Comparison of algorithms for the detection of cancer drivers at subgene resolution
PublisherNature Publishing Group
Rights accessOpen Access
European Commission's projectRD-CONNECT - RD-CONNECT: An integrated platform connecting registries, biobanks and clinical bioinformatics for rare disease research (EC-FP7-305444)
Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.
CitationPorta-Pardo, E. [et al.]. Comparison of algorithms for the detection of cancer drivers at subgene resolution. "Nature Methods", Agost 2017, vol. 14, p. 782-788.
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