Abstract
BackgroundThe rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and scalability. However, the cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters.ResultsWe present CRISPulator, a Monte Carlo method-based computational tool that simulates the impact of screen parameters on the robustness of screen results, thereby enabling users to build intuition and insights that will inform their experimental strategy.CRISPulator enables the simulation of screens relying on either CRISPR interference (CRISPRi) or CRISPR nuclease (CRISPRn). Pooled screens based on cell growth/survival, as well as fluorescence-activated cell sorting according to fluorescent reporter phenotypes are supported. CRISPulator is freely available online (http://crispulator.ucsf.edu).ConclusionsCRISPulator facilitates the design of pooled genetic screens by enabling the exploration of a large space of experimental parameters in silico, rather than through costly experimental trial and error. We illustrate its power by deriving non-obvious rules for optimal screen design.
Highlights
The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPRbased genetic screens in a pooled format
CRISPulator simulates all steps of pooled screens, as visualized in Fig. 1 and explained in the Results section
Here, we present a Monte Carlo method-based computational tool, termed CRISPulator, which simulates how experimental parameters will affect the detection of different types of gene phenotypes in pooled CRISPR-based screens
Summary
The rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPRbased genetic screens in a pooled format. The cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters. Screens in mammalian cells were implemented primarily based on RNA interference (RNAi) technology. Inherent off-target effects of RNAi screens present a major challenge [1]. In principle, this problem can be overcome using optimized ultra-complex RNAi libraries [2, 3], but the resulting scale of the experiment in terms of the number of cells required to be screened can be prohibitive for some applications, such as screens in primary cells or mouse xenografts. Several platforms for mammalian cell screens have been implemented based on CRISPR technology [4]. CRISPR nuclease (CRISPRn) screens [5, 6] perturb gene function by targeting Cas nuclease programmed
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