MCnebula:用于非目标LC-MS/MS数据分析的可视化对分类和提升识别的关键化学类
Lichuang Huang1,2, Qiyuan Shan1,2, Qiang Lyu1
1School of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 310053, China.
Analytical chemistry
|June 14, 2023
概括
MCnebula加速了用于系统生物学的非定向质谱数据分析. 该框架有助于分类化合物和发现生物标志物,提高生物研究的效率.
科学领域:
- 代谢学 代谢学 代谢学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 非定位质谱对于生物分析至关重要,但需要长时间处理数据,特别是在系统生物学中.
- 现有的方法往往难以对复杂的生物样本进行全面分析.
- 需要有效和直观的工具来分析大规模的代谢数据是显著的.
研究的目的:
- 开发和介绍多重化学星云 (MCnebula),这是一个新的框架,旨在简化液态染色体质谱 (LC-MS) 数据分析.
- 提高未知化合物的分类和结构特征,超出光谱图书馆的限制.
- 为了促进直观的途径分析和生物标志物在复杂的生物系统的发现.
主要方法:
- MCnebula使用基于丰度的类 (ABC) 选择算法来确定特征优先级.
- 它将"特征" (化合物) 分类为关键化学类.
- 数据可视化通过带有注释和结构信息的多维儿童星云网络图实现.
主要成果:
- 在人类血清代谢学数据集中,MCnebula成功地将"乙类卡尼丁"确定为生物标志物,与已知的参考资料保持一致.
- 该框架允许在E. ulmoides*的植物性数据集中快速注释和发现化合物.
- MCnebula证明了其在探索未知的化合物的分类和结构特征方面的实用性.
结论:
- MCnebula显著减少了LC-MS数据分析时间,使其成为系统生物学中的一个有价值的工具.
- 该框架为生物标志物发现和途径分析提供了一个直观的平台.
- 在代谢学中,MCnebula为未知化合物的分类和结构阐明提供了一个强大的解决方案.
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