杜雷斯:一种R包,用于否定实验双重质谱和代谢物注释
Shayantan Banerjee1, Prajval Nakrani2, Aviral Singh2
1Centre for Digital Health, Indian Institute of Technology Bombay, Mumbai 400076, India.
Analytical chemistry
|June 6, 2025
概括
在非目标代谢组中精确的代谢物识别得到了新的R包DuReS的改进. 它使用复制光谱来区分真实信号和噪声,减少虚假注释,并保留重要的低强度数据.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 计算生物学 计算生物学
背景情况:
- 使用质谱学的非向代谢学对于小分子分析至关重要.
- 准确的代谢物识别受到协同质谱中的噪声的阻碍,导致错误的注释.
- 传统的降噪强度值通常是特定于数据集的,可能会忽略真实信号.
研究的目的:
- 开发和介绍一个新的R包,Denoising Using Replicate Spectra (DuReS),用于在质谱数据中改进代谢物识别.
- 为了应对将真实信号峰值与随机噪声区分开来的挑战,在并列质谱中.
- 为数据依赖获取 (DDA) 模式的代谢学数据提供无的排泄管道.
主要方法:
- 开发了DuReS R包,它可以处理mzML文件和功能列表.
- 实现了一个基于假设的无声化模块,即真正的信号峰值在复制的并列光谱中重复出现.
- 包括一个可选的调模块来确定特定数据集的最佳复发频率切断值 (Fthreshold).
主要成果:
- 杜雷斯有效地将信号损失降至最低,同时最大限度地降低噪音.
- 该包装保留了诊断上显著的低强度碎片,通常与传统的值丢失.
- 在八个不同的代谢学数据集上进行了测试,证明了改进的光谱匹配指标和减少错误阳性.
结论:
- 杜雷斯提供了一个强大的解决方案,用于在非目标代谢学中拒绝质谱数据.
- 基于复发的方法提高了代谢物注释的准确性.
- 杜雷斯促进了更可靠和更有效的代谢学数据分析.
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