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.
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.
Provides several tools for the analysis of nuclear magnetic resonance (NMR), mass spectrometry (MS), optical and chromatographic data in one interface. Spectrus Processor can process spectra and generate journal-formatted multiplet reports. It can create crisp simulated spectra to produce problem sets. It is compatible with most of data formats from major instrument vendors.
Provides an interface for process, analyze and visualize 1D and 2D nuclear magnetic resonance (NMR) data. MatNMR is an extendable open source software which includes several modules that allows users to run various analysis such as spectral deconvolution or quadrupolar tensor fitting. Besides, it offers a viewer for 2D data providing 2D and 3D contour, surface and line plots in a wide range of formats and standards.
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.
Assists users for metabolomics data analysis. Specmine includes a workflow that can be adapted for specific case studies, addressing tasks as data loading, pre-processing, normalization, metabolite identification, univariate and multivariate statistical analysis, clustering, machine learning and feature selection. It also offers modules for the visualization of data including box plots, volcano plots and spectra.
Allows spectra total line shape analysis of nuclear magnetic resonance (NMR) spectra. ANATOLIA is a program that fits experimental 1D NMR spectra, on the basis of quantum mechanical formalism. The software can be used for identifying J-coupling constants and elucidating complicated multiple structures. It is compatible with the Bruker NMR spectral format, can be executed directly from the TopSpin software package.
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.
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).