MetScribeR:一种半自动化工具,用于内部LC-MS代谢物参考库的数据处理.
Adam M Tisch1, Jason M Inman1, Ewy A Mathé1
1Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), Rockville, Maryland 20850, United States.
Journal of proteome research
|November 17, 2025
概括
非目标代谢组合物识别由metScribeR简化,这是一个R包,用于快速创建保留时间 (RT) 和m/z库. 这个工具加速了代谢物标准库的构建,用于生物学数据的解释.
科学领域:
- 代谢学 代谢学 代谢学
- 质谱测量质量谱测量
- 生物信息学是一种生物信息学.
背景情况:
- 在非目标代谢学中化合物识别对于生物学数据的解释至关重要.
- 建立具有保留时间 (RT) 数据的内部代谢物标准库补充了现有的MS/MS光谱存储库.
- 目前创建这些库的方法是耗时和劳动密集的.
研究的目的:
- 开发metScribeR,一个R包和Shiny应用程序,以加快对代谢物标准的保留时间 (RT) 和m/z库的创建.
- 为处理质谱 (MS) 数据提供一个用户友好的界面,包括峰值检测,过和质量控制.
- 为了实现化合物识别而不需要MS/MS光谱数据,为每种添加物提供识别概率估计.
主要方法:
- 开发metScribeR,一个R包与一个闪亮的应用程序.
- 用于MS数据的峰值查找,过和质量审查算法.
- 与RT和m/z图书馆创建的手动方法进行比较.
主要成果:
- metScribeR显著降低了每个标准所需的精力,约为10秒.
- 在手动和metScribeR衍生的RTs之间实现了高相关性 (0.99),表明了高准确性.
- 成功过出质量差的峰值,并生成了包含身份,m/z,RT和质量信息的全面输出文件.
结论:
- metScribeR提供了一个高效和用户友好的解决方案,用于构建代谢物标准库.
- 该套件通过利用RT和m/z数据,在非目标代谢学中促进了准确的化合物识别.
- metScribeR是开源的,促进了该领域的可访问性和进一步发展.
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