Cancer driver mutations are identified by predicting neutral mutation rates across the entire genome
that were also common to all whole-exome and targeted sequenced samples . For a given cancer, we considered ‘long-tail’ genes to be driver genes that were not on the list of known driver genes for the given cancer . Dig was then used to quantify mutational excess in those long-tail genes.
We directly estimated thevalue of the mutational burden long-tail genes by convolving the neutral mutation distributions for each individual gene and calculating the upper-tail probability of at least the number of observed mutations across all genes occurring by chance under the null distribution. We calculated 95% CIs of excess mutations using the same Monte Carlo approach as in our analysis of cryptic splice SNVs.
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