Computational Infrastructures for biomolecular research

Abstract

[eng] Recently, research processes in Life sciences have evolved at a rapid pace. This evolution, mainly due to technological advances, offers more powerful equipment and generalizes the digital format of research data. In the data deluge context, we need to overcome the current tsunami of data and prepare for the future. The current model, consisting to regularly add hardware resources into centralized core facilities without global coordination, is no longer sustainable. Scientific data management and analysis should be enhanced in order to offer services and developments corresponding to the new e-Science uses, and infrastructures are the vehicles to achieve so. We propose and implement research support infrastructures in line with new science directives, adapting them to the scenarios presented by the divergent use cases. Three different domain-specific infrastructures framed in three different scientific projects are assembly and introduced in this dissertation. The first case is framed in the clinical data management field, and focuses on the data platforms build around two epidemiologic case studies on Immune Mediated Inflammatory diseases (IMIDs), IMID-clinica and IMID-longitudinal. Making the leap to infrastructures more oriented to analysis process support, the transPLANT infrastructure represents a first intrusion into the topical cloud computing model. It is focused on plant genomics and its design became the seed for a more integrative cloud-based solution, this time developed for the non-programmer’s members of the 3D/4D genomics community. MuGVRE is the front cover of the resulting platform. Becoming obvious the transversal potential of cloud-based computational infrastructures as virtual research environments, openVRE is implemented as an abstraction of MuGVRE. It offers a vanilla platform encompassing computation, data and administration services ready to be adopted and customized by other scientific communities. They all represent an opportunity to establish better research processes through enhanced collaboration, data management, analysis practices and resources optimization.