Human Genome Analysis
Identification of noncoding cancer "drivers" from thousands of somatic
alterations is an unsolved problem. Here, we developed a computational
framework to annotate cancer regulatory mutations. The framework
combines an adjustable data context summarizing large-scale genomics
and cancer-relevant datasets with an efficient variant prioritization
pipeline. To prioritize high impact variants, we developed a weighted
scoring scheme to score each mutation's impact.