Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets

Abstract

Access to high quality metabolomics data has become a routine component for biological studies. However, interpreting those datasets in biological contexts remains a challenge, especially because many identified metabolites are not found in biochemical pathway databases. Starting from statistical analyses, a range of new tools are available, including metabolite set enrichment analysis, pathway and network visualization, pathway prediction, biochemical databases and text mining. Integrating these approaches into comprehensive and unbiased interpretations must carefully consider both caveats of the metabolomics dataset itself as well as the structure and properties of the biological study design. Special considerations need to be taken when adopting approaches from genomics for use in metabolomics. R and Python programming language are enabling an easier exchange of diverse tools to deploy integrated workflows. This review summarizes the key ideas and latest developments in regards to these approaches.

Figure 1

Metabolomics interpretation approaches can be combined into study-design specific workflows to provide a comprehensive interpretation.

Figure 2

Biochemical and ontology databases lack entries for the 385 metabolites identified in non-obese diabetic mice [30]. Figure is adopted from [35].

Figure 3

ChemRICH impact plot of chemical similarity enrichment analysis in non-obese diabetic mice [30]. Color indicates direction of change for most of the compounds within a class : red is increased, blue is decreased. Size of cluster indicates the number of metabolites within the class. X-axis shows the cluster order on the Tanimoto similarity tree. The tree order also correlates with average lipophilicity of classes so polar classes are always shown on the right side of the plot. Y-axis shows the negative log of adjusted p-values so significantly important classes are shown at the top of the plot. Figure is adopted from [35].