Introduction

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.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.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.

Publications

  1. BAGEL: a computational framework for identifying essential genes from pooled library screens.
    Cite this
    Hart T, Moffat J, 2016-04-01 - BMC bioinformatics

Credits

  1. Traver Hart
    Developer

    Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, United States of America

  2. Jason Moffat
    Investigator

    Department of Molecular Genetics, University of Toronto, Canada

Community Ratings

UsabilityEfficiencyReliabilityRated By
0 user
Sign in to rate
Summary
AccessionBT000176
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionCanada
Submitted ByJason Moffat