Web Servers and Software

MetaboAnalyst is a comprehensive, web-based tool designed for processing, analyzing, and interpreting metabolomic data. It handles most of the common metabolomic data types including compound concentration lists, spectral bin lists, peak lists, and raw mass spectrometry (MS) spectra.

PubMed: 25897128, 27603023, 22553367, 21637195, 21633943, 19429898, 30909447, 29955821, 29762782

  • MetATT is an easy-to-use, web-based tool designed for time-series and two-factor metabolomics data analysis. MetATT offers a number of complementary approaches including 3D interactive principal component analysis, two-way heatmap visualization, two-way ANOVA, ANOVA-simultaneous component analysis, and multivariate empirical Bayes time-series analysis.
    PubMed: 21712247
  • MetPA (Metabolomics Pathway Analysis) is a free and easy-to-use web application designed to perform pathway analysis and visualization of quantitative metabolomic data.
    PubMed: 2062807
  • MSEA is a web-based tool to help identify and interpret patterns of metabolite concentration changes in a biologically-meaningful context for human and mammalian metabolomic studies.
    PubMed: 20457745
  • ROCCET is a freely-available, web-based tool designed to assist clinicians and bench biologists in performing common ROC-based analyses on their metabolomic data using both classical univariate and more recently developed multivariate approaches. Receiver operating characteristic (ROC) curves are generally considered the method of choice for evaluating the performance of potential biomarkers.
    PubMed: 23543913
Metaboanalyst

Bayesil is a web system that automatically identifies and quantifies metabolites using 1D 1H NMR spectra of ultra-filtered plasma, serum, or cerebrospinal fluid. Bayesil first performs all spectral processing steps, including Fourier transformation, phasing, solvent filtering, chemical shift referencing, baseline correction, and reference line shape convolution automatically. It then deconvolutes the resulting NMR spectrum using a reference spectral library. This deconvolution process determines both the identity and quantity of the compounds in the biofluid mixture. Extensive testing shows that Bayesil meets or exceeds the performance of highly-trained human experts.

PubMed: 26017271

Bayesil

BioTransformer 3.0 is a freely-available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning-based approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut, as well as the environment (soil and water microbiota).

PubMed: 3553652, 30612223

Biotransformer

CFM-ID 4.0 provides a method for accurately and efficiently identifying metabolites in spectra generated by electrospray ionization tandem mass spectrometry (ESI-MS/MS). The program uses competitive fragmentation modeling to produce a probabilistic generative model for the MS/MS fragmentation process and machine learning techniques to adapt the model parameters from data.

PubMed: 24895432, 27381172, 31013937
Other references: Metabolomics 2015 Feb; 11(1): 98–110.

CFM-ID

ClassyFire is a web-based application for automated structural classification of chemical entities. This application uses a rule-based approach that relies on a comprehensible, comprehensive, and computable chemical taxonomy. ClassyFire provides a hierarchical chemical classification of chemical entities (mostly small molecules and short peptide sequences), as well as a structure-based textual description, based on a chemical taxonomy named ChemOnt, which covers 4825 chemical classes of organic and inorganic compounds. It can be accessed via the web interface or via the ClassyFire API. ClassyFire is offered to the public as a freely-accessible web server. Use and redistribution of the data, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgement of the source material (ClassyFire) and the original publication (see below). We ask that users who download portions of the database or use the service (via the server or the API) cite the ClassyFire paper in any resulting publications.

Pubmed: 27867422

Classyfire

GC–AutoFit is a web application that automatically identifies and quantifies metabolites using gas chromatography mass spectrometry (GC-MS) data. GC-AutoFit currently accepts .CDF and .mzXML file formats. It uses alkane standards to calculate the retention index (RI) of each peak in the sample. The extracted EI-MS (electron ionization mass spectrometry) spectra from each peak, along with the RIs, are then compared to reference spectra (RIs and EI-MS) in the specified library to identify and quantify the compounds. The inclusion of blank spectra is optional, however, it is useful for removing noise effects from the query spectra. Extensive testing shows that GC-AutoFit meets or exceeds the performance of highly-trained human experts.

PubMed: 24895432, 27381172, 31013937 Other references: Metabolomics 2015 Feb; 11(1): 98–110.

GC-Autofit

MAGMET is a web-based system that automatically identifies and quantifies metabolites using 1D 1H NMR spectra of serum. MAGMET first performs all spectral processing steps, including Fourier transformation, phasing, solvent filtering, chemical shift referencing, baseline correction, and reference line shape convolution automatically. It then deconvolutes the resulting NMR spectrum using a reference spectral library, which here contains the signatures of more than 60 metabolites. This deconvolution process determines both the identity and quantity of the compounds in the biofluid mixture. Extensive testing shows that MAGMET meets or exceeds the performance of highly-trained human experts.

PubMed: 21360156

MAGMET

PHASTER (PHAge Search Tool – Enhanced Release) is a significant upgrade to the popular PHAST web server for the rapid identification and annotation of prophage sequences within bacterial genomes and plasmids. While the steps in the phage identification pipeline in PHASTER remain largely the same as in the original PHAST, numerous software improvements and significant hardware enhancements have now made PHASTER faster, more efficient, more visually appealing and much more user-friendly. A number of other optimizations have been implemented, including automated algorithms to reduce the size and redundancy of PHASTER’s databases, improvements in handling multiple (metagenomic) queries and high user traffic, and the ability to perform automated look-ups against >14,000 previously PHAST/PHASTER annotated bacterial genomes (which can lead to complete phage annotations in seconds as opposed to minutes). PHASTER’s web interface has also been entirely rewritten. A new graphical genome browser has been added, gene/genome visualization tools have been improved, and the graphical interface is now more modern, robust, and user-friendly.

PubMed: 27141966

Phaster
-->