Abstract
Systems for CRISPR-based combinatorial perturbation of two or more genes are emerging as powerful tools for uncovering genetic interactions. However, systematic identification of these relationships is complicated by sample, reagent, and biological variability. We develop a variational Bayes approach (GEMINI) that jointly analyzes all samples and reagents to identify genetic interactions in pairwise knockout screens. The improved accuracy and scalability of GEMINI enables the systematic analysis of combinatorial CRISPR knockout screens, regardless of design and dimension. GEMINI is available as an open source R package on GitHub at https://github.com/sellerslab/gemini.
Highlights
A genetic interaction can be defined as a combination of genetic alterations that leads to an unforeseen loss or gain of cell viability [1]
We demonstrate a significant improvement over current methods in identifying genetic interactions and show broad applicability to a variety of combinatorial CRISPR knockout screens
Our findings indicate that GEMINI is applicable to various screen formats and enables a systematic integration of combinatorial CRISPR knockout screens for the discovery of novel genetic interactions
Summary
A genetic interaction can be defined as a combination of genetic alterations that leads to an unforeseen loss or gain of cell viability [1]. Genetic interactions have been leveraged to treat a variety of cancer subtypes in humans [5]. In BRAF mutant cancers, combined inhibition of BRAF with other members of the MAP kinase family, such as MAPK1-MAPK3 and MAP2K1-MAP2K2, overcomes emergent mechanisms of resistance [7]. Genetic interactions have been systematically characterized in yeast, comprehensive studies in human cells have not been feasible until recently [8]. This is in part due to the large number of gene combinations (roughly 10 times larger than yeast), functional
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