CRISPR/Cas9 knockout screens have been widely used to interrogate gene functions across a wide range of cell systems. However, the screening outcome is biased in amplified genomic regions, due to the ability of the Cas9 nuclease to induce multiple double-stranded breaks and strong DNA damage responses at these regions. We developed algorithms to correct biases associated with copy number variations (CNV), even when the CNV profiles are unknown. We demonstrated that our methods effectively reduced false positives in amplified regions while preserving signals of true positives. In addition, we developed a sliding window approach to estimate regions of high copy numbers for cases in which CNV information is not available. These copy number estimations can subsequently be used to effectively correct CNV-related biases in CRISPR screening experiments. Our approach is integrated into the existing MAGeCK/MAGeCK-VISPR analysis pipelines and provides a convenient framework to improve the precision of CRISPR screening results.
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