DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes.
Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a method for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. Here we present the implementation of Driver-gene Inference by Genetical-Genomic Information Theory as an R-system package.


METHODS
The diggit package is freely available under the GPL-2 license from Bioconductor (http://www.bioconductor.org).
Department of Systems Biology and.
Mariano J
MJ
Alvarez
Department of Systems Biology and Department of Dermatology, Columbia University, New York, NY 10032 USA.
James C
JC
Chen
Department of Systems Biology and.
Andrea
A
Califano
D015870:Gene Expression; D053263:Gene Regulatory Networks; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D015233:Models, Statistical; D009154:Mutation; D010641:Phenotype; D012984:Software