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.
Highlights
Recent advancements in genomic sequencing provide high quality, deep-coverage DNA sequences for tens of thousands of bacterial genomes. To manage this breadth of data, online tools such as RAST1 leverage the SEED database[2] by using homology and genomic context to determine gene functions encoded in the DNA sequences
The stoichiometry of metabolic networks is represented by a two-dimensional numerical matrix, in which the values are the stoichiometric coefficients of the reactants and products
Feedback from mis-annotations corrects the ambiguities for future bacteria metabolic reconstructions as administrators of KBase, Rapid Annotation using Subsystem Technology (RAST), and the SEED database are informed of these findings
Summary
Recent advancements in genomic sequencing provide high quality, deep-coverage DNA sequences for tens of thousands of bacterial genomes. To manage this breadth of data, online tools such as RAST (http://rast.nmpdr.org/)[1] leverage the SEED database[2] by using homology and genomic context to determine gene functions encoded in the DNA sequences. This automated annotation service generates a raw metabolic reconstruction of the genome for use in in silico experiments. Foreshadowing some comments later, what is the evidence that the phenotype data are correct for all conditions tested?
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