Rxivist logo

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

By Traver Hart, Jason Moffat

Posted 27 Nov 2015
bioRxiv DOI: 10.1101/033068 (published DOI: 10.1186/s12859-016-1015-8)

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 ~2,000 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 with highly variable reagent quality.

Download data

  • Downloaded 932 times
  • Download rankings, all-time:
    • Site-wide: 18,883 out of 118,150
    • In systems biology: 410 out of 2,634
  • Year to date:
    • Site-wide: 104,663 out of 118,150
  • Since beginning of last month:
    • Site-wide: 86,958 out of 118,150

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News