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Metabolomics. 2018;14(3):31. doi: 10.1007/s11306-018-1321-4. Epub 2018 Feb 12.

Recommended strategies for spectral processing and post-processing of 1D 1H-NMR data of biofluids with a particular focus on urine.

Author information

1
Imaging and Characterization Core Lab, KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia. abdelhamid.emwas@kaust.edu.sa.
2
Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Stippeneng 4, 6708 WE, Wageningen, The Netherlands.
3
Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Kingdom of Saudi Arabia.
4
Department of Chemistry, University of Alberta, Edmonton, Canada.
5
Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Lucknow, India.
6
Department of Biological Sciences, University of Alberta, Edmonton, Canada.

Abstract

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.

KEYWORDS:

Baseline correction; Data post-processing; Metabolomics; NMR spectroscopy; Normalization; Scaling; Spectral alignment; Spectral binning; Spectral processing; Urine

Conflict of interest statement

Compliance with ethical standardsThe authors declare that they have no conflict of interest.We declared that, all authors comply with Springer’s ethical policies.

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