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

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.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.     

Ecological cues, gestation length, and birth timing in African buffalo (Syncerus caffer)

We examined annual variation in the timing of conception andparturition in the African buffalo (Syncerus caffer) and thesynchrony of birth timing with resource cues, using 8 yearsof monthly birth, rainfall, and vegetation data, measured asNormalized Difference Vegetation Index (NDVI). Monthly birthshad the strongest significant correlations with NDVI and rainfalllevels 12 and 13 months in the past, respectively. In addition,the synchrony of current year births corresponds most stronglyto the synchrony of the previous year's NDVI distribution. Becausethe gestation period of buffalo has been estimated to be around11 months, these findings suggest that improved protein levels,occurring approximately a month after the first green flushof the wet season, are either a trigger for conception or conceptionhas evolved to be synchronous with correlated environmentalcues that ensure females enter a period of peak body conditionaround the time of conception and/or parturition. With a gestationperiod of approximately 340 days, parturition occurs to takeadvantage of the period when forage has its highest proteincontent. A comparative analysis of gestation periods withinthe subfamily Bovinae indicates that African buffalo have aprotracted gestation for their body size, which we suggest isan adaptation to their seasonal environment. We also found thatinterannual variation in the birth distribution suggests a degreeof plasticity in the date of conception, and variation in thenumber of calves born each year suggest further synchrony ata timescale longer than a single year.