Abstract
Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xia J, Broadhurst DI, Wilson M, Wishart DS (2013) Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 9:280–299. https://doi.org/10.1007/s11306-012-0482-9
Mathew AK, Padmanaban VC (2013) Metabolomics: the apogee of the omics trilogy. Int. J. Pharm. Pharm. Sci. 5:45–48
Puchades-Carrasco L, Pineda-Lucena A (2015) Metabolomics in pharmaceutical research and development. Curr. Opin. Biotechnol. 35:73–77
Holmes E, Wilson ID, Nicholson JK (2008) Metabolic phenotyping in health and disease. Cell 134:714–717
Wei R (2011) Metabolomics and its practical value in pharmaceutical industry. Curr Drug Metab 12:345–358. https://doi.org/10.2174/138920011795202947
Nicholson JK, Lindon JC (2008) Systems biology: metabonomics. Nature 455:1054–1056
Vinayavekhin N, Homan EA, Saghatelian A (2010) Exploring disease through metabolomics. ACS Chem. Biol. 5:91–103
Fiehn O (2001) Combining genomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genomics 2:155–168
Clasquin MF, Melamud E, Rabinowitz JD (2012) LC-MS data processing with MAVEN: a metabolomic analysis and visualization engine. Curr Protoc Bioinforma. https://doi.org/10.1002/0471250953.bi1411s37
Melamud E, Vastag L, Rabinowitz JD (2010) Metabolomic analysis and visualization engine for LC-MS data. Anal Chem 82:9818–9826. https://doi.org/10.1021/ac1021166
Smith CA, Want EJ, O’Maille G et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787. https://doi.org/10.1021/ac051437y
Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) LC/MS preprocessing and analysis with xcms. Anal Chem 78:779–787
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
Pluskal T, Castillo S, Villar-Briones A, Orešič M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11. https://doi.org/10.1186/1471-2105-11-395
Katajamaa M, Miettinen J, Orešič M (2006) MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22:634–636. https://doi.org/10.1093/bioinformatics/btk039
Coble JB, Fraga CG (2014) Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery. J Chromatogr A 1358:155–164. https://doi.org/10.1016/j.chroma.2014.06.100
Myers OD, Sumner SJ, Li S et al (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
Myers OD, Sumner SJ, Li S et al (2017) One step forward for reducing false positive and false negative compound identifications from mass spectrometry metabolomics data: new algorithms for constructing extracted ion chromatograms and detecting chromatographic peaks. Anal Chem 89:8696–8703. https://doi.org/10.1021/acs.analchem.7b00947
Libiseller G, Dvorzak M, Kleb U et al (2015) IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16. https://doi.org/10.1186/s12859-015-0562-8
Lu W, Bennett BD, Rabinowitz JD (2008) Analytical strategies for LC-MS-based targeted metabolomics. J Chromatogr B Anal Technol Biomed Life Sci 871:236–242. https://doi.org/10.1016/j.jchromb.2008.04.031
Lu W, Clasquin MF, Melamud E et al (2010) Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer. Anal Chem 82:3212–3221. https://doi.org/10.1021/ac902837x
Savitzky A, Golay MJE (1964) Smoothing and differentiation of data by simplified least squares procedures. Anal Chem 36:1627–1639. https://doi.org/10.1021/ac60214a047
Acknowledgments
El-MAVEN is an extension of MAVEN, and as such we would like to acknowledge the creators of MAVEN. We would also like to acknowledge El-MAVEN contributors and community on GitHub, especially Victor Chubukov, Lance Parsons, and Eugene Melamud for their help in identifying bugs and fixes. The content and figures in this paper were structured, written, edited, and formatted by Chandni Valiathan at Illumetis, LLC.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Agrawal, S. et al. (2019). El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics. In: D'Alessandro, A. (eds) High-Throughput Metabolomics. Methods in Molecular Biology, vol 1978. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9236-2_19
Download citation
DOI: https://doi.org/10.1007/978-1-4939-9236-2_19
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-4939-9235-5
Online ISBN: 978-1-4939-9236-2
eBook Packages: Springer Protocols