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使用大规模TMT和LFQ实验进行基于组织的绝对量化.

Hong Wang1, Chengxin Dai1,2, Julianus Pfeuffer3

  • 1Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.

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概括
此摘要是机器生成的。

这项研究引入了一种使用双重质量标签 (TMT) 来量化蛋白质的新方法,该方法反映了传统的无标签量化 (LFQ) 方法. 基于TMT的方法为分析大规模蛋白质组数据集提供了有价值的替代方案.

关键词:
LFQQ LFQQ 在线播放TMTT TMTT 是一个很好的方法.绝对蛋白质表达的绝对蛋白质表达大数据就是大数据.蛋白质组学数据重新分析公共数据 公共数据

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 公共可用的蛋白质组数据集,包括细胞系,组织图谱和瘤数据,促进对蛋白质存在,局部化,丰富性和RNA相关性的研究.
  • 使用MS1特征的无标签量化 (LFQ) 是常见的,但同位体双重质量标签 (TMT) 数据集未得到充分利用.
  • 现有的蛋白质定量方法在比较不同类型的数据集方面存在局限性.

研究的目的:

  • 将传统的基于强度的绝对量化 (iBAQ) 与使用TMT数据的新型报告离子蛋白质组丰度排名方法进行比较.
  • 评估基于TMT的新iBAQ方法在大规模组织图谱数据集上的适用性.
  • 为蛋白质组识别,规范化和量化提供可用于LFQ和TMT数据的稳健工作流.

主要方法:

  • 开发了一个基于TMT的记者离子蛋白质组丰度排名方法,类似于iBAQ框架.
  • 将新的TMT方法应用于用LFQ和TMT分析的样本.
  • 在两个独立的大规模组织地图数据集 (一个LFQ,一个TMT) 上使用已建立的蛋白质组工作流验证了TMT-iBAQ方法.

主要成果:

  • 基于TMT的报告者离子丰度排名方法有效地取代了iBAQ框架内的MS1特征强度.
  • 从同一样本中直接比较LFQ-iBAQ和TMT-iBAQ值显示了可比的结果.
  • 当TMT-iBAQ方法应用于大规模组织图谱数据集时,它表现出了稳健性.

结论:

  • 介绍了一种类似于iBAQ的基于TMT的新型蛋白质量化方法,扩大了TMT数据集的实用性.
  • 这种TMT-iBAQ方法提供了一种可靠的方法来比较不同蛋白质组数据集中的蛋白质丰度.
  • 开发的工作流程增强了大规模蛋白质组图谱的分析,使得更深入的生物学见解.