Abstract
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.
Highlights
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
False discovery rate is (FP/FP + TP), precision is 1- false discovery rate (FDR), and recall = TP/(TP + FN), where positives and negatives are defined in the reference sets. We demonstrate this approach with screens from the Toronto KnockOut (TKO) library in four cell lines: a patient-derived glioblastoma cell line (GBM, Fig. 2a), HCT116 colorectal carcinoma cell line (Fig. 2b), HeLa cervical carcinoma cell line (Fig. 2c), and RPE1 retinal pigmented epithelial cells (Fig. 2d) [15]
To maximize potential—and to avoid pitfalls similar to the costly false starts encountered in the RNA interference (RNAi) field—rigorous analytical methods must be applied that are able to effectively discriminate true hits from false positives
Summary
The adaptation of the CRISPR-Cas system to pooled library gene knockout screens in mammalian cells represents a major technological leap over RNA interference, the prior state of the art. The adaptation of CRISPR-Cas technology to pooled library gene knockout screens in mammalian cells allows the identification of genes whose knockout contributes to gene fitness [5,6,7,8,9]. Core essential genes were defined as those genes classified as hits in at half or more of the shRNA screens in [12] or [13], filtered for constitutive mRNA expression across a panel of cell lines, while nonessential
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