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GCMS-ID:一个用于识别气体染色学质谱学实验中的化合物的网络服务器
Julia Wakoli1, Afia Anjum2, Tanvir Sajed1
1Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
GCMS-ID是一个使用机器学习来预测质谱和保持指数的网络服务器,用于气色谱-质谱 (GC-MS) 实验中的化合物识别,以解决对代谢学和暴露学研究的光谱数据缺乏的问题.
科学领域:
- 计算化学是一种计算化学.
- 分析化学是一种分析化学.
- 化学信息学 化学信息学
背景情况:
- 气色谱-质谱 (GC-MS) 对于识别化合物至关重要.
- 有限的实验参考光谱阻碍了化合物识别,特别是在代谢学和暴露学中.
- In silico代谢学提供了一个计算解决方案来扩展光谱数据库.
研究的目的:
- 开发和介绍GCMS-ID,一个使用预测光谱数据进行化合物识别的网络服务器.
- 将现有的预测工具 (CFM-EI和RIpred) 整合到一个统一的平台中.
- 为了解决用于代谢学和暴露学研究的实验EI-MS和RI数据的稀缺性.
主要方法:
- GCMS-ID集成了CFM-EI用于电子电离质谱 (EI-MS) 预测和RIpred用于Kovats保留指数 (RI) 预测.
- 它利用了一个大型库,包含100多万个预测的RI/EI-MS值,用于代谢学/暴露学相关的化合物.
- 网络服务器允许将实验EI-MS,RI或组合数据与此库匹配以进行识别.
主要成果:
- GCMS-ID使用实验RI,EI-MS或组合RI+EI-MS数据准确识别化合物.
- 该工具成功地与>100万预测RI/EI-MS值的库进行了匹配.
- GCMS-ID可以从用户提交的结构中预测RI或EI-MS频谱,并注释用户提交的EI-MS频谱.
结论:
- 在GCMS分析中,GCMS-ID增强了化合物识别能力,特别是在代谢学和暴露学方面.
- 预测的光谱数据的整合大大扩大了可识别化合物的范围.
- GCMS-ID为相关领域的研究人员提供了一个有价值的,免费可访问的资源.
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