Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12.
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Despite the demonstrated success of genome-wide genetic screens and chemical genomics studies at predicting functions for genes of unknown function or predicting new functions for well-characterized genes, their potential to provide insights into gene function has not been fully explored. We systematically reanalyzed a published high-throughput phenotypic dataset for the model Gram-negative bacterium Escherichia coli K-12. The availability of high-quality annotation sets allowed us to compare the power of different metrics for measuring phenotypic profile similarity to correctly infer gene function. We conclude that there is no single best method; the three metrics tested gave comparable results for most gene pairs. We also assessed how converting quantitative phenotypes to discrete, qualitative phenotypes affected the association between phenotype and function. Our results indicate that this approach may allow phenotypic data from different studies to be combined to produce a larger dataset that may reveal functional connections between genes not detected in individual studies.