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.