Improving reproducibility in computational biology research

Abstract

There has been much discussion in the scientific literature on a crisis of reproducibility in science [1,2].It has been reported that the percentage of studies that are reproducible is as low as 10% or less, depending on the discipline [3].This inability to reproduce scientific findings from a given paper has been attributed to a lack of clarity in the methods and inherent variability in the biological system being studied [4].Reproducibility in computational biology research is certainly a problem, yet perhaps a challenge that our field can uniquely tackle.A lack of reproducibility in computational biology research can be attributed to many factors, but incomplete or erroneous descriptions of the simulations (e.g., which software version was used), incomplete documentation on how to run simulations, or simply failing to post the relevant computer code needed to run a given simulation are common issues that occur.Many tools have emerged that we can leverage to make computational biology research more reproducible (e.g., http://co.mbine.org/and https://normsys.h-its.org/)and there exist articles that propose best practices, such as Ten Simple Rules for Reproducible Computational Research [5] or Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks [6].