ABSTRACT The NIH Common Fund program has generated a number of transformative data sets containing a wide variety of multi-dimensional molecular and phenotypic data from human and model organisms. We propose to promote the integration and widen the usage of selected Common Fund data sets (KidsFirst, GTEx, and LINCS L1000) using UCSC Xena. UCSC Xena is a web-based high-performance resource for functional genomics data visualization with a large user base in the cancer genomics research community. Xena is already a visualization resource for a Common Fund data set, the GTEx transcriptome, which cancer researchers can use to compare gene expression between tumors and matched normal tissues. Our proposed work will further widen the usage of the Common Fund data sets by providing the scientific community a web-based interactive avenue to visualize and explore the data. We also propose to integrate the data with other cancer genomics data for pan-cancer across-tissue comparison to identify target genes and pathways in patients tumors. Lastly our proposal will extend Xena Browser software functionality to use differential gene expression and leverage external tools and APIs to search for small molecules that might inhibit tumor growth. We propose the following aims. Aim 1. We will add KidsFirst data to Xena and integrate it with the cancer genomics data already on Xena to enable comparative visualization of molecular profiles across pediatric and adult tumors. Aim 2. Building upon the success of the UCSC RNA-seq compendium, we will deliver an even larger uniformly analyzed RNA-seq data compendium of over 25,0000 samples from KidsFirst, GTEx, TCGA, TARGET, CCLE and other studies. We will openly-share the compendium data using the Xena Browser. This rich data resource will support not only users wishing to compare expression across tumor to normal tissues, but will also support the Treehouse Childhood Cancer initiative in their quest to find treatments for children with cancer. Aim 3. We will extend Xena software functionality to perform genome-wide differential gene expression analysis and connect the analysis results to L1000FWD, a state-of-the-art web-based search and visualization tool for tens of thousands of small-molecule perturbation signatures profiled by the LINCS L1000 assay. This new feature and connectivity will enable users to predict candidate small molecule perturbations that might disrupt tumor growth using the reverse tumor gene expression signature they identified on Xena.