Proposed reporting requirements for the description of NMR-based metabolomics experiments

The amount of data generated by NMR-based metabolomic experiments is increasing rapidly. Furthermore, diverse techniques increase the need for informative and comprehensive meta-data. These factors present a challenge in the dissemination, interpretation, reviewing and comparison of experimental results using this technology. Thus, there is a strong case for unification and standardisation of the data representation for both academia and industry. Here, a systems analysis of an NMR-based metabolomics experiment is presented in order to reveal the reporting requirements. An in-depth analysis of the NMR component of a metabolomics experiment has been produced, and a first round of data standard development completed. This has focussed on both one- and two-dimensional 1H NMR experiments, but is also applicable to higher dimensions and other nuclei. We also report the modelling of this schema using Unified Modelling Language (UML), and have extended this to a proof-of-concept implementation of the standard as an XML schema.

[1]  Nigel W. Hardy,et al.  A proposed framework for the description of plant metabolomics experiments and their results , 2004, Nature Biotechnology.

[2]  Jason E. Stewart,et al.  Design and implementation of microarray gene expression markup language (MAGE-ML) , 2002, Genome Biology.

[3]  Peter Lampen,et al.  An Extension to the JCAMP-DX Standard File Format, JCAMP-DX V.5.01 , 1999 .

[4]  H. Williams,et al.  Metabolic profiling of genetic disorders: a multitissue (1)H nuclear magnetic resonance spectroscopic and pattern recognition study into dystrophic tissue. , 2001, Analytical biochemistry.

[5]  Ivar Jacobson,et al.  Unified Modeling Language , 2020, Definitions.

[6]  Stan Barber.  Status of this Document , 2000 .

[7]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[8]  A R Forrest,et al.  Quality control. , 1978, British medical journal.

[9]  Julian L. Griffin,et al.  Metabolic profiles of cancer cells , 2004, Nature Reviews Cancer.

[10]  I. Wilson,et al.  Gut microorganisms, mammalian metabolism and personalized health care , 2005, Nature Reviews Microbiology.

[11]  P. Lampen,et al.  JCAMP-DX for NMR , 1993 .

[12]  John C. Lindon,et al.  Toxicological applications of magnetic resonance , 2004 .

[13]  The Standard Metabolic Reporting Structures working group.  Summary recommendations for standardization and reporting of metabolic analyses , 2005 .

[14]  T. N. Bhat,et al.  The CCPN project: an interim report on a data model for the NMR community , 2002, Nature Structural Biology.

[15]  M. Viant.  Improved methods for the acquisition and interpretation of NMR metabolomic data. , 2003, Biochemical and biophysical research communications.

[16]  Henrik Antti,et al.  Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project. , 2003, Toxicology and applied pharmacology.

[17]  Daniel Raftery,et al.  Use of selective TOCSY NMR experiments for quantifying minor components in complex mixtures: application to the metabonomics of amino acids in honey. , 2005, Analytical chemistry.

[18]  Wayne Boucher,et al.  The CCPN data model for NMR spectroscopy: Development of a software pipeline , 2005, Proteins.

[19]  Henrik Antti,et al.  Spectral editing and pattern recognition methods applied to high-resolution magic-angle spinning 1H nuclear magnetic resonance spectroscopy of liver tissues. , 2003, Analytical biochemistry.

[20]  Kazuki Saito,et al.  Potential of metabolomics as a functional genomics tool. , 2004, Trends in plant science.

[21]  J. Lindon,et al.  Metabonomics: a platform for studying drug toxicity and gene function , 2002, Nature Reviews Drug Discovery.

[22]  Mark R Viant,et al.  NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health. , 2003, Environmental science & technology.