The metabolomics standards initiative (MSI)

In 2005, the Metabolomics Standards Initiative has been formed. An outline and general introduction is provided to inform about the history, structure, working plan and intentions of this initiative. Comments on any of the suggested minimal reporting standards are welcome to be sent to the open email list Msi-workgroups-feedback@lists.sourceforge.net     

Proposed minimum reporting standards for chemical analysis

There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at or . Further, community input related to this document can also be provided via this electronic forum. The contents of this paper do not necessarily reflect any position of the Government or the opinion of the Food and Drug Administration Sponsor: Metabolomics Society http://www.metabolomicssociety.org/ Reference: http://msi-workgroups.sourceforge.net/bio-metadata/reporting/pbc/ http://msi-workgroups.sourceforge.net/chemical-analysis/ Version: Revision: 5.1 Date: 09 January, 2007     

Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results

Informatics standards and controlled vocabularies are essentialfor allowing information technology to help exchange, manage,interpret and compare large data collections. In a rapidly evolvingfield, the challenge is to work out how best to describe, butnot prescribe, the use of these technologies and methods. AMetabolomics Standards Workshop was held by the US NationalInstitutes of Health (NIH) to bring together multiple ongoingstandards efforts in metabolomics with the NIH research community.The goals were to discuss metabolomics workflows (methods, technologiesand data treatments) and the needs, challenges and potentialapproaches to developing a Metabolomics Standards Initiativethat will help facilitate this rapidly growing field which hasbeen a focus of the NIH roadmap effort. This report highlightsspecific aspects of what was presented and discussed at the1st and 2nd August 2005 Metabolomics Standards Workshop.      

Metabolomics standards initiative: ontology working group work in progress

In this article we present the activities of the Ontology Working Group (OWG) under the Metabolomics Standards Initiative (MSI) umbrella. Our endeavour aims to synergise the work of several communities, where independent activities are underway to develop terminologies and databases for metabolomics investigations. We have joined forces to rise to the challenges associated with interpreting and integrating experimental process and data across disparate sources (software and databases, private and public). Our focus is to support the activities of the other MSI working groups by developing a common semantic framework to enable metabolomics-user communities to consistently annotate the experimental process and to enable meaningful exchange of datasets. Our work is accessible via a public webpage and a draft ontology has been posted under the Open Biological Ontology umbrella. At the very outset, we have agreed to minimize duplications across omics domains through extensive liaisons with other communities under the OBO Foundry. This is work in progress and we welcome new participants willing to volunteer their time and expertise to this open effort. See the MSI Ontology Working Group website for a complete list of members and contributors. Web URL:     

The use of biomarkers for improved retrospective exposure assessment in epidemiological studies: summary of an ECETOC workshop

During a scientific workshop the use of biological monitoring in characterization of retrospective exposure assessment was discussed. The workshop addressed currently available methodology and also novel approaches such as in different fields of ‘omics’. For use in epidemiology requiring retrospective exposure assessment, biomarker levels should not vary too much over time. If variability in exposure over time is large and differences in exposure between individuals are relatively small, this may lead to underestimation of the exposure–response relationship. This means that, for a sound assessment of health risk, biomarkers that reflect cumulative exposure over a long period of time are preferred over biomarkers with short half-lives. Most of the existing biomarkers such as metabolites in body fluids usually have rather short half-lives, typically less than 1–2 days. Some adducts to DNA show somewhat longer half-lives. The current limit to persistence of biomarkers reflecting cumulative exposure over time is from adducts to haemoglobin with a half-life of 4 months. Some specific organic substances may be more persistent due to storage in adipose tissue or metals in kidneys, nails and hair. The metabonomics, proteomics and present gene expression profiling approaches do not provide a perspective to the availability of more persistent biomarkers and most approaches discussed to date show that it is difficult to interpret study outcomes in terms of exposure to a specific xenobiotic factor. Research efforts should focus on improvement and validation of currently available approaches in the field of addition products to DNA and proteins. Promising new developments may be phosphotriester DNA adducts and adducts to more long-lived proteins such as histones.     

Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics

A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.