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- Title
BAGEL: a computational framework for identifying essential genes from pooled library screens.
- Authors
Hart, Traver; Moffat, Jason
- 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.
- Subjects
CRISPRS; GENE knockout; RNA interference; GENETICS; BIOLOGICAL reagents
- Publication
BMC Bioinformatics, 2016, Vol 17, p1
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/s12859-016-1015-8