Gaëlle Lefort1,2, Laurence Liaubet2, Cécile Canlet3,4, Patrick Tardivel5, Marie-Christine Père6, Hélène Quesnel6, Alain Paris7, Nathalie Iannuccelli2, Nathalie Vialaneix1, Rémi Servien8. 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
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.
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.
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