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评估错误发现率控制在并联质谱分析中使用陷的评估

Bo Wen¹, Jack Freestone², Michael Riffle¹

¹Department of Genome Sciences, University of Washington, Seattle, WA, USA.

|June 16, 2025

概括

由于各种软件方法,质谱蛋白质组学中的准确错误控制是很困难的. 我们的研究表明,目前的错误发现率 (FDR) 验证技术是有缺陷的,特别是在数据独立获取 (DIA) 分析中.

科学领域:

蛋白质组学是指蛋白质组学.
质谱测量质量谱测量
生物信息学是一种生物信息学.

背景情况:

质谱蛋白质组学依赖于准确的错误控制,以获得可靠的结果.
目前用于蛋白质组学错误评估的软件工具缺乏透明度,并采用不一致的验证策略.
评估错误发现率 (FDR) 控制效率至关重要,特别是在数据独立获取 (DIA) 分析中.

研究的目的:

在质谱蛋白质组学中批判性地评估验证错误发现率 (FDR) 控制的现有方法.
为FDR控制评估引入一个新的理论框架和更强大的评估方法.
分析当前FDR控制策略的性能,特别是DIA数据集.

主要方法:

开发陷实验的理论框架,以严格描述FDR验证方法.
引入和应用一种更强大的评估方法来评估FDR控制.
使用数据依赖获取 (DDA) 和数据独立获取 (DIA) 数据集验证分析框架.

主要成果:

确定了三种流行的FDR验证方法:一种是无效的,一种是提供下限的,一种是有效的但功率不足的.
在蛋白质组学社区中,对FDR实际控制有效性的洞察力有限.
发现没有一个DIA搜索工具能够一致地控制FDR,单细胞数据集的性能特别差.

结论:

在质谱蛋白质组学中验证错误发现率 (FDR) 控制的现有方法是不充分的.
为严格评估FDR控制提出了一个新的框架和评估方法.
目前的DIA搜索工具在FDR控制中显示出重大缺陷,需要进一步开发.

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