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. 2017 Apr 19;8(30):50252–50272. doi: 10.18632/oncotarget.17225

Table 1. List of driver identification methods used to incorporates the prediction of breast cancer driver genes, their working principle and supporting references.

Driver Identification Method Driver Gene Identification Principle Citations
IntOGen Identifies alterations at transcriptomics level, CN gain and losses in tumor sample. It also integrates OncodriveFM for the identification of accumulation mutations, background mutation rate and OncodriveCLUST for mutation cluster identifications. Further, SIFT, Polyphen and Mutation Assessor are used to predict the impact of mutations. [6265]
SIFT Amino acids substitutions and their deleterious impacts prediction. It find the homologous sequences using PSI-BLAST followed by picking sequences with specific diversity and calculating the SIFT scores. [66]
PolyPhen-2 Analyzes non-synonymous SNP using multiple sequence alignment and structure information followed by predicting the probabilistic damaging variants with confidence prediction and at last interpret the results with mutational impact. [67]
Mutation Assessor Predicts mutational impact by calculating functional impact score derived from addition of conservation score and specificity score. [68]
Driver DBv2 Uses large exome and RNAseq datasets to predict the driver genes using several incorporated tools. [69]
Active Driver It identifies significant mutations of cancer genes in active sites of proteins such as mutations in signaling proteins or domains or regulatory elements. It uses gene-centric logistic regression model including multiple factors to estimate mutation significance. [70]
Dendrix This algorithm discovers driver genes with high coverage and high specificity using mutation data. [71, 72]
MDPFinder It combines mutation and expression data to validate the driver genes and their mutated pathways. [73]
Simon It identifies functional mutation impact on proteins, variations in background mutation frequency and genetic code redundancy among tumors. [74]
NetBox It identifies the driver genes by comparing genes and performing network analysis on human interaction Network (HIN) data. [75]
MutSigCV It uses overall mutation rates and distribution patterns and analyzes background mutation rates with patient specific as well as gene specific mutation rates. Finally it includes expression levels and replication periods. [76]
MEMo It identifies the driver genes based on recurrently mutated genes among tumor data with consistent mutational specificity. [77]
e-Driver It manipulates internal distribution of somatic functional missense mutations amongst functional domains by relating mutation rates with other regions of same protein. [78]
DawnRank Uses gene expression data to construct gene network and rank them based on impact and it analyzes somatic alteration data to identify personalized driver alterations. [79]
DriverNet Driver genes are identified based on genomic aberration states of various patients, genes, gene expression data and it further takes biological pathway data into account and builds the network driver genes. [80]
MSEA It predicts cancer driver genes based on patterns of mutation hotspot. [81]
iPAC Identifies non-random somatic mutations in protein using tertiary protein structure information. [82]
CoMDP It uses mutation data to identify driver genes and their pathways. It also predicts genes with other multiple co-occurring biologically significant pathways. [83]