Abstract
The identification of cancer driver genes through the analysis of mutations detected with high-throughput sequencing is a useful tool and a key challenge in cancer genomics. The workflow presented here relies on unpaired RNA-seq tumoral samples, thus leveraging already available RNA-seq data and providing the intrinsical benefits of directly targeting the transcriptome. Based on well-established methods for variant detection, this workflow also involves thorough data cleaning and extensive annotation, which enable the selection for somatic mutations with functional impact and the prioritization of genes relevant to the carcinogenic processes in the input samples.
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