logo

OR SEARCH CITATIONS

My Activity
Publications
CONTENT TYPES

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:Anal. Chem. 2016, 88, 19, 9821-9829
RETURN TO ISSUEPREVArticleNEXT

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

View Author Information
† Metabolomics Platform, Department of Electronic Engineering (DEEEA), Universitat Rovira i Virgili, 43003 Tarragona, Catalonia, Spain
‡ Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029 Madrid, Spain
§ Institut de Recerca Pediàtrica, Hospital Sant Joan de Déu, University of Barcelona, 08950 Barcelona, Catalonia, Spain
∥ B2SLab, Center for Biomedical Engineering Research (CREB), CIBERBBN, Department of ESAII, Universitat Politècnica de Catalunya, 08028 Barcelona, Catalonia, Spain
*E-mail: [email protected]
*E-mail: [email protected]
Cite this: Anal. Chem. 2016, 88, 19, 9821–9829
Publication Date (Web):September 1, 2016
https://doi.org/10.1021/acs.analchem.6b02927
Copyright © 2016 American Chemical Society
RIGHTS & PERMISSIONS
Article Views
1686
Altmetric
-
Citations
42
LEARN ABOUT THESE METRICS

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

Read OnlinePDF (2 MB)
Get e-Alerts
Supporting Info (5)»
SUBJECTS:

Abstract

Abstract Image

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC/MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-time-of-flight (TOF) MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign, and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available at http://CRAN.R-project.org/package=erah.

Supporting Information

ARTICLE SECTIONS
Jump To

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.6b02927.

  • Supplementary theory, methods, and table (PDF)

  • From processing of the HIAE experiment, XCMS detected m/z features quantified by area (XLSX)

  • From processing of the HIAE experiment, XCMS detected m/z features quantified by intensity (XLSX)

  • From processing of the HIAE experiment, MetAlign detected m/z features quantified by area (XLSX)

  • eRah provided list of putative identifications with their respective relative concentration (XLSX)

Terms & Conditions

Electronic Supporting Information files are available without a subscription to ACS Web Editions. The American Chemical Society holds a copyright ownership interest in any copyrightable Supporting Information. Files available from the ACS website may be downloaded for personal use only. Users are not otherwise permitted to reproduce, republish, redistribute, or sell any Supporting Information from the ACS website, either in whole or in part, in either machine-readable form or any other form without permission from the American Chemical Society. For permission to reproduce, republish and redistribute this material, requesters must process their own requests via the RightsLink permission system. Information about how to use the RightsLink permission system can be found at http://pubs.acs.org/page/copyright/permissions.html.

Cited By

This article is cited by 42 publications.

  1. Aleksandr Smirnov, Yunping Qiu, Wei Jia, Douglas I. Walker, Dean P. Jones, Xiuxia Du. ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography–Mass Spectrometry Metabolomics Data. Analytical Chemistry 2019, 91 (14) , 9069-9077. https://doi.org/10.1021/acs.analchem.9b01424
  2. Drupad K. Trivedi, Eleanor Sinclair, Yun Xu, Depanjan Sarkar, Caitlin Walton-Doyle, Camilla Liscio, Phine Banks, Joy Milne, Monty Silverdale, Tilo Kunath, Royston Goodacre, Perdita Barran. Discovery of Volatile Biomarkers of Parkinson’s Disease from Sebum. ACS Central Science 2019, 5 (4) , 599-606. https://doi.org/10.1021/acscentsci.8b00879
  3. Xavier Domingo-Almenara, J. Rafael Montenegro-Burke, Carlos Guijas, Erica L.-W. Majumder, H. Paul Benton, Gary Siuzdak. Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics. Analytical Chemistry 2019, 91 (5) , 3246-3253. https://doi.org/10.1021/acs.analchem.8b03126
  4. Fatema Bhinderwala, Nishikant Wase, Concetta DiRusso, Robert Powers. Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation. Journal of Proteome Research 2018, 17 (11) , 4017-4022. https://doi.org/10.1021/acs.jproteome.8b00567
  5. Xavier Domingo-Almenara, J. Rafael Montenegro-Burke, H. Paul Benton, and Gary Siuzdak . Annotation: A Computational Solution for Streamlining Metabolomics Analysis. Analytical Chemistry 2018, 90 (1) , 480-489. https://doi.org/10.1021/acs.analchem.7b03929
  6. Linda H. Münger, Alessia Trimigno, Gianfranco Picone, Carola Freiburghaus, Grégory Pimentel, Kathryn J. Burton, François P. Pralong, Nathalie Vionnet, Francesco Capozzi, René Badertscher, Guy Vergères. Identification of Urinary Food Intake Biomarkers for Milk, Cheese, and Soy-Based Drink by Untargeted GC-MS and NMR in Healthy Humans. Journal of Proteome Research 2017, 16 (9) , 3321-3335. https://doi.org/10.1021/acs.jproteome.7b00319
  7. Michael D. M. Dryden, Ryan Fobel, Christian Fobel, and Aaron R. Wheeler . Upon the Shoulders of Giants: Open-Source Hardware and Software in Analytical Chemistry. Analytical Chemistry 2017, 89 (8) , 4330-4338. https://doi.org/10.1021/acs.analchem.7b00485
  8. Chunyan Wang, Yijing Long, Wenwen Li, Wei Dai, Shaohua Xie, Yuanling Liu, Yinchenxi Zhang, Mingxin Liu, Yonghui Tian, Qiang Li, Yixiang Duan. Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-62803-4
  9. Dandan Liang, Quan Liu, Kejun Zhou, Wei Jia, Guoxiang Xie, Tianlu Chen. IP4M: an integrated platform for mass spectrometry-based metabolomics data mining. BMC Bioinformatics 2020, 21 (1) https://doi.org/10.1186/s12859-020-03786-x
  10. Maxim Wilkinson, Iain R White, Royston Goodacre, Tamara Nijsen, Stephen J Fowler. Effects of high relative humidity and dry purging on VOCs obtained during breath sampling on common sorbent tubes. Journal of Breath Research 2020, 14 (4) , 046006. https://doi.org/10.1088/1752-7163/ab7e17
  11. Huamei Yue, Xianquan Huang, Rui Ruan, Huan Ye, Zhong Li, Chuangju Li. Effect of dietary lipid on growth, body composition, serum biochemistry and hepatic metabolite alteration in Chinese rice field eel ( Monopterus albus ) fingerlings. Aquaculture Nutrition 2020, 99 https://doi.org/10.1111/anu.13165
  12. Bruno Rafael Araújo, Danielle Zildeana Sousa Furtado, Fernando Brunale Vilela de Moura Leite, Nilson Antonio de Assunção, Emanuel Carrilho. Metabolic profiling of organic acids in urine samples of Cri Du Chat syndrome individuals by gas chromatography-mass spectrometry. Journal of Chromatography B 2020, 1153 , 122267. https://doi.org/10.1016/j.jchromb.2020.122267
  13. Jarosław Kliks, Joanna Kawa-Rygielska, Alan Gasiński, Adam Głowacki, Antoni Szumny. Analysis of Volatile Compounds and Sugar Content in Three Polish Regional Ciders with Pear Addition. Molecules 2020, 25 (16) , 3564. https://doi.org/10.3390/molecules25163564
  14. Roberta Bonomo, Guido Cavaletti, Debra J. Skene. Metabolomics markers in Neurology: current knowledge and future perspectives for therapeutic targeting. Expert Review of Neurotherapeutics 2020, 20 (7) , 725-738. https://doi.org/10.1080/14737175.2020.1782746
  15. Julie Gaubert-Boussarie, Soizic Prado, Cédric Hubas. An Untargeted Metabolomic Approach for Microphytobenthic Biofilms in Intertidal Mudflats. Frontiers in Marine Science 2020, 7 https://doi.org/10.3389/fmars.2020.00250
  16. Yu-Ying Zhang, Qian Zhang, Yue-Ming Zhang, Wei-Wei Wang, Li Zhang, Yong-Jie Yu, Chang-Cai Bai, Ji-Zhao Guo, Hai-Yan Fu, Yuanbin She. A comprehensive automatic data analysis strategy for gas chromatography-mass spectrometry based untargeted metabolomics. Journal of Chromatography A 2020, 1616 , 460787. https://doi.org/10.1016/j.chroma.2019.460787
  17. Benoît Paix, Nathan Carriot, Raphaëlle Barry-Martinet, Stéphane Greff, Benjamin Misson, Jean-François Briand, Gérald Culioli. A Multi-Omics Analysis Suggests Links Between the Differentiated Surface Metabolome and Epiphytic Microbiota Along the Thallus of a Mediterranean Seaweed Holobiont. Frontiers in Microbiology 2020, 11 https://doi.org/10.3389/fmicb.2020.00494
  18. Émilie Stierlin, Florence Nicolè, Thomas Costes, Xavier Fernandez, Thomas Michel. Metabolomic study of volatile compounds emitted by lavender grown under open-field conditions: a potential approach to investigate the yellow decline disease. Metabolomics 2020, 16 (3) https://doi.org/10.1007/s11306-020-01654-6
  19. Mohammed Ashrafi, Yun Xu, Howbeer Muhamadali, Iain White, Maxim Wilkinson, Katherine Hollywood, Mohamed Baguneid, Royston Goodacre, Ardeshir Bayat, . A microbiome and metabolomic signature of phases of cutaneous healing identified by profiling sequential acute wounds of human skin: An exploratory study. PLOS ONE 2020, 15 (2) , e0229545. https://doi.org/10.1371/journal.pone.0229545
  20. Arthur David, Pawel Rostkowski. Analytical techniques in metabolomics. 2020,,, 35-64. https://doi.org/10.1016/B978-0-12-818196-6.00002-9
  21. Mike Li, X. Rosalind Wang. Peak alignment of gas chromatography–mass spectrometry data with deep learning. Journal of Chromatography A 2019, 1604 , 460476. https://doi.org/10.1016/j.chroma.2019.460476
  22. Jan Stanstrup, Corey Broeckling, Rick Helmus, Nils Hoffmann, Ewy Mathé, Thomas Naake, Luca Nicolotti, Kristian Peters, Johannes Rainer, Reza Salek, Tobias Schulze, Emma Schymanski, Michael Stravs, Etienne Thévenot, Hendrik Treutler, Ralf Weber, Egon Willighagen, Michael Witting, Steffen Neumann. The metaRbolomics Toolbox in Bioconductor and beyond. Metabolites 2019, 9 (10) , 200. https://doi.org/10.3390/metabo9100200
  23. Yue-Ming Zhang, Yu-Ying Zhang, Qian Zhang, Yi Lv, Tao Sun, Lu Han, Chang-Cai Bai, Yong-Jie Yu. Automatic peak detection coupled with multivariate curve resolution–alternating least squares for peak resolution in gas chromatography–mass spectrometry. Journal of Chromatography A 2019, 1601 , 300-309. https://doi.org/10.1016/j.chroma.2019.04.065
  24. Borgsmüller, Gloaguen, Opialla, Blanc, Sicard, Royer, Le Bizec, Durand, Migné, Pétéra, Pujos-Guillot, Giacomoni, Guitton, Beule, Kirwan. WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data. Metabolites 2019, 9 (9) , 171. https://doi.org/10.3390/metabo9090171
  25. Nisha Verma, Mario Pink, Christian Kersch, Albert W. Rettenmeier, Simone Schmitz-Spanke. Benzo[a]pyrene mediated time- and dose-dependent alteration in cellular metabolism of primary pig bladder cells with emphasis on proline cycling. Archives of Toxicology 2019, 93 (9) , 2593-2602. https://doi.org/10.1007/s00204-019-02521-7
  26. Pan Ma, Meijing Li, Hongmei Lu, Zhimin Zhang. MARS 2: A computational tool to resolve and extract features from large-scale GC-MS datasets. Chemometrics and Intelligent Laboratory Systems 2019, 191 , 12-20. https://doi.org/10.1016/j.chemolab.2019.05.010
  27. Julie Gaubert, Stéphane Greff, Olivier P. Thomas, Claude E. Payri. Metabolomic variability of four macroalgal species of the genus Lobophora using diverse approaches. Phytochemistry 2019, 162 , 165-172. https://doi.org/10.1016/j.phytochem.2019.03.002
  28. Kimmo Sirén, Ulrich Fischer, Jochen Vestner. Automated supervised learning pipeline for non-targeted GC-MS data analysis. Analytica Chimica Acta: X 2019, 1 , 100005. https://doi.org/10.1016/j.acax.2019.100005
  29. Min Yan, Guowang Xu. Current and future perspectives of functional metabolomics in disease studies–A review. Analytica Chimica Acta 2018, 1037 , 41-54. https://doi.org/10.1016/j.aca.2018.04.006
  30. Xianliang Zhao, He Chen, Zhaohui Jin, Li Li, Jie Zhang, Xianghui Kong. GC-MS-based metabolomics analysis reveals L-aspartate enhances the antibiotic sensitivity of neomycin sulfate-resistant Aeromonas hydrophila. Journal of Fish Diseases 2018, 41 (12) , 1831-1841. https://doi.org/10.1111/jfd.12894
  31. Peng Lu, Mei-Juan Fan, Qian Zhang, Qing-Xia Zheng, Ping-Ping Liu, Bing Wang, Jun-Wei Guo, Sheng Wang, Hai-Yan Fu, Yong-Jie Yu, Yuanbin She. A novel strategy for extracted ion chromatogram extraction to improve peak detection in UPLC-HRMS. Analytical Methods 2018, 10 (42) , 5118-5126. https://doi.org/10.1039/C8AY01850B
  32. Alessia Trimigno, Linda Münger, Gianfranco Picone, Carola Freiburghaus, Grégory Pimentel, Nathalie Vionnet, François Pralong, Francesco Capozzi, René Badertscher, Guy Vergères. GC-MS Based Metabolomics and NMR Spectroscopy Investigation of Food Intake Biomarkers for Milk and Cheese in Serum of Healthy Humans. Metabolites 2018, 8 (2) , 26. https://doi.org/10.3390/metabo8020026
  33. Dong Kyu Lim, Changyeun Mo, Dong-Kyu Lee, Nguyen Phuoc Long, Jongguk Lim, Sung Won Kwon. Non-destructive profiling of volatile organic compounds using HS-SPME/GC–MS and its application for the geographical discrimination of white rice. Journal of Food and Drug Analysis 2018, 26 (1) , 260-267. https://doi.org/10.1016/j.jfda.2017.04.005
  34. Mario Pink, Nisha Verma, Christian Kersch, Simone Schmitz-Spanke. Identification and characterization of small organic compounds within the corona formed around engineered nanoparticles. Environmental Science: Nano 2018, 5 (6) , 1420-1427. https://doi.org/10.1039/C8EN00161H
  35. Arianna Filntisi, Charalambos Fotakis, Pantelis Asvestas, George K. Matsopoulos, Panagiotis Zoumpoulakis, Dionisis Cavouras. Automated metabolite identification from biological fluid 1H NMR spectra. Metabolomics 2017, 13 (12) https://doi.org/10.1007/s11306-017-1286-8
  36. Benjamin G. Janesko, Li Li, Rodger Mensing. Quantum Chemical Fragment Precursor Tests: Accelerating de novo annotation of tandem mass spectra. Analytica Chimica Acta 2017, 995 , 52-64. https://doi.org/10.1016/j.aca.2017.09.034
  37. Biswapriya B. Misra, Johannes F. Fahrmann, Dmitry Grapov. Review of emerging metabolomic tools and resources: 2015-2016. ELECTROPHORESIS 2017, 38 (18) , 2257-2274. https://doi.org/10.1002/elps.201700110
  38. Rachel Spicer, Reza M. Salek, Pablo Moreno, Daniel Cañueto, Christoph Steinbeck. Navigating freely-available software tools for metabolomics analysis. Metabolomics 2017, 13 (9) https://doi.org/10.1007/s11306-017-1242-7
  39. Chen Wang, Ying-Hao Yin, Ying-Jie Wei, Zi-Qi Shi, Jian-Qun Liu, Li-Fang Liu, Gui-Zhong Xin. Rapid identification of herbal compounds derived metabolites using zebrafish larvae as the biotransformation system. Journal of Chromatography A 2017, 1515 , 100-108. https://doi.org/10.1016/j.chroma.2017.07.076
  40. Xavier Domingo-Almenara, Jesus Brezmes, Gabriela Venturini, Gabriel Vivó-Truyols, Alexandre Perera, Maria Vinaixa. Baitmet, a computational approach for GC–MS library-driven metabolite profiling. Metabolomics 2017, 13 (8) https://doi.org/10.1007/s11306-017-1223-x
  41. Leonardo Perez de Souza, Thomas Naake, Takayuki Tohge, Alisdair R Fernie. From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics. GigaScience 2017, 6 (7) https://doi.org/10.1093/gigascience/gix037
  42. Xavier Domingo-Almenara, Alexandre Perera, Jesus Brezmes. Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods. Journal of Chromatography A 2016, 1474 , 145-151. https://doi.org/10.1016/j.chroma.2016.10.066

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

OOPS

You have to login with your ACS ID befor you can login with your Mendeley account.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.

CONTINUE