Abstract
BackgroundMetabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.ResultsHere we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.ConclusionsIdentification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.
Highlights
Metabolomics is one of most recent omics technologies
Metabolite identification To enable the comparison of Automated Mass Spectral Deconvolution System (AMDIS)’s and MetaBox’s efficacies in metabolite identification, we calculated the percentages of false positives and false negatives reported by each algorithm when analysing 10 samples of a standard mixture of metabolites (i.e. 5 samples of 50 μL and 5 of 100 μL), using match factors of f = 70, 80 and 90 for AMDIS; and match factor of f = 70 and score cut of 13 for MetaBox
Based on the ion mass fragment (IMF) and retention time (RT) in the spectral library used by AMDIS and MetaBox (Additional file 1: Table S3), we identified 19 compounds associated to the total list (387) of IMFs reported by XCMS Online
Summary
Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. Metabolomics, the popular modern approach to screening large numbers of low molecular mass compounds in biological samples, has been successfully applied in drug discovery [1], food science [2] and systems biology [3] studies. The three most commonly used analytical platforms for the identification and quantification of metabolites in biological samples are perhaps gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) and liquid chromatographymass spectrometry (LC-MS) [4] While none of these is stand-alone in the sense that it provides complete coverage of a sample’s metabolome, GC-MS is among the most widely applied because of its ability to separate complex mixtures of metabolites with high efficiency and at low cost [5]. AMDIS is linked to the NIST standard reference database: one of the most popular mass spectral databases for metabolite identification
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