在Spectraverse中对小分子MS/MS库进行全面的策划和协调
Vishu Gupta1,2, Hantao Qiang1,2,3, Hsin-Hsiang Chung1,2
1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08544, United States.
Analytical chemistry
|January 26, 2026
概括
Spectraverse提供高质量的,精心策划的双重质谱 (MS/MS) 图书馆,用于代谢物识别. 本资源解决了公共光谱图书馆的问题,改善了机器学习模型培训的代谢学.
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:
- 公共串联质谱 (MS/MS) 图书馆对于代谢物识别和机器学习模型培训在代谢学方面至关重要.
- 现有的光谱图书馆受到了碎片化,低质量,缺失的元数据和不准确的注释的影响,阻碍了研究.
- 开发定制机器学习模型需要大量的质谱学专业知识和数据预处理,这对许多研究人员来说是一个障碍.
研究的目的:
- 创建Spectraverse,一个全面的,精心策划的公共MS/MS光谱的小分子的图书馆.
- 解决现有光谱数据库的局限性,包括数据质量,元数据标准化和化学结构准确性.
- 提供一个机器学习准备的资源,扩大了对代谢学相关的和电离模式的覆盖范围.
主要方法:
- 从主要存储库和被忽视的来源收集的MS/MS光谱.
- 开发了一条预处理管道,用于元数据协调,化学结构标准化以及去除低质量/多余的光谱.
- 在现有的公共图书馆中发现了影响机器学习模型比较和培训集完整性的陷.
主要成果:
- 创建了Spectraverse,这是迄今为止最全面的化学,机器学习准备的MS/MS光谱库.
- 标准化元数据和化学结构,提高代谢物注释的数据可靠性.
- 揭示了公共图书馆中的问题,这些问题可能会影响机器学习模型的性能和数据的包含.
结论:
- Spectraverse提供了一个高质量的,精心策划的资源,以克服现有的公共MS/MS光谱库的局限性.
- 该图书馆促进了改进的代谢物识别和机器学习模型开发在代谢学.
- Spectraverse 旨在进行持续维护和扩展,以包括未来公开可用的MS/MS光谱数据.
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