Metabolomics

A good place to start would be the Metabolomics Innovation Centre at the University of Alberta and their list of software.

 Biological Magnetic Resonance Data Bank Metabolomics - is a repository for data from NMR spectroscopy on proteins, peptides, nucleic acids, and other biomolecules.

 COLMAR Complex Mixture Analysis by NMR (OSU, Campus Chemical Instrument Center) has a number of tools: Covariance: Generates covariance NMR spectrum from TOCSY-type experiments; DemixC: Deconvolutes 2D TOCSY spectra of complex mixtures into their individual components; 1D NMR: Query 1D 1H and 1D 13C NMR spectra; 13C-TOCCATA: Query 2D 13C-13C TOCSY spectra; 1H(13C)-TOCCATA: Query 2D 1H-1H TOCSY and 2D 13C-1H HSQC-TOCSY spectra; 13C-1H HSQC: Query 2D 13C-1H HSQC spectra; COLMARm: Query Multiple 2D spectra (HSQC, TOCSY and HSQC-TOCSY.

 ECMDB - E.coli Metabolome Database -  is an expertly curated database containing extensive metabolomic data and metabolic pathway diagrams about Escherichia coli (strain K12, MG1655). This database includes significant quantities of “original” data compiled by members of the Wishart laboratory as well as additional material derived from hundreds of textbooks, scientific journals, metabolic reconstructions and other electronic databases. ECMDB currently contains 3755 small molecules with 1402 associated enzymes and 387 associated transporters. It also has 1542 metabolic pathways that are linked to 3011 metabolites. A total of 19,294 NMR and MS spectra (experimental and predicted) for 3098 different E. coli metabolites are also contained in the database. (Reference: T. Sajed et al. (2016). Nucleic Acids Res, 44(D1):D495-501).

 Metabolomics GWAS Server. This site contains the association results of our two genome-wide association studies on the human metabolome. The server is currently under development and new features will be added as soon as they are available. Especially we will add more parameters for a more convenient browsing of the data.

M2IA is a web-based server for the Microbiome and Metabolome data integration analysis. M2IA integrates a variety of statistical analysis methods, including univariate analysis, multivariate modeling, and functional network analysis. Users can upload data, customize parameters, and submit a job by one click. An html-based summarized report will be generated automatically for overview, accompanied by supporting tables and high-resolution figures available for downloading.

        A demo account [Account: demo123] has also been created for users who prefer being familiar with the basic features and main steps of M2IA before registering a personal account. (Reference: Ni Y et al. 2020. Bioinformatics, btaa188, https://doi.org/10.1093/bioinformatics/btaa188)

 MetaboAnalyst - is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources.  (Reference: J.Xia et al. 2015. Nucl. Acids Res. 43 (W1): W251-W257).

 Workflow4Metabolomics - is the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation. (Reference: Giacomoni F et al. 2015. Bioinformatics 31:1493-1495).

 XenoSite Metabolism Prediction Web Server - Cytochrome P450s are metabolic enzymes that process the majority of FDA-approved, small-molecule drugs.  XenoSite predicts which atomic sites of a molecule (sites of metabolism) are modified by P450s. XenoSite server accepts input in common chemical file formats including SDF and SMILES and provides tools for visualizing the likelihood that each atomic site is a site of metabolism for a variety of important P450s.  (Reference: Matlock MK et al. 2015. Bioinformatics. 31:1136-1137).