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Updated: Sep 17, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

MassCube提高了从原始文件到表型分类器的代谢学数据处理的准确性.

Huaxu Yu¹, Jun Ding², Tong Shen¹

¹West Coast Metabolomics Center, University of California Davis, Davis, CA, USA.

Nature communications

|July 2, 2025

概括

此摘要是机器生成的。

MassCube是一个新的开源框架,用于质谱 (MS) 数据处理,它提供了在代谢学中的强大而高效的非定位峰值检测. 它在速度和准确性方面优于现有的工具,使先进的奥米克和生物医学研究成为可能.

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Last Updated: Sep 17, 2025

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

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Published on: January 7, 2019

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Published on: July 27, 2021

科学领域:

分析化学分析化学
计算生物学计算生物学
代谢学代谢学代谢学

背景情况:

在液体染色学-质谱学 (LC-MS) 代谢学中非目标性峰值检测需要强大且具有基准的方法.
现有的算法往往缺乏对大型数据集进行全面的基准测试和效率.

研究的目的:

介绍MassCube,一个开源的Python框架用于MS数据处理.
系统地将MassCube与其他算法和数据类型进行比较.
提供一种用于代谢组信息的表型预测的工具.

主要方法:

通过信号聚类和高斯过器边缘检测进行质量跟踪构造,用于峰值识别.
峰值的分组用于添加和源内碎片检测.
使用身份和模糊搜索进行复合注释,然后进行质量控制.

主要成果:

通过全面的色谱元数据报告,MassCube实现了100%的信号覆盖.
在速度,同位素检测和准确性方面优于MS-DIAL,MZmine3和XCMS.
有效地处理大型数据集 (笔记本电脑上在64分钟内处理105GB),比其他替代方案快得多.
尽管有批量效应,但在小鼠大脑代谢数据中成功识别了年龄,性别和区域差异.

结论:

MassCube为基于LC-MS的代谢学峰值检测提供了一个强大,高效和准确的解决方案.
它的性能和速度使得它适合大规模的电子和生物医学研究.
该框架可用于直接使用或集成到更大的研究应用中.