Gene

Computational science

Negative binomial distribution

Engineering

Biology

Identification (biology)

Statistics

Poisson distribution

CRISPR

R package

Data mining

Computer science

Botany

Beta-binomial distribution

Computational biology

False positive rate

Genetics

Mathematics

Binomial distribution

Multiple comparisons problem

Electronic engineering

False discovery rate

Artificial intelligence

Sensitivity (control systems)

Genome Research

The simplicity and cost-effectiveness of CRISPR technology have made high-throughput pooled screening approaches accessible to virtually any laboratory. 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 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 CB 2 . Based on the beta-binomial distribution, which is better suited to sgRNA data, CB 2 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, CB 2 brings CRISPR screen analysis within reach for a wider community of researchers.

Beta-binomial modeling of CRISPR pooled screen data identifies target genes with greater sensitivity and fewer false negatives