, 13:93 | Cite as

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

  • Xavier Domingo-Almenara
  • Jesus Brezmes
  • Gabriela Venturini
  • Gabriel Vivó-Truyols
  • Alexandre Perera
  • Maria Vinaixa
Short Communication



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.


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.


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


Compound profiling Gas chromatography Mass spectrometry Metabolomics 



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.

Compliance with ethical standards

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.

Supplementary material

11306_2017_1223_MOESM1_ESM.pdf (305 kb)
Supplementary material 1 (PDF 305 KB)


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Xavier Domingo-Almenara
    • 1
    • 2
  • Jesus Brezmes
    • 1
    • 2
  • Gabriela Venturini
    • 3
  • Gabriel Vivó-Truyols
    • 4
  • Alexandre Perera
    • 5
  • Maria Vinaixa
    • 1
    • 2
  1. 1.Metabolomics Platform, Department of Electronic Engineering (DEEEA)Universitat Rovira i VirgiliTarragonaSpain
  2. 2.Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM)MadridSpain
  3. 3.Lab Genetics and Molecular Cardiology, Heart Institute (InCor)Universidade de São PauloSao PauloBrazil
  4. 4.Analytical Chemistry Group, Van’t Hoff Institute for Molecular SciencesUniversiteit van AmsterdamAmsterdamThe Netherlands
  5. 5.B2SLab, Department of ESAII, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)Universitat Politècnica de CatalunyaBarcelonaSpain

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