Abstract

CRISPR–Cas9 screens have emerged as a transformative approach to systematically probe gene functions. The quality and success of these screens depends on the frequencies of loss-of-function alleles, particularly in negative-selection screens widely applied for probing essential genes. Using optimized screening workflows, we performed essentialome screens in cancer cell lines and embryonic stem cells and achieved dropout efficiencies that could not be explained by common frameshift frequencies. We find that these superior effect sizes are mainly determined by the impact of in-frame mutations on protein function, which can be predicted based on amino acid composition and conservation. We integrate protein features into a ‘Bioscore’ and fuse it with improved predictors of single-guide RNA activity and indel formation to establish a score that captures all relevant processes in CRISPR–Cas9 mutagenesis. This Vienna Bioactivity CRISPR score (www.vbc-score.org) outperforms previous prediction tools and enables the selection of sgRNAs that effectively produce loss-of-function alleles.