MAGI: A Method for Metabolite Annotation and Gene Integration

Metabolomics is a
widely used technology for obtaining direct measures
of metabolic activities from diverse biological systems. However,
ambiguous metabolite identifications are a common challenge and biochemical
interpretation is often limited by incomplete and inaccurate genome-based
predictions of enzyme activities (that is, gene annotations). Metabolite
Annotation and Gene Integration (MAGI) generates a metabolite–gene
association score using a biochemical reaction network. This is calculated
by a method that emphasizes consensus between metabolites and genes
via biochemical reactions. To demonstrate the potential of this method,
we applied MAGI to integrate sequence data and metabolomics data collected
from Streptomyces coelicolor A3(2), an extensively
characterized bacterium that produces diverse secondary metabolites.
Our findings suggest that coupling metabolomics and genomics data
by scoring consensus between the two increases the quality of both
metabolite identifications and gene annotations in this organism.
MAGI also made biochemical predictions for poorly annotated genes
that were consistent with the extensive literature on this important
organism. This limited analysis suggests that using metabolomics data
has the potential to improve annotations in sequenced organisms and
also provides testable hypotheses for specific biochemical functions.
MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with
source code available at https://github.com/biorack/magi.