Biologically Consistent Annotation of Metabolomics Data

Annotation
of metabolites remains a major challenge in liquid chromatography–mass
spectrometry (LC–MS) based untargeted metabolomics. The current
gold standard for metabolite identification is to match the detected
feature with an authentic standard analyzed on the same equipment
and using the same method as the experimental samples. However, there
are substantial practical challenges in applying this approach to
large data sets. One widely used annotation approach is to search
spectral libraries in reference databases for matching metabolites;
however, this approach is limited by the incomplete coverage of these
libraries. An alternative computational approach is to match the detected
features to candidate chemical structures based on their mass and
predicted fragmentation pattern. Unfortunately, both of these approaches
can match multiple identities with a single feature. Another issue
is that annotations from different tools often disagree. This paper
presents a novel LC–MS data annotation method, termed Biologically Consistent Annotation
(BioCAn), that combines the results from database searches and in
silico fragmentation analyses and places these results into a relevant
biological context for the sample as captured by a metabolic model.
We demonstrate the utility of this approach through an analysis of
CHO cell samples. The performance of BioCAn is evaluated against several
currently available annotation tools, and the accuracy of BioCAn annotations
is verified using high-purity analytical standards.