Provides a user-friendly, web-based analytical pipeline for high-throughput metabolomics studies. In particular, MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies.
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.
Permits comprehensive metabolomics data pre-processing, statistical analysis and interpretation. W4M includes computational modules for data normalization, multivariate analysis and annotation. It can create interactive web-based documents showing the results of the analyses, and users can share them with collaborators directly on the platform. This tool enables multi-omics analyses in a global systems-biology approach.
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.
Matches correlations of any chemical shifts within an unknown spin-system against all spin systems in a nuclear magnetic resonance (NMR) database. GlycoNMRSearch determines the monosaccharide type, linkage type and often the local chemical environment beyond the next neighbor based on the chemical shifts of an experimental, initially unknown spin system. This tool is useful for carbohydrates of unknown composition.
Assists users in the identification of metabolites. STOCSY is a program for pathway connectivity and biological information recovery. It is designed for simplifying database queries for the identification of compounds in metabolomics studies. This tool can be used for finding statistical correlations among peaks. It allows users to create a peak list as input for the database query.
Allows metabolic annotation and assignment of peak data detected in 2D-Jres nuclear magnetic resonance (NMR) spectra. Spincouple performs quantitative analysis, enabling the absolute quantification of commonly observed major metabolites. It is useful for researchers engaged in the field of metabolomics to identify and quantify metabolites in various biological and environmental systems.
Allows deconvolution of complex nuclear magnetic resonance (NMR) spectra of metabolite mixtures. This approach exploits heteronuclear multiple bond correlation spectroscopy (HMBC) and heteronuclear single quantum correlation spectroscopy (HSQC) correlation data to accelerate the identification of natural metabolites. It can assist to initiate the chemical profiling of natural extracts. This method permits to cross-validate the results obtained.
A software package for user-guided NMR spectral assignment. The overarching goal of the integrated toolbox of MetaboID is to centralize the one dimensional spectral assignment process, from providing access to large chemical shift libraries to providing a straightforward, intuitive means of spectral comparison. Such a toolbox is expected to be attractive to both experienced and new metabolomic researchers as well as general complex mixture analysts.
Manages DICOM spectroscopy data analysis. OXSA includes (i) visualization of 1, 2 and 3D-resolved spectroscopy data volumes of interest (VOIs) overlaid on anatomical localizer images (ii) a robust time-domain analysis routine by using the AMARES algorithm which can include additional prior knowledge (iii) batch processing of spectra. This toolkit aims to give researchers a way for developing customized fitting and processing pipelines.
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.
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.
Allows users to identify and quantify metabolites from free induction decay (FID) input data. IQMNMR is a package that provides an automated method which create prior knowledge data sets of targeted metabolites to provide accurate result about the targeted metabolites and their concentration.
Features an evolutionary algorithm for structure elucidation and is available as a graphical user interface (GUI) client or as a stand-alone command-line executable. The SENECA system is an open-source java-based desktop application to perform Computer Assisted Structure Elucidation (CASE) for organic molecules. It takes a molecular formula generated from high resolution mass spectrometry and spectral data from a suite of nuclear magnetic resonance (NMR) experiments and performs a stochastic search in the constitutional space, guided by a fitness function.
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).
Florence Mondeguer Scientist LC-MS Specialist, Microalgae Metabolomic data
French Research Institute for Exploitation of the Sea
Lepinay Alexandra, Turpin Vincent, Mondeguer Florence, Grandet-Marchant Quentin, Capiaux Herve, Baron Regis, Lebeau Thierry (2018). First insight on interactions between bacteria and the marine diatom Haslea ostrearia : Algal growth and metabolomic fingerprinting . Algal Research , 31, 395-405 . http://doi.org/10.1016/j.algal.2018.02.023
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