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Bioinformatics. 2019 Jun 1;35(11):1916-1922. doi: 10.1093/bioinformatics/bty837.

pJRES Binning Algorithm (JBA): a new method to facilitate the recovery of metabolic information from pJRES 1H NMR spectra.

Author information

1
Division of Integrative Systems Medicine and Digestive Diseases, Department of Surgery and Cancer, Imperial College London, London, UK.
2
Department of Epidemiology and Biostatistics School of Public Health, Imperial College London, London, UK.
3
Section of Structural Biology, Department of Medicine, Shimadzu Corporation, Kyoto, Japan.
4
Life Science Research Center, Technology Research Laboratory, Shimadzu Corporation, Kyoto, Japan.
5
Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
6
School of Medicine, Lebanese American University, Beirut, Lebanon.
7
Cordeliers Research Centre, INSERM UMR_S, Paris, France.

Abstract

MOTIVATION:

Data processing is a key bottleneck for 1H 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 1H 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 1H 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.

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