Baitmet, a computational approach for GC–MS library-driven metabolite profiling
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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 d ata reviewing. Metabolomics ad vent has fostered the development of public metabolite repositories containing mass spectra and retentio n indices, two orthogonal prop erties needed for metabol ite 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 i dentities 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 w ere assa yed in a metabolomics study in volving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high - thr oughput and wide scope interro gation on the metabolic composition of complex sa mples analyzed using GC – MS via freely available spectral data
CitacióDomingo, X., Brezmes, J., Venturini, G., Vivó-Truyols, G., Perera, A., Vinaixa, M. Baitmet, a computational approach for GC–MS library-driven metabolite profiling. "Metabolomics", 24 Juny 2017, vol. 13, núm. 93.
Versió de l'editorhttps://link.springer.com/article/10.1007/s11306-017-1223-x