Genetic Interaction Motif Finding by Expectation Maximization – a novel statistical framework for inferring gene modules from synthetic lethality
نویسندگان
چکیده
Synthetic lethality experiments identify pairs of genes with complementary function: two genes are synthetic lethal if each mutant is viable, but the double mutant combination is lethal. More direct functional associations may be inferred between genes that share synthetic lethal interaction partners than genes that are directly synthetic lethal. We describe an unsupervised algorithm, Genetic Interaction Motif Finding (GIMF), which uses probabilistic motifs to identify gene modules based on synthetic lethal interaction data. The dataset is obtained from SGA analysis in Saccharomyces cerevisiae [3], where a single mutant (query gene) is introduced into the complete pool of viable yeast single-deletion (library gene) strains.
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