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A combined chemoinformatic approach for objective and systematic analysis of untargeted 1H NMR-based metabolic profiles in quantitative genetic contexts. The R/Bioconductor mQTL.NMR package was designed to (i) perform a series of preprocessing steps restoring spectral dependency in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accurate metabotype quantitative trait locus (mQTL) mapping in human cohorts as well as in rodent models, (iii) statistically enhance structural assignment of genetically determined metabolites, and (iv) illustrate results with a series of visualization tools. Built-in flexibility and implementation in the powerful R/Bioconductor framework allow key preprocessing steps such as peak alignment, normalization, or dimensionality reduction to be tailored to specific problems.
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A tool for the alignment of spectral datasets. The icoshift tool for Matlab is an open source program specifically designed for solving signal alignment problems in metabonomic NMR data analysis, but it can also properly deal with other spectra-like datasets (e.g. data from other spectroscopic methods or chromatographic data). The icoshift algorithm is based on COrrelation SHIFTing of spectral Intervals and employs an FFT engine that aligns all spectra simultaneously.
dataChord Spectrum Analyst
Provides a rapid workflow from raw Nuclear Magnetic Resonance (NMR) data to patent and publication quality output. dataChord Spectrum Analyst is a graphic application for small molecule NMR Analysis that provides a variety of analytical tools for interpreting NMR Spectra. It can work in a client server mode with the spectrum server maintaining an archive of annotated raw data. This module can compare multiple spectra with each other and generates reports that include a variety of information, including molecular structures.
HiRes / High Resolution spectroscopy
Combines standard nuclear magnetic resonance (NMR) spectral processing functionalities with techniques for multi-spectral dataset analysis. HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. It couples rigorous data pre-processing, artifact removal and identification of metabolic patterns via principal component analysis (PCA).
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