Background: A bottleneck in investigating the cellular metabolism and physiology of organisms is the presence of metabolic gaps in the genome-scale metabolic networks. Metabolic gaps are reactions in the network that the corresponding genes have not yet been identified. Previous gap filling methods are generally based on identifying protein family in related organisms and then use this family to help for finding the target gene in a given genome. However, these methods fail when the protein family is not well-defined. There are therefore still many gaps in current metabolic networks. Here, we attempt to fill these gaps via an indirect approach by retrofitting protein function predictors and post-processing their results to identify the candidate genes.
For more information : Nguyen NN, Vongsangnak W, Shen B, Nguyen PV, Leong HW (2014) Megafiller: A Retrofitted Protein Function Predictor for Filling Gaps in Metabolic Networks. J Proteomics Bioinform S9: 003