Abstract
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 20~100× 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.
Highlights
Global or untargeted metabolomics is increasingly used to investigate metabolic changes of various biological or environmental systems in an unbiased manner [1,2]
3.0 adopts an optimization strategy based on regions of interest (ROI) to avoid the time-consuming step of recursive peak detection using complete spectra
The algorithm first scans the whole spectra across m/z and retention time dimensions to select several Regions of Interest (ROIs) that are enriched for real peaks
Summary
Global or untargeted metabolomics is increasingly used to investigate metabolic changes of various biological or environmental systems in an unbiased manner [1,2]. The typical LC-HRMS metabolomics workflow involves spectra collection, raw data processing, statistical and functional analysis [5]. Despite significant progress made in recent years, critical issues remain with regard to several key steps involved in the current metabolomics workflow. Default parameters provided by common spectra processing tools are not applicable to all experiments [7], and misuse of parameters can lead to significant issues in data quality [8]. To mitigate this issue, commercial tools such as Waters MassLynxTM and open-source software such as XCMS [9] and MZmine [10] allow users to specify multiple parameters to define
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