While both the number and the diversity of sequenced prokaryotic genomes grow rapidly, the number of specific assignments of gene functions in the databases remains low and skewed toward the model prokaryote Escherichia coli. To aid in understanding the full set of newly sequenced genes, we created a computational model for assignment of function to prokaryotic genomes.
The result is an innovative framework for orthology and paralogy-aware phyletic profiling that provides a large number of computational annotations with high predictive accuracy in train/test evaluations. Our predictions include annotations for 1.3 million genes with the estimated Precision of 90%; these, and many more predictions for 998 prokaryotic genomes are freely available at http://gorbi.irb.hr/. More importantly, we show a proof of principle that our functional annotation model can be used to generate new biological hypotheses: we performed experiments on 38 E. coli knockout mutants and showed that our annotation model provides realistic estimates of predictive accuracy. With this, our work will contribute to making experimental validation of computational predictions more approachable, both in cost and time.
This article was published in PLOS Computational Biology.