ASICS: an R package for a whole analysis workflow of 1D 1H NMR spectra

Author:

Lefort Gaëlle12ORCID,Liaubet Laurence2,Canlet Cécile34,Tardivel Patrick5,Père Marie-Christine6,Quesnel Hélène6,Paris Alain7,Iannuccelli Nathalie2,Vialaneix Nathalie1,Servien Rémi8ORCID

Affiliation:

1. MIAT, Université de Toulouse, INRA, Castanet Tolosan, France

2. GenPhySE, Université de Toulouse, INRA, ENVT, Castanet Tolosan, France

3. Toxalim, Université de Toulouse, INRA, ENVT, INP-Purpan, UPS, Toulouse, France

4. Axiom Platform, MetaToul-MetaboHUB, National Infrastructure for Metabolomics and Fluxomics, Toulouse, France

5. Institute of Mathematics, University of Wroclaw, Wroclaw 50-384, Poland

6. PEGASE, INRA, Agrocampus Ouest, Saint-Gilles, France

7. Unité Molécules de Communication et Adaptation des Microorganismes (MCAM), Muséum national d’Histoire naturelle, CNRS, CP54, Paris, France

8. INTHERES, Université de Toulouse, INRA, ENVT, Toulouse, France

Abstract

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.

Funder

French National Agency of Research

PORCINET

Digital Agriculture Convergence Lab

INRA Mathematics and Computer Science Division

INRA Animal Genetics Division

INRA Animal Health Divison

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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