Alban Gaignard

Data analysis pipelines are now established as an effective means for specifying and executing bioinformatics data analysis and experiments. While scripting languages, particularly Python, R and notebooks, are popular and sufficient for developing small-scale pipelines that are often intended for a single user, it is now widely recognized that they are by no means enough to support the development of large-scale, shareable, maintainable and reusable pipelines capable of handling large volumes of data and running on high performance computing clusters. This review outlines the key requirements for building large-scale data pipelines and provides a mapping of existing solutions that fulfill them. We then highlight the benefits of using scientific workflow systems to get modular, reproducible and reusable bioinformatics data analysis pipelines. We finally discuss current workflow reuse practices based on an empirical study we performed on a large collection of workflows.

With the development of new experimental technologies, biologists are faced with an avalanche of data to be computationally analyzed for scientific advancements and discoveries to emerge. Faced with the complexity of analysis pipelines, the large number of computational tools, and the enormous amount of data to manage, there is compelling evidence that many if not most scientific discoveries will not stand the test of time: increasing the reproducibility of computed results is of paramount importance. The objective we set out in this paper is to place scientific workflows in the context of reproducibility. To do so, we define several kinds of reproducibility that can be reached when scientific workflows are used to perform experiments. We characterize and define the criteria that need to be catered for by reproducibility-friendly scientific workflow systems, and use such criteria to place several representative and widely used workflow systems and companion tools within such a framework. We also discuss the remaining challenges posed by reproducible scientific workflows in the life sciences. Our study was guided by three use cases from the life science domain involving in silico experiments.