A multi-tenant computational platform for translational medicine

Translational biomedical research has become a science driven by big data. Improving patient care by developing personalized therapies and new drugs depends increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of largescale internal and external, partner and public, data sources. As analysing these large-scale and complex datasets has become increasingly computationally expensive, it is of paramount importance to enable researchers to seamlessly scale up their computation platform while being able to manage complex yet flexible scenario that biomedical scientists are asking for. We developed a new platform as an answer to those needs of analysing and exploring massive amounts of medical data with the constrain of enabling the broadest audience, ranging from the medical doctor to the advanced coders, to easily and intuitively exploit this new resource. The platform consists of three main components: Borderline UI, the eTRIKS Analytical Environment (eAE)(1) [I] and the eTRIKS Data Platform (eDP). Each component has been designed to operate on specific sets of problems statelessly, developed as independent open-source software packages, and connected loosely in a cloud-native fashion [2] to form a coherent platform for large scale medical data analysis. The code is fully open source and available on GitHub at this address: https://github.com/dsi-icl