BACKGROUND: In order to understand how biological systems function it is 
necessary to determine the interactions and associations between proteins. Gene 
fusion prediction is one approach to detection of such functional relationships. 
Its use is however known to be problematic in higher eukaryotic genomes due to 
the presence of large homologous domain families. Here we introduce CODA 
(Co-Occurrence of Domains Analysis), a method to predict functional associations 
based on the gene fusion idiom.
METHODOLOGY/PRINCIPAL FINDINGS: We apply a novel scoring scheme which takes 
account of the genome-specific size of homologous domain families involved in 
fusion to improve accuracy in predicting functional associations. We show that 
CODA is able to accurately predict functional similarities in human with 
comparison to state-of-the-art methods and show that different methods can be 
complementary. CODA is used to produce evidence that a currently uncharacterised 
human protein may be involved in pathways related to depression and that another 
is involved in DNA replication.
CONCLUSIONS/SIGNIFICANCE: The relative performance of different gene fusion 
methodologies has not previously been explored. We find that they are largely 
complementary, with different methods being more or less appropriate in 
different genomes. Our method is the only one currently available for download 
and can be run on an arbitrary dataset by the user. The CODA software and 
datasets are freely available from 
ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/v6.1.0/CODA/. Predictions are also 
available via web services from http://funcnet.eu/.