RT Journal Article
SR Electronic
T1 BAGEL: A computational framework for identifying essential genes from pooled library screens
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 033068
DO 10.1101/033068
A1 Traver Hart
A1 Jason Moffat
YR 2015
UL http://biorxiv.org/content/early/2015/11/27/033068.abstract
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.