Large-scale Metabolomics Data Research Articles

MET-IDEA version 2.06; improved efficiency and additional functions for mass spectrometry-based metabolomics data processing

Metabolomics Ion-based Data Extraction Algorithm (MET-IDEA) is a computer program for processing large-scale metabolomics data. MET-IDEA utilizes network Common Data Form (netCDF) data files available from a diversity of chromatographically coupled mass spectrometry (MS) systems, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized data. This article reports on recent improvements to MET-IDEA which include new visualization of peak integrations, display of mass spectra associated with integrated peaks, and optional manual peak integration. The computational performance of MET-IDEA has also been improved to avoid memory overflow during the processing of large data sets and the software made compatible with 64 bit CPUs and operating systems. These new functions improve the performance of MET-IDEA, and they allow users to visualize peak integrations and curate the results through manual integration if desired. The improved version of MET-IDEA better facilitates the quantitative analysis of complex MS-based metabolomics data. MET-IDEA is freely available for academic and non commercial use at ( http://bioinfo.noble.org/gateway/index.php?option=com_wrapper&Itemid=57 ). Commercial use is available via licensing agreement.

Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data

BackgroundWith the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions.ResultsIn our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination.ConclusionsIn summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.

Putative regulatory sites unraveled by network‐embedded thermodynamic analysis of metabolome data

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.

Behavioural problems in infants and toddlers were associated with later psychiatric disorders

<h3>ABSTRACT</h3> Type 2 diabetes (T2D) and coronary artery disease (CAD) both have known genetic determinants, but the mechanisms through which their associated genetic variants lead to disease onset remain poorly...