Fig. 2.

The multi-targeting effect correction reduces false positives from off-targets with 1-bp mismatch. a, b Increment of Bayes Factors of multi-targeting gRNAs but targeting only a single protein-coding gene in comparison with Bayes Factor of gRNAs targeting the protein-coding gene without any other targets a before the multi-targeting effect correction and b after the multi-targeting effect correction. c The number of essential genes across good quality cell lines (F-measure > 0.85) in the Avana dataset predicted by BAGEL2 with or without CRISPRcleanR and other algorithms, CERES, MAGeCK, and JACKS with cut-off threshold BF 10, BF 7, score − 0.6, FDR 0.15, and p value 0.001, respectively. The cut-off threshold was aimed for obtaining similar numbers of essential genes. d The number false positives predicted by each algorithm. False positives were defined by non-expressed genes in RNA-seq data of corresponding cell lines. BAGEL2 after multi-targeting effect correction shows comparable results with CERES and much lower numbers than results of MAGeCK and JACKS. e The number of false positives in predicted essential genesets when the scope is limited to genes having gRNAs mapped over than five 1-bp mismatched targets that are likely from multi-targeting effects of 1-bp mismatched targets. The result of BAGEL2 after correction shows the best performance among algorithms