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在DIA-NN的图书馆免费搜索中错误发现率的评估,使用人体蛋白质组在体外

Kongxin Gu¹, Masanaga Kenko², Koji Ogawa³

¹Department of Pharmaceutical Microbiology, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto 862-0973, Japan.

Journal of proteome research

|July 18, 2025

概括

深度学习的光谱库改善了数据独立获取 (DIA) 分析. 较新的DIA-NN软件版本提供更好的错误发现率 (FDR) 控制,以更准确地识别.

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.
计算生物学计算生物学

背景情况:

基于深度学习的光谱库在蛋白质组学中越来越多地使用.
这些库允许在数据独立采集 (DIA) 质谱学中进行无库搜索.
准确的错误发现率 (FDR) 控制仍然是大型in silico库的一个挑战.

研究的目的:

在DIA软件中引入一个严格的方法来评估FDR控制.
为了比较不同DIA-NN软件版本在FDR控制和标识方面的性能.
为改善DIA分析工具和FDR评估提供见解.

主要方法:

从全长的人类cDNA库中合成的重组蛋白质.
分析样品使用液态染色体质谱法 (LC-MS) 与DIA.
将实验结果与已知的蛋白质序列进行比较,以计算FDR.
评估的DIA-NN版本是1.8.1,1.9.2和2.1.0.0的版本.

主要成果:

在DIA-NN版本1.9.2和2.1.0中,鉴定出比1.8.1.0版本更多的.
较新的版本采用了更保守的方法,显著改善了FDR控制.
平均前体水平的FDR:0.538% (1.8.1),0.389% (1.9.2),0.385% (2.1.0). 这是一个很大的变化.
平均蛋白质水平的FDR:2.85% (1.8.1),1.81% (1.9.2),1.81% (2.1.0). 这些蛋白质的含量均为FDR.

结论:

该研究提出了一种强大的方法来评估DIA软件中的FDR控制.
更新的DIA-NN版本在鉴定和FDR管理方面表现出卓越的性能.
这些发现有助于生物信息学家改进DIA工具,以便进行更可靠的蛋白质组分析.

关键词:

这就是DIA-NNN的意义.数据独立的获取采集.错误发现率错误发现率在试验室中,人类蛋白质组.软件比较软件比较合成的重组蛋白质混合物.

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