1H Nuclear Magnetic Resonance (NMR) is a high-throughput technology that allows to obtain metabolomic profile from easy-to-obtain fluids (such as blood) at moderate cost. It is thus a promising tool to detect practically usable biomarkers. However, its interpretation can be hard to make, because metabolites present from the 1H NMR spectrum of a complex mixture are not easily identified and quantified. To facilitate the use of such data, we developed a new R package, ASICS, that implements a method for automatic identification and quantification of metabolites in 1H NMR spectra. The package combines all the steps of the analysis (management of a reference library with pure metabolite spectra, preprocessing, quantification, diagnosis tools to assess the quality of the quantification, post-quantification statistical analyses). To assess the performance of ASICS, data from PORCINET (ANR-09-GENM-005) were used. Both the quantification and its impact on a post-quantification differential analysis were evaluated. First, correlations between ASICS relative quantifications and biochemical dosages of three metabolites were computed and a similar analysis was performed with other quantification methods like Autofit or batman. Second, a differential standard approach (based on a bucket analysis and a manual expert identification) was compared with the same analysis based on ASICS quantifications. These comparisons showed that ASICS allows for a faster and simpler direct biological interpretation than the classical bucket approach and obtains more precisely identified and quantified metabolites than other quantification methods. ASICS is released as a Bioconductor package and is also available as a GALAXY module.