Measuring error rates in genomic perturbation screens: gold standards for human functional genomics

• Published in: Molecular systems biology Vol. 10; no. 7; pp. 733 - n/a
• Main Authors: Hart, Traver, Brown, Kevin R, Sircoulomb, Fabrice, Rottapel, Robert, Moffat, Jason
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-07-2014; EMBO Press; Blackwell Publishing Ltd; Springer Nature
• Subjects: Analytical methods; Bayes Theorem; Bayesian analysis; Cancer; Cell growth; Cell Line, Tumor; Clustered Regularly Interspaced Short Palindromic Repeats; Computational Biology - methods; Computational Biology - standards; CRISPR; EMBO09; EMBO22; Error analysis; essential genes; Fitness; Gene expression; Genes; Genes, Essential; Genetic Fitness; Genetics; Genome, Human; Genomes; Genomics; Genotype & phenotype; Humans; Mammalian cells; Medical research; Models, Theoretical; Neoplasms - genetics; Organisms; Performance evaluation; Perturbation; Proteins; Reagents; Reference Standards; Reproductive fitness; RNA Interference; RNA-mediated interference; RNAi; Screens; Sensitivity and Specificity; shRNA; Software; Technology; CRISPR; RNAi; cancer; shRNA; essential genes
• ISSN: 1744-4292; 1744-4292
• DOI: 10.15252/msb.20145216

Technological advancement has opened the door to systematic genetics in mammalian cells. Genome‐scale loss‐of‐function screens can assay fitness defects induced by partial gene knockdown, using RNA interference, or complete gene knockout, using new CRISPR techniques. These screens can reveal the basic blueprint required for cellular proliferation. Moreover, comparing healthy to cancerous tissue can uncover genes that are essential only in the tumor; these genes are targets for the development of specific anticancer therapies. Unfortunately, progress in this field has been hampered by off‐target effects of perturbation reagents and poorly quantified error rates in large‐scale screens. To improve the quality of information derived from these screens, and to provide a framework for understanding the capabilities and limitations of CRISPR technology, we derive gold‐standard reference sets of essential and nonessential genes, and provide a Bayesian classifier of gene essentiality that outperforms current methods on both RNAi and CRISPR screens. Our results indicate that CRISPR technology is more sensitive than RNAi and that both techniques have nontrivial false discovery rates that can be mitigated by rigorous analytical methods.
Synopsis
This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens.
Gold‐standard reference sets of human essential and nonessential genes are leveraged to improve analyses of RNAi and CRISPR screens.
Characteristics of human essential genes are derived from the cumulative analysis of RNAi screens.
The Daisy model of gene essentiality is derived from the difference between core and context‐specific cell line essentials.
A computational framework is presented for the prediction of human essential genes from reverse genetic screening data.
Graphical Abstract
This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens.