Information-based methods for predicting gene function from systematic gene knock-downs

The rapid annotation of genes on a genome-wide scale is now possible for several organisms using high-throughput RNA interference assays to knock down the expression of a specific gene. To date, dozens of RNA interference phenotypes have been recorded for the nematode Caenorhabditis elegans. Although previous studies have demonstrated the merit of using knock-down phenotypes to predict gene function, it is unclear how the data can be used most effectively. An open question is how to optimally make use of phenotypic observations, possibly in combination with other functional genomics datasets, to identify genes that share a common role.

We compared several methods for detecting gene-gene functional similarity from phenotypic knock-down profiles. We found that information-based measures, which explicitly incorporate a phenotype's genomic frequency when calculating gene-gene similarity, outperform non-information-based methods. We report the presence of newly predicted modules identified from an integrated functional network containing phenotypic congruency links derived from an information-based measure. One such module is a set of genes predicted to play a role in regulating body morphology based on their multiply-supported interactions with members of the TGF-beta signaling pathway.

Information-based metrics significantly improve the comparison of phenotypic knock-down profiles, based upon their ability to enhance gene function prediction and identify novel functional modules.