Abstract
Metabolomics Ion-based Data Extraction Algorithm (MET-IDEA) is a computer program for processing large-scale metabolomics data. MET-IDEA utilizes network Common Data Form (netCDF) data files available from a diversity of chromatographically coupled mass spectrometry (MS) systems, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized data. This article reports on recent improvements to MET-IDEA which include new visualization of peak integrations, display of mass spectra associated with integrated peaks, and optional manual peak integration. The computational performance of MET-IDEA has also been improved to avoid memory overflow during the processing of large data sets and the software made compatible with 64 bit CPUs and operating systems. These new functions improve the performance of MET-IDEA, and they allow users to visualize peak integrations and curate the results through manual integration if desired. The improved version of MET-IDEA better facilitates the quantitative analysis of complex MS-based metabolomics data. MET-IDEA is freely available for academic and non commercial use at (http://bioinfo.noble.org/gateway/index.php?option=com_wrapper&Itemid=57). Commercial use is available via licensing agreement.
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References
Baran, R., Kochi, H., Saito, N., Suematsu, M., Soga, T., Nishioka, T., et al. (2006). MathDAMP: A package for differential analysis of metabolite profiles. BMC Bioinformatics, 7(530), doi:10.1186/1471-2105-7-530.
Benkeblia, N., Shinano, T., & Osaki, M. (2007). Metabolite profiling and assessment of metabolome compartmentation of soybean leaves using non-aqueous fractionation and GC–MS analysis. Metabolomics, 3(3), 297–305.
Brechenmacher, L., Lei, Z., Libault, M., Findley, S., Sugawara, M., Sadowsky, M. J., et al. (2010). Soybean metabolites regulated in root hairs in response to the symbiotic bacterium Bradyrhizobium japonicum. Plant Physiology, 153(4), 1808–1822. doi:10.1104/pp.110.157800.
Broeckling, C. D., Reddy, I. R., Duran, A. L., Zhao, X. J., & Sumner, L. W. (2006). MET-IDEA: data extraction tool for mass spectrometry-based metabolomics. Analytical Chemistry, 78(13), 4334–4341. doi:10.1021/ac0521596.
Broeckling, C. D., Broz, A. K., Bergelson, J., Manter, D. K., & Vivanco, J. M. (2008). Root exudates regulate soil fungal community composition and diversity. Applied and Environmental Microbiology, 74(3), 738–744. doi:10.1128/aem.02188-07.
Dolan, J. W. (2009). Integration problems. LCGC North America, 27(10), 892–899.
Duran, A. L., Yang, J., Wang, L. J., & Sumner, L. W. (2003). Metabolomics spectral formatting, alignment and conversion tools (MSFACTs). Bioinformatics, 19(17), 2283–2293. doi:10.1093/bioinformatics/btg315.
Farag, M. A. (2008). Headspace analysis of volatile compounds in leaves from the Juglandaceae (Walnut) family. Journal of Essential Oil Research, 20(4), 323–327.
Farag, M. A. (2009). Chemical composition and biological activities of Asimina triloba leaf essential oil. Pharmaceutical Biology, 47(10), 982–986. doi:10.1080/13880200902967995.
Farag, M. A., Ryu, C. M., Sumner, L. W., & Pare, P. W. (2006). GC–MS SPME profiling of rhizobacterial volatiles reveals prospective inducers of growth promotion and induced systemic resistance in plants. Phytochemistry, 67(20), 2262–2268. doi:10.1016/j.phytochem.2006.07.021.
Farag, M. A., Huhman, D. V., Dixon, R. A., & Sumner, L. W. (2008). Metabolomics reveals novel pathways and differential mechanistic and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago truncatula cell cultures. Plant Physiology, 146(2), 387–402. doi:10.1104/pp.107.108431.
Farag, M. A., Deavours, B. E., de Fatima, A., Naoumkina, M., Dixon, R. A., & Sumner, L. W. (2009). Integrated metabolite and transcript profiling identify a biosynthetic mechanism for hispidol in Medicago truncatula cell cultures. Plant Physiology, 151(3), 1096–1113. doi:10.1104/pp.109.141481.
FDA. (2001). Guidance for industry: Bioanalytical method validation. Rockville, MD: FDA, pp. 1–25.
Fiehn, O., Wohlgemuth, G., Scholz, M. (2005). Setup and annotation of metabolomic experiments by integrating biological and mass spectrometric metadata. In B. Ludascher, L. Raschid (Eds.), Data Integration in the Life Sciences, Proceedings (vol. 3615, pp. 224–239, Lecture Notes in Computer Science). Berlin: Springer-Verlag.
Florida_DEP. (2011). CM-018-1.7 Laboratory policy regarding manual chromatographic peak integration. pp. 1–5. http://www.dep.state.fl.us/labs/cgi-bin/sop/sop1.asp?sect=CHEMISTRY.
Hamzehzarghani, H., Paranidharan, V., Abu-Nada, Y., Kushalappa, A. C., Mamer, O., & Somers, D. (2008). Metabolic profiling to discriminate wheat near isogenic lines, with quantitative trait loci at chromosome 2DL, varying in resistance to Fusarium head blight. Canadian Journal of Plant Science, 88(4), 789–797.
Hiller, K., Hangebrauk, J., Jager, C., Spura, J., Schreiber, K., & Schomburg, D. (2009). MetaboliteDetector: Comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. Analytical Chemistry, 81(9), 3429–3439. doi:10.1021/ac802689c.
Katajamaa, M., Miettinen, J., & Oresic, M. (2006). MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics, 22(5), 634–636. doi:10.1093/bioinformatics/btk039.
Kopka, J., Schauer, N., Krueger, S., Birkemeyer, C., Usadel, B., Bergmuller, E., et al. (2005). GMD@CSB.DB: the Golm metabolome database. Bioinformatics, 21(8), 1635–1638. doi:10.1093/bioinformatics/bti236.
Lisec, J., Schauer, N., Kopka, J., Willmitzer, L., & Fernie, A. R. (2006). Gas chromatography mass spectrometry-based metabolite profiling in plants. Nature Protocols, 1(1), 387–396. doi:10.1038/nprot.2006.59.
Lommen, A. (2009). MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Analytical Chemistry, 81(8), 3079–3086. doi:10.1021/ac900036d.
Luedemann, A., Strassburg, K., Erban, A., & Kopka, J. (2008). TagFinder for the quantitative analysis of gas chromatography–mass spectrometry (GC–MS)-based metabolite profiling experiments. Bioinformatics, 24(5), 732–737. doi:10.1093/bioinformatics/btn023.
Naoumkina, M., Vaghchhipawala, S., Tang, Y. H., Ben, Y. X., Powell, R. J., & Dixon, R. A. (2008). Metabolic and genetic perturbations accompany the modification of galactomannan in seeds of Medicago truncatula expressing mannan synthase from guar (Cyamopsis tetragonoloba L.). Plant Biotechnology Journal, 6(6), 619–631. doi:10.1111/j.1467-7652.2008.00345.x.
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3), 779–787. doi:10.1021/ac051437y.
Xu, P., Chen, F., Mannas, J. P., Feldman, T., Sumner, L. W., & Roossinck, M. J. (2008). Virus infection improves drought tolerance. New Phytologist, 180(4), 911–921. doi:10.1111/j.1469-8137.2008.02627.x.
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Z. Lei and H. Li contributed equally to the work.
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Lei, Z., Li, H., Chang, J. et al. MET-IDEA version 2.06; improved efficiency and additional functions for mass spectrometry-based metabolomics data processing. Metabolomics 8 (Suppl 1), 105–110 (2012). https://doi.org/10.1007/s11306-012-0397-5
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DOI: https://doi.org/10.1007/s11306-012-0397-5