Abstract 2490: R2: Genomics analysis and visualization platform

Given the complexity, omics data like RNA-seq are often analyzed by bioinformaticians, and not the wetlab researchers that performed the experiments. With the biomedical researcher (with limited or no bioinformatics skills) in mind as the end-user, we developed the user-friendly, publicly accessible R2 platform (http://r2.amc.nl), enabling biomedical scientists to work with their own data from anywhere at any time. R2 (cited in >850 peer-reviewed scientific publications) consists of an omics database, coupled to an extensive set of interactive tools to analyze/visualize the datasets. Interactive analyses within the software are highly connected, allowing quick navigation between various aspects of the datamining process. Besides password protected private data, R2 also contains an extensive body of publicly available omics data, enabling users to relate their findings to other diseases / tissues, or as validation in integrative analyses. Next to gene expression (bulk as well as single cell), R2 is also being employed for the integration, analysis and visualization of copynumber, SNP, methylation, miRNA, drug response, ChIP-seq, and NGS exome/WGS DNA sequencing data. Analyses include correlation, differential expression, gene sets, gene ontology, transcription factor binding sites, tSNE, PCA, k-means, Kaplan Meier survival scans, signature creation etc. Visualizations include various gene oriented plots, heatmaps, Circos plots, embedded genome browser, Venn diagrams, etc. The real power of the platform lies in the chaining of results of analyses; e.g. the results of a differential expression analysis can be further trimmed down using a Kaplan Meier analysis that in turn can be used for a Gene Ontology over-representation analysis. Furthermore, the webserver allows for overviews across different datasets (such as MegaSampler and 2D distribution), where a user can harness the power of thousands of measurements. Recently, integrated analyses for personalized medicine, where copy number, mRNA expression, methylation and mutation data are combined into a comprehensive overview, occasionally supplemented with PDX/organoid drug response data. These serve clinical decision making for a patient, but as growing cohorts also form a treasure trove for scientific discovery. R2 provides a central starting point from where data mining and analysis paths can be followed within one environment. Data/analyses can be shared between R2-users via our community options, making R2 an outstanding environment for discovery, hypothesis testing and scientific collaboration. Citation Format: Jan Koster, Richard Volckmann, Danny Zwijnenburg, Piet Molenaar, Rogier Versteeg. R2: Genomics analysis and visualization platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2490.