DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values.
The R package `MADGiC‘ fits an empirical Bayesian hierarchical model to obtain posterior probabilities that each gene is a driver. The model accounts for (1) frequency of mutation compared to a sophisticated background model that accounts for gene-specific factors in addition to mutation type and nucleotide context, (2) predicted functional impact (in the form of SIFT scores) of each specific change, and (3) positional patterns in mutations that have been deposited into the COSMIC (Catalogue of Somatic Mutations in Cancer) database. Example data from the The Cancer Genome Atlas (TCGA) project ovarian cohort is provided.
Oncodrive-FM detects candidate cancer driver genes and pathways from catalogs of somatic mutations in a cohort of tumors by computing the bias towards the accumulation of functional mutations (FM bias).