Research

The society of genes: networks of functional links between genes from comparative genomics

Itai Yanai^1,2 and Charles DeLisi¹

¹  Bioinformatics Graduate Program and Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA

²  Current address: Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel

author email corresponding author email

Genome Biology 2002, 3:research0064.1-0064.12doi:10.1186/gb-2002-3-11-research0064

Published:	25 October 2002

Subject areas: Bioinformatics, Genome studies, Microbiology and parasitology , Genetics

Abstract

Background

Comparative genomics provides at least three methods beyond traditional sequence similarity for identifying functional links between genes: the examination of common phylogenetic distributions, the analysis of conserved proximity along the chromosomes of multiple genomes, and observations of fusions of genes into a multidomain gene in another organism. We have previously generated the links according to each of these methods individually for 43 known microbial genomes. Here we combine these results to construct networks of functional associations.

Results

We show that the functional networks obtained by applying these methods have different topologies and that the information they provide is largely additive. In particular, the combined networks of functional links contain an average of 57% of an organism's complete genetic complement, uncover substantial portions of known pathways, and suggest the function of previously unannotated genes. In addition, the combined networks are qualitatively different from the networks obtained using individual methods. They have a dominant cluster that contains approximately 80%-90% of the genes, independent of genome size, and the dominant clusters show the small world behavior expected of a biological system, with global connectivity that is nearly random, and local properties that are highly ordered.

Conclusions

When the information on functional linkage provided by three emerging computational methods is combined, the integrated network uncovers large numbers of conserved pathways and identifies clusters of functionally related genes. It therefore shows considerable utility and promise as a tool for understanding genomic structure, and for guiding high throughput experimental investigations.