Identifying metabolic enzymes with multiple types of association evidence.

ABSTRACT:

Background

Existing large-scale metabolic models of sequenced organisms commonly include enzymatic functions which can not be attributed to any gene in that organism. Existing computational strategies for identifying such missing genes rely primarily on sequence homology to known enzyme-encoding genes.

Results

We present a novel method for identifying genes encoding for a specific metabolic function based on a local structure of metabolic network and multiple types of functional association evidence, including clustering of genes on the chromosome, similarity of phylogenetic profiles, gene expression, protein fusion events and others. Using E. coli and S. cerevisiae metabolic networks, we illustrate predictive ability of each individual type of association evidence and show

SUBMITTER: Kharchenko P 

PROVIDER: S-EPMC1450304 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature