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用同位素标签进行蛋白质定量测定,对Tandem质量报告器离子强度进行属性加权聚合

Jiahua Tan¹, Gian L Negri², Gregg B Morin^2,3

¹Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, Canada.

Journal of proteome research

|September 23, 2025

概括

本研究引入了一种属性加权聚合 (AWA) 方法,用于在蛋白质组学中更准确地估计蛋白质丰度. 通过根据它们的属性加权谱匹配 (PSM),AWA提高了定量准确性,提高了差异表达分析的回忆和精度.

科学领域:

蛋白质组学是指蛋白质组学.
定量生物学定量生物学
生物信息学是一种生物信息学.

背景情况:

同标签是一种标准的蛋白质组学技术,用于蛋白质的量化.
自下而上的蛋白质组学聚合物-光谱匹配 (PSMs) 用于蛋白质丰度估计.
目前的聚合方法假定所有PSM的定量准确性相同,但由于电离性和干扰的变化,这通常不是真的.

研究的目的:

开发一种新的方法,属性加权聚合 (AWA),用于更准确的蛋白质丰度估计.
通过考虑其内在属性来提高PSM的定量准确性.
在蛋白质组学研究中提高差异表达分析的性能.

主要方法:

开发了一种以属性加权聚合 (AWA) 方法,将PSM属性和报告离子强度结合起来.
在PSM特征上训练一个随机森林模型,使用三个尖端数据集来预测定量不准确性.
将PSM聚合到基于预测不准确性的蛋白质水平,并相应加权.

主要成果:

在多个数据集的微分表达式分析中,AWA表现出更好的回忆,同时保持高精度.
该方法已成功应用于两个大型癌症队列,验证了其实用性.
开发了一个R包,AWAggregator,以方便实现和重新训练模型.

结论:

属性加权聚合 (AWA) 在蛋白质组学中为蛋白质丰度估计提供了更准确的方法.
AWA提高了定量蛋白质组学数据的可靠性,特别是在差异表达研究中.
该AWAggregator R包促进了这种改进的定量方法的采用和应用.

关键词:

标签上的异酸标签.公共服务管理 (PSM) 聚合.蛋白质量化量化的蛋白质量.

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