对高分辨率MS/MS数据的XCorr评分进行精确的p值计算
Kishankumar Bhimani1, Arina Peresadina1, Dmitrii Vozniuk1
1Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, Moscow, Russian Federation.
Proteomics
|September 19, 2023
概括
高分辨率精确p值 (HR-XPV) 方法改善了质谱学中的谱匹配得分. 这种方法准确地从高分辨率数据中校准得分,从而产生更可靠的频谱注释.
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
- 计算生物学 计算生物学
背景情况:
- 谱匹配 (PSM) 评分对于在MS/MS数据中识别来说至关重要.
- 现有的精确的p值 (XPV) 方法由于分类不准确而与高分辨率质谱数据作斗争.
- 类似点点产品的分数 (例如,XCorr) 是常见的,但需要精确的校准.
研究的目的:
- 开发和验证一种使用高分辨率MS/MS数据校准PSM得分的新方法.
- 解决标准XPV方法在处理高分辨率碎片化模式方面的局限性.
- 为了提高质谱测量中标识的准确性和可靠性.
主要方法:
- 引入了高分辨率精确p值 (HR-XPV) 方法,这是XPV的延伸.
- 整合了在整个碎片化过程中的残余质量跟踪,以改善碎片箱分配.
- 应用HR-XPV来校准高分辨率光谱的点产品样分数 (例如XCorr).
- 使用四个不同的质谱数据集验证了该方法.
主要成果:
- HR-XPV有效地对高分辨率MS/MS光谱进行PSM评分校准.
- 该方法准确地将碎片分配到正确的容器中,利用高分辨率数据.
- 实验结果显示,各种数据集的得分都得到了良好的校准.
- 在所有测试的虚假发现率水平上都观察到改善的频谱注释.
结论:
- HR-XPV为高分辨率质谱测量中校准PSM得分提供了强大的解决方案.
- 该方法通过改进分数校准来提高频谱注释的可靠性.
- HR-XPV能够从现代的高分辨率质谱数据中更准确地识别.
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