Likelihood-based gene annotations for gap filling and quality assessment in genome-scale metabolic models.

ABSTRACT: Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homolo

SUBMITTER: Benedict MN 

PROVIDER: S-EPMC4199484 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature