MOTIVATION: Major tumor sequencing projects have been conducted in the past few 
years to identify genes that contain 'driver' somatic mutations in tumor 
samples. These genes have been defined as those for which the non-silent 
mutation rate is significantly greater than a background mutation rate estimated 
from silent mutations. Several methods have been used for estimating the 
background mutation rate.
RESULTS: We propose a new method for identifying cancer driver genes, which we 
believe provides improved accuracy. The new method accounts for the functional 
impact of mutations on proteins, variation in background mutation rate among 
tumors and the redundancy of the genetic code. We reanalyzed sequence data for 
623 candidate genes in 188 non-small cell lung tumors using the new method. We 
found several important genes like PTEN, which were not deemed significant by 
the previous method. At the same time, we determined that some genes previously 
reported as drivers were not significant by the new analysis because mutations 
in these genes occurred mainly in tumors with large background mutation rates.
AVAILABILITY: The software is available at: 
http://linus.nci.nih.gov/Data/YounA/software.zip.