Abstract

Many bioinformatic applications require to exploit the capabilities of several computational resources to effectively access and process large and distributed datasets. In this context, Grid computing has been largely used to face unprecedented challenges in Computational Biology, at the cost of complex workarounds needed to make applications successfully running. The Grid computing paradigm, in fact, has always suffered from a lack of flexibility. Although this has been partially solved by Cloud computing, the on-demand approach is way distant from the original idea of volunteering computing that boosted the Grid paradigm. A solution to outpace the impossibility of creating custom environments for running applications in Grid is represented by the containerization technology. In this paper, we describe our experience in exploiting a Docker-based approach to run in a Grid environment a novel, computationally intensive, bioinformatic application, which models the DNA spatial conformation inside the nucleus of eukaryotic cells. Results assess the feasibility of this approach in terms of performance and efforts to run large experiments.

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