Pang Zhiqiang, Chong Jasmine, Li Shuzhao, Xia Jianguo
Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, Quebec, H9X 3V9, Canada.
The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, Canada.
Metabolites. 2020 May 7;10(5):186. doi: 10.3390/metabo10050186.
Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.
液相色谱与高分辨率质谱平台联用越来越多地用于全面测量系统生物学和复杂疾病中的代谢组变化。在过去十年中,已经开发了几种强大的计算流程用于光谱处理、注释和分析。然而,在参数设置、计算效率、批次效应和功能解释方面仍然存在重大障碍。在此,我们介绍MetaboAnalystR 3.0,这是一个有显著改进的流程,具有三个关键新特性:(1)用于峰提取的高效参数优化;(2)自动批次效应校正;以及(3)更准确的通路活性预测。我们的基准研究表明,与其他成熟的工作流程相比,此工作流程快20至100倍,并产生更具生物学意义的结果。总之,MetaboAnalystR 3.0提供了一个高效的流程,以支持在开源R环境中的高通量全局代谢组学。