Metabolomics is a key approach in modern functional genomics and systems biology.Due to the complexity of metabolomics data, the variety of experimental designs, and the variety of existing bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M; http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues.Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI).Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows.
: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, Springer Verlag, 2017, 10.1007/s11306-017-1244
Abstract:Introduction Experiments in metabolomics rely on the identification and quantification of metabolites in complex biological mixtures. This remains one of the major challenges in NMR/mass spectrometry analysis of metabolic profiles. These features are mandatory to make metabolomics asserting a general approach to test a priori formulated hypotheses on the basis of exhaustive metabolome characterization rather than an exploratory tool dealing with unknown metabolic features.Objectives In this article we propose a method, named ASICS, based on a strong statistical theory that handles automatically the metabolites identification and quantification in proton NMR spectra.Methods A statistical linear model is built to explain a complex spectrum using a library containing pure metabolite spectra. This model can handle local or global chemical shift variations due to experimental conditions using a warping function. A statistical lasso-type estimator identifies and quantifies the metabolites in the complex spectrum. This estimator shows good statistical properties and handles peak overlapping issues.
ResultsThe performances of the method were investigated on known mixtures (such as synthetic urine) and on plasma datasets from duck and human. Results show noteworthy performances, outperforming current existing methods.Conclusion ASICS is a completely automated procedure to identify and quantify metabolites in 1 H NMR spectra of biological mixtures. It will enable empowering NMR-based metabolomics by quickly and accurately helping experts to obtain metabolic profiles.
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