 My name is Katelyn O'Shea, and I am presenting meningiomics, a web-based analysis and visualization tool for meningioma, omic datasets using R-Shiny. Meningiomas are tumors of the central nervous system. They grow on the meninges which surround the brain. These tumors are understudied relative to other brain tumors, for example glial blastoma, and there are no targeted therapies available for these tumors. Recent literature, however, suggests that omics data, a term used to describe biologic data such as expression, methylation, or proteomic data, are differentiating among more aggressive subtypes for prediction of progression and treatment purposes. Though an important hallmark across all disciplines in the big data era, a hub for collective analysis is of particular benefit in an understudied disease such as meningioma. Meningiomics was created to fill this role, and the application provides robust and easily interpretable graphs and analysis, and will serve as a platform for integration of future meningioma datasets. There are multiple repositories which hold publicly available omics data, but for the purposes of this initial meningiomics build, we focused on the data available in the gene extraction omnibus. We selected 11 datasets with RNA, microarray, and clinical data. These datasets were harmonized to allow for collective analysis. Clinical data were harmonized to a single common data dictionary, and expression data were lightly cleaned identically for each dataset. The application itself was constructed using R Shiny, utilizing the shiny nav bar structure to organize the application. There are no hard-coded analyses or visualizations. Anything displayed in the application is reactive to user selection. Additionally, all plots utilize poly for an interactive user experience. The application offers analysis and visualizations on both a per-gene and multi-gene basis, allowing for researchers to easily and reproducibly explore basic relationships between gene expression and clinical data. All visuals are paired with relevant analyses, including warnings if insufficient data is available to complete an analysis. A full summary of the functionality offered by this tool is available on the front home page of the application, available at the link on screen. This is a screen grab of the single gene analysis tab of meningiomics. This gene phenotype analysis describes the relationship between the gene PTTG1 and grade in the Schmitt M dataset. The user can change inputs and download the analysis and associated data if they want. Please visit the application to view further functionality or my GitHub for construction details if interested. Doctors Denise Shultons and Craig Grubinski were supported by Northwestern University's brain score. Dr. Grubinski received additional support from the funding sources listed below. References for this presentation are available upon request, but you can view the application or my GitHub for more information. Thank you.