The identification and accurate quantitation of protein abundance has been a major objective of proteomics research. Abundance studies have the potential to provide users with data that can be used to gain a deeper understanding of protein function and regulation and can also help identify cellular pathways and modules that operate under various environmental stress conditions. One of the central missions of the Saccharomyces Genome Database (SGD; https://www.yeastgenome.org) is to work with researchers to identify and incorporate datasets of interest to the wider scientific community, thereby enabling hypothesis-driven research. A large number of studies have detailed efforts to generate proteome-wide abundance data, but deeper analyses of these data have been hampered by the inability to compare results between studies. Recently, a unified protein abundance dataset was generated through the evaluation of more than 20 abundance datasets, which were normalized and converted to common measurement units, in this case molecules per cell. We have incorporated these normalized protein abundance data and associated metadata into the SGD database, as well as the SGD YeastMine data warehouse, resulting in the addition of 56 487 values for untreated cells grown in either rich or defined media and 28 335 values for cells treated with environmental stressors. Abundance data for protein-coding genes are displayed in a sortable, filterable table on Protein pages, available through Locus Summary pages. A median abundance value was incorporated, and a median absolute deviation was calculated for each protein-coding gene and incorporated into SGD. These values are displayed in the Protein section of the Locus Summary page. The inclusion of these data has enhanced the quality and quantity of protein experimental information presented at SGD and provides opportunities for researchers to access and utilize the data to further their research.

中文翻译：

---

将统一的蛋白质丰度数据集整合到酿酒酵母基因组数据库中。

蛋白质丰度的鉴定和准确定量一直是蛋白质组学研究的主要目标。丰度研究有潜力为用户提供数据，这些数据可用于更深入地了解蛋白质的功能和调控，还可以帮助确定在各种环境压力条件下运行的细胞途径和模块。酿酒酵母基因组数据库（SGD; https://www.yeastgenome.org）的中心任务之一是与研究人员合作，以识别和整合更广泛的科学界感兴趣的数据集，从而进行假设驱动的研究。大量研究为生成蛋白质组范围的丰度数据做出了详尽的努力，但是由于无法比较研究之间的结果，因此无法对这些数据进行更深入的分析。最近，通过评估20多个丰度数据集，生成了统一的蛋白质丰度数据集，将其标准化并转换为通用的测量单位，在这种情况下为每个细胞分子。我们已经将这些标准化的蛋白质丰度数据和相关的元数据合并到SGD数据库以及SGD YeastMine数据仓库中，从而为在富液或限定培养基中生长的未处理细胞增加了56487的值，为处理过的细胞增加了28335的值与环境压力。蛋白质编码基因的丰度数据显示在“蛋白质”页面的可排序，可过滤表格中，可通过“基因座摘要”页面获得。合并中值丰度值，并为每个蛋白质编码基因计算中值绝对偏差，并将其合并到SGD中。这些值显示在“基因座摘要”页面的“蛋白质”部分中。包含这些数据提高了SGD上展示的蛋白质实验信息的质量和数量，并为研究人员提供了访问和利用数据以进一步进行研究的机会。