, 15:5 | Cite as

RANCM: a new ranking scheme for assigning confidence levels to metabolite assignments in NMR-based metabolomics studies

  • William C. Joesten
  • Michael A. KennedyEmail author
Original Article



The Metabolomics Standards Initiative has recommended four categories for metabolite assignments in NMR-based metabolic profiling studies. The “putatively annotated compound” category is most commonly reported by metabolomics investigators. However, there is significant ambiguity in reliability of “putatively annotated compound” assignments, which can range from low confidence made on minimal corroborating data to high confidence made on substantial corroborating data.


To introduce a new ranking system, Rank and AssigN Confidence to Metabolites (RANCM), to assign confidence levels to “putatively annotated compound” assignments in NMR-based metabolic profiling studies.


The ranking system was constructed with three confidence levels ranging from Rank 1 for the lowest confidence assignment level to Rank 3 for the highest confidence assignment level. A decision tree was constructed to guide rank selection for each metabolite assignment.


Examples are provided from experimental data demonstrating how to use the decision tree to make confidence level assignments to “putatively annotated compounds” in each of the three rank levels. A standard Excel sheet template is provided to facilitate decision-making, documentation and submission to data repositories.


RANCM is intended to reduce the ambiguity in “putatively annotated compound” assignments, to facilitate effective communication of the degree of confidence in “putatively annotated compound” assignments, and to make it easier for non-experts to evaluate the significance and reliability of NMR-based metabonomics studies. The system is straightforward to implement, based on the most common datasets collected in NMR-based metabolic profiling studies, and can be used with equal rigor and significance with any set of NMR datasets.


Nuclear magnetic resonance NMR Metabolomics Metabonomics 



The research was conducted with the support of Miami University. The instrumentation used in this work was obtained with the support of Miami University and the Ohio Board of Regents with funds used to establish the Ohio Eminent Scholar Laboratory where the work was performed.

Author contributions

MAK conceived the ranking scheme. WCJ tested and contributed to the development of the new ranking scheme. WJC and MAK wrote the manuscript. WCJ and MAK read and approved the manuscript.

Compliance with ethical standards

Conflict of interest Statements

William C Joesten declares that he has no conflict of interest. Michael A. Kennedy declares that he has no conflict of interest.

Ethical approval

Research involving Human Participants and/or Animals. This study did not involve the use of human participants. All procedures involving mice were approved by both the ethics committee and the Institutional Animal Care and Use Committee at Miami University (Animal Welfare Assurance Number: D16-00100). The protocol approved by the Miami University IACUC was assigned Project Number 898.

Supplementary material

11306_2018_1465_MOESM1_ESM.pdf (3.3 mb)
Supplementary material Figures S1–S4.
11306_2018_1465_MOESM2_ESM.xlsx (12 kb)
To facilitate use and documentation of the decision tree algorithm.
11306_2018_1465_MOESM3_ESM.pdf (45 kb)
A complete description of the NMR data files included in the both the figshare and MetaboLights data repositories.


  1. Bingol, K., Bruschweiler-Li, L., Li, D. W., & Bruschweiler, R. (2014). Customized metabolomics database for the analysis of NMR H-1-H-1 TOCSY and C-13-H-1 HSQC-TOCSY spectra of complex mixtures. Analytical Chemistry, 86, 5494–5501. Scholar
  2. Bingol, K., Li, D.-W., Zhang, B., & Bruschweiler, R. (2016). Comprehensive metabolite identification strategy using multiple 2D NMR spectra of a complex mixture implemented in the COLMARm web server. Analytical Chemistry, 88, 12411–12418.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Canueto, D., Gomez, J., Salek, R. M., Correig, X., & Canellas, N. (2018). rDolphin: a GUI R package for proficient automatic profiling of 1D 1H-NMR spectra of study datasets. Metabolomics, 14, 24.CrossRefGoogle Scholar
  4. Chihanga, T., Hausmann, S. M., Ni, S. S., & Kennedy, M. A. (2018a). Influence of media selection on NMR based metabolic profiling of human cell lines. Metabolomics, Scholar
  5. Chihanga, T., Ma, Q., Nicholson, J. D., Ruby, H. N., Edelmann, R. E., Devarajan, P., et al. (2018b). NMR spectroscopy and electron microscopy identification of metabolic and ultrastructural changes to the kidney following ischemia-reperfusion injury. American Journal of Physiology-Renal Physiology, 314, F154–F166. Scholar
  6. Chihanga, T., Ruby, H. N., Ma, Q., Bashir, S., Devarajan, P., & Kennedy, M. A. (2018c). NMR-based urine metabolic profiling and immunohistochemistry analysis of nephron changes in a mouse model of hypoxia-induced acute kidney injury. American Journal of Physiology-Renal Physiology. Scholar
  7. Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., et al. (2005). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77, 1282–1289. Scholar
  8. Creek, D. J., Dunn, W. B., Fiehn, O., Griffin, J. L., Hall, R. D., Lei, Z., et al. (2014). Metabolite identification: Are you sure? And how do your peers gauge your confidence? Metabolomics 10, 350–353.CrossRefGoogle Scholar
  9. Dona, A. C., Kyriakides, M., Scott, F., Shephard, E. A., Varshavi, D., Veselkov, K., et al. (2016). A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments. Computational and Structural Biotechnology Journal, 14, 135–153. Scholar
  10. Everett, J. R. (2015). A new paradigm for known metabolite identification in metabonomics/metabolomics: Metabolite identification efficiency. Computational and Structural Biotechnology Journal, 13, 131–144. Scholar
  11. Fiehn, O., Robertson, D., Griffin, J., van der Werf, M., Nikolau, B., Morrison, N., et al. (2007). The Metabolomics Standards Initiative (MSI). Metabolomics, 3, 175–178. Scholar
  12. Guijas, C., Montenegro-Burke, J. R., Domingo-Almenara, X., Palermo, A., Warth, B., Hermann, G., et al. (2018). METLIN: A technology platform for identifying knowns and unknowns. Analytical Chemistry, 90, 3156–3164. Scholar
  13. Haug, K., Salek, R. M., Conesa, P., Hastings, J., de Matos, P., Rijnbeek, M., et al. (2013). MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Research, 41, D781–D786. Scholar
  14. Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). ‘Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 29, 1181–1189 doi. Scholar
  15. Posma, J. M., Garcia-Perez, I., Heaton, J. C., Burdisso, P., Mathers, J. C., Draper, J., et al. (2017). Integrated analytical and statistical two-dimensional spectroscopy strategy for metabolite identification: Application to dietary biomarkers. Analytical Chemistry, 89, 3300–3309. Scholar
  16. Rohnisch, H. E., Eriksson, J., Mullner, E., Agback, P., Sandstrom, C., & Moazzami, A. A. (2018). AQuA: An automated quantification algorithm for high-throughput nmr-based metabolomics and its application in human plasma. Analytical Chemistry, 90, 2095–2102. Scholar
  17. Romick-Rosendale, L. E., Goodpaster, A. M., Hanwright, P. J., Patel, N. B., Wheeler, E. T., Chona, D. L., et al. (2009). NMR-based metabonomics analysis of mouse urine and fecal extracts following oral treatment with the broad-spectrum antibiotic enrofloxacin (Baytril). Magnetic Resonance in Chemistry, 47(Suppl 1), 36–46. Scholar
  18. Romick-Rosendale, L. E., Legomarcino, A., Patel, N. B., Morrow, A. L., & Kennedy, M. A. (2014). Prolonged antibiotic use induces intestinal injury in mice that is repaired after removing antibiotic pressure: implications for empiric antibiotic therapy. Metabolomics, 10, 8–20. Scholar
  19. Romick-Rosendale, L. E., Schibler, K. R., & Kennedy, M. A. (2012). A potential biomarker for acute kidney injury in preterm infants from metabolic profiling. Journal of Molecular Biomarkers & Diagnosis, Scholar
  20. Salek, R. M., Steinbeck, C., Viant, M. R., Goodacre, R., & Dunn, W. B. (2013). The role of reporting standards for metabolite annotation and identification in metabolomic studies. Gigascience, 2, 13. Scholar
  21. Sanchon-Lopez, B., & Everett, J. R. (2016). New Methodology for known metabolite identification in metabonomics/metabolomics: Topological metabolite identification carbon efficiency (tMICE). Journal of Proteome Research, 15, 3405–3419. Scholar
  22. Schmahl, M. J., Regan, D. P., Rivers, A. C., Joesten, W. C., & Kennedy, M. A. (2018). NMR-based metabolic profiling of urine, serum, fecal, and pancreatic tissue samples from the Ptf1a-Cre; LSL-KrasG12D transgenic mouse model of pancreatic cancer. PLoS ONE, 13, e0200658. Scholar
  23. Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., et al. (2005). METLIN—a metabolite mass spectral database. Therapeutic Drug Monitoring, 27, 747–751 doi. Scholar
  24. Spicer, R. A., Salek, R., & Steinbeck, C. (2017). Compliance with minimum information guidelines in public metabolomics repositories. Scientific Data, 4, 170137. Scholar
  25. Sud, M., Fahy, E., Cotter, D., Azam, K., Vadivelu, I., Burant, C., et al. (2016). Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools. Nucleic Acids Research, 44, D463–D470. Scholar
  26. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3, 211–221. Scholar
  27. Tardivel, P. J. C., Canlet, C., LeFort, G., Tremblay-Franco, M., Debrauwer, L., Concordet, D., et al. (2017). ASICS: An automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, 13, 109.CrossRefGoogle Scholar
  28. Ulrich, E. L., Akutsu, H., Doreleijers, J. F., Harano, Y., Ioannidis, Y. E., Lin, J., et al. (2008). BioMagResBank. Nucleic Acids Research, 36, D402–D408. Scholar
  29. van der Hooft, J. J. J., & Rankin, N. (2016). Metabolite identification in complex mixtures using nuclear magnetic resonance spectroscopy. In G. A. Webb (Ed.), Modern magnetic resonance. Glascow: Springer.Google Scholar
  30. Wang, B., Sheriff, S., Balasubramaniam, A., & Kennedy, M. A. (2015). NMR based metabolomics study of Y2 receptor activation by neuropeptide Y in the SK-N-BE2 human neuroblastoma cell line. Metabolomics, 11, 1243–1252. Scholar
  31. Watanabe, M., Sheriff, S., Kadeer, N., Cho, J., Lewis, K. B., Balasubramaniam, A., et al. (2012a). NMR based metabonomics study of NPY Y5 receptor activation in BT-549, a human breast carcinoma cell line. Metabolomics, 8, 854–868. Scholar
  32. Watanabe, M., Sheriff, S., Lewis, K. B., Cho, J., Tinch, S. L., Balasubramaniam, A., et al. (2012b). Metabolic profiling comparison of human pancreatic ductal epithelial cells and three pancreatic cancer cell lines using NMR based metabonomics. Journal of Molecular Biomarkers and Diagnosis, S3, 1–17.Google Scholar
  33. Watanabe, M., Sheriff, S., Ramelot, T. A., Kadeer, N., Cho, J., Lewis, K. B., et al. (2011). NMR Based metabonomics study of DAG treatment in a C2C12 mouse skeletal muscle cell line myotube model of burn-injury. International Journal of Peptide Research and Therapeutics, 17, 281–299. Scholar
  34. Willker, W., Leibfritz, D., Kerssebaum, R., & Bermel, W. (1993). Gradient selection in inverse heteronuclear correlation spectroscopy. Magnetic Resonance in Chemistry, 31, 287–292 doi. Scholar
  35. Wishart, D. S., Feunang, Y. D., Marcu, A., Guo, A. C., Liang, K., Vazquez-Fresno, R., et al. (2018). HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Research, 46, D608–D617. Scholar
  36. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41, D801–D807. Scholar
  37. Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., et al. (2009). HMDB: a knowledgebase for the human metabolome. Nucleic Acids Research, 37, D603–D610. Scholar
  38. Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., Young, N., et al. (2007). HMDB: the human metabolome database. Nucleic Acids Research, 35, D521–D526. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryMiami UniversityOxfordUSA

Personalised recommendations