The recent deluge of cancer genomics data provides a tremendous opportunity for the discovery of detailed mechanisms of tumorigenesis and the development of therapeutics. However, identifying the functionally relevant genomic alterations (‘drivers’) among the many non-oncogenic events (‘passengers’) presents a major challenge. Several new methods have been developed over the past few years that identify recurrently altered genes. Mapping the recurrent genomic alterations, such as somatic mutations and focal DNA copy-number alterations, onto individual tumor samples as tumor-specific event calls facilitates the identification of altered processes and pathways. The resulting reduction in complexity makes cancer genomics data more easily interpretable by cancer researchers and is now driving the development of powerful yet intuitive web-based analysis tools.