Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

用于分析完整的生物分子类型的方法，例如蛋白质组学或代谢组学，会产生大量复杂的数据。用于分析组学数据的软件工具重塑了现代生物学的格局，并成为生物医学研究的重要组成部分。这些工具本身相当复杂，通常需要安装其他支持软件、库和/或数据库。研究人员还可能使用多种不同的工具，这些工具需要相同支持材料的不同版本。生物医学科学家对这些强大工具的依赖日益增加，因此需要更轻松的安装和更高的可用性。打包和容器化是满足这种需求的不同方法，它们提供已经封装在附加软件中的组学工具，使这些工具更易于安装和使用。在这篇系统综述中，我们描述并比较了著名的包装和集装箱化平台的特征。我们从用户、软件开发人员和系统管理员的角度概述了每种方法以及一些最广泛使用的平台的挑战、优点和局限性。我们还提出了使组学软件的分发更加可持续和稳健的原则，以提高生物医学和生命科学研究的可重复性。