Abstract
Metabolomics is one of the omics’ sciences that has been gaining a lot of interest due to its potential on correlating an organism’s biochemical activity and its phenotype. While nuclear magnetic resonance (NMR) is one of the main analytical techniques in metabolomics, one-dimensional NMR suffers from some limitations. NMR’s two-dimensional approaches (2D-NMR) deliver a solution to one of its main disadvantages, low sensitivity. Addressing a growing need for integrated frameworks to handle data analysis and mining in this domain, new functionalities regarding 2D-NMR were added to specmine, an R package for metabolomics and spectral data analysis/mining. These functionalities allow reading, visualization, and analysis of 2D-NMR data within the same environment, making possible to the user to establish its own pipeline. Two case studies, from Bruker and Varian datasets, were used to validate the functions developed and a pipeline was implemented and made available through R Markdown.
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References
Carrari, F., et al..: Integrated analysis of metabolite and transcript levels reveals the metabolic shifts that underlie tomato fruit development and highlight regulatory aspects of metabolic network behavior. Plant Physiol. 142(4), 1380–1396 (2006)
Clendinen, C.S., Pasquel, C., Ajredini, R., Edison, A.S.: 13C NMR metabolomics: INADEQUATE network analysis. Anal.l Chem. 87(11), 5698–5706 (2015)
Costa, C., Maraschin, M., Rocha, M.: An R package for the integrated analysis of metabolomics and spectral data. Comput. Methods Prog. Biomed. 129, 117–124 (2016)
Čuperlović-Culf, M.: Experimental methodology. In: NMR Metabolomics in Cancer Research, Chap. 3, pp. 193–213. Woodhead Publishing, Beijing (2013)
Dumas, M., Maibaum, E., Teague, C., Ueshima, H., Zhou, B., Lindon, J., Nicholson, J., Stamler, J., Elliott, P., Chan, Q., Holmes, E.: Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP study. Anal. Chem. 78(7), 2199–2208 (2006)
Emwas, A.H., et al.: Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis. J. Proteome Res. 15(2), 360–373 (2016)
Emwas, A.H., et al.: NMR spectroscopy for metabolomics research. Metabolites 9(7), 123 (2019)
Hanson, B.A.: ChemoSpec2D: exploratory Chemometrics for 2D Spectroscopy (2020), https://CRAN.R-project.org/package=ChemoSpec2D. r package version 0.4.176
Helmus, J.J., Jaroniec, C.P.: Nmrglue: an open source Python package for the analysis of multidimensional NMR data. J. Biomol. NMR 55(4), 355–367 (2013). http://nmrglue.com
Inc., P.T.: Collaborative data science (2015). https://plot.ly
Jézéquel, T., Deborde, C., Maucourt, M., Zhendre, V., Moing, A., Giraudeau, P.: Absolute quantification of metabolites in tomato fruit extracts by fast 2D NMR. Metabolomics 11(5), 1231–1242 (2015)
Klein, M.: mrbin: Magnetic Resonance Binning, Integration and Normalization (2021). https://CRAN.R-project.org/package=mrbin. r package version 1.5.0
Lewis, I.A., Schommer, S.C., Markley, J.L.: rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Mag. Reson. Chem. 47(SUPPL. 1), S123 (2009)
Mahrous, E.A., Farag, M.A.: Two dimensional NMR spectroscopic approaches for exploring plant metabolome: a review. J. Adv. Res. 6(1), 3–15 (2015)
Öman, T., et al.: Identification of metabolites from 2D H-C HSQC NMR using peak correlation plots. BMC Bioinform. 15(1) (2014)
Parsons, H., Ekman, D., Collette, T., Viant, M.: Spectral relative standard deviation: a practical benchmark in metabolomics. Analyst 134(3), 478–485 (2009)
ur Rahman, A., Choudhary, M.I., tul Wahab, A.: Chapter 5 - the second dimension. In: ur Rahman, A., Choudhary, M.I., tul Wahab, A. (eds.) Solving Problems with NMR Spectroscopy, 2nd edn, pp. 191–225. Academic Press, Boston (2016)
Robinette, S.L., et al.: Hierarchical alignment and full resolution pattern recognition of 2D NMR spectra: Appl. Nematode Chem. Ecol. Anal. Chem. 83(5), 1649–1657 (2011)
Takis, P.G., Ghini, V., Tenori, L., Turano, P., Luchinat, C.: Uniqueness of the NMR approach to metabolomics. TrAC Trends Anal. Chem. 120, 115300 (2019)
Teng, Q.: Structural Biology. 2nd edn. Springer US, New York (2013)
Wang, B., Goodpaster, A., Kennedy, M.: Coefficient of variation, signal-to-noise ratio, and effects of normalization in validation of biomarkers from NMR-based metabonomics studies. Chemomet. Intell. Lab. Syst. 128, 9–16 (2013)
Xia, J., Bjorndahl, T.C., Tang, P., Wishart, D.S.: MetaboMiner - semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinform. 9(1), 507 (2008)
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Pereira, B., Maraschin, M., Rocha, M. (2022). Computational Tools for the Analysis of 2D-Nuclear Magnetic Resonance Data. In: Rocha, M., Fdez-Riverola, F., Mohamad, M.S., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 15th International Conference (PACBB 2021). PACBB 2021. Lecture Notes in Networks and Systems, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-86258-9_6
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