In Fig. 4, this is shown for a selected set of well-studied model organisms, from yeast to mouse.
Although this approach can be used to generally classify genes, it lacks the necessary flexibility to compare specific pathways of interest in single organisms or groups of organisms to a selected set of genes.
By combining a whole-organism approach using C. elegans with complementary experimental work done on human cell lines, this analysis extends currently available information on the selected set of genes.
Until recently, the application of microarrays has largely focused on studies from a select set of organisms (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus) that constitute some of the most well characterized laboratory models in the life sciences.
We have attempted to assuage this issue by selecting a representative set of organisms related to the most commonly analyzed mammals.
We selected datasets for a diverse set of organisms to explore the impact of genome complexity and repeat content on error correction performance, from short genomes (E scherichia coli, P seudomonas syringae) to longer and more complex ones (D rosophila melanogaster, H omo sapiens).
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The utility of this approach was validated by comparing the distribution of Receiver Operating Characteristic (ROC) scores of our intelligently selected subsets to random sets of organisms, as well as to sets of organisms selected using maximal mutual information (data not shown).
In order to identify the set of organisms in which the retention of a gene is most predictive of essentiality, we first selected a small group of organisms likely to be predictive for the E. coli essential list, and then computed the accuracy for all subsets of this reduced set.
