Mapping genetically compensatory pathways from synthetic lethal interactions in yeast.
BACKGROUND
Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as protein-protein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways.


RESULTS
In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties.


CONCLUSIONS
We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contains abundant compensatory properties.
Ma
Xiaotu
X
Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, California, United States of America. xma@usc.edu
Tarone
Aaron M
AM
Li
Wenyuan
W
D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D015966:Gene Expression Regulation, Fungal; D005800:Genes, Fungal; D005821:Genetic Techniques; D016681:Genome, Fungal; D006056:Golgi Apparatus / Q000378:metabolism; D008869:Microtubule-Associated Proteins; D008957:Models, Genetic; D015233:Models, Statistical; D025941:Protein Interaction Mapping; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D015003:Yeasts
D008869:Microtubule-Associated Proteins; D029701:Saccharomyces cerevisiae Proteins