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

BMC Bioinformatics

Software

Traver Hart¹  Jason Moffat² 

[1] Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA;Donnelly Centre, University of Toronto, Toronto, Canada;Department of Molecular Genetics, University of Toronto, Toronto, Canada;

关键词: CRISPR;    Genetic screens;    Cancer;    Essential genes;    Functional genomics;   

DOI  :  10.1186/s12859-016-1015-8

 received in 2015-11-26, accepted in 2016-04-06,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsWe 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.ConclusionsUsing 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.

【 授权许可】

CC BY   
© Hart and Moffat. 2016