清理化学性MS/MS光谱用于LC-MS/MS基代谢物
Tingting Zhao1, Shipei Xing1, Huaxu Yu1
1Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1 British Columbia, Canada.
Analytical chemistry
|August 21, 2023
概括
这项研究介绍了DNMS2Purifier,这是一种新的生物信息学工具,可以在代谢学中清除模拟双重质谱 (MS/MS) 光谱. 它准确地识别和删除虚假碎片,改善代谢物注释和分子网络.
科学领域:
- 分析化学 分析化学
- 代谢学 代谢学 代谢学
- 生物信息学是一种生物信息学.
背景情况:
- 双重质谱 (MS/MS) 光谱的纯度对于准确的代谢物识别和在代谢学中的探索至关重要.
- 化学性MS/MS光谱,由同隔离的前体产生的,引入错误的碎片,危及光谱相似性分析和分子网络.
- 现有的MS/MS光谱清洗方法通常需要额外的数据采集或库匹配.
研究的目的:
- 开发一种 de novo 计算方法,用于清理基于液体染色学-并联质谱学 (LC-MS/MS) 的代谢学中的仿真MS/MS光谱.
- 识别关键片段峰值属性,以区分真实片段与虚假 (污染物) 片段在虚构光谱中.
- 创建一个自动化生物信息平台,用于处理和净化MS/MS光谱,而不需要额外的实验步骤.
主要方法:
- 通过数据模拟,研究了嵌合式光谱对光谱分析的影响.
- 分析了化学标准的MS/MS光谱,以确定真伪片段的特征,重点关注强度比率波动,出现率和相对强度.
- 训练和评估机器学习模型,选择XGBoost是因为它在区分真假片段方面具有卓越的性能 (AUC 0.98).
- 开发了DNMS2Purifier,这是一个实现训练XGBoost模型的自动化平台,用于MS/MS光谱清洁.
主要成果:
- 确定了三个关键的碎片峰值属性 (强度比率波动,出现率,相对强度),表明虚假碎片.
- 在XGBoost模型实现高精度 (AUC 0.98) 在区分真假碎片.
- DNMS2Purifier成功处理了模拟的MS/MS光谱,显示出更好的光谱质量和更高的代谢物注释和分子网络的准确性.
- 该工具有效地识别了具有更大的MS / MS前体隔离窗口的虚假碎片增加.
- DNMS2Purifier通过独特识别虚假碎片来补充现有的基于DIA的方法.
结论:
- DNMS2Purifier提供了一种有效的新型计算解决方案,用于在代谢学中清理模拟的MS/MS光谱.
- 该平台通过提高MS/MS光谱质量,提高了代谢物注释和分子网络的可靠性.
- 这种方法为研究人员提供了一种有价值的工具,因为它允许更准确的数据分析,而不需要额外的实验程序或图书馆依赖.
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