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Modern science is built on automated data generation, software development, and large-scale data analysis. In recent years, data analysis has become more complex, moving from single applications to convoluted and integrated workflows,1,2  creating two major challenges for software developers and the bioinformatics community: (i) software availability and (ii) reproducible experiments. The increasing use of complex workflows integrating several bioinformatics tools require substantial effort for correct installation and configuration (e.g. conflicting system dependencies). To overcome these challenges, the bioinformatics community have created different groups, communities and platforms that define guidelines for best practices bioinformatics software development, deployment and deposition.3 

Journal editors, funding agencies, and individual scientists have increasingly made calls for the scientific community to embrace best practices to support computational reproducibility.4  Recently, Ioannidis et al. evaluated 18 published research studies that used computational methods to evaluate gene expression data, but they were able to reproduce only two of those studies.5  Most of the failures were related with incomplete descriptions of software-based analyses and lack of description of the data analysis protocol. Recreating analyses that lack such details can require hundreds of hours of effort and may be impossible, even after consulting the original authors; which can lead to retractions.

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