PT - JOURNAL ARTICLE AU - Traver Hart AU - Jason Moffat TI - BAGEL: A computational framework for identifying essential genes from pooled library screens AID - 10.1101/033068 DP - 2015 Jan 01 TA - bioRxiv PG - 033068 4099 - http://biorxiv.org/content/early/2015/11/27/033068.short 4100 - http://biorxiv.org/content/early/2015/11/27/033068.full AB - 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 ~2,000 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 with highly variable reagent quality.