Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol
  • Published:

Optimization of metabolomic data processing using NOREVA

Nature Protocols volume 17, pages 129–151 (2022)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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.

Similar content being viewed by others

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/.

References

  1. Pareek, V., Tian, H., Winograd, N. & Benkovic, S. J. Metabolomics and mass spectrometry imaging reveal channeled de novo purine synthesis in cells. Science 368, 283–290 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Delzenne, N. M. & Bindels, L. B. Microbiome metabolomics reveals new drivers of human liver steatosis. Nat. Med. 24, 906–907 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Paschos, G. K. & FitzGerald, G. A. Circadian clocks and metabolism: implications for microbiome and aging. Trends Genet. 33, 760–769 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wishart, D. S. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15, 473–484 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Edison, A. S. et al. NMR: unique strengths that enhance modern metabolomics research. Anal. Chem. 93, 478–499 (2021).

    Article  CAS  PubMed  Google Scholar 

  6. Li, P., Gawaz, M., Chatterjee, M. & Lammerhofer, M. Targeted profiling of short-, medium-, and long-chain fatty acyl-coenzyme as in biological samples by phosphate methylation coupled to liquid chromatography-tandem mass spectrometry. Anal. Chem. 93, 4342–4350 (2021).

    Article  CAS  PubMed  Google Scholar 

  7. Mamani-Huanca, M., Gradillas, A., Lopez-Gonzalvez, A. & Barbas, C. Unraveling the cyclization of l-argininosuccinic acid in biological samples: a study via mass spectrometry and NMR spectroscopy. Anal. Chem. 92, 12891–12899 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Amodei, D. et al. Improving precursor selectivity in data-independent acquisition using overlapping windows. J. Am. Soc. Mass Spectrom. 30, 669–684 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hoffmann, N. et al. mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Anal. Chem. 91, 3302–3310 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bearden, D. W. et al. Metabolomics test materials for quality control: a study of a urine materials suite. Metabolites 9, 270 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  11. Huan, T. et al. Systems biology guided by XCMS online metabolomics. Nat. Methods 14, 461–462 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. McLean, C. & Kujawinski, E. B. AutoTuner: high fidelity and robust parameter selection for metabolomics data processing. Anal. Chem. 92, 5724–5732 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wen, B., Mei, Z., Zeng, C. & Liu, S. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics 18, 183 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yang, Q. et al. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res. 48, W436–W448 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cambiaghi, A., Ferrario, M. & Masseroli, M. Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Brief. Bioinform. 18, 498–510 (2017).

    CAS  PubMed  Google Scholar 

  16. Chong, J. et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486–W494 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Seyednasrollah, F., Rantanen, K., Jaakkola, P. & Elo, L. L. ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer. Nucleic Acids Res. 44, e1 (2016).

    Article  PubMed  Google Scholar 

  18. Considine, E. C. & Salek, R. M. A tool to encourage minimum reporting guideline uptake for data analysis in metabolomics. Metabolites 9, 43 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  19. Martinez-Arranz, I. et al. Enhancing metabolomics research through data mining. J. Proteom. 127, 275–288 (2015).

    Article  CAS  Google Scholar 

  20. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Schober, D. et al. nmrML: a community supported open data standard for the description, storage, and exchange of NMR data. Anal. Chem. 90, 649–656 (2018).

    Article  CAS  PubMed  Google Scholar 

  22. Gowda, H. et al. Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal. Chem. 86, 6931–6939 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Jacob, D., Deborde, C., Lefebvre, M., Maucourt, M. & Moing, A. NMRProcFlow: a graphical and interactive tool dedicated to 1D spectra processing for NMR-based metabolomics. Metabolomics 13, 36 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Giacomoni, F. et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31, 1493–1495 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Forsberg, E. M. et al. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS online. Nat. Protoc. 13, 633–651 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Xia, J. & Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 6, 743–760 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Ludewig, A. H. et al. An excreted small molecule promotes C. elegans reproductive development and aging. Nat. Chem. Biol. 15, 838–845 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Bachem, A. et al. Microbiota-derived short-chain fatty acids promote the memory potential of antigen-activated CD8(+) T cells. Immunity 51, 285–297 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Han, T. L., Yang, Y., Zhang, H. & Law, K. P. Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy. F1000Res. 6, 967 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Andres, D. A. et al. Improved workflow for mass spectrometry-based metabolomics analysis of the heart. J. Biol. Chem. 295, 2676–2686 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N. & Khoomrung, S. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Comput. Struct. Biotechnol. J. 17, 611–618 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Shen, X. & Zhu, Z. J. MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery. Bioinformatics 35, 2870–2872 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Willforss, J., Chawade, A. & Levander, F. NormalyzerDE: online tool for improved normalization of omics expression data and high-sensitivity differential expression analysis. J. Proteome Res. 18, 732–740 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Lee, C. K. et al. Tumor metastasis to lymph nodes requires YAP-dependent metabolic adaptation. Science 363, 644–649 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Tiwari, S. et al. Arginine-deprivation-induced oxidative damage sterilizes Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 115, 9779–9784 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tang, J. et al. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies. Brief. Bioinform. 21, 621–636 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Valikangas, T., Suomi, T. & Elo, L. L. A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief. Bioinform. 19, 1–11 (2018).

    CAS  PubMed  Google Scholar 

  38. Li, B. et al. NOREVA: normalization and evaluation of MS-based metabolomics data. Nucleic Acids Res. 45, W162–W170 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yang, Q. et al. A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies. Brief. Bioinform. 21, 2142–2152 (2020).

    Article  PubMed  Google Scholar 

  40. Li, B. et al. Performance evaluation and online realization of data-driven normalization methods used in LC/MS based untargeted metabolomics analysis. Sci. Rep. 6, 38881 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Lee, N. Y. et al. Lactobacillus and Pediococcus ameliorate progression of non-alcoholic fatty liver disease through modulation of the gut microbiome. Gut Microbes 11, 882–899 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ayoola, M. B. et al. Polyamine synthesis effects capsule expression by reduction of precursors in Streptococcus pneumoniae. Front. Microbiol. 10, 1996 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Franciosi, E. et al. Microbial community dynamics in phyto-thermotherapy baths viewed through next generation sequencing and metabolomics approach. Sci. Rep. 10, 17931 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Taverna, F. et al. BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization. Nucleic Acids Res. 48, W385–W394 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Liu, R. & Yang, Z. Single cell metabolomics using mass spectrometry: techniques and data analysis. Anal. Chim. Acta 1143, 124–134 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Whitson, J. A. et al. SS-31 and NMN: two paths to improve metabolism and function in aged hearts. Aging Cell 19, e13213 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cui, X. et al. Assessing the effectiveness of direct data merging strategy in long-term and large-scale pharmacometabonomics. Front. Pharmacol. 10, 127 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Woollam, M. et al. Urinary volatile terpenes analyzed by gas chromatography-mass spectrometry to monitor breast cancer treatment efficacy in mice. J. Proteome Res. 19, 1913–1922 (2020).

    Article  CAS  PubMed  Google Scholar 

  49. Lee, S. M. et al. Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia. Sci. Rep. 10, 16142 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee, C. W. et al. Lipidomic profiles disturbed by the internet gaming disorder in young Korean males. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 1114–1115, 119–124 (2019).

    Article  Google Scholar 

  51. Han, W. & Li, L. Evaluating and minimizing batch effects in metabolomics. Mass Spectrom. Rev. https://doi.org/10.1038/1002/mas.21672 (2020).

  52. Zullig, T. & Kofeler, H. C. High resolution mass spectrometry in lipidomics. Mass Spectrom. Rev. 40, 162–176 (2021).

    Article  PubMed  Google Scholar 

  53. Narduzzi, L. et al. Ammonium fluoride as suitable additive for HILIC-based LC-HRMS metabolomics. Metabolites 9, 292 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  54. Park, S. J. et al. Exposure of ultrafine particulate matter causes glutathione redox imbalance in the hippocampus: a neurometabolic susceptibility to Alzheimer’s pathology. Sci. Total Environ. 718, 137267 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Lee, B. M. et al. Discovery study of integrative metabolic profiles of sesame seeds cultivated in different countries. LWT Food Sci. Technol. 129, 109454 (2020).

    Article  CAS  Google Scholar 

  56. Gonzalez-Riano, C. et al. Recent developments along the analytical process for metabolomics workflows. Anal. Chem. 92, 203–226 (2020).

    Article  CAS  PubMed  Google Scholar 

  57. Deng, K. et al. WaveICA: a novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis. Anal. Chim. Acta 1061, 60–69 (2019).

    Article  CAS  PubMed  Google Scholar 

  58. De Livera, A. M., Olshansky, G., Simpson, J. A. & Creek, D. J. NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data. Metabolomics 14, 54 (2018).

    Article  PubMed  Google Scholar 

  59. Drotleff, B. & Lammerhofer, M. Guidelines for selection of internal standard-based normalization strategies in untargeted lipidomic profiling by LC-HR-MS/MS. Anal. Chem. 91, 9836–9843 (2019).

    Article  CAS  PubMed  Google Scholar 

  60. Noonan, M. J., Tinnesand, H. V. & Buesching, C. D. Normalizing gas-chromatography-mass spectrometry data: method choice can alter biological inference. Bioessays 40, e1700210 (2018).

    Article  PubMed  Google Scholar 

  61. Zheng, F. et al. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry. Nat. Protoc. 15, 2519–2537 (2020).

    Article  CAS  PubMed  Google Scholar 

  62. Sans, M., Feider, C. L. & Eberlin, L. S. Advances in mass spectrometry imaging coupled to ion mobility spectrometry for enhanced imaging of biological tissues. Curr. Opin. Chem. Biol. 42, 138–146 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Petras, D., Jarmusch, A. K. & Dorrestein, P. C. From single cells to our planet—recent advances in using mass spectrometry for spatially resolved metabolomics. Curr. Opin. Chem. Biol. 36, 24–31 (2017).

    Article  CAS  PubMed  Google Scholar 

  64. Alexandrov, T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu. Rev. Biomed. Data Sci. 3, 61–87 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Hao, L. et al. Metandem: an online software tool for mass spectrometry-based isobaric labeling metabolomics. Anal. Chim. Acta 1088, 99–106 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Verhoeven, A., Giera, M. & Mayboroda, O. A. KIMBLE: a versatile visual NMR metabolomics workbench in KNIME. Anal. Chim. Acta 1044, 66–76 (2018).

    Article  CAS  PubMed  Google Scholar 

  67. Cardoso, S., Afonso, T., Maraschin, M. & Rocha, M. WebSpecmine: a website for metabolomics data analysis and mining. Metabolites 9, 237 (2019).

    Article  CAS  PubMed Central  Google Scholar 

  68. Liang, D. et al. IP4M: an integrated platform for mass spectrometry-based metabolomics data mining. BMC Bioinformatics 21, 444 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Franceschi, P. et al. MetaDB a data processing workflow in untargeted MS-based metabolomics experiments. Front. Bioeng. Biotechnol. 2, 72 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Calderon-Santiago, M., Lopez-Bascon, M. A., Peralbo-Molina, A. & Priego-Capote, F. MetaboQC: a tool for correcting untargeted metabolomics data with mass spectrometry detection using quality controls. Talanta 174, 29–37 (2017).

    Article  CAS  PubMed  Google Scholar 

  71. Brunius, C., Shi, L. & Landberg, R. Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction. Metabolomics 12, 173 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Wang, S. et al. MetaboGroup S: a group entropy-based web platform for evaluating normalization methods in blood metabolomics data from maintenance hemodialysis patients. Anal. Chem. 90, 11124–11130 (2018).

    Article  CAS  PubMed  Google Scholar 

  73. Hughes, G. et al. MSPrep-summarization, normalization and diagnostics for processing of mass spectrometry-based metabolomic data. Bioinformatics 30, 133–134 (2014).

    Article  CAS  PubMed  Google Scholar 

  74. Wang, S. & Yang, H. pseudoQC: a regression-based simulation software for correction and normalization of complex metabolomics and proteomics datasets. Proteomics 19, e1900264 (2019).

    Article  PubMed  Google Scholar 

  75. Schiffman, C. et al. Filtering procedures for untargeted LC-MS metabolomics data. BMC Bioinformatics 20, 334 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Wei, R. et al. Missing value imputation approach for mass spectrometry-based metabolomics data. Sci. Rep. 8, 663 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K. & van der Werf, M. J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7, 142 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  78. De Livera, A. M. et al. Statistical methods for handling unwanted variation in metabolomics data. Anal. Chem. 87, 3606–3615 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Dunn, W. B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).

    Article  CAS  PubMed  Google Scholar 

  80. Khodadadi, M. & Pourfarzam, M. A review of strategies for untargeted urinary metabolomic analysis using gas chromatography-mass spectrometry. Metabolomics 16, 66 (2020).

    Article  CAS  PubMed  Google Scholar 

  81. Parca, L., Beck, M., Bork, P. & Ori, A. Quantifying compartment-associated variations of protein abundance in proteomics data. Mol. Syst. Biol. 14, e8131 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell Proteom. 13, 2513–2526 (2014).

    Article  CAS  Google Scholar 

  83. Dai, W. et al. Characterization of white tea metabolome: comparison against green and black tea by a nontargeted metabolomics approach. Food Res. Int. 96, 40–45 (2017).

    Article  CAS  PubMed  Google Scholar 

  84. Haug, K. et al. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 48, D440–D444 (2020).

    CAS  PubMed  Google Scholar 

  85. Navarro, P. et al. A multicenter study benchmarks software tools for label-free proteome quantification. Nat. Biotechnol. 34, 1130–1136 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Li, S. X. et al. Circadian alteration in neurobiology during 30 days of abstinence in heroin users. Biol. Psychiatry 65, 905–912 (2009).

    Article  CAS  PubMed  Google Scholar 

  87. Dos Santos, R. O. et al. Kynurenine elevation correlates with T regulatory cells increase in acute Plasmodium vivax infection: a pilot study. Parasite Immunol. 42, e12689 (2020).

    Article  PubMed  Google Scholar 

  88. Hunt, N. H. et al. The kynurenine pathway and parasitic infections that affect CNS function. Neuropharmacology 112, 389–398 (2017).

    Article  CAS  PubMed  Google Scholar 

  89. Wehrens, R., Franceschi, P., Vrhovsek, U. & Mattivi, F. Stability-based biomarker selection. Anal. Chim. Acta 705, 15–23 (2011).

    Article  CAS  PubMed  Google Scholar 

  90. Skarke, C. et al. A pilot characterization of the human chronobiome. Sci. Rep. 7, 17141 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Meinicke, P. et al. Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps. Algorithms Mol. Biol. 3, 9 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Hussein, M. et al. The killing mechanism of teixobactin against methicillin-resistant Staphylococcus aureus: an untargeted metabolomics study. mSystems 5, e00077 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Sayqal, A. et al. Metabolic analysis of the response of Pseudomonas putida DOT-T1E strains to toluene using Fourier transform infrared spectroscopy and gas chromatography mass spectrometry. Metabolomics 12, 112 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Gardinassi, L. G. et al. Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling. Redox Biol. 17, 158–170 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. O’Callaghan, S. et al. PyMS: a python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. BMC Bioinform. 13, 115 (2012).

    Article  Google Scholar 

  96. Cui, F. et al. Identification of metabolites and transcripts involved in salt stress and recovery in peanut. Front. Plant Sci. 9, 217 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Weidt, S. et al. A novel targeted/untargeted GC-orbitrap metabolomics methodology applied to Candida albicans and Staphylococcus aureus biofilms. Metabolomics 12, 189 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Sayers, E. W. et al. Database resources of the national center for biotechnology information. Nucleic Acids Res. 49, D10–D17 (2021).

    Article  CAS  PubMed  Google Scholar 

  99. Benito, S. et al. Plasma biomarker discovery for early chronic kidney disease diagnosis based on chemometric approaches using LC-QTOF targeted metabolomics data. J. Pharm. Biomed. Anal. 149, 46–56 (2018).

    Article  CAS  PubMed  Google Scholar 

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Jianbo Fu, Ying Zhang.

Authors and Affiliations

  1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China

    Jianbo Fu, Ying Zhang, Yunxia Wang, Hongning Zhang, Jin Liu, Jing Tang, Qingxia Yang, Huaicheng Sun, Mingyue Zheng & Feng Zhu

  2. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China

    Huaicheng Sun, Zhaorong Li & Feng Zhu

  3. Department of Surgery, HKU-SZH and Faculty of Medicine, The University of Hong Kong, Hong Kong, China

    Wenqi Qiu

  4. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, China

    Yinghui Ma

  5. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Mingyue Zheng

Authors
  1. Jianbo Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qingxia Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Huaicheng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wenqi Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yinghui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Zhaorong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mingyue Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Feng Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding author

Correspondence to Feng Zhu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Jianguo Xia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–6, Supplementary Tables 1–8 and Supplementary Methods 1–4.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00636-9

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research