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Bioinformatics. 2017 Feb 15;33(4):612-614. doi: 10.1093/bioinformatics/btw695.

EGAD: ultra-fast functional analysis of gene networks.

Author information

1
Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Woodbury, NY 11797, USA.
2
Department of Mathematics and Computer Science, University of Leipzig, Leipzig, Germany.
3
Department of Psychiatry and Michael Smith Laboratories, University of British Columbia, Vancouver, Canada.

Abstract

Summary:

Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many computational experiments are difficult to apply exhaustively in network analysis due to run-times. To permit high-throughput analysis of gene networks, we have implemented a set of very efficient tools to calculate functional properties in networks based on guilt-by-association methods. ( xtending ' uilt-by- ssociation' by egree) allows gene networks to be evaluated with respect to hundreds or thousands of gene sets. The methods predict novel members of gene groups, assess how well a gene network groups known sets of genes, and determines the degree to which generic predictions drive performance. By allowing fast evaluations, whether of random sets or real functional ones, provides the user with an assessment of performance which can easily be used in controlled evaluations across many parameters.

Availability and Implementation:

The software package is freely available at https://github.com/sarbal/EGAD and implemented for use in R and Matlab. The package is also freely available under the LGPL license from the Bioconductor web site ( http://bioconductor.org ).

Contact:

JGillis@cshl.edu.

Supplementary information:

Supplementary data are available at Bioinformatics online.

PMID:
27993773
PMCID:
PMC6041978
DOI:
10.1093/bioinformatics/btw695
[Indexed for MEDLINE]
Free PMC Article

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