来自数千个HeLa对照样本的基于质谱的蛋白质组学数据
Henry Webel1, Yasset Perez-Riverol2, Annelaura Bach Nielsen1
1Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen N, Denmark.
Scientific data
|January 23, 2024
概括
这项研究介绍了来自HeLa细胞的大规模,无标签的质谱蛋白质组学数据集,简化了机器学习分析. 它提供精选的元数据和分析工具,以便更容易访问和重新处理数据.
科学领域:
- 蛋白质组学是指蛋白质组学
- 机器学习 机器学习
- 生物技术是生物技术.
背景情况:
- 基于质谱的蛋白质组学产生了很大的数据集,难以访问和分析.
- 研究人员花费大量时间过和准备用于机器学习应用程序的数据.
研究的目的:
- 从HeLa细胞系提供一个精选的,大规模的,无标签的质谱蛋白质组学数据集.
- 通过提供机器可读元数据和预先过的数据集来促进机器学习和数据分析.
- 通过标准化的仪表设置注释,实现自动化数据再处理.
主要方法:
- 基于无标签质谱的蛋白质组学在HeLa细胞系上进行.
- 使用MaxQuant软件进行数据处理和分析.
- 为所有原始数据生成机器可读元数据和标准化的SDRF文件.
主要成果:
- 创建了一个包含7444个原始文件的全面蛋白质组学数据集.
- 基于机器的元数据是为了有效的数据选择和概述而创建的.
- 提供了三个聚合的开发数据集 (蛋白质组,,前体).
- 包含详细仪表设置的SDRF文件用于自动再处理.
结论:
- 提供的数据集和相关工具大大减少了蛋白质组学数据分析所需的时间和精力.
- 本资源促进机器学习在蛋白质组学研究中的更广泛采用.
- 开放式访问性质和提供的工作流程鼓励社区贡献和数据集扩展.
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