Practical Computational Reproducibility in the Life Sciences

Many areas of research suffer from poor reproducibility, particularly in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but implementation of these practices remains difficult due to the challenge of assembling software tools plus associated libraries, connecting tools together into pipelines, and specifying parameters. Here, we discuss a suite of cutting-edge technologies that make computational reproducibility not just possible, but practical in both time and effort. This suite combines three well-tested components—a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and workflow systems that automatically orchestrate the composition of these packages for entire pipelines—to achieve an unprecedented level of computational reproducibility. We also provide a practical implementation and five recommendations to help set a typical researcher on the path to performing data analyses reproducibly. Many areas of research suffer from poor reproducibility, particularly in computationally intensive domains where results rely on a series of complex methodological decisions that are not well captured by traditional publication approaches. Various guidelines have emerged for achieving reproducibility, but implementation of these practices remains difficult due to the challenge of assembling software tools plus associated libraries, connecting tools together into pipelines, and specifying parameters. Here, we discuss a suite of cutting-edge technologies that make computational reproducibility not just possible, but practical in both time and effort. This suite combines three well-tested components—a system for building highly portable packages of bioinformatics software, containerization and virtualization technologies for isolating reusable execution environments for these packages, and workflow systems that automatically orchestrate the composition of these packages for entire pipelines—to achieve an unprecedented level of computational reproducibility. We also provide a practical implementation and five recommendations to help set a typical researcher on the path to performing data analyses reproducibly.