eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

Gas
chromatography coupled to mass spectrometry (GC/MS) has been
a long-standing approach used for identifying small molecules due
to the highly reproducible ionization process of electron impact ionization
(EI). However, the use of GC-EI MS in untargeted metabolomics produces
large and complex data sets characterized by coeluting compounds and
extensive fragmentation of molecular ions caused by the hard electron
ionization. In order to identify and extract quantitative information
on metabolites across multiple biological samples, integrated computational
workflows for data processing are needed. Here we introduce eRah,
a free computational tool written in the open language R composed
of five core functions: (i) noise filtering and baseline removal of
GC/MS chromatograms, (ii) an innovative compound deconvolution process
using multivariate analysis techniques based on compound match by
local covariance (CMLC) and orthogonal signal deconvolution (OSD),
(iii) alignment of mass spectra across samples, (iv) missing compound
recovery, and (v) identification of metabolites by spectral library
matching using publicly available mass spectra. eRah outputs a table
with compound names, matching scores and the integrated area of compounds
for each sample. The automated capabilities of eRah are demonstrated
by the analysis of GC-time-of-flight (TOF) MS data from plasma samples
of adolescents with hyperinsulinaemic androgen excess and healthy
controls. The quantitative results of eRah are compared to centWave,
the peak-picking algorithm implemented in the widely used XCMS package,
MetAlign, and ChromaTOF software. Significantly dysregulated metabolites
are further validated using pure standards and targeted analysis by
GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available
at http://CRAN.R-project.org/package=erah.