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.
Original language | English (US) |
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Article number | 164 |
Journal | BMC bioinformatics |
Volume | 17 |
Issue number | 1 |
DOIs | |
State | Published - Apr 16 2016 |
Keywords
- CRISPR
- Cancer
- Essential genes
- Functional genomics
- Genetic screens
ASJC Scopus subject areas
- Structural Biology
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
MD Anderson CCSG core facilities
- Bioinformatics Shared Resource