Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models
Figure 4
Genes added to the model using likelihood-based and parsimony-based gap filling.
Likelihood-based gap filling produced more new gene annotations than post-processing gap filled reactions generated using the parsimony-based approach. The plot shows the number of uniquely-added genes by likelihood-based and parsimony-based gap filling approaches (genes in common with both approaches are omitted for clarity but tended to be more than those unique to either approach). A) Number of genes added after targeted gap filling to activate biomass production. B) Number of genes added after iterative gap filling.