Computational Tools for the Secondary Analysis of Metabolomics Experiments

Abstract

Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before.

Tear metabolite changes in keratoconus

“…Only metabolites that were present on all biological samples were considered for further analysis. Data is plotted as fold enrichment in order to highlight those abundant metabolites that are being affected (Booth et al, 2013). …”

Future perspectives for metabolomics in nutrition research

“…In recent years, metabolomics has progressed from improvements in the sensitivity and precision of measuring devices, through the development of new technologies, to the synergistic use of different approaches combined with advances in signal analysis (Zeng et al, 2014) and data mining (Booth et al, 2013). All of these developments offer metabolomics the potential to couple the characterisation and quantification of metabolites with a spatiotemporal dimension, giving it greater relevance for the biological interpretation of data.…”

Metabolomic strategies for aquaculture research: a primer

“…The recent development of secondary bioinformatics tools (reviewed by Booth et al . ) to analyse biochemical data within the context of predefined metabolite sets are changing the way that the results of metabolomics projects are interpreted. Pathway Activity Profiling (PAPi) is one example of such a technique (Aggio et al .…”