Klarman Cell Observatory, Broad Institute of MIT and Harvard University, Cambridge, MA, 02142, USA.
School of Biomedical Engineering, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
Genome Biol. 2020 Jun 3;21(1):134. doi: 10.1186/s13059-020-02046-8.
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.
需要改进方法来对 CRISPR 筛选数据进行建模，以研究改变报告基因表达读数的遗传元件。我们创建了 MAUDE（使用离散表达进行平均改变），用于量化向导 RNA 对基于池和分选的表达筛选中靶基因表达的影响。MAUDE 通过对分选表达箱中细胞分布建模来量化向导级别的影响。然后，它结合向导来估计靶向遗传元件的统计显著性和效应大小。我们证明 MAUDE 优于以前的方法，并提供了实验设计指南，以最大程度地利用 MAUDE，MAUDE 可在 https://github.com/Carldeboer/MAUDE 上获得。
