Abstract
Motivation
In metabolomics, the detection of new biomarkers from Nuclear Magnetic Resonance (NMR) spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way.
Results
We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketing. It also found new relevant metabolites involved in metabolic pathways related to risk factors associated with the condition.
Availability and implementation
ASICS is distributed as an R package, available on Bioconductor.
Supplementary information
Supplementary data are available at Bioinformatics online.
Conjugation of a novel histidine derivative to biomolecules and labelling with [99mTc(OH2)3(CO)3]+Electronic supplementary information (ESI) available: complete 1H and 13C NMR spectra of 14, 15, 16 and 19. See http://www.rsc.org/suppdata/ob/b4/b405575f/