OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes

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A Landscape of Pharmacogenomic Interactions in Cancer (2016, methodology): “CGs were detected per cancer type by combining the outputs of three algorithms: MutSigCV, OncodriveFM, and OncodriveCLUST (Lawrence et al., 2013; Rubio-Perez et al., 2015; Tamborero et al., 2013a).”
FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer (2014, background): “Systematic studies of human cancer genomes have discovered a wide range of driver genes [2-6].”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, unknown): “OncodriveCLUST [Tamboreroetal.,2013]identiﬁesgenesthathavesigniﬁcantclus-tersofvariationsinproteins.”
Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, background): “OncodriveCLUST OncodriveCLUST (Tamborero et al., 2013a) identifies genes with non-silent mutations that cluster together in protein sequencemore than expected based on a background distribution of synonymous mutations.”
Maftools: efficient and comprehensive analysis of somatic variants in cancer (2018, methodology): “To identify disease-associated cancer genes, we reimplemented OncodriveCLUST algorithm as previously described by Tamborero et al. (2013).”