R-based software for the integration of pathway data into bioinformatic algorithms.
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.
University Medical Center Göttingen, Department of Medical Statistics, Humboldtallee 32, D-37073 Göttingen, Germany. frank.kramer@med.uni-goettingen.de.
Frank
F
Kramer
University Medical Center Göttingen, Department of Medical Statistics, Humboldtallee 32, D-37073 Göttingen, Germany. michaela.bayerlova@med.uni-goettingen.de.
Michaela
M
Bayerlová
University Medical Center Göttingen, Department of Medical Statistics, Humboldtallee 32, D-37073 Göttingen, Germany. tim.beissbarth@med.uni-goettingen.de.
Tim
T
Beißbarth