caa a22 4500 445324910 CHVBK 20180317142717.0 cr unu---uuuuu 170323e20110401xx s 000 0 eng 10.1007/s10858-011-9480-x doi (NATIONALLICENCE)springer-10.1007/s10858-011-9480-x Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra [Elektronische Daten] [Pascal Mercier, Michael Lewis, David Chang, David Baker, David Wishart] Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative” metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis.Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values. Springer Science+Business Media B.V., 2011 Metabolomics nationallicence Nuclear magnetic resonance nationallicence Targeted profiling nationallicence Automated targeted spectral profiling nationallicence Mercier Pascal Chenomx Inc, T5K 2J1, Edmonton, AB, Canada aut Lewis Michael Chenomx Inc, T5K 2J1, Edmonton, AB, Canada aut Chang David Chenomx Inc, T5K 2J1, Edmonton, AB, Canada aut Baker David Pfizer Inc, Groton, CT, USA aut Wishart David Department of Computing Science and Biological Sciences, University of Alberta, T6G 2E8, Edmonton, AB, Canada aut Journal of Biomolecular NMR Springer Netherlands 49/3-4(2011-04-01), 307-323 0925-2738 49:3-4<307 2011 49 10858 https://doi.org/10.1007/s10858-011-9480-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10858-011-9480-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Mercier Pascal Chenomx Inc, T5K 2J1, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Lewis Michael Chenomx Inc, T5K 2J1, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Chang David Chenomx Inc, T5K 2J1, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Baker David Pfizer Inc, Groton, CT, USA aut NATIONALLICENCE 700 1- Wishart David Department of Computing Science and Biological Sciences, University of Alberta, T6G 2E8, Edmonton, AB, Canada aut NATIONALLICENCE 773 0- Journal of Biomolecular NMR Springer Netherlands 49/3-4(2011-04-01), 307-323 0925-2738 49:3-4<307 2011 49 10858 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer