Signature Mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Signature Mapping (SigMa) : an efficient approach for processing complex human urine 1H NMR metabolomics data. / Khakimov, Bekzod; Mobaraki, Nabiollah; Trimigno, Alessia; Aru, Violetta; Engelsen, Søren Balling.

In: Analytica Chimica Acta, Vol. 1108, 2020, p. 142-151.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Khakimov, B, Mobaraki, N, Trimigno, A, Aru, V & Engelsen, SB 2020, 'Signature Mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data', Analytica Chimica Acta, vol. 1108, pp. 142-151. https://doi.org/10.1016/j.aca.2020.02.025

APA

Khakimov, B., Mobaraki, N., Trimigno, A., Aru, V., & Engelsen, S. B. (2020). Signature Mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data. Analytica Chimica Acta, 1108, 142-151. https://doi.org/10.1016/j.aca.2020.02.025

Vancouver

Khakimov B, Mobaraki N, Trimigno A, Aru V, Engelsen SB. Signature Mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data. Analytica Chimica Acta. 2020;1108:142-151. https://doi.org/10.1016/j.aca.2020.02.025

Author

Khakimov, Bekzod ; Mobaraki, Nabiollah ; Trimigno, Alessia ; Aru, Violetta ; Engelsen, Søren Balling. / Signature Mapping (SigMa) : an efficient approach for processing complex human urine 1H NMR metabolomics data. In: Analytica Chimica Acta. 2020 ; Vol. 1108. pp. 142-151.

Bibtex

@article{95b9e5a0c72841abb6efcc298243b378,
title = "Signature Mapping (SigMa): an efficient approach for processing complex human urine 1H NMR metabolomics data",
abstract = "Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.",
keywords = "MCR, Metabolomics, NMR, Signature Mapping, Urine",
author = "Bekzod Khakimov and Nabiollah Mobaraki and Alessia Trimigno and Violetta Aru and Engelsen, {S{\o}ren Balling}",
year = "2020",
doi = "10.1016/j.aca.2020.02.025",
language = "English",
volume = "1108",
pages = "142--151",
journal = "Analytica Chimica Acta",
issn = "0003-2670",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Signature Mapping (SigMa)

T2 - an efficient approach for processing complex human urine 1H NMR metabolomics data

AU - Khakimov, Bekzod

AU - Mobaraki, Nabiollah

AU - Trimigno, Alessia

AU - Aru, Violetta

AU - Engelsen, Søren Balling

PY - 2020

Y1 - 2020

N2 - Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.

AB - Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.

KW - MCR

KW - Metabolomics

KW - NMR

KW - Signature Mapping

KW - Urine

U2 - 10.1016/j.aca.2020.02.025

DO - 10.1016/j.aca.2020.02.025

M3 - Journal article

C2 - 32222235

AN - SCOPUS:85080998572

VL - 1108

SP - 142

EP - 151

JO - Analytica Chimica Acta

JF - Analytica Chimica Acta

SN - 0003-2670

ER -

ID: 238732774