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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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产品离子光谱的数据处理:控制错误检测率的方法 复合搜索结果中的错误检测率 针对非目标代谢物
1Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan.
Mass spectrometry (Tokyo, Japan)
|November 18, 2024
概括
本研究介绍了四种简单的方法来控制在非目标的代谢学化合物搜索中错误发现率 (FDR). 第二级方法在准确的FDR估计和识别新型化合物方面被证明是最有效的.
科学领域:
- 分析化学 分析化学
- 代谢学 代谢学 代谢学
- 质谱测量质量谱测量
背景情况:
- 非定位代谢学依赖于数据库搜索光谱注释.
- 在分析有助于将光谱与分子结构相匹配.
- 假阳性需要假发现率 (FDR) 控制.
研究的目的:
- 建议和评估用于复合数据库搜索中控制FDR的简单方法.
- 将诱光谱数据集生成技术与传统诱数据库进行比较.
主要方法:
- 开发了四种使用诱光谱数据集的FDR控制方法 (极性切换,镜像,光谱采样,二级).
- 通过对复合数据库搜索目标和诱光谱来比较诱生成方法.
- 使用MassBank数据和SIRIUS 5 CSI:FingerID.使用的评估方法性能.
主要成果:
- 第二级方法提供了与真正的FDR最接近的FDR估计.
- 在0.05FDR的FDR控制搜索中,在人类代谢数据集中成功识别了新型化合物.
- 诱光谱数据集为诱复合数据库提供了一个替代方案.
结论:
- 第二级方法是FDR控制在代谢化合物识别中的可靠方法.
- 精确的FDR控制提高了化合物注释在非目标代谢学中的可靠性.
- 这种方法有助于发现以前未知的代谢产物.
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