We present a study on computational identification of uber-operons in a 
prokaryotic genome, each of which represents a group of operons that are 
evolutionarily or functionally associated through operons in other (reference) 
genomes. Uber-operons represent a rich set of footprints of operon evolution, 
whose full utilization could lead to new and more powerful tools for elucidation 
of biological pathways and networks than what operons have provided, and a 
better understanding of prokaryotic genome structures and evolution. Our 
prediction algorithm predicts uber-operons through identifying groups of 
functionally or transcriptionally related operons, whose gene sets are conserved 
across the target and multiple reference genomes. Using this algorithm, we have 
predicted uber-operons for each of a group of 91 genomes, using the other 90 
genomes as references. In particular, we predicted 158 uber-operons in 
Escherichia coli K12 covering 1830 genes, and found that many of the 
uber-operons correspond to parts of known regulons or biological pathways or are 
involved in highly related biological processes based on their Gene Ontology 
(GO) assignments. For some of the predicted uber-operons that are not parts of 
known regulons or pathways, our analyses indicate that their genes are highly 
likely to work together in the same biological processes, suggesting the 
possibility of new regulons and pathways. We believe that our uber-operon 
prediction provides a highly useful capability and a rich information source for 
elucidation of complex biological processes, such as pathways in microbes. All 
the prediction results are available at our Uber-Operon Database: 
http://csbl.bmb.uga.edu/uber, the first of its kind.