Elucidating genomic gaps using phenotypic profiles

Abstract

Advances in genomic sequencing provide the ability to model the metabolism of organisms from their genome annotation. The bioinformatics tools developed to deduce gene function through homology-based methods are dependent on public databases; thus, novel discoveries are not readily extrapolated from current analysis tools with a homology dependence. Multi-phenotype Assay Plates (MAPs) provide a high-throughput method to profile bacterial phenotypes by growing bacteria in various growth conditions, simultaneously. More robust and accurate computational models can be constructed by coupling MAPs with current genomic annotation methods.PMAnalyzeris an online tool that analyzes bacterial growth curves from the MAP system which are then used to optimize metabolic models duringin silicogrowth simulations. UsingCitrobacter sedlakiias a prototype, the Rapid Annotation using Subsystem Technology (RAST) tool produced a model consisting of 1,367 enzymatic reactions. After the optimization, 44 reactions were added to, or modified within, the model. The model correctly predicted the outcome on 93% of growth experiments.