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MAUDE: inferring expression changes in sorting-based CRISPR screens.

Carl G de Boer^1,2, John P Ray³, Nir Hacohen^4,3,5

¹Klarman Cell Observatory, Broad Institute of MIT and Harvard University, Cambridge, MA, 02142, USA. carl.deboer@ubc.ca.

|June 5, 2020

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This summary is machine-generated.

We developed MAUDE (Mean Alterations Using Discrete Expression), a new method to analyze CRISPR screen data. MAUDE accurately quantifies genetic element effects on gene expression, outperforming existing approaches.

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Accurate modeling of CRISPR screen data is crucial for understanding gene function.
Existing methods for analyzing pooled, sorting-based expression screens have limitations.

Purpose of the Study:

To introduce MAUDE (Mean Alterations Using Discrete Expression), a novel computational method for analyzing CRISPR screen data.
To quantify the impact of guide RNAs on target gene expression in pooled, sorting-based screens.

Main Methods:

MAUDE models the distribution of cells across expression bins to quantify guide-level effects.
It aggregates guide data to estimate the statistical significance and effect size of targeted genetic elements.

Main Results:

Keywords:

CRISPR/Cas9 Enhancers Gene expression Gene regulation Pooled screen R Transcriptional regulation

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MAUDE demonstrates superior performance compared to previous analytical approaches.
The method provides a robust framework for quantifying genetic element impacts on reporter gene expression.

Conclusions:

MAUDE offers an improved method for modeling CRISPR screen data, enhancing the interrogation of genetic elements.
The study provides guidelines for experimental design to optimize MAUDE's application and is available open-source.