NMR-based profiling of low molecular weight (LMW) compounds provides an ‘unbiased’ and broad view on the composition of foods and biofluids and has been proven beneficial to control the quality and safety of foods as well as to assess the effect of foods/nutrients in biological systems. The quantification of LMW compounds from 1D 1H NMR profiles has recently gained in importance for the analysis of complex mixtures, yet still suffers from major hurdles such as signal overlap preventing the accurate integration of NMR signals.
Several estrogenic compounds including the isoflavonoid genistein have been reported to induce a higher maximal response than the natural estrogen 17β-estradiol in in vitro luciferase based reporter gene bioassays for testing estrogenicity. The phenomenon has been referred to as superinduction.
Estragole is a natural constituent of several herbs and spices including sweet basil. In rodent bioassays, estragole induces hepatomas, an effect ascribed to estragole bioactivation to 1'-sulfooxyestragole resulting in DNA adduct formation.
Distributed computing has been considered for decades as a promising way of speeding up software execution, resulting in a valuable collection of safe and efficient concurrent algorithms. With the pervasion of multi-core processors, parallelization has moved to the center of attention with new challenges, especially regarding scalability to tens or even hundreds of parallel cores. In this paper, we present a scalable multi-core tool for the metabolomics community. This tool addresses the problem of metabolite identification which is currently a bottleneck in metabolomics pipeline.