Table 2 Methods for prediction of driver mutations and genes

Recurrent somatic mutation identification

SNV

MutSigCV[48]

Uses coverage information and genomic features (e.g. DNA replication time) to estimate the background mutation rate of a gene.

MuSiC[49]

Uses a per-gene background mutation rate; allows for user-defined regions of interest.

Youn et al.[51]

Includes predicted impact on protein function in determining recurrent mutations.

Sjöblom et al.[52]

Defines a cancer mutation prevalence score for each gene.

DrGaP[139]

Uses Bayesian approach to estimate background mutation rate; helpful for cancer types with low mutation rate.

CNA

GISTIC2[61], JISTIC[63]

Uses ‘peel-off’ techniques to find smaller recurrent aberrations inside larger aberrations.

CMDS[62]

Identifies recurrent CNAs from unsegmented data.

ADMIRE[65]

Multi-scale smoothing of copy number profiles.

Functional impact prediction

General

SIFT[72]

Uses conservation of amino acids to predict functional impact of a non-synonymous amino-acid change.

Polyphen-2[74]

Infers functional impact of non-synonymous amino-acid changes through alignments of related peptide sequences and a machine-learning-based probabilistic classifier.

MutationAssessor[75]

Uses protein homologs to calculate a score based on the divergence in conservation caused by an amino-acid change.

PROVEAN[73]

Benchmarks favorably against MutationAssessor, Polyphen-2 and SIFT.

Cancer-specific

CHASM[77]

Uses a machine-learning approach to classify mutations as drivers or passengers based on sequence conservation, protein domains, and protein structure.

Oncodrive-FM[79]

Combines scores from SIFT, Polyphen-2, and MutationAccessor into a single ranking.

Positional or structural clustering

NMC[83]

Finds clusters of non-synonymous mutations across patients. Typically used with missense mutations to detect so-called ‘activating’ mutations.

iPAC[84]

Extends the NMC approach to search for clusters of mutations in three-dimensional space using crystal structures of proteins.

Pathway analysis and combinations of mutations

Known pathways

GSEA[92]

A general technique for testing ranked lists of genes for enrichment in known gene sets. Can be used on rankings derived from significance of observed mutations.

PathScan[95]

Finds pathways with excess of mutations in a gene set (pathway), by combining P-values of enrichment across samples.

Patient-oriented gene sets[94]

Tests known pathways using a binary indicator for a pathway in each patient.

Interaction networks

NetBox[140]

Finds network modules in a user-provided list of genes. Significance depends only on the topology of the genes in the network, and not on mutation scores.

HotNet[102]

Finds subnetworks with significantly more aberrations than would be expected by chance, using both network topology and user-defined gene or protein scores.

MEMo[104]

Finds subnetworks whose interacting pairs of genes have mutually exclusive aberrations[105]; recommends including only recurrent SNVs and CNAs in the analysis.

De novo

Dendrix[102]

Identifies groups of genes with mutually exclusive aberrations.

Multi-Dendrix[112]

Simultaneously finds multiple groups of genes with mutually exclusive aberrations.

RME[110]

Finds groups of genes with mutually exclusive aberrations by building from gene pairs; best results obtained when restricting to genes with high mutation frequencies (e.g. > 10%).