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Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis
Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectr...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145719/ https://www.ncbi.nlm.nih.gov/pubmed/33947160 http://dx.doi.org/10.3390/metabo11050285 |
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author | Haslauer, Kristina E. Schmitt-Kopplin, Philippe Heinzmann, Silke S. |
author_facet | Haslauer, Kristina E. Schmitt-Kopplin, Philippe Heinzmann, Silke S. |
author_sort | Haslauer, Kristina E. |
collection | PubMed |
description | Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine (1)H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis. |
format | Online Article Text |
id | pubmed-8145719 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81457192021-05-26 Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis Haslauer, Kristina E. Schmitt-Kopplin, Philippe Heinzmann, Silke S. Metabolites Article Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine (1)H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis. MDPI 2021-04-29 /pmc/articles/PMC8145719/ /pubmed/33947160 http://dx.doi.org/10.3390/metabo11050285 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Haslauer, Kristina E. Schmitt-Kopplin, Philippe Heinzmann, Silke S. Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis |
title | Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis |
title_full | Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis |
title_fullStr | Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis |
title_full_unstemmed | Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis |
title_short | Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis |
title_sort | data processing optimization in untargeted metabolomics of urine using voigt lineshape model non-linear regression analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145719/ https://www.ncbi.nlm.nih.gov/pubmed/33947160 http://dx.doi.org/10.3390/metabo11050285 |
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