Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A proposed framework for the description of plant metabolomics experiments and their results

Abstract

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: The nine ArMet components and their interdependencies describing the key concepts of a metabolomics experiment modeled using standard UML packages.

Bob Crimi

Figure 2: ArMet core components and detailed subcomponents.

Bob Crimi

Figure 3: The 'MetabolomeEstimate' component.

Bob Crimi

References

  1. 1

    Quackenbush, J. Data standards for 'omic' science. Nat. Biotechnol. 22, 613–614 (2004).

    CAS  Article  Google Scholar 

  2. 2

    Brazma, A. et al. Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29, 365–371 (2001).

    CAS  Article  Google Scholar 

  3. 3

    Spellman, P.T. et al. Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol. 3, research0046.0041–0046.0049 (2002).

    Article  Google Scholar 

  4. 4

    Brazma, A. et al. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 31, 68–71 (2003).

    CAS  Article  Google Scholar 

  5. 5

    Killion, P.J., Sherlock, G. & Iyer, V.R. The Longhorn Array Database (LAD): An open-source, MIAME compliant implementation of the Stanford Microarray database (SMD). BMC Bioinformatics 4, 32 (2003). http://biomedcentral.com/1471-2105/4

    Google Scholar 

  6. 6

    Editorial. Microarray standards at last. Nature 419, 323 (2002).

  7. 7

    Glueck, S.B. & Dzau, V.J. Our new requirement for MIAME standards. Physiol. Genomics 13, 1–2 (2003).

    Article  Google Scholar 

  8. 8

    Oliver, S. On the MIAME standards and central repositories, of microarray. Comp. Funct. Genomics 4, 1 (2003).

    CAS  Article  Google Scholar 

  9. 9

    Taylor, C.F. et al. A systematic approach to modeling, capturing, and disseminating proteomics experimental data. Nat. Biotechnol. 21, 247–254 (2003).

    CAS  Article  Google Scholar 

  10. 10

    Booch, G., Rumbaugh, J. & Jacobson, I. The Unified Modeling Language User Guide (Addison-Wesley, Reading, MA, 1999).

    Google Scholar 

  11. 11

    Orchard, S., Hermjakob, H. & Apweiler, R. The proteomics standards initiative. Proteomics 3, 1374–1376 (2003).

    CAS  Article  Google Scholar 

  12. 12

    Orchard, S. et al. Common interchange of standards for proteomics data: public availability of tools and schema. Proteomics 4, 490–491 (2004).

    CAS  Article  Google Scholar 

  13. 13

    Fiehn, O. Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genomics 2, 155–168 (2001).

    CAS  Article  Google Scholar 

  14. 14

    Fiehn, O. Metabolomics—the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    CAS  Article  Google Scholar 

  15. 15

    Fiehn, O. & Weckwerth, W. Deciphering metabolic networks. Eur. J. Biochem. 270, 579–588 (2003).

    CAS  Article  Google Scholar 

  16. 16

    Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G. & Kell, D.B. Metabolomics by numbers:acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245–252 (2004).

    CAS  Article  Google Scholar 

  17. 17

    Harrigan, G.G. & Goodacre, R. (eds.) Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis (Kluwer Academic Publishers, Boston, 2003).

    Book  Google Scholar 

  18. 18

    Mendes, P. Emerging bioinformatics for the metabolome. Brief. Bioinform. 3, 134–145 (2002).

    CAS  Article  Google Scholar 

  19. 19

    Oliver, S.G., Winson, M.K., Kell, D.B. & Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373–378 (1998).

    CAS  Article  Google Scholar 

  20. 20

    Raamsdonk, L.M. et al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 (2001).

    CAS  Article  Google Scholar 

  21. 21

    Roessner, U. et al. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell 13, 11–29 (2001).

    CAS  Article  Google Scholar 

  22. 22

    Roessner, U., Wagner, C., Kopka, J., Trethewey, R.N. & Willmitzer, L. Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 23, 131–142 (2000).

    CAS  Article  Google Scholar 

  23. 23

    Sumner, L.W., Mendes, P. & Dixon, R.A. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817–836 (2003).

    CAS  Article  Google Scholar 

  24. 24

    Weckwerth, W. Metabolomics in systems biology. Annu. Rev. Plant Biol. 54, 669–689 (2003).

    CAS  Article  Google Scholar 

  25. 25

    Hall, R. et al. Plant metabolomics: the missing link in functional genomics strategies (Meeting report). Plant Cell 14, 1437–1440 (2002).

    CAS  Article  Google Scholar 

  26. 26

    van der Greef, J., van der Heiiden, R. & Verheij, E.R. The role of mass spectrometry in system biology: data processing and identification strategies in metabolomics. in Advances in Mass Spectrometry, vol. 16. (eds. Ashcroft, A.E., Brenton, G. & Monaghan, J.J.) 145–165 (Elsevier, Amsterdam, 2004).

    Google Scholar 

  27. 27

    Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    CAS  Article  Google Scholar 

  28. 28

    Krieger, C.J. et al. MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 32, D438–D442 (2004).

    CAS  Article  Google Scholar 

  29. 29

    Mueller, L.A., Zhang, P. & Rhee, S.Y. AraCyc: A biochemical pathway database for Arabidopsis. Plant Physiol. 132, 453–460 (2003).

    CAS  Article  Google Scholar 

  30. 30

    Harris, M.A. et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32 Special Issue, D258–D261 (2004).

    CAS  Article  Google Scholar 

  31. 31

    Nicholson, J.K., Lindon, J.C. & Homes, E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29, 1181–1189 (1999).

    CAS  Article  Google Scholar 

  32. 32

    Bino, R.J. et al. Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 9, 418–425 (2004).

    CAS  Article  Google Scholar 

  33. 33

    Stein, S.E. & Scott, D.R. Optimization and testing of mass spectral library search algorithms for compound identification. J. Am. Soc. Mass Spectrom. 5, 859–866 (1994).

    CAS  Article  Google Scholar 

  34. 34

    McLafferty, F.W., Zhang, M-Y., Stauffer, D.B. & Loh, S.Y. Comparison of algorithms and databases for matching unknown mass spectra. J. Am. Soc. Mass Spectrom. 9, 92–95 (1998).

    CAS  Article  Google Scholar 

  35. 35

    Xirasagar, S. et al. CEBS object model for systems biology data, SysBio-OM. Bioinformatics 20, 2004–2015 (2004).

    CAS  Article  Google Scholar 

  36. 36

    Lampen, P. et al. An extension to the JCAMP-DX standard file format, JCAMP-DX V.5.01 (IUPAC Recommendations 1999). Pure Appl. Chem. 71, 1549–1556 (1999).

    CAS  Article  Google Scholar 

  37. 37

    Griffiths, P.R. & de Haseth, J.A. Fourier Transform Infrared Spectrometry, vol. 83 (John Wiley & Sons, Chichester, England, 1986).

    Google Scholar 

  38. 38

    Allen, J.K. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).

    CAS  Article  Google Scholar 

  39. 39

    Smedsgaard, J. Terverticillate penicillia studied by direct electrospray mass spectrometric profiling of crude extracts: II. Database and identification. Biochemical Systematics and Ecology 25, 65–71 (1997).

    CAS  Article  Google Scholar 

  40. 40

    Smedsgaard, J. & Frisvad, J.C. Using direct electrospray mass spectrometry in taxonomy and secondary metabolite profiling of crude fungal extracts. J Microbiol. Methods 25, 5–17 (1996).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the United Kingdom Food Standards Agency (under the G02006 project), the United Kingdom Biotechnology and Biological Sciences Research Council (particularly under the HiMet project) and the United Kingdom Engineering and Physical Sciences Research Council for support of their work in metabolomics. We would also like to thank personnel at the United Kingdom Institute of Grassland and Environmental Research and delegates at the 1st, 2nd and 3rd International Plant Metabolomics Conferences for many useful discussions.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nigel Hardy.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Jenkins, H., Hardy, N., Beckmann, M. et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat Biotechnol 22, 1601–1606 (2004). https://doi.org/10.1038/nbt1041

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing