Department of Chemistry and Biochemistry, Miami University, 106 Hughes Laboratories, Oxford, OH, 45056, USA.
Metabolomics. 2019 Jan 3;15(1):5. doi: 10.1007/s11306-018-1465-2.
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.
代谢组学标准倡议建议将基于 NMR 的代谢物图谱研究中的代谢物分配分为四类。“推测注释化合物”类别是代谢组学研究人员最常报告的类别。然而，“推测注释化合物”分配的可靠性存在很大的模糊性，其置信度范围从基于少量佐证数据的低置信度到基于大量佐证数据的高置信度。
引入一种新的排序系统，即代谢物的排序和赋值置信度（RANCM），以对基于 NMR 的代谢物图谱研究中的“推测注释化合物”分配赋予置信度级别。
该排序系统由三个置信度级别组成，从置信度最低的级别 1 到置信度最高的级别 3。构建了一个决策树来指导每个代谢物分配的等级选择。
提供了来自实验数据的示例，演示如何使用决策树对每个等级的“推测注释化合物”进行置信度级别分配。提供了一个标准的 Excel 表格模板，以方便决策制定、文档记录和提交到数据存储库。
RANCM 旨在减少“推测注释化合物”分配的模糊性，促进对“推测注释化合物”分配的置信度的有效沟通，并使非专家更容易评估基于 NMR 的代谢组学研究的意义和可靠性。该系统基于最常见的基于 NMR 的代谢物图谱研究中收集的数据集，实施起来非常简单，可以与任何一组 NMR 数据集一样严格和有意义地使用。
