Frontiers in Bioengineering and Biotechnology (Jul 2015)
PhenoMeter: a metabolome database search tool using statistical similarity matching of metabolic phenotypes for high-confidence detection of functional links
Abstract
This article describes PhenoMeter, a new type of metabolomics database search that accepts metabolite response patterns as queries and searches the MetaPhen database of reference patterns for responses that are statistically significantly similar or inverse for the purposes of detecting functional links. To identify a similarity measure that would detect functional links as reliably as possible, we compared the performance of four statistics in correctly top-matching metabolic phenotypes of Arabidopsis thaliana metabolism mutants affected in different steps of the photorespiration metabolic pathway to reference phenotypes of mutants affected in the same enzymes by independent mutations. The best performing statistic, the PhenoMeter Score (PM Score), was a function of both Pearson correlation and Fisher’s Exact Test of directional overlap. This statistic outperformed Pearson correlation, biweight midcorrelation and Fisher’s Exact Test used alone. To demonstrate general applicability, we show that the PhenoMeter reliably retrieved the most closely functionally-linked response in the database when queried with responses to a wide variety of environmental and genetic perturbations. Attempts to match metabolic phenotypes between independent studies were met with varying success and possible reasons for this are discussed. Overall, our results suggest that integration of pattern-based search tools into metabolomics databases will aid functional annotation of newly recorded metabolic phenotypes analogously to the way sequence similarity search algorithms have aided the functional annotation of genes and proteins. PhenoMeter is freely available at MetabolomeExpress (https://www.metabolome-express.org/phenometer.php).
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