Software containerization has become a cornerstone of modern computational biology, enabling researchers to package code, dependencies, and execution environments in portable and reusable units. Containers support reproducibility, facilitate collaboration, and lower barriers to deploying complex computational workflows across heterogeneous systems. At the same time, inappropriate or superficial use of containers can undermine these benefits, leading to brittle environments, security risks, or false confidence in reproducibility. In this article, we present nine practical and actionable tips for using software containers effectively in computational biology research. Rather than focusing narrowly on container syntax or tooling, we address conceptual decisions that arise throughout the research lifecycle: when containerization is appropriate, how to balance reproducibility with flexibility, how to manage dependencies and data, and how to share containers responsibly. These tips are intended for researchers with varying levels of experience, from those adopting containers for the first time to those maintaining mature, containerized workflows.
Moreau et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: