Improved methods are needed to model CRISPR screen data for interrogation of genetic elements that alter reporter gene expression readout. We create MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target gene’s expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We demonstrate that MAUDE outperforms previous approaches and provide experimental design guidelines to best leverage MAUDE, which is available on https://github.com/Carldeboer/MAUDE .

中文翻译：

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MAUDE：推断基于分选的 CRISPR 筛选中的表达变化


需要改进的方法来模拟 CRISPR 筛选数据，以询问改变报告基因表达读数的遗传元件。我们创建了 MAUDE（使用离散表达的平均改变），用于在汇集的、基于排序的表达筛选中量化引导 RNA 对目标基因表达的影响。 MAUDE 通过对细胞在排序表达箱中的分布进行建模来量化指导水平效应。然后，它结合指南来估计目标遗传元素的统计显着性和效应大小。我们证明 MAUDE 优于以前的方法，并提供实验设计指南以最好地利用 MAUDE，该指南可在 https://github.com/Carldeboer/MAUDE 上找到。