Next-generation sequencing (NGS) has allowed substantial advances in cancer genomics. In fact, large-scale discovery efforts have propelled the identification of hundreds of cancer-related genes in recent years. To be truly transforming, however, key cancer-associated mutations must be profiled systematically in the clinical and translational arena to guide rational cancer therapeutics. This aim has yet to be achieved on a larges-cale, mainly because many methodologies cannot be applied efficiently and reliably on formalin-fixed paraffin embedded (FFPE) tumor samples that are routinely encountered in the clinic and in archived tumor banks. This project is a part of the computational effort to develop and apply a robust and cost-effective methodology, empowered by solution-phase exon capture and massively parallel next-generation sequencing, by which any FFPE tumor may be characterized for somatic base mutations and copy number changes in all known cancer genes. With the programming language-R, the computational analysis of NGS data for assays running clinical samples has been redeveloped, automated, and graphically represented. Moreover, such analysis, such as copy-number graphs orQC metrics, can be computed at a speed that is 568 times as fast as the traditional, and manual, computational techniques of alternative methodologies. Read More

• Biology