uafR:一个R包,自动化质谱数据处理
Chase A Stratton1,2, Yvonne Thompson1, Konilo Zio3
1The Land Institute, Salina, KS, United States of America.
PloS one
|July 5, 2024
概括
该uafR包自动化了气色谱-质谱 (GC-MS) 数据分析,使得结构相似性的快速匹配和化合物识别成为可能. 这个R包显著提高了化学数据解释的速度和准确性,用于各种研究应用.
科学领域:
- 化学信息学 化学信息学
- 分析化学 分析化学
- 计算生物学 计算生物学
背景情况:
- 化学数据的生成正在迅速增加,超过目前的分析能力.
- 传统气相色谱-质谱 (GC-MS) 数据的解释是耗时和劳动密集的.
- 需要自动化和高效的工具来处理大规模的化学数据集.
研究的目的:
- 开发一个R包,uafR,用于自动化GC-MS数据检索和分析.
- 为了快速进行结构相似性匹配和初步化合物识别.
- 为了减少与化学数据解释相关的时间,成本和精力.
主要方法:
- 开发了uafR R包,集成化学信息学工作流程.
- 使用公共化学数据库 (如PubChem) 进行化合物识别.
- 使用纯化的内部标准和以前发布的大量GC-MS数据集验证了该包.
主要成果:
- 在高R2值 (0.827-0.999) 的净化标准中,uafR包准确地识别了已知的化合物.
- 对大量数据集的分析产生了与手工方法相比较或相同的化合物识别.
- 该套件在GC-MS数据处理速度和准确性方面取得了显著的改进.
结论:
- uafR极大地提高了GC-MS数据处理的速度和准确性.
- 该套件促进了对大型数据集的高效分析,支持诸如曝光学和代谢学等领域.
- uafR是免费可用的,适用于使用GC-MS分析的任何研究.
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