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

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.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Hart T,Moffat J

doi

10.1186/s12859-016-1015-8

subject

Has Abstract

pub_date

2016-04-16 00:00:00

pages

164

issn

1471-2105

pii

10.1186/s12859-016-1015-8

journal_volume

17

pub_type

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