J Proteome Res. 2021 May 7;20(5):2182-2186. doi: 10.1021/acs.jproteome.1c00177. Epub 2021 Mar 13.
Proteomics is, by definition, comprehensive and large-scale, seeking to unravel ome-level protein features with phenotypic information on an entire system, an organ, cells, or organisms. This scope consistently involves and extends beyond single experiments. Multitudinous resources now exist to assist in making the results of proteomics experiments more findable, accessible, interoperable, and reusable (FAIR), yet many tools are awaiting to be adopted by our community. Here we highlight strategies for expanding the impact of proteomics data beyond single studies. We show how linking specific terminologies, identifiers, and text (words) can unify individual data points across a wide spectrum of studies and, more importantly, how this approach may potentially reveal novel relationships. In this effort, we explain how data sets and methods can be rendered more linkable and how this maximizes their value. We also include a discussion on how data linking strategies benefit stakeholders across the proteomics community and beyond.
蛋白质组学从定义上讲是全面的、大规模的，旨在利用整个系统、器官、细胞或生物体的表型信息来揭示蛋白质组水平的特征。这一范围始终涉及并超出了单个实验的范围。现在有许多资源可以帮助使蛋白质组学实验的结果更易于查找、访问、互操作和重复使用（FAIR），但仍有许多工具有待我们的社区采用。在这里，我们强调了扩大蛋白质组学数据影响超出单个研究的策略。我们展示了如何将特定的术语、标识符和文本（单词）链接起来，从而将广泛研究中的各个数据点统一起来，更重要的是，这种方法如何有可能揭示新的关系。在这方面，我们解释了如何使数据集和方法更具可链接性，以及这如何最大化它们的价值。我们还讨论了数据链接策略如何使蛋白质组学界内外的利益相关者受益。
