MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

Li, Wei; Han, Xu; Xiao, Tengfei; Cong, Le; Love, Michael I.; Zhang, Feng; Irizarry, Rafael A.; Liu, Jun S.; Brown, Myles; Liu, X Shirley

doi:10.1186/s13059-014-0554-4

Cited by 1,710 publications

(1,351 citation statements)

References 36 publications

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“…In each screen, cells were treated with either DMSO or 30 nM gefitinib for ∼17 d. This intermediate concentration of gefitinib was not completely lethal but caused a significant lengthening of the doubling time of PC9 cells, allowing more subtle suppressors of reduced EGFR function to be detected. We used the MAGeCK (model-based analysis of genomewide CRISPR-Cas9 knockout) scoring algorithm (Li et al 2014) to rank the performance of individual genes.…”

Section: Resultsmentioning

confidence: 99%

“…The MAGeCK scoring algorithm (Li et al 2014) was used to rank the performance of individual genes based on enrichment, comparing the gefitinib treatment group with the DMSO treatment group. The negative controls were incorporated in the MAGeCk analysis to generate null distributions and calculate the P-value and FDR for each gene.…”

Section: Crispr and Shrna Screenmentioning

confidence: 99%

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A genetic interaction analysis identifies cancer drivers that modify EGFR dependency

et al. 2017

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A large number of cancer drivers have been identified through tumor sequencing efforts, but how they interact and the degree to which they can substitute for each other have not been systematically explored. To comprehensively investigate how cancer drivers genetically interact, we searched for modifiers of epidermal growth factor receptor (EGFR) dependency by performing CRISPR, shRNA, and expression screens in a non-small cell lung cancer (NSCLC) model. We elucidated a broad spectrum of tumor suppressor genes (TSGs) and oncogenes (OGs) that can genetically modify proliferation and survival of cancer cells when EGFR signaling is altered. These include genes already known to mediate EGFR inhibitor resistance as well as many TSGs not previously connected to EGFR and whose biological functions in tumorigenesis are not well understood. We show that mutation of PBRM1, a subunit of the SWI/SNF complex, attenuates the effects of EGFR inhibition in part by sustaining AKT signaling. We also show that mutation of Capicua (CIC), a transcriptional repressor, suppresses the effects of EGFR inhibition by partially restoring the EGFR-promoted gene expression program, including the sustained expression of Ets transcription factors such as ETV1. Together, our data provide strong support for the hypothesis that many cancer drivers can substitute for each other in certain contexts and broaden our understanding of EGFR regulation.

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Section: Resultsmentioning

confidence: 99%

Section: Crispr and Shrna Screenmentioning

confidence: 99%

A genetic interaction analysis identifies cancer drivers that modify EGFR dependency

et al. 2017

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“…highscoring) shRNAs or of all shRNAs targeting a specific gene are combined. Several algorithms have been proposed to select or combine shRNA-level evidence, including choosing the second best or most depleted shRNA (Luo et al, 2008) (RIGER_SB), averaging the two shRNAs that produced the largest scores (Luo et al, 2008) (RIGER_WS), performing enrichment analysis of all shRNAs targeting one gene against all shRNAs in the library (Luo et al, 2008) (RIGER_KS), comparing rank distributions of effective size of all shRNAs per gene (Konig et al, 2007) (RSA) and more recent modelbased MAGeCK (Li et al, 2014) and HitSelect (Diaz et al, 2015). An intrinsic limitation with all of these approaches is that they rely on the accurate assessment of an individual sh/sgRNA activity, which is difficult to achieve in large-scale screens that typically have a relatively small number of replicate samples.…”

Section: Introductionmentioning

confidence: 99%

ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling

Silva

Califano

2015

Bioinformatics

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Motivation: Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives. Indeed, rigorous statistical analysis of high-throughput FG screening data remains challenging, particularly when integrative analyses are used to combine multiple sh/sgRNAs targeting the same gene in the library. Method: We use large RNAi and CRISPR repositories that are publicly available to evaluate a novel meta-analysis approach for FG screens via Bayesian hierarchical modeling, Screening Bayesian Evaluation and Analysis Method (ScreenBEAM). Results: Results from our analysis show that the proposed strategy, which seamlessly combines all available data, robustly outperforms classical algorithms developed for microarray data sets as well as recent approaches designed for next generation sequencing technologies. Remarkably, the ScreenBEAM algorithm works well even when the quality of FG screens is relatively low, which accounts for about 80-95% of the public datasets. Availability and implementation: R package and source code are available at: https://github.com/

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“…The source code of MAGeCK is freely available at [40] under the 3-clause Berkeley Software Distribution (BSD) open-source license.…”

Section: Availabilitymentioning

confidence: 99%

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

et al. 2014

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We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation.

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MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

Cited by 1,710 publications

References 36 publications

A genetic interaction analysis identifies cancer drivers that modify EGFR dependency

A genetic interaction analysis identifies cancer drivers that modify EGFR dependency

ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

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