Metabolite quantification software tools | NMR-based metabolomics data analysis
Recent advances in metabolomics have enabled investigation of human diseases at metabolite level, revealing mechanistic and diagnostic information. High-throughput metabolic phenotyping in epidemiological studies has identified several molecular species present in complex biofluids as being associated with disease risk. Metabolomics therefore provides a useful tool in systems biology research. Due to its quantitative nature, nuclear magnetic resonance (NMR) is considered one of the key analytical platforms for metabolomics.
Analyses advanced time-domain of magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) data. jMRUI can significantly help the user to track the processing history performed on data. The approach offers basic processing tracking and is not suitable for complex data processing and extensive studies. It helps to better organize the processing history and increases the reproducibility and documentability of all spectroscopic processing.
An R package for estimating metabolite concentrations from Nuclear Magnetic Resonance spectral data using a specialised MCMC algorithm. BATMAN deconvolutes peaks from 1-dimensional NMR spectra, automatically assigns them to specific metabolites from a target list and obtains concentration estimates. The Bayesian model incorporates information on characteristic peak patterns of metabolites and is able to account for shifts in the position of peaks commonly seen in NMR spectra of biological samples. It applies a Markov Chain Monte Carlo (MCMC) algorithm to sample from a joint posterior distribution of the model parameters and obtains concentration estimates with reduced error compared with conventional numerical integration and comparable to manual deconvolution by experienced spectroscopists.
Provides a method that supports fully automated and quantitative nuclear magnetic resonance (NMR)-based metabolomics of complex mixtures. Bayesil was developed to divide the spectrum into small blocks and represents the sparse dependencies between these blocks. It then performs approximate inference over this model as a surrogate for spectral profiling, yielding the most probable metabolic profile.
Identifies metabolites in H-NMR spectra of complex mixtures. MetaboHunter is a web-server application based on two manually curated reference libraries. The software provides three distinct methods: (i) a scoring function, (ii) an iterative greedy selective approach and, (iii) selection approaches with a user adjustable chemical peak drift parameter. There are 4 functional views: (i) a Processing View, (ii) a Search Results View, (iii) a Plot View and, (iv) a Peaks Hit Map view.
A probabilistic approach Bayesian Quantification for fully automated database-based identification. BQuant also automated quantification of metabolites in local regions of 1H NMR spectra. It represents the spectra as mixtures of reference profiles from a database, and infers the identities and the abundances of metabolites by Bayesian model selection. BQuant outperforms the available automated alternatives in accuracy for both identification and quantification.