Oncodrive-fm is an approach to uncover driver genes or gene modules. It computes a metric of functional impact using three well-known methods (SIFT, PolyPhen2 and MutationAssessor) and assesses how the functional impact of variants found in a gene across several tumor samples deviates from a null distribution. It is thus based on the assumption that any bias towards the accumulation of variants with high functional impact is an indication of positive selection and can thus be used to detect candidate driver genes or gene modules.
DriverDB is a database that aggregates results from exome sequencing data sets and applies various tools to identify driver genes.
They have precomputed results from (notes taken from their user manual and edited for clarity):
ActiveDriver (Abstract) - ActiveDriver focuses on principal loci in a gene like phosphorylation sites and kinase domain to predict driver genes.
Dendrix (Abstract) - Dendrix finds genes of a module which are mutual exclusive in accordance with the value of K which is the number of genes in a module, but it can’t support gene expression data or reference network.
MDPFinder (Abstract) - MDPFinder finds gene modules that are mutual exclusive in accordance with the value of K which is the number of genes in a module. This tool additionally supports gene expression data.
Simon - Simon’s tool has a background mutation model which can use for every cancer types. It is also considering multiple codons encoded the same amino acids.
Netbox (Abstractenter link description here) - NetBox consults reference network to find driver gene modules. This tool additionally provides network results that can be viewed in Cytoscape.
OncodriveFM - Oncodrive-FM hypothesizes that any bias toward the accumulation of variants with high functional impact observed in a Gene and employs a method to measure this bias (FM bias).
MutSigCv (Abstract) - MutSigCV uses patient-specific mutation frequency and spectrum, as well as gene-specific background mutation rates.
Memo (Abstract) - MEMo considers driver genes containing somatic mutations and copy number variations and uses a reference network to find correlations among driver genes.
The Raphael lab has various tools in this general vein:
Hotnet - HotNet is an algorithm for finding significanlty altered subnetworks in a large gene interaction network.
HotNet2 - HotNet2 is an algorithm for the discovery of significantly mutated subnetworks in a protein-protein interaction network. HotNet2 uses an insulated heat diffusion model to simultaneously analyze both the mutations in and local topology of sets of proteins.
Multi-Dendrix - Multi-Dendrix (Multiple Pathways De novo Driver Exclusivity) is an algorithm that identifies sets of driver pathways in cancer without prior information by simultaneously identifying sets of genes with approximately exclusive mutations and high coverage.
Dendrix - Dendrix (De novo Driver Ex Exclusivity) is an algorithm that identifies driver groups of mutations without prior information, using the exclusivity property observed for mutations in a driver pathway.
W e present a high throughput pipeline for identifying cancer mutation targets, capable of processing billions of variations across thousands of samples. This pipeline is coupled with our Human Variation Database to provide more complex down stream analysis on the variations hosted in the database. Most notably, these analysis include finding significantly mutated regions across multiple genomes and regions with mutational preferences within certain types of cancers. The results of the analysis is presented in HTML summary reports that incorporate gene annotations from various resources for the reported regions.