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BMC Bioinformatics. 2016 Apr 16;17:164. doi: 10.1186/s12859-016-1015-8.

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

Author information

1
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA. traver.hart@gmail.com.
2
Donnelly Centre, University of Toronto, Toronto, Canada.
3
Department of Molecular Genetics, University of Toronto, Toronto, Canada.

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.

KEYWORDS:

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

PMID:
27083490
PMCID:
PMC4833918
DOI:
10.1186/s12859-016-1015-8
[Indexed for MEDLINE]
Free PMC Article

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