Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/btz248 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Computational science,Proton NMR,Workflow,R package | Journal | 35 |
Issue | ISSN | Citations |
21 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 0 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaëlle Lefort | 1 | 0 | 0.34 |
| Laurence Liaubet | 2 | 3 | 0.79 |
| Cécile Canlet | 3 | 0 | 0.34 |
| Patrick Tardivel | 4 | 0 | 0.34 |
| Marie-Christine Père | 5 | 0 | 0.34 |
| Hélène Quesnel | 6 | 0 | 0.34 |
| Alain Paris | 7 | 0 | 0.34 |
| Nathalie Iannuccelli | 8 | 0 | 0.34 |
| Nathalie Villa-Vialaneix | 9 | 72 | 10.94 |
| Rémi Servien | 10 | 1 | 1.06 |