Project Summary Proteogenomics involves the integrative multi-omic analysis of genomic, transcriptomic, proteomic and post- translational modification data produced by next-generation sequencing and mass spectrometry-based proteomics. Several publications by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and others have highlighted the impact of proteogenomics in enabling deeper insight into the biology of cancer and identification of potential drug targets. Integrative analysis of multi-omic data requires the deployment of complex algorithms and data processing techniques, which are generally inaccessible to scientists without a background in bioinformatics and computational biology. Our proposed center for comprehensive proteogenomic data analysis will encapsulate a comprehensive set of analysis methods in a platform that will be (i) simple to use (ii) flexible (iii) automated, facilitating the routine application to all CPTAC proteogenomic datasets as they become available, and (iv) able to incorporate new methods with minimal effort. We will leverage PANOPLY--a cloud-based platform for automated and reproducible proteogenomic data analysis--as the foundation, with specific, carefully chosen algorithms targeted for addition to provide an expansive set of analysis capabilities that can be easily harnessed to provide a rapid and extensive baseline analysis for proteogenomic studies, leading to many disease specific hypotheses that can be explored further using additional computational and wet-lab experiments. Our collection of tools, algorithms and interactive reports will enable unprecedented, automated and integrative systems-biology level analyses of proteome, post-translational modification and metabolomic data combined with genomic data for individual disease cohorts and pan-cancer analysis across cohorts, leading to deeper understanding of cancer biology and enabling identification of therapeutic targets and disease/prognostic biomarkers.