Nucleic Acids Research Advance Access published online on March 11, 2007
Nucleic Acids Research, doi:10.1093/nar/gkl1114
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Computational Biology |
Hierarchical classification of functionally equivalent genes in prokaryotes
Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
*To whom correspondence should be addressed. Email: xyn{at}csbl.bmb.uga.edu
Received September 22, 2006. Revised November 15, 2006. Accepted December 6, 2006.
Functional classification of genes represents a fundamental problem to many biological studies. Most of the existing classification schemes are based on the concepts of homology and orthology, which were originally introduced to study gene evolution but might not be the most appropriate for gene function prediction, particularly at high resolution level. We have recently developed a scheme for hierarchical classification of genes (HCGs) in prokaryotes. In the HCG scheme, the functional equivalence relationships among genes are first assessed through a careful application of both sequence similarity and genomic neighborhood information; and genes are then classified into a hierarchical structure of clusters, where genes in each cluster are functionally equivalent at some resolution level, and the level of resolution goes higher as the clusters become increasingly smaller traveling down the hierarchy. The HCG scheme is validated through comparisons with the taxonomy of the prokaryotic genomes, Clusters of Orthologous Groups (COGs) of genes and the Pfam system. We have applied the HCG scheme to 224 complete prokaryotic genomes, and constructed a HCG database consisting of a forest of 5339 multi-level and 15 770 single-level trees of gene clusters covering 
93% of the genes of these 224 genomes. The validation results indicate that the HCG scheme not only captures the key features of the existing classification schemes but also provides a much richer organization of genes which can be used for functional prediction of genes at higher resolution and to help reveal evolutionary trace of the genes.
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.