Data Analytics in Bioinformatics: Data Science in Practice for Genomics Analysis Workflows

Workflow systems manage large-scale experiments and deliver a large volume of provenance data traces. The provenance repository of these systems contains information about the workflow execution, which allows for tracking and analyzing data transformations. However, provenance data may still be considered a black-box, when it comes to analyze the contents of resulting data files. Current solutions are focused on data transformation at coarse grain, they point to input and output files, but do not allow for exploring domain-specific data. Data analytics is essential for managing large-scale workflows executed in parallel, especially when tracking anomalous executions. In this paper, we present a data analytics approach, which is based on the use of provenance data enriched with domain-specific data coupled to a data mining tool. A real bioinformatics workflow was modeled and executed in parallel on top of Amazon clouds. It manipulates complex biological data, which is difficult to monitor like many other genomic workflows. We evaluate the benefits of using domain-specific data and provenance data for user steering while monitoring the execution with detailed filters, steering on specific conditions and performance evaluation. Results show that the provenance database coupled to workflow systems has an unexplored potential for raw data analytics, which may improve the user confidence and reduce overall execution time.