MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

Jasmine Chong, Othman Soufan, Carin Li, Iurie Caraus, Shuzhao Li, Guillaume Bourque, David S Wishart, Jianguo Xia, Jasmine Chong, Othman Soufan, Carin Li, Iurie Caraus, Shuzhao Li, Guillaume Bourque, David S Wishart, Jianguo Xia

Abstract

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

Figures

Figure 1.
Figure 1.
Overview of MetaboAnalyst 4.0 framework. The current modules can be organized into four general categories: (i) exploratory statistical analysis, (ii) functional analysis, (iii) data integration & systems biology and (iv) data processing & utility functions.
Figure 2.
Figure 2.
Summary of new features introduced in MetaboAnalyst 4.0. (A) An illustration showing the R command history panel and the companion MetaboAnalystR package will allow users to easily reproduce their analyses. In this case, the R command history captures all R commands leading to the generation the PLS-DA 2D score plot, which can then be reproduced in MetaboAnalystR using identical R commands (except the file path parameter for user input). (B) A zoomed-in view of the KEGG metabolic network showing the potential metabolite hits predicted from the mummichog algorithm. Clicking on a highlighted node will display all possible matched adduct forms of the corresponding compound. (C) An interactive Venn diagram showing the results from a biomarker meta-analysis. Clicking on an area will show the corresponding hits. (D) An example of the metabolite-gene-disease interaction network created based on user input.

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