title: Identifying and quantifying metabolites by scoring peaks of GC-MS data.
abstract: BACKGROUND
  Metabolomics 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.
  
  
  RESULTS
  Here 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.
  
  
  CONCLUSIONS
  Identification 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.
authors.affiliation: The University of Auckland, 3A Symonds Street, Auckland, 1142, New Zealand. ragg005@aucklanduni.ac.nz.|Department of Gastroenterology, Institute of Translational Medicine, University of Liverpool, Nuffield Building, Crown Street, L693BX Liverpool, UK. ragg005@aucklanduni.ac.nz.
authors.forename: Raphael B M
authors.initials: RB
authors.lastname: Aggio
authors.affiliation: Department of Gastroenterology, Institute of Translational Medicine, University of Liverpool, Nuffield Building, Crown Street, L693BX Liverpool, UK. arnom@liverpool.ac.uk.
authors.forename: Arno
authors.initials: A
authors.lastname: Mayor
authors.affiliation: Department of Gastroenterology, Institute of Translational Medicine, University of Liverpool, Nuffield Building, Crown Street, L693BX Liverpool, UK. S.Reade@student.liverpool.ac.uk.
authors.forename: Sophie
authors.initials: S
authors.lastname: Reade
authors.affiliation: Department of Gastroenterology, Institute of Translational Medicine, University of Liverpool, Nuffield Building, Crown Street, L693BX Liverpool, UK. Chris.Probert@liverpool.ac.uk.
authors.forename: Chris S J
authors.initials: CS
authors.lastname: Probert
authors.affiliation: The University of Auckland, 3A Symonds Street, Auckland, 1142, New Zealand. k.ruggiero@auckland.ac.nz.
authors.forename: Katya
authors.initials: K
authors.lastname: Ruggiero
mesh_terms: D000465:Algorithms; D000818:Animals; D005243:Feces / Q000737:chemistry*; D005260:Female; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D008297:Male; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D051379:Mice; D008810:Mice, Inbred C57BL; D012015:Reference Standards; D012984:Software; D055549:Volatile Organic Compounds / Q000032:analysis*
chemical_list: D055549:Volatile Organic Compounds