BACKGROUND: The proportion of conserved DNA sequences with no clear function is 
steadily growing in bioinformatics databases. Studies of sequence and structural 
homology have indicated that many uncharacterized protein domain sequences are 
variants of functionally described domains. If these variants promote an 
organism's ecological fitness, they are likely to be conserved in the genome of 
its progeny and the population at large. The genetic composition of microbial 
communities in their native ecosystems is accessible through metagenomics. We 
hypothesize the co-variation of protein domain sequences across metagenomes from 
similar ecosystems will provide insights into their potential roles and aid 
further investigation.
METHODOLOGY/PRINCIPAL FINDINGS: We calculated the correlation of Pfam protein 
domain sequences across the Global Ocean Sampling metagenome collection, 
employing conservative detection and correlation thresholds to limit results to 
well-supported hits and associations. We then examined intercorrelations between 
domains of unknown function (DUFs) and domains involved in known metabolic 
pathways using network visualization and cluster-detection tools. We used a 
cautious "guilty-by-association" approach, referencing knowledge-level resources 
to identify and discuss associations that offer insight into DUF function. We 
observed numerous DUFs associated to photobiologically active domains and 
prevalent in the Cyanobacteria. Other clusters included DUFs associated with DNA 
maintenance and repair, inorganic nutrient metabolism, and sodium-translocating 
transport domains. We also observed a number of clusters reflecting known 
metabolic associations and cases that predicted functional reclassification of 
DUFs.
CONCLUSION/SIGNIFICANCE: Critically examining domain covariation across 
metagenomic datasets can grant new perspectives on the roles and associations of 
DUFs in an ecological setting. Targeted attempts at DUF characterization in the 
laboratory or in silico may draw from these insights and opportunities to 
discover new associations and corroborate existing ones will arise as more 
large-scale metagenomic datasets emerge.