A gene cluster refers to a set of functionally related genes that reside within close proximity of each other in the genome. For example, in prokaryotes, multiple genes can be organized into operons that are in-turn transcribed as polycistronic mRNA (e.g. the lac operon consisting of three genes, controlling lactose metabolism). In eukaryotes, the well-known Hox gene cluster that controls the morphology of the anterio-posterior body axis during embryonic development in metazoans is a good example. It is of enormous interest to biologists to examine the conservation of such "gene clusters" across multiple genomes.

Existing tools seem to rely heavily on pre-computing of orthologs for all gene families on a genome-wide scale. Such strategies suffers from two major limitations. Firstly, automating the task of distinguishing orthologs from paralogs is very difficult on a genome-wide scale. The second drawback is that genome-wide comparison is also very time consuming. As a result, when new genomes become available, users often have to wait for a long time until the computationally-intensive updates are finished.

CGCV is designed for biologists who are often only interested in studying a small set of gene clusters at a time and hence we find that there is no need to perform such expensive genome-wide comparisons. We have developed a web-based system that allows users to investigate their gene clusters against multiple genomes. On account of a small input data size, such searches can be done very quickly, thus enabling the users with the option to explore different search criteria.

The results are interactively visualized via our novel multi-genome browser which allows the manual inspection of the sequence similarity among the members of each gene family. Because our strategy employs absolutely no sort of pre-computing, the back-end of the system is very easy to maintain and keep up to date, thus providing the users with access to the latest sequence data.

The Tutorial page provides step-by-step instructions.

Please contact us at biohelp@cgb.indiana.edu for tech support.

Yes. You can download the source code from the Software page.