An Integrated Computational Platform for Metabolomics Data Analysis

  • Christopher Costa
  • Marcelo Maraschin
  • Miguel Rocha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 375)


The field of metabolomics, one of the omics technologies that have recently revolutionized biological research, provides multiple challenges for data analysis, that have been addressed by several computational tools. However, none addresses the multiplicity of existing techniques and data analysis tasks. Here, we propose a novel R package that provides a set of functions for metabolomics data analysis, including data loading in different formats, pre-processing, univariate and multivariate data analysis, machine learning and feature selection. The package supports the analysis of data from the main experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment, promoting the rapid development and sharing of data analysis pipelines.


Metabolomics R NMR MS IR UV-vis package data analysis 



The work is partially funded by Project 23060, PEM - Technological Support Platform for Metabolic Engineering, co- funded by FEDER through Portuguese QREN under the scope of the Technological Research and Development Incentive system, North Operational and by Project PropMine, funded by the agreement between Portuguese FCT and Brazilian CNPq.


  1. 1.
    Banskota, A.H., Tezuka, Y., Kadota, S.H.: Recent progress in pharmacological research of propolis. Phytother. Res. 15, 561–571 (2001)CrossRefGoogle Scholar
  2. 2.
    Beleites, C.: hyperSpec introduction. URL CENMAT and DI3, University of Trieste Spectroscopy - Imaging, IPHT Jena e.V (2014)
  3. 3.
    Burdock, G.A.: Review of the biological properties and toxicity of bee propolis (propolis). Food Chem. Toxicol. 36, 347–363 (1998)CrossRefGoogle Scholar
  4. 4.
    Eisner, R., Stretch, C., Eastman, T., Xia, J., Hau, D., Damaraju, S., Greiner, R., Wishart, D.S., Baracos, V.E.: Learning to predict cancer-associated skeletal muscle wasting from 1h-nmr profiles of urinary metabolites. Metabolomics 7, 25–34 (2010)CrossRefGoogle Scholar
  5. 5.
    Evans, W.J., Morleya, J.E., Argilésa, J., Balesa, C., Baracosa, V., Guttridgea, D., Jatoia, A., Kalantar-Zadeha, K., Lochsa, H., Mantovania, G., Marksa, D., Mitcha, W.E., Muscaritolia, M., Najanda, A., Ponikowskia, P., Fanellia, F.R., Schambelana, M., Scholsa, A., Schustera, M., Thomas, D., Wolfea, R., Anker, S.D.: Cachexia: a new definition. Clin. Nutr. 27, 793–799 (2008)CrossRefGoogle Scholar
  6. 6.
    Gekker, G., Hu, S., Spivak, M., Lokensgard, J.R., Peterson, P.K.: Anti-hiv-1 activity of propolis in cd4+ lymphocyte and microglial cell cultures. J. Ethnopharmacol. 102, 158–163 (2005)CrossRefGoogle Scholar
  7. 7.
    Hanson, B.A.: Chemospec: an r package for chemometric analysis of spectroscopic data and chromatograms (2013)Google Scholar
  8. 8.
    Kumazawa, S., Ueda, R., Hamasaka, T., Fukumoto, S., Fujimoto, T., Nakayama, T.: Antioxidant prenylated flavonoids from propolis collected in Okinawa, Japan. J. Agric. Food Chem. 55, 7722–7725 (2007)CrossRefGoogle Scholar
  9. 9.
    Mozzi, F., Ortiz, M.E., Bleckwedel, J., Vuyst, L.D., Pescuma, M.: Metabolomics as a tool for the comprehensive understanding of fermented and functional foods with lactic acid bacteria. Food Res. Int. (2012)Google Scholar
  10. 10.
    Nielsen, J., Jewett, M.C.: Metabolomics: A Powerful Tool in Systems Biology. Springer, Berlin (2007)Google Scholar
  11. 11.
    Sforcin, J.M.: Propolis and the immune system: a review. J. Ethnopharmacol. 113, 1–14 (2007)CrossRefGoogle Scholar
  12. 12.
    Tan-No, K., Nakajima, K.T., Shoii, T., Nakagawasai, O., Niijima, F., Ishikawa, M., Endo, Y., Sato, T., Satoh, S., Tadano, K.: Anti-inflammatory effect of própolis through nitric oxide production on carrageenin-induced mouse paw edema. Biol. Pharm. Bull 29, 96–99 (2006)CrossRefGoogle Scholar
  13. 13.
    Uarrota, V.G., Moresco, R., Coelho, B., da Costa, E.: In: Nunes, L.A., Martins Peruch, E., de Oliveira Neubert, M., Rocha, M.M. (eds.) Metabolomics combined with chemometric tools (pca, hca, pls-da and svm) for screening cassava (manihot esculenta crantz) roots during postharvest physiological deterioration. Food Chem. 161, 67–78 (2014)Google Scholar
  14. 14.
    Varmuza, K., Filzmoser, P.: Introduction to multivariate statistical analysis in chemometrics. (2008, CRC Press)Google Scholar
  15. 15.
    Villas-Boas, S., Roessner, U., Hansen, M.A.E., Smedsgaard, J., Nielsen, J.: Metabolome analysis: an introduction. Wiley, Hoboken (2007)Google Scholar
  16. 16.
    Xia, J., Mandal, R., Sinelnikov, I.V., Broadhurst, D., Wishart, D.S.: Metaboanalyst 2.0-a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 40, 127–33 (2012)Google Scholar
  17. 17.
    Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., Siuzdak, G.: XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification. Anal. Chem. 78, 779–787 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christopher Costa
    • 1
  • Marcelo Maraschin
    • 2
  • Miguel Rocha
    • 1
  1. 1.CEB - Centre Biological EngineeringUniversity of MinhoBragaPortugal
  2. 2.Plant Morphogenesis and Biochemistry LaboratoryFederal University of Santa CatarinaFlorianopolisBrazil

Personalised recommendations