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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 263910, 7 pages
doi:10.1155/2012/263910
Methodology Report

MarVis-Filter: Ranking, Filtering, Adduct and Isotope Correction of Mass Spectrometry Data

Alexander Kaever,1 Manuel Landesfeind,1 Mareike Possienke,2 Kirstin Feussner,2,3 Ivo Feussner,2 and Peter Meinicke1

1Department of Bioinformatics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, 37077 Göttingen, Germany
2Department for Plant Biochemistry, Albrecht-von-Haller-Institute for Plant Sciences, Georg-August-University Göttingen, 37077 Göttingen, Germany
3Department of Molecular Microbiology and Genetics, Institute of Microbiology and Genetics, Georg-August-University Göttingen, 37077 Göttingen, Germany

Received 28 July 2011; Revised 18 January 2012; Accepted 18 January 2012

Academic Editor: Brad Upham

Copyright © 2012 Alexander Kaever et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. O. Fiehn, J. Kopka, P. Dörmann, T. Altmann, R. N. Trethewey, and L. Willmitzer, “Metabolite profiling for plant functional genomics,” Nature Biotechnology, vol. 18, no. 11, pp. 1157–1161, 2000. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  2. J. K. Nicholson, J. C. Lindon, and E. Holmes, “'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data,” Xenobiotica, vol. 29, no. 11, pp. 1181–1189, 1999. View at Scopus
  3. J. Lisec, N. Schauer, J. Kopka, L. Willmitzer, and A. R. Fernie, “Gas chromatography mass spectrometry-based metabolite profiling in plants,” Nature Protocols, vol. 1, no. 1, pp. 387–396, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  4. R. C. H. De Vos, S. Moco, A. Lommen, J. J. B. Keurentjes, R. J. Bino, and R. D. Hall, “Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry,” Nature Protocols, vol. 2, no. 4, pp. 778–791, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  5. K. Dettmer, P. A. Aronov, and B. D. Hammock, “Mass spectrometry-based metabolomics,” Mass Spectrometry Reviews, vol. 26, no. 1, pp. 51–78, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  6. M. Katajamaa and M. Orešič, “Data processing for mass spectrometry-based metabolomics,” Journal of Chromatography A, vol. 1158, no. 1-2, pp. 318–328, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  7. C. A. Smith, E. J. Want, G. O'Maille, R. Abagyan, and G. Siuzdak, “XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification,” Analytical Chemistry, vol. 78, no. 3, pp. 779–787, 2006. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  8. P. Meinicke, T. Lingner, A. Kaever et al., “Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps,” Algorithms for Molecular Biology, vol. 3, no. 1, article 9, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. V. Shulaev, D. Cortes, G. Miller, and R. Mittler, “Metabolomics for plant stress response,” Physiologia Plantarum, vol. 132, no. 2, pp. 199–208, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  10. L. Tarpley, A. L. Duran, T. H. Kebrom, and L. W. Sumner, “Biomarker metabolites capturing the metabolite variance present in a rice plant developmental period,” BMC Plant Biology, vol. 5, article 8, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  11. K. Nahlik, M. Dumkow, Ö Bayram, et al., “The COP9 signalosome mediates transcriptional and metabolic response to hormones, oxidative stress protection and cell wall rearrangement during fungal development,” Molecular Microbiology, vol. 78, no. 4, pp. 964–979, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  13. M. Beckmann, D. P. Enot, D. P. Overy, and J. Draper, “Representation, comparison, and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato cultivars,” Journal of Agricultural and Food Chemistry, vol. 55, no. 9, pp. 3444–3451, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  14. J. Gibbons and S. Chakraborti, Nonparametric Statistical Inference, CRC Press, 2003.
  15. A. Koulman, B. A. Tapper, K. Fraser, M. Cao, G. A. Lane, and S. Rasmussen, “High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics,” Rapid Communications in Mass Spectrometry, vol. 21, no. 3, pp. 421–428, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  16. D. A. MacKenzie, M. Defernez, W. B. Dunn et al., “Relatedness of medically important strains of Saccharomyces cerevisiae as revealed by phylogenetics and metabolomics,” Yeast, vol. 25, no. 7, pp. 501–512, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  17. M. Kanehisa and S. Goto, “KEGG: Kyoto encyclopedia of genes and genomes,” Nucleic Acids Research, vol. 28, no. 1, pp. 27–30, 2000. View at Scopus
  18. P. Zhang, H. Foerster, C. P. Tissier et al., “MetaCyc and AraCyc. Metabolic pathway databases for plant research,” Plant Physiology, vol. 138, no. 1, pp. 27–37, 2005. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  19. M. Sud, E. Fahy, D. Cotter et al., “LMSD: LIPID MAPS structure database,” Nucleic Acids Research, vol. 35, no. 1, pp. D527–D532, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  20. D. S. Wishart, C. Knox, A. C. Guo et al., “HMDB: a knowledgebase for the human metabolome,” Nucleic Acids Research, vol. 37, supplement 1, pp. D603–D610, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  21. Y. Wang, J. Xiao, T. O. Suzek, J. Zhang, J. Wang, and S. H. Bryant, “PubChem: a public information system for analyzing bioactivities of small molecules,” Nucleic Acids Research, vol. 37, no. 2, pp. W623–W633, 2009. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  22. J. Draper, D. P. Enot, D. Parker et al., “Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules',” BMC Bioinformatics, vol. 10, article 227, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Tautenhahn, C. Böttcher, and S. Neumann, “Annotation of LC/ESI-MS mass signals,” in 1st International Conference on Bioinformatics Research and Development (BIRD '07), vol. 4414 of Lecture Notes in Computer Science, pp. 371–380, Berlin, Germany, March 2007. View at Scopus
  24. T. Kind and O. Fiehn, “Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry,” BMC Bioinformatics, vol. 8, article 105, 2007. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  25. R. Gentleman, R. Ihaka, et al., http://www.r-project.org/.
  26. B. Jones, MATLAB: Statistics Toolbox User's Guide, MathWorks, 1993.
  27. A. Lommen, “Metalign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing,” Analytical Chemistry, vol. 81, no. 8, pp. 3079–3086, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Sturm, A. Bertsch, C. Gröpl et al., “OpenMS—an open-source software framework for mass spectrometry,” BMC Bioinformatics, vol. 9, article 163, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  29. A. Kaever, T. Lingner, K. Feussner, C. Göbel, I. Feussner, and P. Meinicke, “MarVis: a tool for clustering and visualization of metabolic biomarkers,” BMC Bioinformatics, vol. 10, article 92, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Holm, “A simple sequentially rejective multiple test procedure,” Scandinavian Journal of Statistics, vol. 6, no. 2, pp. 65–70, 1979.
  31. Y. Benjamini and Y. Hochberg, “Controlling the false discovery rate: a practical and powerful approach to multiple testing,” Journal of the Royal Statistical Society. Series B, vol. 57, pp. 289–300, 1995.
  32. B. Von Malek, E. Van Der Graaff, K. Schneitz, and B. Keller, “The Arabidopsis male-sterile mutant dde2-2 is defective in the ALLENE OXIDE SYNTHASE gene encoding one of the key enzymes of the jasmonic acid biosynthesis pathway,” Planta, vol. 216, no. 1, pp. 187–192, 2002. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus
  33. C. Göbel and I. Feussner, “Methods for the analysis of oxylipins in plants,” Phytochemistry, vol. 70, no. 13-14, pp. 1485–1503, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. A. Ibrahim, A. Schütz, J. Galano et al., “The alphabet of galactolipids in Arabidopsis thaliana,” Frontiers in Plant Physiology, vol. 2, article 95, 2011.
  35. A. Andreou, F. Brodhun, and I. Feussner, “Biosynthesis of oxylipins in non-mammals,” Progress in Lipid Research, vol. 48, no. 3-4, pp. 148–170, 2009. View at Publisher · View at Google Scholar · View at Scopus
  36. G. A. Howe and G. Jander, “Plant immunity to insect herbivores,” Annual Review of Plant Biology, vol. 59, pp. 41–66, 2008. View at Publisher · View at Google Scholar · View at PubMed · View at Scopus