Abstract
A new technique for analysis of data from synthetic genetic array and E-MAP technology generates high confidence quantitative epistasis scores.
Highlights
Genetic interactions, which describe the extent to which a mutation in one gene modulates the phenotype associated with altering a second gene, have long been used as a tool to investigate the relationship between pairs of genes participating in common or compensatory biological pathways [1,2]
Processing raw synthetic genetic array (SGA) data The utility of large-scale interaction data sets is highly dependent on the confidence that can be assigned to their results
The extra information contained in the varying strengths of genetic interactions may be extremely useful for teasing apart the organizational structure of the cell and for determining gene functions
Summary
Genetic (or epistatic) interactions, which describe the extent to which a mutation in one gene modulates the phenotype associated with altering a second gene, have long been used as a tool to investigate the relationship between pairs of genes participating in common or compensatory biological pathways [1,2]. It has become possible to expand the study of genetic interactions to a genomic scale [3,4,5,6,7], and these new approaches provide a previously unseen perspective of the functional organization of a cell. The structure of this network of genetic interactions contains information that will be critical for understanding cellular function, the interplay between genotypes and drug efficacy, as well as aspects of the process of evolution, such as the maintenance of sexual reproduction [8,9]. It is clear that the phenotypes associated with each individual
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