Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data

Zhiqiang Pang, Guangyan Zhou, Jessica Ewald, Le Chang, Orcun Hacariz, Niladri Basu, Jianguo Xia

Published: 2022-06-16 DOI: 10.1038/s41596-022-00710-w

data processing

statistical analysis

multi-omics integration

MetaboAnalyst 5.0

LC-HRMS

AI 解读

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Supplementary Table 1.

Reporting Summary

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Dual detection of chromatin accessibility and DNA methylation using ATAC-Me

Single-particle tracking photoactivated localization microscopy of membrane proteins in living plant tissues

Design and simulation of DNA, RNA and hybrid protein–nucleic acid nanostructures with oxView

Stabilization and structure determination of integral membrane proteins by termini restraining

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Design, assembly, and characterization of membrane-spanning DNA nanopores

Hyperosmolar blood–brain barrier opening using intra-arterial injection of hyperosmotic mannitol in mice under real-time MRI guidance

Analysis framework and experimental design for evaluating synergy-driving gene expression

Medulloblastoma and high-grade glioma organoids for drug screening, lineage tracing, co-culture and in vivo assay

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Supplementary information

Alseekh, S. et al. Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nat. Methods 18, 747–756 (2021).
Alseekh, S. & Fernie, A. R. Metabolomics 20 years on: what have we learned and what hurdles remain? Plant J. 94, 933–942 (2018).
Doerr, A. Global metabolomics. Nat. Methods 14, 32–32 (2017).
Cajka, T. & Fiehn, O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal. Chem. 88, 524–545 (2016).
Vermeulen, R., Schymanski, E. L., Barabasi, A. L. & Miller, G. W. The exposome and health: where chemistry meets biology. Science 367, 392–396 (2020).
Beger, R. D. et al. Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 12, 149 (2016).
Want, E. J. et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat. Protoc. 8, 17–32 (2013).
Zhou, G. et al. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 47, W234–W241 (2019).
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).
Pang, Z. et al. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 49, W388–W396 (2021).
Xia, J., Psychogios, N., Young, N. & Wishart, D. S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 37, W652–W660 (2009).
Xia, J., Mandal, R., Sinelnikov, I. V., Broadhurst, D. & Wishart, D. S. MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 40, W127–W133 (2012).
Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res. 43, W251–W257 (2015).
Chong, J. et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486–W494 (2018).
Stanstrup, J. et al. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites https://doi.org/10.3390/metabo9100200 (2019).
Gardinassi, L. G., Xia, J., Safo, S. E. & Li, S. Bioinformatics tools for the interpretation of metabolomics data. Curr. Pharmacol. Rep. 3, 374–383 (2017).
Chang, H. Y. et al. A practical guide to metabolomics software development. Anal. Chem. 93, 1912–1923 (2021).
Tautenhahn, R., Patti, G. J., Rinehart, D. & Siuzdak, G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).
Giacomoni, F. et al. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31, 1493–1495 (2015).
Yang, Q. et al. NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data. Nucleic Acids Res. 48, W436–W448 (2020).
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).
Du, X. X., Smirnov, A., Pluskal, T., Jia, W. & Sumner, S. Metabolomics data preprocessing using ADAP and MZmine 2. Computational Methods Data Anal. Metabolomics 2104, 25–48 (2020).
Tsugawa, H. et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 38, 1159–1163 (2020).
Tsugawa, H. et al. Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software. Anal. Chem. 88, 7946–7958 (2016).
Rost, H. L. et al. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 13, 741–748 (2016).
Danczak, R. E. et al. Using metacommunity ecology to understand environmental metabolomes. Nat. Commun. 11, 6369 (2020).
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
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).
Xia, J. & Wishart, D. S. Metabolomic data processing, analysis, and interpretation using MetaboAnalyst. Curr. Protoc. Bioinformatics https://doi.org/10.1002/0471250953.bi1410s34 (2011).
Xia, J. & Wishart, D. S. Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr. Protoc. Bioinforma. 55, 14 10 11–14 10 91 (2016).
Xia, J., Broadhurst, D. I., Wilson, M. & Wishart, D. S. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 9, 280–299 (2013).
Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinforma. 68, e86 (2019).
Chong, J. & Xia, J. Using MetaboAnalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data. Methods Mol. Biol. 2104, 337–360 (2020).
Tautenhahn, R., Bottcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinforma. 9, 504 (2008).
Yu, T., Park, Y., Johnson, J. M. & Jones, D. P. apLCMS—adaptive processing of high-resolution LC/MS data. Bioinformatics 25, 1930–1936 (2009).
Kenar, E. et al. Automated label-free quantification of metabolites from liquid chromatography-mass spectrometry data. Mol. Cell Proteom. 13, 348–359 (2014).
Pang, Z., Chong, J., Li, S. & Xia, J. MetaboAnalystR 3.0: toward an optimized workflow for global metabolomics. Metabolites https://doi.org/10.3390/metabo10050186 (2020).
Chaleckis, R., Meister, I., Zhang, P. & Wheelock, C. E. Challenges, progress and promises of metabolite annotation for LC–MS-based metabolomics. Curr. Opin. Biotechnol. 55, 44–50 (2019).
Sindelar, M. & Patti, G. J. Chemical discovery in the era of metabolomics. J. Am. Chem. Soc. 142, 9097–9105 (2020).
Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).
Senan, O. et al. CliqueMS: a computational tool for annotating in-source metabolite ions from LC-MS untargeted metabolomics data based on a coelution similarity network. Bioinformatics 35, 4089–4097 (2019).
Kind, T. & Fiehn, O. Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics https://doi.org/10.1186/1471-2105-8-105 (2007).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Xia, J. & Wishart, D. S. MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res. 38, W71–W77 (2010).
Tarca, A. L. et al. A novel signaling pathway impact analysis. Bioinformatics 25, 75–82 (2009).
Xia, J. & Wishart, D. S. MetPA: a web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).
Li, S. et al. Predicting network activity from high throughput metabolomics. PLoS Comput. Biol. 9, e1003123 (2013).
Xia, J. et al. INMEX—a web-based tool for integrative meta-analysis of expression data. Nucleic Acids Res. 41, W63–W70 (2013).
Zhou, G. & Xia, J. OmicsNet: a web-based tool for creation and visual analysis of biological networks in 3D space. Nucleic Acids Res. 46, W514–W522 (2018).
Schriml, L. M. et al. COVID-19 pandemic reveals the peril of ignoring metadata standards. Sci. Data 7, 188 (2020).
Kahan, B. C., Jairath, V., Dore, C. J. & Morris, T. P. The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. Trials 15, 139 (2014).
Chong, J. & Xia, J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics 34, 4313–4314 (2018).
Chong, J., Yamamoto, M. & Xia, J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites https://doi.org/10.3390/metabo9030057 (2019).
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).
Pang, Z., Zhou, G., Chong, J. & Xia, J. Comprehensive meta-analysis of COVID-19 global metabolomics datasets. Metabolites https://doi.org/10.3390/metabo11010044 (2021).
Walker, D. I. et al. High-resolution metabolomics of occupational exposure to trichloroethylene. Int. J. Epidemiol. 45, 1517–1527 (2016).
Gatto, L., Gibb, S. & Rainer, J. MSnbase, efficient and elegant R-based processing and visualization of raw mass spectrometry data. J. Proteome Res. 20, 1063–1069 (2021).
Conley, C. J. et al. Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection. Bioinformatics 30, 2636–2643 (2014).
Wishart, D. S. et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res. 50, D622–D631 (2022).
Vaughan, A. M. et al. Type II fatty acid synthesis is essential only for malaria parasite late liver stage development. Cell Microbiol. 11, 506–520 (2009).
Cumnock, K. et al. Host energy source is important for disease tolerance to malaria. Curr. Biol. 28, 1635–1642 e1633 (2018).
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).
Prince, J. T. & Marcotte, E. M. Chromatographic alignment of ESI–LC–MS proteomics data sets by ordered bijective interpolated warping. Anal. Chem. 78, 6140–6152 (2006).
Libiseller, G. et al. IPO: a tool for automated optimization of XCMS parameters. BMC Bioinforma. 16, 118 (2015).
McLean, C. & Kujawinski, E. B. AutoTuner: high fidelity and robust parameter selection for metabolomics data processing. Anal. Chem. 92, 5724–5732 (2020).
Zhou, G., Ewald, J. & Xia, J. OmicsAnalyst: a comprehensive web-based platform for visual analytics of multi-omics data. Nucleic Acids Res. 49, W476–W482 (2021).
Rappoport, N. & Shamir, R. Multi-omic and multi-view clustering algorithms: review and cancer benchmark. Nucleic Acids Res. 47, 1044 (2019).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data

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