Identifying and quantifying metabolites by scoring peaks of GC-MS data

BMC Bioinformatics

Software

Sophie Reade¹  Chris SJ Probert¹  Arno Mayor¹  Katya Ruggiero²  Raphael BM Aggio³ 

[1] Department of Gastroenterology, Institute of Translational Medicine, University of Liverpool, Nuffield Building, Crown Street, L693BX, Liverpool, UK;The University of Auckland, 3A Symonds Street, 1142, Auckland, New Zealand;The University of Auckland, 3A Symonds Street, 1142, Auckland, New Zealand;Department of Gastroenterology, Institute of Translational Medicine, University of Liverpool, Nuffield Building, Crown Street, L693BX, Liverpool, UK;

关键词: Metabolomics;    Identification;    GC-MS;    Data analysis;   

DOI  :  10.1186/s12859-014-0374-2

 received in 2014-04-19, accepted in 2014-11-03,  发布年份 2014

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Aggio et al.; licensee BioMed Central Ltd. 2014