MyCompoundID: Using an
Evidence-Based Metabolome Library
for Metabolite Identification
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Abstract
Identification of unknown metabolites is a major challenge
in metabolomics.
Without the identities of the metabolites, the metabolome data generated
from a biological sample cannot be readily linked with the proteomic
and genomic information for studies in systems biology and medicine.
We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting
mass spectrometry (MS) data against a newly constructed metabolome
library composed of 8 021 known human endogenous metabolites
and their predicted metabolic products (375 809 compounds from
one metabolic reaction and 10 583 901 from two reactions).
As an example, in the analysis of a simple extract of human urine
or plasma and the whole human urine by liquid chromatography-mass
spectrometry and MS/MS, we are able to identify at least two times
more metabolites in these samples than by using a standard human metabolome
library. In addition, it is shown that the evidence-based metabolome
library (EML) provides a much superior performance in identifying
putative metabolites from a human urine sample, compared to the use
of the ChemPub and KEGG libraries