BAGEL: a computational framework for identifying essential genes from pooled library screens

BMC Bioinformatics. 2016 Apr 16;17:164. doi: 10.1186/s12859-016-1015-8.

Abstract

Background: The adaptation of the CRISPR-Cas9 system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. New methods for analyzing the data and evaluating results are needed.

Results: We offer BAGEL (Bayesian Analysis of Gene EssentiaLity), a supervised learning method for analyzing gene knockout screens. Coupled with gold-standard reference sets of essential and nonessential genes, BAGEL offers significantly greater sensitivity than current methods, while computational optimizations reduce runtime by an order of magnitude.

Conclusions: Using BAGEL, we identify ~2000 fitness genes in pooled library knockout screens in human cell lines at 5 % FDR, a major advance over competing platforms. BAGEL shows high sensitivity and specificity even across screens performed by different labs using different libraries and reagents.

Keywords: CRISPR; Cancer; Essential genes; Functional genomics; Genetic screens.

MeSH terms

  • Cell Line, Tumor
  • Computational Biology / methods*
  • Epithelial Cells / metabolism
  • Gene Knockout Techniques
  • Gene Library*
  • Genes, Essential*
  • Genetic Fitness
  • Glioblastoma / genetics
  • HCT116 Cells
  • HeLa Cells
  • Humans
  • Machine Learning
  • Models, Genetic
  • RNA Interference
  • Sensitivity and Specificity