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NMRProcFlow: A graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics

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      Abstract

      Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks, and so far the best way is to proceed interactively with a spectra viewer. NMRProcFlow is a graphical and interactive 1D NMR (1H \& 13C) spectra processing tool dedicated to metabolic fingerprinting and targeted metabolomic, covering all spectra processing steps including baseline correction, chemical shift calibration, alignment. It does not require programming skills. Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.

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      Quantitative 1H NMR spectroscopy

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        Improved parametric time warping for proteomics

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          Highly Repeatable Dissolution Dynamic Nuclear Polarization for Heteronuclear NMR Metabolomics.

          At natural (13)C abundance, metabolomics based on heteronuclear NMR is limited by sensitivity. We have recently demonstrated how hyperpolarization by dissolution dynamic nuclear polarization (D-DNP) assisted by cross-polarization (CP) provides a reliable way of enhancing the sensitivity of heteronuclear NMR in dilute mixtures of metabolites. In this Technical Note, we evaluate the precision of this experimental approach, a critical point for applications to metabolomics. The higher the repeatability, the greater the likelihood that one can detect small biologically relevant differences between samples. The average repeatability of our state-of-the-art D-DNP NMR equipment for samples of metabolomic relevance (20 mg dry weight tomato extracts) is 3.6% for signals above the limit of quantification (LOQ) and 6.4% when all the signals above the limit of detection (LOD) are taken into account. This first report on the repeatability of D-DNP highlights the compatibility of the technique with the requirements of metabolomics and confirms its potential as an analytical tool for such applications.
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            Author and article information

            Journal
            2016-11-23
            1611.07801

            http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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            q-bio.QM stat.AP
            ccsd

            Applications, Quantitative & Systems biology

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