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Abstract B1-05: R2: Accessible web-based genomics analysis and visualization platform for biomedical researchers

Jan Koster, Jan J. Molenaar, Rogier Versteeg

Abstract: In this era of explosive high throughput (HT) genomics data generation, there is a growing need for accessible software solutions that can help unlock biological/clinical characteristics from such data. With the biomedical researcher (with limited or no bioinformatics skills) in mind, we developed a comprehensive web-based system called R2 ( R2 aids in the analysis and visualization of private/shielded as well as public high throughput data and their associated annotated features. The R2 platform consists of a database storing both publicly accessible as well as shielded datasets with unified gene annotation, supplemented with a large suite of tools that can be used on these data, and their associated annotation. As such the user experiences the same look & feel throughout the mining process. In the public section, R2 hosts over 60,000 HT samples. Next to gene expression (microarray and RNA-seq), the platform is also being employed in the integration, analysis and visualization of other data types, such as aCGH, SNP, ChIP, methylation, miRNA, and NGS whole genome sequencing information. R2 contains an expanding set of interactive analyses which are heavily inter-connected, allowing users to quickly hop from one view (representation of the data) to another. Analyses include, correlation, differential expression, gene sets, gene ontology, transcription factor binding sites, PCA, kmeans, Kaplan Meier scans, signature creation etc. Visualizations include, various gene oriented plots, heatmaps, circos, embedded genome browser, Venn, etc. Many parts of the R2 platform are publicly accessible through the portal. The R2 gene expression analysis tools have thus far been used in more than 180 peer-reviewed scientific publications including journals as Nature, Nature Genetics and Cell. R2 also supports restricted / shielded use and is used in many (inter)national collaborative efforts involving unpublished datasets. The webservers have been serving over 900.000 pages over the past 12 months (Nov 2014). Citation Format: Jan Koster, Jan J. Molenaar, Rogier Versteeg. R2: Accessible web-based genomics analysis and visualization platform for biomedical researchers. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-05.
Keywords: gene expression / gene ontology / transcription factor / Accessible web based / visualization platform / based genomics analysis / web based genomics

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