Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine
Type
ArticleAuthors
Emwas, Abdul-Hamid M.Saccenti, Edoardo
Gao, Xin

McKay, Ryan T.
dos Santos, Vitor A. P. Martins
Roy, Raja
Wishart, David S.
KAUST Department
Computational Bioscience Research Center (CBRC)Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Imaging and Characterization Core Lab
NMR
KAUST Grant Number
URF/1/1976-04Date
2018-02-12Online Publication Date
2018-02-12Print Publication Date
2018-03Permanent link to this record
http://hdl.handle.net/10754/627182
Metadata
Show full item recordAbstract
1H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.Citation
Emwas A-H, Saccenti E, Gao X, McKay RT, dos Santos VAPM, et al. (2018) Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine. Metabolomics 14. Available: http://dx.doi.org/10.1007/s11306-018-1321-4.Sponsors
The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/1976-04.Publisher
Springer NatureJournal
MetabolomicsPubMed ID
29479299Additional Links
https://link.springer.com/article/10.1007%2Fs11306-018-1321-4ae974a485f413a2113503eed53cd6c53
10.1007/s11306-018-1321-4
Scopus Count
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