Metabolomic Software


Bayesian Automated meTabolite Analyser for NMR spectra, written in the free programming language R. Like most R stuff it can be a bit programming intensive but once up and running the program deconvolutes peaks from one-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 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. You can read more about it at the R forge site at or in papers at and


A powerful desktop application with a highly sophisticated graphical user interface for mapping out Baysian networks . It provides a neat  “lab” environment for machine learning, knowledge modelling, diagnosis, analysis, simulation, and optimization. It is not specifically designed for metabolomics to be fair; it i​s for any multi-node network but since that essentially describes metabolism the kit could be very handy for metabolomics work. You can find out more about it at and there is a free ebook at that will show you more.

Chenomx release version 8.1 of NMR Suite

The new release included a batch processing wizard (to perform processing steps on a group of .cnx files with a single operation), the export spectrum image feature now supports resolution independent vector graphic file formats, there is improved Region-Based Fitting and in good news for the metabolomics community there are new reference compounds in the library added at all frequencies for this release, they include, 2, 3, 4-Trihydroxybenzoate, 2, 6-Dihydroxybenzoate, 5-Aminopentanoate, Desaminotyrosine, Guanidinosuccinate, and Xylitol

Chenomx HMDB database Software Update Chenomx have announced that their software is now supporting data contained within the HMDB library from the University of Alberta. Chenomx has downloaded the spectra and compound meta-data describing many of the compounds contained within the HMDB.   The result is over 600 signatures that can be used within the latest Chenomx software (see  Many of these are for the same compounds that already exist within Chenomx proprietary libraries (but about half of them are not already in the Chenomx library) see for info.

DEXSI Developed by Dr. Michael Dagley in Prof. Malcolm McConville‘s laboratory at The University of Melbourne, this user-friendly software, called DExSI, performs targeted automated metabolite identification, isotopologue mass (fractional labelling) and abundance determination and natural isotope abundance correction. DExSI provides a range of output options (i.e. tables, graphs and heatmaps) and is suitable for high throughput analyses.  As DExSI is designed to detect metabolites irrespective of the extent of isotopic labelling, this software may also be used to perform targeted analysis on unlabelled data sets. The paper below describes new software for the targeted analysis of stable-isotope labelled data from GC-MS metabolomics experiments.  The paper is available at and the software is available at

EBI Reactome Pathway Browser Update The Reactome Pathway Browser ( an open-source, open access, manually curated and peer-reviewed pathway database. Pathway annotations are authored by expert biologists, in collaboration with Reactome editorial staff and cross-referenced to many bioinformatics databases. These include NCBI Gene, Ensembl and UniProt databases, the UCSC and HapMap Genome Browsers, the KEGG Compound and ChEBI small molecule databases, PubMed, and Gene Ontology. The system lets you undertake ID mapping, pathway assignment and over-representation or enrichment analysis. The rationale behind Reactome is to convey the rich information in the visual representations of biological pathways familiar from textbooks and articles in a detailed, computationally accessible format. The system has undergone a revamp and is now packed with extra features. Have a look at

Elements Software for Metabolomics Elements software for metabolomics is made by Proteome Software Inc. It is based around the ideas that unlike transcripts and proteins the molecular identity of metabolites cannot be deduced from genomic information. Thus the identification and quantification of metabolites relies on sophisticated instrumentation such as mass spectrometry and nuclear magnetic resonance spectroscopy. Elements for Metabolomics is a new software tool designed to help researchers in the field of metabolomics to identify metabolites included in samples analyzed using liquid chromatography-mass spectrometry or LC-MS-MS. It has a lot of functionality in terms of data analysis. It is quite expensive but you can test it out first via

Genedata Expressionist for Mass Spectrometry Those of you who work in mass spectrometry based metabolomics will know that mass spectrometers have been improving in performance at an astonishing rate in the last decade or so and concomitant improvements in automation and sensitivity are enabling us to collect huge quantities of high-quality data fairly easily. However, although data generation is fairly well automated, for most users, data processing is still a bit of a bottleneck. There is a new bit of software called Genedata Expressionist® for Mass Spectrometry has been designed for those people with especially large data sets, such metabolomics. You can read more about it at and and there is a techsheet at


Is a bioinformatics platform contains an internal graph database and the R package for omics studies. Grinn databases incorporate data from several databases including KEGG, SMPDB, HMDB, REACTOME, CheBI, UniProt and ENSEMBL. The R package allows reconstruction of different network types e.g. metabolite-protein-gene, metabolite-protein, metabolite-pathway, protein-gene, protein-pathway and gene-pathway. Grinn applies different correlation-based network analyses to estimate relationships among different omics levels independently from domain knowledge, and with the internal graph database it provides rapid integration of domain knowledge i.e. to aid annotation of unknown metabolites. Find out more at

KNIME Analytics Platform KNIME is a modern data analytics platform that allows you to perform sophisticated statistics and data mining on your data to analyze trends and predict potential results. Its visual workbench combines data access, data transformation, initial investigation, powerful predictive analytics and visualization. KNIME also provides the ability to develop reports based on your information or automate the application of new insight back into production systems. KNIME could be very handy for metabolomics and systems biology and is open source and available under GPL license. It can be extended with KNIME Commercial Software to include professional support, productivity and collaboration functionality, providing the best of both worlds. You can see this at

Metabolomic MUSCLE MUSCLE (Multi-platform Unbiased optimisation of Spectrometry via Closed Loop Experimentation) uses genetic algorithms and closed-loop optimisation to make your LC-MS methods better and you don’t need any programming skills to use it. The program was developed by a team of top notch computer scientists, analytical chemists and biochemists from the Universities of Birmingham and Manchester in the UK, including Professor Mark Viant and Drs. Shan He, Warwick Dunn, Gregory Genta-Jouve and Mr. James Bradbury from Birmingham, and Dr. Joshua Knowles and Professor Roy Goodacre from Manchester and you would be hard pushed to get a better team than that Software for GC-MS Metabolomics Data Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) ( It also performs statistical hypothesis tests (t-test) and analysis of variance (ANOVA).  Metab was developed by Raphael Aggio, Silas Villas−Boas and Katya Ruggiero at the University of Auckland. You can see more about it at and there is also a paper at


Is an, in development, publicly available site with software for the analysis of genome-wide mRNA, protein, and metabolite profiling data. The software is designed to enable the biologist to visualize, statistically analyse, and model a metabolic and regulatory network map combined with gene expression profiling data

MetScape Metscape maps human mouse and rat metabolomics and gene expression data to human metabolic networks and enables pathway and correlation analysis of the data. The idea is that by providing a bioinformatics framework for the visualization and interpretation of metabolomic data via the use of Cytoscape, (an open source software platform for visualizing complex networks and integrating these with any type of attribute data) MetScape allows one to build and analyze networks of genes and compounds identify enriched pathways from expression profiling data and visualize changes in metabolite data. Please see or or you can contact the MetScape development

nmrML: A vendor-neutral open exchange format for NMR-based metabolomics

It is currently under heavy development and is not yet ready for public use but once ready it will be a vendor-neutral open exchange format for NMR-based metabolomics, which will be really neat. The project is part of the COSMOS project (COordination Of Standards In MetabOlomicS, has been created to improve the adoption of open standards for metabolomics data, annotation with agreed metadata, and support by open source data management and capturing tools. COSMOS delivers an ecosystem of formats, tools, and resources such as MetaboLights (, a database for capturing information obtained in metabolomics experiments. You can read more at and at See also for COSMOS project videos.

Paintomics Software Paintomics is a web tool for the integration and visualization of transcriptomics and metabolomics data. It consists of three simple steps:  i) data upload: typically two data matrices, containing gene expression and metabolite levels for the same set of samples for example​. ii) Metabolite assignment: specification of metabolite assignments in ambiguity cases and iii) pathway selection: pathway(s) to be painted on a two-color scheme indicating the over/under abundance of pathway elements. Currently Paintomics supports integrated visualization of about one hundred top species of different biological kingdoms and offers user the possibility to request any other organism present in the KEGG database. Please check for all the details. Users can also send a mail to if they have questions on the software.

SCIEX and Mass Consortium Announce Reseller Agreement of XCMSplus XCMSplus has a very neat set of algorithms to align features, identify peaks, perform statistical tests, and visualize complex results. It offers improved multi-group analysis capabilities, fast on-site data processing, and, local data storage and sharing capabilities. SCIEX say this will mean that metabolomic researchers will be able to accelerate their discovery workflows and shorten their timeframes for translating data into biological information and I think they could be right. You can read more about it at and

Waters Metabolomics Software

Progenesis is advanced LC-MS data analysis software that enables researchers to quantify and identify small molecule metabolites as well as larger lipids and proteins. There are two pieces of software: ‘Progenesis QI’ for small molecules and lipids and‘Progenesis QI for proteomics’ – for protein work. They both have support for all common vendor data formats and a visually guided workflow. Perhaps more interestingly, as well as conventional data-dependent analysis (DDA), Progenesis QI supports data independent analysis (DIA – The software also takes advantage of the additional dimension of resolution offered by ion-mobility separations to give improvements in the accuracy and precision of compound identification and quantification.