Progress in Chemometrics and Biostatistics for Plant Applications, or: A Good Red Wine is a Bad White Wine

Annual Plant Reviews Volume 43: Biology of Plant Metabolomics

Volumn 43, Issue , 2011, Pages 317-342

  Kopka, Joachim ^a   Walther, Dirk ^a   William Allwood, J ^b   Goodacre, Royston ^b  

^a MAX PLANCK INSTITUTE OF MOLECULAR PLANT PHYSIOLOGY   (Germany)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Non targeted fingerprint analysis; Standard operating procedure; Statistical data mining; Targeted profiling analysis

Indexed keywords

EID: 84948986242     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781444339956.ch10     Document Type: Chapter

References (37)

1. Allwood, J.W., Erban, A., de Koning, S. et al. (2009) Inter-laboratory reproducibility of fast gas chromatography-electron impact-time of flight mass spectrometry (GC-EI-TOF/MS) based plant metabolomics. Metabolomics 5, 479-496.
2. Bartowsky, E.J. and Pretorius, I.S. (2009) Microbial formation and modification of flavor and off-flavor compounds in wine. In: König, H., Unden, G. and Fröhlich, J. (eds.) Biology ofMicroorganisms on Grapes, in Must and inWine. Springer Publishers, Berlin, Heidelberg, pp. 209-231.
3. Biais, B., Allwood, J.W., Deborde, C. et al. (2009) 1H-NMR, GC-EI-TOF/MS, and dataset correlation for fruit metabolomics: application to spatial metabolite analysis in melon. Analytical Chemistry 81, 2884-2894.
4. Birkemeyer, C., Luedemann, A., Wagner, C. et al. (2005) Metabolome analysis: the potential of in vivo-labeling with stable isotopes for metabolite profiling. Trends in Biotechnology 23(1), 28-33.
5. Breiman, L., Friedman, J.H., Olshen, R.A. et al. (1984) Classification and Regression Trees. Wadsworth, Inc., Pacific Grove, California.
6. Broadhurst, D. and Kell, D.B. (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171-196.
7. Brown, M., Dunn, W.B., Ellis, D.I. et al. (2005) A metabolome pipeline: from concept to data to knowledge. Metabolomics 1, 39-51.
8. Büscher, N., Zyprian, E., Bachmann, O. et al. (1994) On the origin of the grapevine variety 'Müller-Thurgau' as investigated by the inheritance of random amplified polymorphic DNA (RAPD). Vitis 33 (1), 15-17.
9. Camacho, D., de la Fuente, A. and Mendes, P. (2005) The origin of correlations in metabolomics data. Metabolomics 1, 53-63.
10. Dettweiler, E., Jung, A., Zyprian, E. et al. (2000) Grapevine cultivar 'Müller-Thurgau' and its true to type descent. Vitis 39(2), 63-65.
11. De Vos, R.C.H., Moco, S., Lommen, A. et al. (2007) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nature Protocols 2 (4), 778-791.
12. Ebbels, T.M.D., Buxton, B.F. and Jones, D.T. (2006) SpringScape:visualisation of microarray and contextual bioinformatic data using spring embedding an 'information landscape'. Bioinformatics 22, e99-e108.
13. Erban, A., Schauer, N., Fernie, A.R. et al. (2007) Non-supervised construction and application of mass spectral and retention time index libraries from time-of-flight GC-MS metabolite profiles. In: Weckwerth, W. (ed.) Metabolomics: Methods and Protocols. Humana Press, Totowa, USA, pp. 19-38.
14. Goodacre, R. (2005) Making sense of the metabolome using evolutionary computation: seeing the wood with the trees. Journal of Experimental Botany 56, 245-254.
15. Goodacre, R., Timmins, É.M., Burton, R. et al. (1998) Rapid identification of urinary tract infection bacteria using hyperspectral, whole organism fingerprinting and artificial neural networks. Microbiology 144, 1157-1170.
16. Goodacre, R., Broadhurst, D., Smilde, A. et al. (2007) Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics 3, 231-241.
17. Guy, C.L., Kaplan, F., Kopka, J. et al. (2008) Metabolomics of temperature stress. Physiologica Plantarum 132(2), 220-235
18. Jolliffe, I.T. (1986) Principal Component Analysis. Springer-Velarg, New York.
19. Jonsson, P., Gullberg, J., Nordström, A. et al. (2004) A strategy for identifying differences in large series of metabolomic samples analysed by GC/MS. Analytical Chemistry 76, 1738-1745.
20. Jonsson, P., Johansson, E.S., Wuolikainen, A. et al. (2006) Predictive metabolite profiling applying hierarchicalmultivariate curve resolution to GC-MS data - a potential tool for multi-parametric diagnosis. Journal of Proteome Research 5, 1407-1414.
21. Kaplan, F., Kopka, J., Haskell, D.W. et al. (2004) Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiology 136(4), 4159-4168.
22. Kaplan, F., Kopka, J., Sung, D.Y. et al. (2007) Transcript and metabolite profiling during cold acclimation of Arabidopsis reveals an intricate relationship of coldregulated gene expression with modifications in metabolite content. Plant Journal 50(6), 967-981.
23. Koza, J.R. (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
24. Lommen, A. (2009) MetAlign: Interface-Driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Analytical Chemistry 81(8): 3079-3086.
25. Lisec, J., Schauer, N., Kopka, J. et al. (2006) Gas chromatography mass spectrometrybased metabolite profiling in plants. Nature Protocols 1, 387-396.
26. Luedemann, A., Strassburg, K., Erban, A. et al. (2008) TagFinder for the quantitative analysis of gas chromatography - mass spectrometry (GC-MS) based metabolite profiling experiments. Bioinformatics 24 (5), 732-737.
27. Manly, B.F.J. (1994) Multivariate Statistical Methods: A Primer. Chapman & Hall, London.
28. Meyer, R.C., Steinfath, M., Lisec, J. et al. (2007) Themetabolic signature related to high plant growth rate in Arabidopsis thaliana. PNAS 104(11), 4759-4764.
29. Sanchez, D.H., Szymanski, J., Erban, A. et al. (2009) Mining for robust transcriptional and metabolic responses to long-term salt stress: a case study on the model legume Lotus japonicus. Plant Cell and Environment (DOI: 10.1111/j.1365-3040.2009.02047.x).
30. Sanchez, D.H., Lippold, F., Redestig, H. et al. (2008a) Integrative functional genomics of salt acclimation in the model legume Lotus japonicus. Plant Journal 53(6), 973-987.
31. Sanchez, D.H., Redestig, H., Kraemer, U. et al. (2008b) Metabolome-ionome-biomass interactions: What can we learn about salt stress by multiparallel phenotyping? Plant Signaling and Behavior 3 (8), 598-600.
32. Sanchez, D.H., Siahpoosh, M.R., Roessner, U. et al. (2008c) Plant metabolomics reveals conserved and divergent metabolic responses to salinity. Physiologica Plantarum 132(2), 209-219.
33. Schauer, N., Semel, Y., Roessner, U. et al. (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology 24 (4), 447-454.
34. Scholz, M., Kaplan, F., Guy, C.L. et al. (2005) Non-linear PCA: a missing data approach. Bioinformatics 21(20), 3887-3895.
35. Seasholtz, M.B. and Kowalski, B. (1993) The parsimony principle applied to multivariate calibration. Analytica Chimica Acta 277, 165-177.
36. Smilde, A.K., Westerhuis, J.A. and Jong, S.J. (2003) A framework for sequential multiblock component methods. Journal of Chemometrics 17 (6), 323-337.
37. Waterhouse, A.L. Kennedy, J.A. (ed.) (2004) ACS Symposium Series, 886. American Chemical Society, Washington, D.C.