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pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra
MOTIVATION: Data processing is a key bottleneck for (1)H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been p...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546129/ https://www.ncbi.nlm.nih.gov/pubmed/30351417 http://dx.doi.org/10.1093/bioinformatics/bty837 |
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author | Rodriguez-Martinez, Andrea Ayala, Rafael Posma, Joram M Harvey, Nikita Jiménez, Beatriz Sonomura, Kazuhiro Sato, Taka-Aki Matsuda, Fumihiko Zalloua, Pierre Gauguier, Dominique Nicholson, Jeremy K Dumas, Marc-Emmanuel |
author_facet | Rodriguez-Martinez, Andrea Ayala, Rafael Posma, Joram M Harvey, Nikita Jiménez, Beatriz Sonomura, Kazuhiro Sato, Taka-Aki Matsuda, Fumihiko Zalloua, Pierre Gauguier, Dominique Nicholson, Jeremy K Dumas, Marc-Emmanuel |
author_sort | Rodriguez-Martinez, Andrea |
collection | PubMed |
description | MOTIVATION: Data processing is a key bottleneck for (1)H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D (1)H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra. RESULTS: The performance of JBA is comprehensively evaluated using 617 plasma (1)H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented using the MWASTools R/Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-6546129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-65461292019-06-13 pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra Rodriguez-Martinez, Andrea Ayala, Rafael Posma, Joram M Harvey, Nikita Jiménez, Beatriz Sonomura, Kazuhiro Sato, Taka-Aki Matsuda, Fumihiko Zalloua, Pierre Gauguier, Dominique Nicholson, Jeremy K Dumas, Marc-Emmanuel Bioinformatics Original Papers MOTIVATION: Data processing is a key bottleneck for (1)H NMR-based metabolic profiling of complex biological mixtures, such as biofluids. These spectra typically contain several thousands of signals, corresponding to possibly few hundreds of metabolites. A number of binning-based methods have been proposed to reduce the dimensionality of 1 D (1)H NMR datasets, including statistical recoupling of variables (SRV). Here, we introduce a new binning method, named JBA (“pJRES Binning Algorithm”), which aims to extend the applicability of SRV to pJRES spectra. RESULTS: The performance of JBA is comprehensively evaluated using 617 plasma (1)H NMR spectra from the FGENTCARD cohort. The results presented here show that JBA exhibits higher sensitivity than SRV to detect peaks from low-abundance metabolites. In addition, JBA allows a more efficient removal of spectral variables corresponding to pure electronic noise, and this has a positive impact on multivariate model building AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented using the MWASTools R/Bioconductor package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2019-06-01 2018-10-23 /pmc/articles/PMC6546129/ /pubmed/30351417 http://dx.doi.org/10.1093/bioinformatics/bty837 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Rodriguez-Martinez, Andrea Ayala, Rafael Posma, Joram M Harvey, Nikita Jiménez, Beatriz Sonomura, Kazuhiro Sato, Taka-Aki Matsuda, Fumihiko Zalloua, Pierre Gauguier, Dominique Nicholson, Jeremy K Dumas, Marc-Emmanuel pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra |
title | pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra |
title_full | pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra |
title_fullStr | pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra |
title_full_unstemmed | pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra |
title_short | pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES (1)H NMR spectra |
title_sort | pjres binning algorithm (jba): a new method to facilitate the recovery of metabolic information from pjres (1)h nmr spectra |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546129/ https://www.ncbi.nlm.nih.gov/pubmed/30351417 http://dx.doi.org/10.1093/bioinformatics/bty837 |
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