Good practices in computational biology have gained the spotlight among researchers thanks to several guiding principles published, as well as the increasing usage of Git-based repositories and workflow managers. This review adds to the existing literature by introducing a comprehensive list of good practices and associated tools that can be applied to any computational biology project, regardless of the specific subfield or the experience of the researcher.
We are aware that the many tools and practices introduced in this study and their ever-changing nature may seem overwhelming, especially for someone new to the field. To overcome this, we encourage you to implement only a few practices and tools first, starting from your personal research, and expanding your repertoire over time. More important than any specific tool is keeping a mindset of striving for reproducibility. We also note that our highlighted list of tools is not comprehensive, with many new tools being released. Updated reviews will be essential to help new computational biologists enter the field as well as to keep experienced computational biologists up to date with the latest trends.
The consequences of not following good computational practices are often not seen immediately but become evident and detrimental towards project progress over time. As with all scientific endeavors, computational biology heavily relies on previous knowledge; as such, the good practices we adopt serve as building blocks for the overall reproducibility of the field, propelling novel and exciting future discoveries.