MAGeCK is a method for identifying essential genes from genome-scale CRISPR/Cas9 knockout screens. It outperforms existing methods in controlling the false discovery rate (FDR) and sensitivity. MAGeCK identifies both positively and negatively selected genes and pathways, and is robust across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells with a BRAF mutation. It also detected cell type-specific essential genes, such as BCR and ABL1 in KBM7 cells with a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation. The CRISPR/Cas9 system allows for genome-wide knockout screens, but data analysis is challenging due to variability in sgRNA abundance and over-dispersion compared to Poisson models. MAGeCK uses a negative binomial model to estimate variance and test sgRNA abundance differences between treatments and controls. It ranks sgRNAs based on P-values and applies a modified robust ranking aggregation (RRA) algorithm to identify positively or negatively selected genes. MAGeCK also identifies enriched pathways by applying RRA to gene rankings. MAGeCK was tested against other methods, including edgeR, DESeq, RIGER, and RSA. It outperformed these methods in detecting significant sgRNAs and genes, with higher sensitivity and lower FDR. MAGeCK is robust to sequencing depth and number of sgRNAs per gene, and can identify cell type-specific essential genes. It also identified novel biologically relevant genes and pathways, such as CDH13 and PPT1 in the melanoma dataset. MAGeCK can perform both positive and negative selection screens and identify cell type-specific essential genes, making it a useful tool for CRISPR/Cas9 knockout screening. The method is also applicable to RNAi screens and can identify consensus hits between different screening technologies. MAGeCK is a reliable and robust method for identifying essential genes from CRISPR/Cas9 knockout screens.MAGeCK is a method for identifying essential genes from genome-scale CRISPR/Cas9 knockout screens. It outperforms existing methods in controlling the false discovery rate (FDR) and sensitivity. MAGeCK identifies both positively and negatively selected genes and pathways, and is robust across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells with a BRAF mutation. It also detected cell type-specific essential genes, such as BCR and ABL1 in KBM7 cells with a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation. The CRISPR/Cas9 system allows for genome-wide knockout screens, but data analysis is challenging due to variability in sgRNA abundance and over-dispersion compared to Poisson models. MAGeCK uses a negative binomial model to estimate variance and test sgRNA abundance differences between treatments and controls. It ranks sgRNAs based on P-values and applies a modified robust ranking aggregation (RRA) algorithm to identify positively or negatively selected genes. MAGeCK also identifies enriched pathways by applying RRA to gene rankings. MAGeCK was tested against other methods, including edgeR, DESeq, RIGER, and RSA. It outperformed these methods in detecting significant sgRNAs and genes, with higher sensitivity and lower FDR. MAGeCK is robust to sequencing depth and number of sgRNAs per gene, and can identify cell type-specific essential genes. It also identified novel biologically relevant genes and pathways, such as CDH13 and PPT1 in the melanoma dataset. MAGeCK can perform both positive and negative selection screens and identify cell type-specific essential genes, making it a useful tool for CRISPR/Cas9 knockout screening. The method is also applicable to RNAi screens and can identify consensus hits between different screening technologies. MAGeCK is a reliable and robust method for identifying essential genes from CRISPR/Cas9 knockout screens.