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如何训练后处理器进行并列质谱蛋白质学数据库搜索,同时保持对错误发现率的控制

Jack Freestone¹, Lukas Käll², William Stafford Noble^3,4

¹School of Mathematics and Statistics F07, University of Sydney, New South Wales 2006, Australia.

Journal of proteome research

|March 31, 2025

概括

穿透器-RESET为质谱学中的检测提供了有效的统计控制. 这种新方法确保了准确的错误发现率控制,改进了现有的蛋白质组学数据分析工具.

科学领域:

蛋白质组学是指蛋白质组学.
计算生物学计算生物学
质谱测量质量谱测量

背景情况:

基于诱的方法对于验证双重质谱中的检测至关重要.
像Percolator这样的后处理器通过学习更好的评分函数来提高统计能力.
透器可以显示控制鉴定中的错误发现率 (FDR) 的问题.

研究的目的:

引入Percolator-RESET,这是RESET元程序的调整,用于检测.
在蛋白质组学数据分析中确保有效的错误发现率控制.
改进现有的标识统计验证方法.

主要方法:

穿透器-RESET将穿透器的代SVM训练与RESET元程序集成在一起.
该方法在单引诱和双引诱模式中进行评估.
理论和经验分析用于验证FDR控制.

主要成果:

穿透器-RESET在单诱和双诱模式中都显示出有效的FDR控制.
单个诱模式报告了与Percolator相似的发现数量.
两种诱模式提供了略高的统计能力和减少的可变性.

结论:

在检测中,Percolator-RESET提供了一个可靠的解决方案,用于准确控制检测中的错误发现率.
两种诱模式在蛋白质组学研究中为增加功率和稳定性提供了有利的选择.
这种方法提高了从质谱数据中鉴定的可靠性.

关键词:

错误发现率控制错误发现率控制蛋白质组学蛋白质组学双联质谱质量测量技术

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