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

Genome Res. 2019 Jun;29(6):999-1008. doi: 10.1101/gr.245571.118. Epub 2019 Apr 23.

Abstract

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 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 brings CRISPR screen analysis within reach for a wider community of researchers.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • CRISPR-Cas Systems*
  • Clustered Regularly Interspaced Short Palindromic Repeats*
  • Computational Biology* / methods
  • Computational Biology* / standards
  • Gene Editing
  • Gene Targeting
  • Genetic Association Studies / methods
  • Models, Statistical*
  • RNA, Guide, CRISPR-Cas Systems
  • Sensitivity and Specificity

Substances

  • RNA, Guide, CRISPR-Cas Systems