TopNEXt:用于多样本质谱实验的自动DDA排除框架
Ross McBride1, Joe Wandy2, Stefan Weidt2
1School of Computing Science, University of Glasgow, Glasgow G12 8RZ, United Kingdom.
Bioinformatics (Oxford, England)
|June 26, 2023
概括
TopNEXt通过改进数据采集来增强多样本液体染色学并列质谱. 这一框架提高了碎片化光谱的质量和数量,以更好地识别代谢物.
科学领域:
- 代谢学 代谢学 代谢学
- 分析化学 分析化学
- 生物信息学是一种生物信息学.
背景情况:
- 液体色谱 双重质谱 (LC-MS/MS) 对于代谢物注释至关重要.
- 目前LC-MS/MS中的数据依赖获取 (DDA) 方法往往产生不够的光谱质量和数量.
- 现有的DDA方法难以进行多样本分析,缺乏信息共享,需要手动干预.
研究的目的:
- 推出TopNEXt,一个新的实时扫描优先级框架,用于在多样本LC-MS/MS代谢学中增强数据采集.
- 提高在代谢学实验中收集的碎片化光谱的质量和数量.
- 为了促进在不同的实验环境中更准确和更全面地识别代谢物.
主要方法:
- TopNEXt使用兴趣区域和基于强度的评分系统来扩展数据依赖获取排除方法.
- 该框架在实时运行,在数据采集期间对扫描进行优先排序.
- TopNEXt 集成到现有的 ViMMS 框架中.
主要成果:
- TopNEXt成功地增加了对额外10%的目标峰值的碎片化光谱采集.
- 该框架实现了收购强度额外增加20%.
- 模拟和实验室实验验证实了TopNEXt在改善光谱数据方面的有效性.
结论:
- TopNEXt显著提高了多样本LC-MS/MS代谢学中的数据采集.
- 该框架对光谱质量和数量的改进直接有助于更好地识别代谢物.
- TopNEXt提供了一个有价值的工具,在代谢学中的各种实验环境中具有广泛的适用性.
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