SmartR: an open-source platform for interactive visual analytics for translational research data

In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly acce...

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Published in:Bioinformatics (Oxford, England) Vol. 33; no. 14; pp. 2229 - 2231
Main Authors: Herzinger, Sascha, Gu, Wei, Satagopam, Venkata, Eifes, Serge, Rege, Kavita, Barbosa-Silva, Adriano, Schneider, Reinhard
Format: Journal Article
Language:English
Published: England Oxford University Press 15-07-2017
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Summary:In translational research, efficient knowledge exchange between the different fields of expertise is crucial. An open platform that is capable of storing a multitude of data types such as clinical, pre-clinical or OMICS data combined with strong visual analytical capabilities will significantly accelerate the scientific progress by making data more accessible and hypothesis generation easier. The open data warehouse tranSMART is capable of storing a variety of data types and has a growing user community including both academic institutions and pharmaceutical companies. tranSMART, however, currently lacks interactive and dynamic visual analytics and does not permit any post-processing interaction or exploration. For this reason, we developed SmartR , a plugin for tranSMART, that equips the platform not only with several dynamic visual analytical workflows, but also provides its own framework for the addition of new custom workflows. Modern web technologies such as D3.js or AngularJS were used to build a set of standard visualizations that were heavily improved with dynamic elements. The source code is licensed under the Apache 2.0 License and is freely available on GitHub: https://github.com/transmart/SmartR . reinhard.schneider@uni.lu. Supplementary data are available at Bioinformatics online.
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ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/btx137