title: Measuring error rates in genomic perturbation screens: gold standards for human functional genomics.
abstract: 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.
authors.affiliation: Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada.
authors.forename: Traver
authors.initials: T
authors.lastname: Hart
authors.affiliation: Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada.
authors.forename: Kevin R
authors.initials: KR
authors.lastname: Brown
authors.affiliation: Campbell Family Cancer Research Institute, Ontario Cancer Institute, Princess Margaret Hospital University Health Network, Toronto, ON, Canada.
authors.forename: Fabrice
authors.initials: F
authors.lastname: Sircoulomb
authors.affiliation: Campbell Family Cancer Research Institute, Ontario Cancer Institute, Princess Margaret Hospital University Health Network, Toronto, ON, Canada Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada Division of Rheumatology, Department of Medicine, St. Michael's Hospital, Toronto, ON, Canada.
authors.forename: Robert
authors.initials: R
authors.lastname: Rottapel
authors.affiliation: Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada j.moffat@utoronto.ca.
authors.forename: Jason
authors.initials: J
authors.lastname: Moffat
mesh_terms: D001499:Bayes Theorem; D045744:Cell Line, Tumor; D064112:Clustered Regularly Interspaced Short Palindromic Repeats; D019295:Computational Biology / Q000379:methods* / Q000592:standards*; D020043:Genes, Essential; D056084:Genetic Fitness; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics*; D034622:RNA Interference; D012015:Reference Standards; D012680:Sensitivity and Specificity; D012984:Software
keywords: CRISPR; RNAi; cancer; essential genes; shRNA