integration; R-project; bioconductor; BioPAX; rBiopaxParser; Cytoscape Pathway data; data integration; R-project; bioconductor; BioPAX; rBiopaxParser; Cytoscape

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Kramer, F.; Bayerlová, M.; Beißbarth, T. R-Based Software for the Integration of Pathway Data into Bioinformatic Algorithms. Biology 2014, 3, 85-100.

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