Baitmet, a computational approach for GC–MS library-driven metabolite profiling

Abstract

Introduction

Current computational tools for gas chromatography—mass spectrometry (GC–MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC–MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently.

Results

This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC–MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library.

Conclusions

Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC–MS via freely available spectral data. Baitmet is freely available at http://CRAN.R-project.org/package=baitmet.

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

Fig. 1

References

  1. Cuadros-Inostroza, A., Caldana, C., Redestig, H., Kusano, M., Lisec, J., Peña-Cortés, H., et al. (2009). TargetSearch—A bioconductor package for the efficient preprocessing of GC–MS metabolite profiling data. BMC Bioinformatics, 10, 428.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Domingo-Almenara, X., Brezmes, J., Vinaixa, M., Samino, S., Ramirez, N., Ramon-Krauel, M., et al. (2016). eRah: A computational tool integrating spectral deconvolution and alignment with quantification and identification of metabolites in GC–MS-based metabolomics. Analytical Chemistry, 88(19), 9821–9829.

    CAS  Article  PubMed  Google Scholar 

  3. Domingo-Almenara, X., Perera, A., Ramírez, N., Cañellas, N., Correig, X., & Brezmes, J. (2015). Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. Journal of Chromatography A, 1409, 226–233.

    CAS  Article  PubMed  Google Scholar 

  4. Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., et al. (2010). MassBank: A public repository for sharing mass spectral data for life sciences. Journal of Mass Spectrometry, 45(7), 703–714.

    CAS  Article  PubMed  Google Scholar 

  5. Hummel, J., Selbig, J., Walther, D., & Kopka, J. (2007). The Golm metabolome database: A database for GC–MS based metabolite profiling. Topics in Current Genetics, 18, 75–95.

    CAS  Article  Google Scholar 

  6. Hummel, J., Strehmel, N., Selbig, J., Walther, D., & Kopka, J. (2010). Decision tree supported substructure prediction of metabolites from GC–MS profiles. Metabolomics, 6(2), 322–333.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Luedemann, A., Strassburg, K., Erban, A., & Kopka, J. (2008). TagFinder for the quantitative analysis of gas chromatography–mass spectrometry (GC–MS)-based metabolite profiling experiments. Bioinformatics, 24(5), 732–737.

    CAS  Article  PubMed  Google Scholar 

  8. Stein, S. E. (1999). An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data. Journal of the American Society for Mass Spectrometry, 10(8), 770–781.

    CAS  Article  Google Scholar 

  9. Stein, S. E., & Scott, D. R. (1994). Optimization and testing of mass spectral library search algorithms for compound identification. Journal of the American Society for Mass Spectrometry, 5(9), 859–866.

    CAS  Article  PubMed  Google Scholar 

  10. Strehmel, N., Hummel, J., Erban, A., Strassburg, K., & Kopka, J. (2008). Retention index thresholds for compound matching in GC–MS metabolite profiling. Journal of Chromatography B, 871(2), 182–190.

    CAS  Article  Google Scholar 

  11. Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3(3), 211–221.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. Vinaixa, M., Schymanski, E. L., Neumann, S., Navarro, M., Salek, R. M., & Yanes, O. (2016). Mass spectral databases for LC/MS and GC/MS-based metabolomics: State of the field and future prospects. Trends in Analytical Chemistry, 78, 23–35.

    CAS  Article  Google Scholar 

  13. Wehrens, R., Weingart, G., & Mattivi, F. (2014). metaMS: An open-source pipeline for GC–MS-based untargeted metabolomics. Journal of Chromatography B, 966, 109–116.

    CAS  Article  Google Scholar 

  14. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0-The human metabolome database in 2013. Nucleic Acids Research, 41, 801–807.

    Article  Google Scholar 

Download references

Funding

This research was partially funded by MINECO Grant TEC2014–60337-R and TEC2015–69076-P; and FAPESP Grant 2012/12042-7. CIBER–BBN and CIBER–DEM are initiatives of the Spanish ISCIII.

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Xavier Domingo-Almenara or Maria Vinaixa.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

The ethics committee of the Hospital das Clinicas, University of São Paulo (Brazil) approved the study (Protocol Number 3759/12/015).

Informed consent

Informed written consent was obtained from all participants in the study.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 305 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Domingo-Almenara, X., Brezmes, J., Venturini, G. et al. Baitmet, a computational approach for GC–MS library-driven metabolite profiling. Metabolomics 13, 93 (2017). https://doi.org/10.1007/s11306-017-1223-x

Download citation

Keywords

  • Compound profiling
  • Gas chromatography
  • Mass spectrometry
  • Metabolomics