Global probabilistic annotation of metabolic networks enables enzyme discovery.

ABSTRACT: Annotation of organism-specific metabolic networks is one of the main challenges of systems biology. Importantly, owing to inherent uncertainty of computational annotations, predictions of biochemical function need to be treated probabilistically. We present a global probabilistic approach to annotate genome-scale metabolic networks that integrates sequence homology and context-based correlations under a single principled framework. The developed method for global biochemical reconstruction using sampling (GLOBUS) not only provides annotation probabilities for each functional assignment but also suggests likely alternative functions. GLOBUS is based on statistical Gibbs sampling of probable metabolic annotations and is able to make accurate functional assignments even in cases of remote se

SUBMITTER: Plata G 

PROVIDER: S-EPMC3696893 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature