Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier.

ABSTRACT:

Background

Identifying essential genes in genome-wide loss-of-function screens is a critical step in functional genomics and cancer target finding. We previously described the Bayesian Analysis of Gene Essentiality (BAGEL) algorithm for accurate classification of gene essentiality from short hairpin RNA and CRISPR/Cas9 genome-wide genetic screens.

Results

We introduce an updated version, BAGEL2, which employs an improved model that offers a greater dynamic range of Bayes Factors, enabling detection of tumor suppressor genes; a multi-target correction that reduces false positives from off-target CRISPR guide RNA; and the implementation of a cross-validation strategy that improves performance ~ 10× over the prior bootstrap resampling approach. We also describe a metric for scr

SUBMITTER: Kim E 

PROVIDER: S-EPMC7789424 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature