Isotope
Cluster-Based Compound Matching in Gas Chromatography/Mass
Spectrometry for Non-Targeted Metabolomics
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Abstract
Gas chromatography coupled to mass
spectrometry (GC/MS) has emerged
as a powerful tool in metabolomics studies. A major bottleneck in
current data analysis of GC/MS-based metabolomics studies is compound
matching and identification, as current methods generate high rates
of false positive and false -negative identifications. This is especially
true for data sets containing a high amount of noise. In this work,
a novel spectral similarity measure based on the specific fragmentation
patterns of electron impact mass spectra is proposed. An important
aspect of these algorithmic methods is the handling of noisy data.
The performance of the proposed method compared to the dot product,
the current gold standard, was evaluated on a complex biological data
set. The analysis results showed significant improvements of the proposed
method in compound matching and chromatogram alignment compared to
the dot product