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BioMed Research International
Volume 2014 (2014), Article ID 194812, 11 pages
http://dx.doi.org/10.1155/2014/194812
Research Article

Tools and Databases of the KOMICS Web Portal for Preprocessing, Mining, and Dissemination of Metabolomics Data

1Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba 292-0818, Japan
2JST, National Bioscience Database Center (NBDC), 5-3 Yonbancho, Chiyoda-ku, Tokyo 102-0081, Japan
3Department of Nutrition and Life Science, Kanagawa Institute of Technology, 1030 Shimo-ogino, Atsugi, Kanagawa 243-0292, Japan
4Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan

Received 5 December 2013; Revised 7 February 2014; Accepted 24 February 2014; Published 9 April 2014

Academic Editor: Shigehiko Kanaya

Copyright © 2014 Nozomu Sakurai 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. N. Yamamoto, T. Suzuki, T. Ara et al., “MatchedIonsFinder: a software tool for revising alignment matrices of spectrograms from liquid chromatography-mass spectrometry,” Plant Biotechnology, vol. 29, no. 1, pp. 109–113, 2012. View at Google Scholar
  2. Y. Ogata, N. Sakurai, K. Aoki et al., “KAGIANA: an excel-based tool for retrieving summary information on Arabidopsis genes,” Plant and Cell Physiology, vol. 50, no. 1, pp. 173–177, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. N. Sakurai, T. Ara, S. Kanaya et al., “An application of a relational database system for high-throughput prediction of elemental compositions from accurate mass values,” Bioinformatics, vol. 29, no. 2, pp. 290–291, 2013. View at Publisher · View at Google Scholar
  4. K. Yano, K. Aoki, H. Suzuki, and D. Shibata, “DAGViz: a directed acyclic graph browser that supports analysis of gene ontology annotation,” Plant Biotechnology, vol. 26, no. 1, pp. 9–13, 2009. View at Google Scholar · View at Scopus
  5. N. Sakurai, T. Ara, Y. Ogata et al., “KaPPA-View4: a metabolic pathway database for representation and analysis of correlation networks of gene co-expression and metabolite co-accumulation and omics data,” Nucleic Acids Research, vol. 39, no. 1, pp. D677–D684, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Ogata, H. Suzuki, N. Sakurai, and D. Shibata, “CoP: a database for characterizing co-expressed gene modules with biological information in plants,” Bioinformatics, vol. 26, no. 9, pp. 1267–1268, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Yano, T. Dansako, N. Sakurai, H. Suzuki, and D. Shibata, “KATANA: a web-based guide to public databases for Arabidopsis genomic information,” Plant Biotechnology, vol. 22, no. 3, pp. 225–229, 2005. View at Google Scholar · View at Scopus
  8. T. Ohba, K. Suzuki, T. Oura et al., “ARTRA: a new database of the Arabidopsis transcriptome and gene-specific sequences for microarray probes and RNAi triggers,” Plant Biotechnology, vol. 26, no. 1, pp. 161–165, 2009. View at Google Scholar · View at Scopus
  9. S. G. Oliver, M. K. Winson, D. B. Kell, and F. Baganz, “Systematic functional analysis of the yeast genome,” Trends in Biotechnology, vol. 16, no. 9, pp. 373–378, 1998. View at Publisher · View at Google Scholar · View at Scopus
  10. W. B. Dunn, “Current trends and future requirements for the mass spectrometric investigation of microbial, mammalian and plant metabolomes,” Physical Biology, vol. 5, no. 1, Article ID 011001, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. 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 Scopus
  12. K. Hollywood, D. R. Brison, and R. Goodacre, “Metabolomics: current technologies and future trends,” Proteomics, vol. 6, no. 17, pp. 4716–4723, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Nielsen and S. Oliver, “The next wave in metabolome analysis,” Trends in Biotechnology, vol. 23, no. 11, pp. 544–546, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. W. B. Dunn and T. Hankemeier, “Mass spectrometry and metabolomics: past, present and future,” Metabolomics, vol. 9, no. 1, supplement, pp. 1–3, 2013. View at Publisher · View at Google Scholar
  15. A. Zhang, H. Sun, P. Wang, Y. Han, and X. Wang, “Modern analytical techniques in metabolomics analysis,” Analyst, vol. 137, no. 2, pp. 293–300, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. D. S. Wishart, “Computational strategies for metabolite identification in metabolomics,” Bioanalysis, vol. 1, no. 9, pp. 1579–1596, 2009. View at Google Scholar · View at Scopus
  17. Z. Lei, D. V. Huhman, and L. W. Sumner, “Mass spectrometry strategies in metabolomics,” The Journal of Biological Chemistry, vol. 286, no. 29, pp. 25435–25442, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. R. D. Hall, “Plant metabolomics: from holistic hope, to hype, to hot topic,” New Phytologist, vol. 169, no. 3, pp. 453–468, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Saito and F. Matsuda, “Metabolomics for functional genomics, systems biology, and biotechnology,” Annual Review of Plant Biology, vol. 61, pp. 463–489, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. S. H. Khoo and M. Al-Rubeai, “Metabolomics as a complementary tool in cell culture,” Biotechnology and Applied Biochemistry, vol. 47, part 2, pp. 71–84, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. M. R. Mashego, K. Rumbold, M. de Mey, E. Vandamme, W. Soetaert, and J. J. Heijnen, “Microbial metabolomics: past, present and future methodologies,” Biotechnology Letters, vol. 29, no. 1, pp. 1–16, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Kopka, A. Fernie, W. Weckwerth, Y. Gibon, and M. Stitt, “Metabolite profiling in plant biology: platforms and destinations,” Genome Biology, vol. 5, no. 6, article 109, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Koulman, G. A. Lane, S. J. Harrison, and D. A. Volmer, “From differentiating metabolites to biomarkers,” Analytical and Bioanalytical Chemistry, vol. 394, no. 3, pp. 663–670, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Jansson, B. Willing, M. Lucio et al., “Metabolomics reveals metabolic biomarkers of Crohn's disease,” PLoS ONE, vol. 4, no. 7, Article ID e6386, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Madsen, T. Lundstedt, and J. Trygg, “Chemometrics in metabolomics—a review in human disease diagnosis,” Analytica Chimica Acta, vol. 659, no. 1-2, pp. 23–33, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. M. A. Fitzgerald, S. R. McCouch, and R. D. Hall, “Not just a grain of rice: the quest for quality,” Trends in Plant Science, vol. 14, no. 3, pp. 133–139, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. W. Pongsuwan, T. Bamba, K. Harada, T. Yonetani, A. Kobayashi, and E. Fukusaki, “High-throughput technique for comprehensive analysis of Japanese green tea quality assessment using ultra-performance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS),” Journal of Agricultural and Food Chemistry, vol. 56, no. 22, pp. 10705–10708, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. A. E. Ricroch, J. B. Bergé, and M. Kuntz, “Evaluation of genetically engineered crops using transcriptomic, proteomic, and metabolomic profiling techniques,” Plant Physiology, vol. 155, no. 4, pp. 1752–1761, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. M. Kusano, H. Redestig, T. Hirai et al., “Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment,” PLoS ONE, vol. 6, no. 2, Article ID e16989, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Krauss, H. Singer, and J. Hollender, “LC-high resolution MS in environmental analysis: from target screening to the identification of unknowns,” Analytical and Bioanalytical Chemistry, vol. 397, no. 3, pp. 943–951, 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Y. Lin, M. R. Viant, and R. S. Tjeerdema, “Metabolomics: methodologies and applications in the environmental sciences,” Journal of Pesticide Science, vol. 31, no. 3, pp. 245–251, 2006. View at Publisher · View at Google Scholar · View at Scopus
  32. A. Fukushima and M. Kusano, “Recent progress in the development of metabolome databases for plant systems biology,” Frontiers in Plant Science, vol. 4, article 73, 2013. View at Publisher · View at Google Scholar
  33. T. Tohge and A. R. Fernie, “Web-based resources for mass-spectrometry-based metabolomics: a user's guide,” Phytochemistry, vol. 70, no. 4, pp. 450–456, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. G. Blekherman, R. Laubenbacher, D. F. Cortes et al., “Bioinformatics tools for cancer metabolomics,” Metabolomics, vol. 7, no. 3, pp. 329–343, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. M. Hur, A. A. Campbell, M. Almeida-de-Macedo et al., “A global approach to analysis and interpretation of metabolic data for plant natural product discovery,” Natural Product Reports, vol. 30, no. 4, pp. 565–583, 2013. View at Publisher · View at Google Scholar
  36. H. P. Benton, D. M. Wong, S. A. Trauger, and G. Siuzdak, “XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization,” Analytical Chemistry, vol. 80, no. 16, pp. 6382–6389, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. T. Pluskal, S. Castillo, A. Villar-Briones, and M. Orešič, “MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data,” BMC Bioinformatics, vol. 11, article 395, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Baran, H. Kochi, N. Saito et al., “MathDAMP: a package for differential analysis of metabolite profiles,” BMC Bioinformatics, vol. 7, article 530, 2006. View at Publisher · View at Google Scholar · View at Scopus
  39. 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
  40. Z. Lei, H. Li, J. Chang, P. X. Zhao, and L. W. Sumner, “MET-IDEA version 2.06; improved efficiency and additional functions for mass spectrometry-based metabolomics data processing,” Metabolomics, vol. 8, pp. 105–110, 2012. View at Publisher · View at Google Scholar · View at Scopus
  41. H. Horai, M. Arita, S. Kanaya et al., “MassBank: a public repository for sharing mass spectral data for life sciences,” Journal of Mass Spectrometry, vol. 45, no. 7, pp. 703–714, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. C. A. Smith, G. O'Maille, E. J. Want et al., “METLIN: a metabolite mass spectral database,” Therapeutic Drug Monitoring, vol. 27, no. 6, pp. 747–751, 2005. View at Publisher · View at Google Scholar · View at Scopus
  43. T. Sakurai, Y. Yamada, Y. Sawada et al., “PRIMe update: innovative content for plant metabolomics and integration of gene expression and metabolite accumulation,” Plant and Cell Physiology, vol. 54, no. 2, article e5, 2013. View at Publisher · View at Google Scholar
  44. D. S. Wishart, T. Jewison, A. C. Guo et al., “HMDB 3.0—the human metabolome database in 2013,” Nucleic Acids Research, vol. 41, pp. D801–D807, 2013. View at Publisher · View at Google Scholar
  45. F. M. Afendi, T. Okada, M. Yamazaki et al., “KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research,” Plant and Cell Physiology, vol. 53, no. 2, article e1, 2012. View at Publisher · View at Google Scholar · View at Scopus
  46. 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 Scopus
  47. M. Kanehisa, S. Goto, Y. Sato, M. Furumichi, and M. Tanabe, “KEGG for integration and interpretation of large-scale molecular data sets,” Nucleic Acids Research, vol. 40, pp. D109–D114, 2012. View at Publisher · View at Google Scholar
  48. R. Caspi, T. Altman, K. Dreher et al., “The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases,” Nucleic Acids Research, vol. 38, no. 1, pp. D742–D753, 2009. View at Publisher · View at Google Scholar
  49. D. Croft, G. O'Kelly, G. Wu et al., “Reactome: a database of reactions, pathways and biological processes,” Nucleic Acids Research, vol. 39, no. 1, pp. D691–D697, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. P. Bais, S. M. Moon, K. He et al., “Plantmetabolomics.org: a web portal for plant metabolomics experiments,” Plant Physiology, vol. 152, no. 4, pp. 1807–1816, 2010. View at Publisher · View at Google Scholar · View at Scopus
  51. J. Kopka, N. Schauer, S. Krueger et al., “GMD@CSB.DB: the Golm metabolome database,” Bioinformatics, vol. 21, no. 8, pp. 1635–1638, 2005. View at Publisher · View at Google Scholar · View at Scopus
  52. A. J. Carroll, M. R. Badger, and A. H. Millar, “The MetabolomeExpress project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets,” BMC Bioinformatics, vol. 11, article 376, 2010. View at Publisher · View at Google Scholar · View at Scopus
  53. K. Haug, R. M. Salek, P. Conesa et al., “MetaboLights-an open-access general-purpose repository for metabolomics studies and associated meta-data,” Nucleic Acids Research, vol. 41, pp. D781–D786, 2013. View at Publisher · View at Google Scholar
  54. B. P. Bowen and T. R. Northen, “Dealing with the unknown: metabolomics and metabolite atlases,” Journal of the American Society for Mass Spectrometry, vol. 21, no. 9, pp. 1471–1476, 2010. View at Publisher · View at Google Scholar · View at Scopus
  55. S. Neumann and S. Böcker, “Computational mass spectrometry for metabolomics: identification of metabolites and small molecules,” Analytical and Bioanalytical Chemistry, vol. 398, no. 7-8, pp. 2779–2788, 2010. View at Publisher · View at Google Scholar · View at Scopus
  56. R. Goodacre, S. Vaidyanathan, W. B. Dunn, G. G. Harrigan, and D. B. Kell, “Metabolomics by numbers: acquiring and understanding global metabolite data,” Trends in Biotechnology, vol. 22, no. 5, pp. 245–252, 2004. View at Publisher · View at Google Scholar · View at Scopus
  57. T. Kind, M. Scholz, and O. Fiehn, “How large is the metabolome? A critical analysis of data exchange practices in chemistry,” PLoS ONE, vol. 4, no. 5, Article ID e5440, 2009. View at Publisher · View at Google Scholar · View at Scopus
  58. H. A. Piwowar, R. S. Day, and D. B. Fridsma, “Sharing detailed research data is associated with increased citation rate,” PLoS ONE, vol. 2, no. 3, article e308, 2007. View at Publisher · View at Google Scholar · View at Scopus
  59. N. Sakurai, Y. Ogata, T. Ara et al., “Development of KaPPA-View4 for omics studies on Jatropha and a database system KaPPA-Loader for construction of local omics databases,” Plant Biotechnology, vol. 29, no. 2, pp. 131–135, 2012. View at Google Scholar
  60. R. Sano, T. Ara, N. Akimoto et al., “Dynamic metabolic changes during fruit maturation in Jatropha curcas L,” Plant Biotechnology, vol. 29, no. 2, pp. 175–178, 2012. View at Google Scholar