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Recurrent somatic mutation identification
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SNV
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MutSigCV[48]
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Uses coverage information and genomic features (e.g. DNA replication time) to estimate the background mutation rate of a gene.
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MuSiC[49]
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Uses a per-gene background mutation rate; allows for user-defined regions of interest.
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Youn et al.[51]
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Includes predicted impact on protein function in determining recurrent mutations.
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Sjöblom et al.[52]
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Defines a cancer mutation prevalence score for each gene.
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DrGaP[139]
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Uses Bayesian approach to estimate background mutation rate; helpful for cancer types with low mutation rate.
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CNA
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GISTIC2[61], JISTIC[63]
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Uses ‘peel-off’ techniques to find smaller recurrent aberrations inside larger aberrations.
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CMDS[62]
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Identifies recurrent CNAs from unsegmented data.
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ADMIRE[65]
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Multi-scale smoothing of copy number profiles.
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Functional impact prediction
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General
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SIFT[72]
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Uses conservation of amino acids to predict functional impact of a non-synonymous amino-acid change.
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Polyphen-2[74]
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Infers functional impact of non-synonymous amino-acid changes through alignments of related peptide sequences and a machine-learning-based probabilistic classifier.
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MutationAssessor[75]
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Uses protein homologs to calculate a score based on the divergence in conservation caused by an amino-acid change.
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PROVEAN[73]
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Benchmarks favorably against MutationAssessor, Polyphen-2 and SIFT.
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Cancer-specific
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CHASM[77]
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Uses a machine-learning approach to classify mutations as drivers or passengers based on sequence conservation, protein domains, and protein structure.
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Oncodrive-FM[79]
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Combines scores from SIFT, Polyphen-2, and MutationAccessor into a single ranking.
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Positional or structural clustering
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NMC[83]
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Finds clusters of non-synonymous mutations across patients. Typically used with missense mutations to detect so-called ‘activating’ mutations.
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iPAC[84]
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Extends the NMC approach to search for clusters of mutations in three-dimensional space using crystal structures of proteins.
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Pathway analysis and combinations of mutations
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Known pathways
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GSEA[92]
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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.
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PathScan[95]
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Finds pathways with excess of mutations in a gene set (pathway), by combining P-values of enrichment across samples.
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Patient-oriented gene sets[94]
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Tests known pathways using a binary indicator for a pathway in each patient.
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Interaction networks
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NetBox[140]
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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.
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HotNet[102]
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Finds subnetworks with significantly more aberrations than would be expected by chance, using both network topology and user-defined gene or protein scores.
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MEMo[104]
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Finds subnetworks whose interacting pairs of genes have mutually exclusive aberrations[105]; recommends including only recurrent SNVs and CNAs in the analysis.
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De novo
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Dendrix[102]
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Identifies groups of genes with mutually exclusive aberrations.
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Multi-Dendrix[112]
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Simultaneously finds multiple groups of genes with mutually exclusive aberrations.
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| | |
RME[110]
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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%).
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