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将统一的蛋白质丰度数据集纳入酿酒酵母基因组数据库。

Incorporation of a unified protein abundance dataset into the Saccharomyces genome database.

机构信息

Department of Genetics, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304, USA.

出版信息

Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baaa008.

Abstract

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.

摘要

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。丰度研究有可能为用户提供可用于深入了解蛋白质功能和调控的数据,还可以帮助识别在各种环境胁迫条件下运作的细胞途径和模块。酿酒酵母基因组数据库 (SGD; https://www.yeastgenome.org) 的核心任务之一是与研究人员合作,识别和整合更广泛科学界感兴趣的数据集,从而支持基于假设的研究。大量研究详细地致力于生成蛋白质组范围的丰度数据,但由于无法在研究之间比较结果,这些数据的深入分析受到了阻碍。最近,通过评估 20 多个丰度数据集,生成了一个统一的蛋白质丰度数据集,这些数据集经过标准化并转换为常见的测量单位,在这种情况下为每个细胞的分子数。我们已经将这些标准化的蛋白质丰度数据及其相关元数据纳入 SGD 数据库以及 SGD YeastMine 数据仓库,从而为在丰富或定义培养基中生长的未处理细胞添加了 56487 个值,为用环境胁迫处理的细胞添加了 28335 个值。蛋白质编码基因的丰度数据显示在蛋白质页面的可排序、可筛选表中,可通过基因摘要页面访问。为每个蛋白质编码基因纳入了中位数丰度值,并计算了中位数绝对偏差,并将其纳入 SGD。这些值显示在基因摘要页面的蛋白质部分。这些数据的纳入提高了 SGD 提供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这些数据以推进其研究的机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a116/7054198/3f9f4ecb8bda/baaa008f1.jpg

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