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Metabolomics

, 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
  • 106 Downloads

Abstract

Introduction

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.

Objectives

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

Nuclear magnetic resonance NMR Metabolomics Metabonomics 

Notes

Acknowledgements

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.

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Copyright information

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

Authors and Affiliations

  1. 1.Department of Chemistry and BiochemistryMiami UniversityOxfordUSA

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