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

作者：Hart Traver^*; Moffat Jason

来源：BMC Bioinformatics, 2016, 17(1): 164.

DOI：10.1186/s12859-016-1015-8

• CRISPR
• Genetic screens
• Cancer
• Essential genes
• Functional genomics

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 similar to 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.

• 出版日期2016-4-16