Abstract
MetaboAnalyst (www.metaboanalyst.ca) is an easy-to-use, comprehensive web-based tool, freely available for metabolomics data processing, statistical analysis, functional interpretation, as well as integration with other omics data. This chapter first provides an introductory overview to the current MetaboAnalyst (version 4.0) with regards to its underlying design concepts and user interface structure. Subsequent sections describe three common metabolomics data analysis workflows covering targeted metabolomics, untargeted metabolomics, and multi-omics data integration.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Xia J, Psychogios N, Young N, Wishart DS (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37:W652–W660
Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, Wishart DS, Xia J (2018) MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res 46:W486–W494. https://doi.org/10.1093/nar/gky310
Xia J, Mandal R, Sinelnikov IV, Broadhurst D, Wishart DS (2012) MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis. Nucleic Acids Res 40:W127–W133
Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res 43:W251–W257
Chong J, Xia J (2018) MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics 34:4313–4314. https://doi.org/10.1093/bioinformatics/bty528
Chong J, Yamamoto M, Xia J (2019) MetaboAnalystR 2.0: from raw spectra to biological insights. Meta 9:57
van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJC (2006) Scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142. https://doi.org/10.1186/1471-2164-7-142.
Temmerman L, Livera A, Bowne JB, Sheedy JR, Callahan DL, Nahid A, Souza D, Schoofs L, Tull DL, McConville M (2012) Cross-platform urine metabolomics of experimental hyperglycemia in type 2 diabetes. Diabetes Metab S 6:002
De Livera AM, Dias DA, De Souza D, Rupasinghe T, Pyke J, Tull D, Roessner U, McConville M, Speed TP (2012) Normalizing and integrating metabolomics data. Anal Chem 84:10768–10776. https://doi.org/10.1021/ac302748b
Eisner R, Stretch C, Eastman T, Xia J, Hau D, Damaraju S, Greiner R, Wishart DS, Baracos VE (2011) Learning to predict cancer-associated skeletal muscle wasting from 1 H-NMR profiles of urinary metabolites. Metabolomics 7:25–34
Dieterle F, Ross A, Schlotterbeck G, Senn H (2006) Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem 78:4281–4290
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114. https://doi.org/10.1093/nar/gkr988
Puchalska P, Crawford PA (2017) Multi-dimensional roles of ketone bodies in fuel metabolism, signaling, and therapeutics. Cell Metab 25:262–284. https://doi.org/10.1016/j.cmet.2016.12.022
Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D et al (2014) SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res 42:D478–D484. https://doi.org/10.1093/nar/gkt1067
Flint TR, Janowitz T, Connell CM, Roberts EW, Denton AE, Coll AP, Jodrell DI, Fearon DT (2016) Tumor-induced IL-6 reprograms host metabolism to suppress anti-tumor immunity. Cell Metab 24:672–684. https://doi.org/10.1016/j.cmet.2016.10.010
Ham DJ et al (2014) Glycine administration attenuates skeletal muscle wasting in a mouse model of cancer cachexia. Clin Nutr 33(3):448–458
Cui P et al (2019) Metabolic derangements of skeletal muscle from a murine model of glioma cachexia. Skelet Muscle 9(1):3
Gowda GN, Djukovic D (2014) Overview of mass spectrometry-based metabolomics: opportunities and challenges. In: Mass spectrometry in metabolomics. Springer, New York, pp 3–12
Theodoridis G, Gika HG, Wilson ID (2008) LC-MS-based methodology for global metabolite profiling in metabonomics/metabolomics. Trac Trend Anal Chem 27:251–260. https://doi.org/10.1016/j.trac.2008.01.008
Li S, Park Y, Duraisingham S, Strobel FH, Khan N, Soltow QA, Jones DP, Pulendran B (2013) Predicting network activity from high throughput metabolomics. PLoS Comput Biol 9:e1003123
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550. https://doi.org/10.1073/pnas.0506580102
Integrative HMPRNC (2014) The integrative human microbiome project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16:276–289. https://doi.org/10.1016/j.chom.2014.08.014
Li S, Pozhitkov A, Ryan RA, Manning CS, Brown-Peterson N, Brouwer M (2010) Constructing a fish metabolic network model. Genome Biol 11:R115. https://doi.org/10.1186/gb-2010-11-11-r115
Tiratterra E, Franco P, Porru E, Katsanos KH, Christodoulou DK, Roda G (2018) Role of bile acids in inflammatory bowel disease. Ann Gastroenterol 31:266–272. https://doi.org/10.20524/aog.2018.0239
Ogilvie LA, Jones BV (2012) Dysbiosis modulates capacity for bile acid modification in the gut microbiomes of patients with inflammatory bowel disease: a mechanism and marker of disease? Gut 61:1642–1643. https://doi.org/10.1136/gutjnl-2012-302137.
Duboc H, Rajca S, Rainteau D, Benarous D, Maubert MA, Quervain E, Thomas G, Barbu V, Humbert L, Despras G et al (2013) Connecting dysbiosis, bile-acid dysmetabolism and gut inflammation in inflammatory bowel diseases. Gut 62:531–539. https://doi.org/10.1136/gutjnl-2012-302578
Murakami Y, Kubo S, Tamori A, Itami S, Kawamura E, Iwaisako K, Ikeda K, Kawada N, Ochiya T, Taguchi Y (2015) Comprehensive analysis of transcriptome and metabolome analysis in intrahepatic Cholangiocarcinoma and hepatocellular carcinoma. Sci Rep 5:16294
Worley B, Powers R (2013) Multivariate analysis in metabolomics. Curr Metabolomics 1:92–107. https://doi.org/10.2174/2213235X11301010092.
Vinaixa M, Samino S, Saez I, Duran J, Guinovart JJ, Yanes O (2012) A guideline to univariate statistical analysis for LC/MS-based untargeted metabolomics-derived data. Meta 2:775–795. https://doi.org/10.3390/metabo2040775
Rubingh CM, Bijlsma S, Derks EP, Bobeldijk I, Verheij ER, Kochhar S, Smilde AK (2006) Assessing the performance of statistical validation tools for megavariate metabolomics data. Metabolomics 2:53–61. https://doi.org/10.1007/s11306-006-0022-6
Bartel J, Krumsiek J, Theis FJ (2013) Statistical methods for the analysis of high-throughput metabolomics data. Comput Struct Biotechnol J 4:e201301009. https://doi.org/10.5936/csbj.201301009
Marco-Ramell A, Palau-Rodriguez M, Alay A, Tulipani S, Urpi-Sarda M, Sanchez-Pla A, Andres-Lacueva C (2018) Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data. BMC Bioinformatics 19(1). https://doi.org/10.1186/s12859-017-2006-0
Hackstadt AJ, Hess AM (2009) Filtering for increased power for microarray data analysis. BMC Bioinformatics 10:11. https://doi.org/10.1186/1471-2105-10-11
Hendriks MMWB, van Eeuwijk FA, Jellema RH, Westerhuis JA, Reijmers TH, Hoefsloot HCJ, Smilde AK (2011) Data-processing strategies for metabolomics studies. Trac Trend Anal Chem 30:1685–1698. https://doi.org/10.1016/j.trac.2011.04.019
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (2012) XCMS online: a web-based platform to process untargeted metabolomic data. Anal Chem 84:5035–5039. https://doi.org/10.1021/ac300698c
Myers OD, Sumner SJ, Li S, Barnes S, Du X (2017) Detailed investigation and comparison of the XCMS and MZmine 2 chromatogram construction and chromatographic peak detection methods for preprocessing mass spectrometry metabolomics data. Anal Chem 89:8689–8695. https://doi.org/10.1021/acs.analchem.7b01069
Dudzik D, Barbas-Bernardos C, Garcia A, Barbas C (2018) Quality assurance procedures for mass spectrometry untargeted metabolomics. A review. J Pharm Biomed Anal 147:149–173. https://doi.org/10.1016/j.jpba.2017.07.044
Lange E, Tautenhahn R, Neumann S, Gröpl C (2008) Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics 9:375
Tautenhahn R, Bottcher C, Neumann S (2008) Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9:504. https://doi.org/10.1186/1471-2105-9-504
Zhou B, Xiao JF, Tuli L, Ressom HW (2012) LC-MS-based metabolomics. Mol BioSyst 8:470–481
Acknowledgement
This work has been supported in part by the US National Institutes of Health grant U01 CA235493, Natural Sciences and Engineering Research Council of Canada and Canada Research Chairs program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Chong, J., Xia, J. (2020). Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_17
Download citation
DOI: https://doi.org/10.1007/978-1-0716-0239-3_17
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-0238-6
Online ISBN: 978-1-0716-0239-3
eBook Packages: Springer Protocols