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Optimization of metabolomic data processing using NOREVA

Abstract

A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users’ data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.

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Fig. 1: Processing of the peak table generated by MS/NMR-based metabolomics.
Fig. 2: Performance plots before and after each stage (S1–S4) shown in Fig. 1.
Fig. 3: Performance assessment based on five independent criteria for processing workflow.
Fig. 4: A collective performance assessment from multiple perspectives and comprehensive performance ranking among all processing workflows based on benchmark PMID28528106 in Table 1.
Fig. 5: Three representative workflows and their processing results for metabolite cortisol based on the dataset PMID29215023 in Table 1.
Fig. 6: Two representative workflows (ranked first and last by the protocol of this study) and their processing results for spike-in compounds (aspartic acid and malic acid) based on dataset PMID22647087 in Table 1.

Data availability

All data used in this publication have been made available on the NOREVA website (https://idrblab.org/noreva/NOREVA_exampledata.zip) or are available from the corresponding author upon request.

Code availability

All code that constitutes the protocol provided in this study is available for use under a GPL v3 license and can be downloaded from GitHub at https://github.com/idrblab/NOREVA. The NOREVA service is freely available for academic use at https://idrblab.org/noreva/.

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Acknowledgements

Funded by Natural Science Foundation of Zhejiang Province (LR21H300001); National Natural Science Foundation of China (81872798 and U1909208); Leading Talent of the ‘Ten Thousand Plan’–National High-Level Talents Special Support Plan of China; Fundamental Research Fund for Central Universities (2018QNA7023); ‘Double Top-Class’ University Project (181201*194232101); Key R&D Program of Zhejiang Province (2020C03010). This work was supported by Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare; Alibaba Cloud; Information Technology Center of Zhejiang University.

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F.Z. conceived the idea and designed the study. J.B.F., Y.Z., Y.H.M., Q.X.Y. and J.T. wrote and debugged codes. J.B.F. and Y.Z. performed the benchmark data analyses. J.B.F., Y.Z., Y.X.W., H.N.Z., J.L., J.T., Q.X.Y., H.C.S., W.Q.Q., Z.R.L. and M.Y.Z. contributed to statistics and data visualization. F.Z. wrote the manuscript.

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Correspondence to Feng Zhu.

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Key references using this protocol

Lee, N. Y. et al. Gut Microbes 11, 882–899 (2020): https://www.tandfonline.com/doi/full/10.1080/19490976.2020.1712984

Taverna, F. et al. Nucleic Acids Res. 48, W385–W394 (2020): https://academic.oup.com/nar/article/48/W1/W385/5835814

Whitson, J. A. et al. Aging Cell 19, e13213 (2020): https://onlinelibrary.wiley.com/doi/10.1111/acel.13213

González-Riano, C. et al. Anal. Chem. 92, 203–226 (2020): https://pubs.acs.org/doi/10.1021/acs.analchem.9b04553

Woollam, M. et al. J. Proteome Res. 19, 1913–1922 (2020): https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00722

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Fu, J., Zhang, Y., Wang, Y. et al. Optimization of metabolomic data processing using NOREVA. Nat Protoc 17, 129–151 (2022). https://doi.org/10.1038/s41596-021-00636-9

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