Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives
Abstract
The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches accessible to virtually any lab. Analyzing the large sequencing data derived from these studies, however, still demands considerable bioinformatics expertise. Various methods have been developed to lessen this requirement, but there are still three tasks for accurate CRISPR screen analysis that involve bioinformatic know-how if not prowess: designing a proper statistical hypothesis test for robust target identification, developing an accurate mapping algorithm to quantify sgRNA levels, and minimizing the parameters necessary that need to be fine-tuned. To make CRISPR screen analysis more reliable as well as more readily accessible, we have developed a new algorithm, called CRISPRBetaBinomial or CB2 (https://CRAN.R-project.org/package=CB2). Based on the beta-binomial distribution, which is better suited to sgRNA data, CB2 outperforms the eight most commonly used methods (HiTSelect, MAGeCK, PBNPA, PinAPL-Py, RIGER, RSA, ScreenBEAM, and sgRSEA) in both accurately quantifying sgRNAs and identifying target genes, with greater sensitivity and a much lower false discovery rate. It also accommodates staggered sgRNA sequences. In conjunction with CRISPRcloud, CB2 will bring CRISPR screen analysis within reach for a wider community of researchers.
- Received October 23, 2018.
- Accepted April 3, 2019.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International license), as described at http://creativecommons.org/licenses/by-nc/4.0/.
