A benchmark of computational methods for correcting biases of established and unknown origin in CRISPR-Cas9 screening data.

ABSTRACT:

Background

CRISPR-Cas9 dropout screens are formidable tools for investigating biology with unprecedented precision and scale. However, biases in data lead to potential confounding effects on interpretation and compromise overall quality. The activity of Cas9 is influenced by structural features of the target site, including copy number amplifications (CN bias). More worryingly, proximal targeted loci tend to generate similar gene-independent responses to CRISPR-Cas9 targeting (proximity bias), possibly due to Cas9-induced whole chromosome-arm truncations or other genomic structural features and different chromatin accessibility levels.

Results

We benchmarked eight computational methods, rigorously evaluating their ability to reduce both CN and proximity bias in the two large

SUBMITTER: Vinceti A 

PROVIDER: S-EPMC11264729 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature