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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.


An extensible web component that can be easily integrated in current web applications and databases, providing NMR processing and visualization functionalities. NMRPro is highly extensible to include new functionalities according to the needs of each application. It integrates server-side processing with client-side interactive visualization through three parts: a python package to efficiently process large NMR datasets on the server-side, a Django App managing server-client interaction, and SpecdrawJS for client-side interactive visualization.

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).