MetaNetter 2:A Cytoscape plugin for ab initio network analysis and metabolite feature classification

Metabolomics frequently relies on the use of high resolution mass spectrometry data. Classification and filtering of this data remain a challenging task due to the plethora of complex mass spectral artefacts, chemical noise, adducts and fragmentation that occur during ionisation and analysis. Additionally, the relationships between detected compounds can provide a wealth of information about the nature of the samples and the biochemistry that gave rise to them. We present a biochemical networking tool: MetaNetter 2 that is based on the original MetaNetter, a Cytoscape plugin that creates ab initio networks. The new version supports two major improvements: the generation of adduct networks and the creation of tables that map adduct or transformation patterns across multiple samples, providing a readout of compound relationships. We have applied this tool to the analysis of adduct patterns in the same sample separated under two different chromatographies, allowing inferences to be made about the effect of different buffer conditions on adduct detection, and the application of the chemical transformation analysis to both a single fragmentation analysis and an all-ions fragmentation dataset. Finally, we present an analysis of a dataset derived from anaerobic and aerobic growth of the organism Staphylococcus aureus demonstrating the utility of the tool for biological analysis

Toward global metabolomics snalysis with Hydrophilic Interaction Liquid Chromatography–Mass Spectrometry: improved metabolite identification by retention time prediction

Metabolomics is an emerging field of postgenomic biology concerned with comprehensive analysis of small molecules in biological systems. However, difficulties associated with the identification of detected metabolites currently limit its application. Here we demonstrate that a retention time prediction model can improve metabolite identification on a hydrophilic interaction chromatography (HILIC)-high-resolution mass spectrometry metabolomics platform. A quantitative structure retention relationship (QSRR) model, incorporating six physicochemical variables in a multiple-linear regression based on 120 authentic standard metabolites, shows good predictive ability for retention times of a range of metabolites (cross-validated R(2) = 0.82 and mean squared error = 0.14). The predicted retention times improved metabolite identification by removing 40% of the false identifications that occurred with identification by accurate mass alone. The importance of this procedure was demonstrated by putative identification of 690 metabolites in extracts of the protozoan parasite Trypanosoma brucei, thus allowing identified metabolites to be mapped onto an organism-wide metabolic network, providing opportunities for future studies of cellular metabolism from a global systems biology perspectiv