R2platform Data Science Annals

Chromosomal abberations in Cancer come in 6 different flavours Single nucleotide variations in Cancer come in 3 different flavours 

The Cancer Genome Atlas Program (TCGA) is one of the cornerstones of cancer research. Every sample has a unique identifier, also known as barcode. A basic understanding of the naming convention is very helpful to quickly assess the 'type of sample' that you are working with. The sample type is contained in the numerical part of the 4th element of a barcode, that can be up to 7 parts. Barcodes most commonly are composed of 4 parts, which is informative for most use cases. Code Definition 1 Primary Solid Tumor 2 Recurrent Solid Tumor 3 Primary Blood Derived Cancer - Peripheral Blood 4 Recurrent Blood Derived Cancer - Bone Marrow 5 Additional - New Primary 6 Metastatic 7 Additional Metastatic 8 Human Tumor Original Cells 9 Primary Blood Derived Cancer - Bone Marrow 10 Blood Derived Normal 11 Solid Tissue Normal 12 Buccal Cell Normal 13 EBV Immortalized Normal 14 Bone Marrow Normal 15 sample type 15 16 sample type 16 20 Control Analyte 40 Recur...

  Relate the expression of any gene to survival potential using the embedded tools available in the R2 open online datascience tool  (  https://r2.amc.nl  ) . Scan for genes with potential using Cox proportional hazard analysis, and explore those in more details, using the interactive visualization tools. Or alternatively find genes with the optimal logrank separation and view those in interactive Kaplan Meier plots. With the identified cut-off(s), you can easily create a new grouping variable and use those to perform in depth analyses, such as differential expression within a few click of the mouse. Any gene of interest can also be validated in numerous other resources that are publicly available as well (n>2500 resources). All this and much much,more is available in the free open online R2 platform (  https://r2.amc.nl  ).

The RNA atlas that is hosted in the R2 Platform ( https://r2.amc.nl ) is a great 300 samples reseource where all of the samples have been analysed on polyA isolation, ribo depletion as well as small RNA sequencing. As such it is an invaluable resource that can also be used to investigate the effects of the different isolation methods. You can use the 'two-set view' to analyze andvisualize 2 resources head-to-head h For example check out the expression of a Histone gene (that lacks a poly A tail), where you can clearly see that the poly A isolation lacks the aility to assess the expression of such genes.  Or visualize the profiles in the landscape views in the embedded genome browser of R2. Just a couple of simple things that R2 can be of great value with a few mouse clicks. Visit  https://r2.amc.nl  and explore this, or one of the hundreds of other public resources from the comfort of your web browser

  Meta cohorts can be very interesting to get a quick comprehensive overview of the gene expression pattern for your gene of interest. Here a meta cohort that has been around for some time, but still provides valuable information at a single glance. The integration of a harmonised Affymetrix cohort of nearly 28.000 bulk profiles. Using the 'sample maps' feature in the free open online R2 platform (  https://r2.amc.nl  ), you can use data driven representations, such as UMAP, tSNE, PCA etc for exploration. Or make use of a wealth of other features available in the versatile data science tool, intended for biomedical researchers.

Genomic data from The Cancer Genome Atlas (TCGA) project has enabled comprehensive molecular profiling of diverse cancer types. The extensive sample size within TCGA provides an invaluable resource for investigating tumor heterogeneity. Effective exploration of this dataset by researchers and clinicians is essential for discovering novel therapeutic and diagnostic biomarkers.  The R2Platform, provides an easy interface to explore the rich resource at different levels. By example, subset the cohort to paired tumor / normal patients only and discover how the expression of your gene of interest changes from normal to tumor. The R2 data science platform for biomedical researchers serves as a robust tool for in silico validation of target genes and the identification of candidate biomarkers for tumor subtype-specific research. The R2 portal has the potential to accelerate cancer research by providing accessible and comprehensive analytical capabilities. R2 is already cited in more than ...