An Automated Infrastructure to Support High-Throughput Bioinformatics

The number of domains affected by the big data phenomenon is constantly increasing, both in science and industry, with high-throughput DNA sequencers being among the most massive data producers. Building analysis frameworks that can keep up with such a high production rate, however, is only part of the problem: current challenges include dealing with articulated data repositories where objects are connected by multiple relationships, managing complex processing pipelines where each step depends on a large number of configuration parameters and ensuring reproducibility, error control and usability by nontechnical staff. Here we describe an automated infrastructure built to address the above issues in the context of the analysis of the data produced by the CRS4 next-generation sequencing facility. The system integrates open source tools, either written by us or publicly available, into a framework that can handle the whole data transformation process, from raw sequencer output to primary analysis results.