@article{Buchumenski19102021,
author = {Buchumenski, Ilana and Roth, Shalom Hillel and Kopel, Eli and Katsman, Efrat and Feiglin, Ariel and Levanon, Erez Y and Eisenberg, Eli}, 
title = {Global quantification exposes abundant low-level off-target activity by base editors},
year = {2021}, 
doi = {10.1101/gr.275770.121}, 
elocation-id = {gr.275770.121}, 
abstract ={Base editors are dedicated engineered deaminases that enable directed conversion of specific bases in the genome or transcriptome in a precise and efficient manner, and hold promise for correcting pathogenic mutations. A major concern limiting application of this powerful approach is the issue of off-target edits. Several recent studies have shown substantial off-target RNA activity induced by base editors and demonstrated that off-target mutations may be suppressed by improved deaminases versions or optimized guide RNAs. Here we describe a new class of off-target events that are invisible to the established methods for detection of genomic variations, and were thus far overlooked.  We show that much of the off-target activity of the deaminases is nonspecific, seemingly stochastic, affecting a large number of sites throughout the genome or the transcriptome and accounting for the majority of off-target activity. We develop and employ a different, complementary, approach that is sensitive to the stochastic off-targets activity, and use it to quantify the abundant off-target RNA mutations due to current optimized deaminase editors. Engineered base editors enable directed manipulation of the genome or transcriptome at single-base resolution. We believe that implementation of this computational approach would facilitate design of more specific base editors.  We provide a computational tool to quantify global off-target activity, which can be used to optimize future base editors.}, 
URL = {http://genome.cshlp.org/content/early/2021/10/18/gr.275770.121.abstract}, 
eprint = {http://genome.cshlp.org/content/early/2021/10/18/gr.275770.121.full.pdf+html}, 
journal = {Genome Research} 
}