Welcome to R2, an online datamining and discovery platform designed to assist the bio-medical researchers with limited to no Bioinformatics skills to perform datascience tasks in the omics field.
Our mission is to provide biomedical scientists with a comprehensive, user-friendly platform for analyzing and interpreting genomic data, enabling them to make more informed decisions and advance their research in the field of genomics without the need for bioinformatics or coding expertise. By leveraging cutting-edge technology and incorporating the latest scientific insights, our platform aims to streamline the genome analysis process and facilitate the discovery of new insights and breakthroughs in the field of genomics. Through our commitment to excellence and innovation, we strive to be a leading provider of online genome analysis tools for scientists.
The interface and design of R2 has been created to easily follow your path by inter connecting analyses and data visualization there of. This allows you to follow an hypothesis from a bird's eye view up to the details of a statistical test result and vice versa.
Use our KaplanScanner tool to find the optimal 2 group segregation (based on the expression of gene) on the logrank p-value for Kaplan Meier curves with the best survival difference. Next to the KaplanScan option, the R2platform also provides other separation options to assess segregation from gene expression, such as median, quartiles, of the average expression of a gene.
Convert lists of genes (gene sets) into a single value and store those as a new meta feature in your account. These meta genes can then be used for association analyses and represent e.g. pathway activities. The R2 platform provides a large resource with public gene set databases such as MSigDB, KEGG pathways and many more. In addition, you can start creating your own gene sets from analyses performed in R2, or by cut and paste in your own account.
Generate new embeddings or explore (published) dimensionality reduction views (PCA, tSNE, UMAP) of single cell experiments. Overlay those with annotations (grouping variables), meta features, gene expression data or other multi omics overlays. You can also define regions of your interest with the lasso tool, or aut-detect clusters using the DBscan algorithm. R2 typically stores multiple embeddings for a data set, from parameter sweeps in UMAP and tSNE settings. These can be helpful in assessing robustness, and finding the optimal view to tell your story.
Use our different entry points (e.g. onco plot, circos views, mutation view etc. ) to assess targets for potential treatment options from the R2 integrated single sample overviews. These features are also insightful for (PDX / Cell line) model systems. Simply use one of our public data scopes to see how insightful these representations can be.
Use our embedded genome browser to change perspective on your differential expression analyses, or simply use the chromosome location to integrate the many genome plugins (with adaptable settings) to gain new insights. If you register an account, you can also store these representations as presets for later re-use.
R2 hosts many public histone modification / transcription factor ChIPseq profiles that can be useful in understanding transcriptional changes. Use the embedded options in the ChIP section such as, TSS plots, multi region views, ROSE super enhancer options and more. In addition, it is also possible to have your private profiles added to R2
Share your data resources from a publication or a consortium in an explorable fashion via the R2 Platform. Allow readers of your work to dig deeper or check their favourite genes / associations via quick jumps into the many functions hat R2 has to offer, with pre-filled settings. For some data scopes we have implemented dedicated functionalities into the platform. Get in touch with us if you are interested to get your (private) scope.
The R2 plaform has been cited as a webcite in 2238 PubMed listed publications, a listing of which can be accessed from within the platform. These citations also include high impact journals such as Nature and Cell. The author network feature in R2 allows you to see how authors are connected in a playful way.