Likelihood-Based Gene Annotations for Gap Filling and Quality Assessment in Genome-Scale Metabolic Models
Figure 6
Knockout lethality accuracy for genes added in gap filling.
Gene knockout simulations were performed for models gap filled with each of the four workflows to assess the consistency between lethality prediction and knockout lethality data for genes added in gap filling. Likelihood-based gap filling was able to produce the most candidate gene associations, with high specificity and low sensitivity in lethality predictions. The difference in accuracy between likelihood-based and parsimony-based gap filling was not statistically significant. A) Number of positive growth predictions, B) Number of negative growth predictions.