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 http://CRAN.R-project.org/package=baitmet.
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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.
Conflict of interest
The authors declare no conflict of interest.
The ethics committee of the Hospital das Clinicas, University of São Paulo (Brazil) approved the study (Protocol Number 3759/12/015).
Informed written consent was obtained from all participants in the study.
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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
- Compound profiling
- Gas chromatography
- Mass spectrometry