NMR data simulation software tools | NMR-based metabolomics analysis
A major technique for capturing metabolite data is 1H-NMR spectroscopy and this yields highly complex profiles that require sophisticated statistical analysis methods. However, experimental data is difficult to control and expensive to obtain. Thus data simulation is a productive route to aid algorithm development.
Simulates datasets of realistic nuclear magnetic resonance (NMR) spectra. MetAssimulo provides a graphical interface which aims to test data analysis techniques, hypotheses and experimental designs. The software is able to create groups of spectra for representing controls and cases subjects. It can also perform pre-processing of pure spectra, simulate metabolite concentrations, incorporate peak shifts and generate the final mixture spectrum.
A strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. CIMA selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The proposed approach focuses on the general metabolomic hypothesis-testing problem, whereas incorporating structure information into other common metabolomic inference practices, such as multivariate predictive model construction and network inference, is warranted for further research.
Models the kinetics of different metabolites in samples over different temperature and time conditions. sampleDrift is able to correct for pre-analytical errors. It predicts pre-centrifugation temperature and time parameters. This tool was tested and approved by external validation of 111 ethylene diamine tetra-acetic acid (EDTA) plasma samples. It improves experimental data by taking into account reproducible pre-analytical, sample history-derived changes in individual metabolite levels, where handling conditions could be predicted from experimental data.