MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

Abstract

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Graphical Abstract

Figure 1.

MetaboAnalyst 6.0 workflow for targeted and untargeted metabolomics data. Multiple data input types are accepted. Untargeted metabolomics inputs require extra steps for spectra processing and peak annotation. The result table can be used for statistical and functional analysis within a consistent workflow in the same manner as for targeted metabolomics data.

Figure 2.

Example outputs from MetaboAnalyst 6.0. (A) Integrated 3D PCA score and loading plots summarizing the raw spectra processing results. (B) An interactive mirror plot showing the MS2 matching result. Matched fragments are marked with a red diamond. (C) Functional analysis results with the top four significant pathways labelled. (D) A forest plot comparing the effect sizes calculated based on individual SNPs (black) or using all SNPs by different MR methods (red). (E) Bar plots of the dose response curve fitting results showing how many times each model type was identified as the best fit. (F) A dose-response curve fitting result showing each of the concentration values (black points), the fitted curve (solid blue line), and the estimated benchmark dose (solid red line) with its lower and upper 95% confidence intervals (dashed red lines), respectively.