Am I ready for CRISPR? A user's guide to genetic screens

Key Points

  • Pooled screens are a cost-effective approach to investigating phenotypes at the genome scale and build on technological innovations with lentivirus, oligonucleotide synthesis and massively parallel sequencing.

  • CRISPR technology is extremely powerful, with many modalities for perturbing gene function. A successful genetic screen, however, requires not only a good library of perturbations but also a relevant model and optimized assay.

  • When executing a screen, maintaining representation of the library is essential to good quantification. All steps in the process should be investigated experimentally in advance of the screen to ensure efficiency.

  • Designing a library of single-guide RNAs (sgRNAs) requires genomic information from multiple sources, which will change over time as the genome is better annotated and may vary depending on the type of cell under investigation.

  • Defining a systematic follow-up path, both analytically and experimentally, should be thought through before conducting a screen. Secondary pooled screens with customized libraries can be very powerful at this stage.


Exciting new technologies are often self-limiting in their rollout, as access to state-of-the-art instrumentation or the need for years of hands-on experience, for better or worse, ensures slow adoption by the community. CRISPR technology, however, presents the opposite dilemma, where the simplicity of the system enabled the parallel development of many applications, improvements and derivatives, and new users are now presented with an almost paralyzing abundance of choices. This Review intends to guide users through the process of applying CRISPR technology to their biological problems of interest, especially in the context of discovering gene function at scale.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 2: Diversity of activities with Cas9.
Figure 1: Maintaining representation in pooled screens.
Figure 3: Design considerations for CRISPR-based knockout.


  1. 1

    Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  2. 2

    Plenge, R. M. Disciplined approach to drug discovery and early development. Sci. Transl Med. 8, 349ps15 (2016).

  3. 3

    Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004).

  4. 4

    Westbrook, T. F. et al. A genetic screen for candidate tumor suppressors identifies REST. Cell 121, 837–848 (2005).

  5. 5

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

  6. 6

    Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343, 80–84 (2014).

  7. 7

    Koike-Yusa, H., Li, Y., Tan, E.-P., Velasco-Herrera, M. D. C. & Yusa, K. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32, 267–273 (2014). References 5–7 provide the first examples of the use of CRISPR technology for large-scale screens in mammalian cells.

  8. 8

    Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008).

  9. 9

    Wang, T., Lander, E. S. & Sabatini, D. M. Viral packaging and cell culture for CRISPR-based screens. Cold Spring Harb. Protoc. 2016, db.prot090811 (2016).

  10. 10

    Hartenian, E. & Doench, J. G. Genetic screens and functional genomics using CRISPR/Cas9 technology. FEBS J. 282, 1383–1393 (2015).

  11. 11

    Wang, T., Lander, E. S. & Sabatini, D. M. Single guide RNA library design and construction. Cold Spring Harb. Protoc. 2016, db.prot090803 (2016).

  12. 12

    Tan, J. & Martin, S. E. Validation of synthetic CRISPR reagents as a tool for arrayed functional genomic screening. PLoS ONE 11, e0168968 (2016).

  13. 13

    Esvelt, K. M. et al. Orthogonal Cas9 proteins for RNA-guided gene regulation and editing. Nat. Methods 10, 1116–1121 (2013).

  14. 14

    Hou, Z. et al. Efficient genome engineering in human pluripotent stem cells using Cas9 from Neisseria meningitidis. Proc. Natl Acad. Sci. USA 110, 15644–15649 (2013).

  15. 15

    Shmakov, S. et al. Discovery and functional characterization of diverse class 2 CRISPR-Cas systems. Mol. Cell 60, 385–397 (2015).

  16. 16

    Ran, F. A. et al. In vivo genome editing using Staphylococcus aureus Cas9. Nature 520, 186–191 (2015).

  17. 17

    Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771 (2015).

  18. 18

    Mohanraju, P. et al. Diverse evolutionary roots and mechanistic variations of the CRISPR-Cas systems. Science 353, aad5147 (2016).

  19. 19

    Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).

  20. 20

    Cheung, H. W. et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc. Natl Acad. Sci. USA 108, 12372–12377 (2011).

  21. 21

    Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903.e15 (2017).

  22. 22

    Tzelepis, K. et al. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016).

  23. 23

    Kryukov, G. V. et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351, 1214–1218 (2016).

  24. 24

    Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

  25. 25

    McDonald, E. R. et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170, 577–592.e10 (2017).

  26. 26

    Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

  27. 27

    Arroyo, J. D. et al. A genome-wide CRISPR death screen identifies genes essential for oxidative phosphorylation. Cell Metab. 24, 875–885 (2016).

  28. 28

    Zwang, Y. et al. Synergistic interactions with PI3K inhibition that induce apoptosis. eLife 6, e24523 (2017).

  29. 29

    Rotem, A. et al. Alternative to the soft-agar assay that permits high-throughput drug and genetic screens for cellular transformation. Proc. Natl Acad. Sci. USA 112, 5708–5713 (2015).

  30. 30

    Jain, I. H. et al. Hypoxia as a therapy for mitochondrial disease. Science 352, 54–61 (2016).

  31. 31

    Wang, B. et al. ATXN1L, CIC, and ETS transcription factors modulate sensitivity to MAPK pathway inhibition. Cell Rep. 18, 1543–1557 (2017).

  32. 32

    Krall, E. B. et al. KEAP1 loss modulates sensitivity to kinase targeted therapy in lung cancer. eLife 6, e18970 (2017).

  33. 33

    Vecchione, L. et al. A vulnerability of a subset of colon cancers with potential clinical utility. Cell 165, 317–330 (2016).

  34. 34

    Johannessen, C. M. et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504, 138–142 (2013).

  35. 35

    Orchard, R. C. et al. Discovery of a proteinaceous cellular receptor for a norovirus. Science 353, 933–936 (2016).

  36. 36

    Ma, H. et al. A CRISPR-based screen identifies genes essential for West-Nile-virus-induced cell death. Cell Rep. 12, 673–683 (2015).

  37. 37

    Blondel, C. J. et al. CRISPR/Cas9 screens reveal requirements for host cell sulfation and fucosylation in bacterial type III secretion system-mediated cytotoxicity. Cell Host Microbe 20, 226–237 (2016).

  38. 38

    Zhang, R. et al. A CRISPR screen defines a signal peptide processing pathway required by flaviviruses. Nature 535, 164–168 (2016).

  39. 39

    Marceau, C. D. et al. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens. Nature 535, 159–163 (2016).

  40. 40

    Deans, R. M. et al. Parallel shRNA and CRISPR-Cas9 screens enable antiviral drug target identification. Nat. Chem. Biol. 12, 361–366 (2016).

  41. 41

    Ma, Y. et al. CRISPR/Cas9 screens reveal Epstein-Barr virus-transformed B cell host dependency factors. Cell Host Microbe 21, 580–591.e7 (2017).

  42. 42

    Parnas, O. et al. A genome-wide CRISPR screen in primary immune cells to dissect regulatory networks. Cell 162, 675–686 (2015).

  43. 43

    Brockmann, M. et al. Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling. Nature 546, 307–311 (2017).

  44. 44

    Park, R. J. et al. A genome-wide CRISPR screen identifies a restricted set of HIV host dependency factors. Nat. Genet. 49, 193–203 (2017).

  45. 45

    Braun, C. J. et al. Versatile in vivo regulation of tumor phenotypes by dCas9-mediated transcriptional perturbation. Proc. Natl Acad. Sci. USA 113, E3892–E3900 (2016).

  46. 46

    Beronja, S. et al. RNAi screens in mice identify physiological regulators of oncogenic growth. Nature 501, 185–190 (2013).

  47. 47

    Schramek, D. et al. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 343, 309–313 (2014).

  48. 48

    Heckl, D. et al. Generation of mouse models of myeloid malignancy with combinatorial genetic lesions using CRISPR-Cas9 genome editing. Nat. Biotechnol. 32, 941–946 (2014).

  49. 49

    Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).

  50. 50

    Quintana, E. et al. Efficient tumour formation by single human melanoma cells. Nature 456, 593–598 (2008). This study provides a demonstration of the complex interplay between cell type and mouse background in the determination of xenograft efficiency.

  51. 51

    Godec, J. et al. Inducible RNAi in vivo reveals that the transcription factor BATF is required to initiate but not maintain CD8+ T-cell effector differentiation. Proc. Natl Acad. Sci. USA 112, 512–517 (2015).

  52. 52

    Bhang, H.-E. C. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

  53. 53

    Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

  54. 54

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

  55. 55

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

  56. 56

    Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896.e15 (2016). References 53–56 combine CRISPR screens with single-cell RNA sequencing readouts.

  57. 57

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

  58. 58

    Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

  59. 59

    Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

  60. 60

    Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

  61. 61

    Mali, P. et al. CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat. Biotechnol. 31, 833–838 (2013).

  62. 62

    Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

  63. 63

    Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014). This study presents the first use of dCas9 for genetic screens in mammalian cells.

  64. 64

    Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

  65. 65

    Rajagopal, N. et al. High-throughput mapping of regulatory DNA. Nat. Biotechnol. 34, 167–174 (2016).

  66. 66

    Vojta, A. et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 44, 5615–5628 (2016).

  67. 67

    Choudhury, S. R., Cui, Y., Lubecka, K., Stefanska, B. & Irudayaraj, J. CRISPR-dCas9 mediated TET1 targeting for selective DNA demethylation at BRCA1 promoter. Oncotarget 7, 46545–46556 (2016).

  68. 68

    Hilton, I. B. et al. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33, 510–517 (2015).

  69. 69

    Kearns, N. A. et al. Functional annotation of native enhancers with a Cas9–histone demethylase fusion. Nat. Methods 12, 401–403 (2015).

  70. 70

    Kwon, D. Y., Zhao, Y.-T., Lamonica, J. M. & Zhou, Z. Locus-specific histone deacetylation using a synthetic CRISPR-Cas9-based HDAC. Nat. Commun. 8, 15315 (2017).

  71. 71

    Polstein, L. R. et al. Genome-wide specificity of DNA binding, gene regulation, and chromatin remodeling by TALE- and CRISPR/Cas9-based transcriptional activators. Genome Res. 25, 1158–1169 (2015).

  72. 72

    Thakore, P. I. et al. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 1143–1149 (2015).

  73. 73

    Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016). This study presents the development of 'Base Editor' Cas9, which enables specific nucleotide changes without the need for double-stranded DNA breaks and homology-directed repair.

  74. 74

    Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016).

  75. 75

    Ma, Y. et al. Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells. Nat. Methods 13, 1029–1035 (2016).

  76. 76

    Kampmann, M. et al. Next-generation libraries for robust RNA interference-based genome-wide screens. Proc. Natl Acad. Sci. USA 112, E3384–E3391 (2015).

  77. 77

    Smith, I. et al. Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the connectivity map. Cold Spring Harb. Lab. (2017).

  78. 78

    Morgens, D. W., Deans, R. M., Li, A. & Bassik, M. C. Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes. Nat. Biotechnol. 34, 634–636 (2016).

  79. 79

    Evers, B. et al. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol. 34, 631–633 (2016).

  80. 80

    Anderson, E. M. et al. Experimental validation of the importance of seed complement frequency to siRNA specificity. RNA 14, 853–861 (2008).

  81. 81

    Singh, S. et al. Morphological profiles of RNAi-induced gene knockdown are highly reproducible but dominated by seed effects. PLoS ONE 10, e0131370 (2015).

  82. 82

    Berger, A. H. et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell 30, 214–228 (2016).

  83. 83

    Majithia, A. R. et al. Prospective functional classification of all possible missense variants in PPARG. Nat. Genet. 48, 1570–1575 (2016).

  84. 84

    Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34, 184–191 (2016). This study applies machine learning to sgRNA design.

  85. 85

    Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

  86. 86

    Horlbeck, M. A. et al. Nucleosomes impede Cas9 access to DNA in vivo and in vitro. eLife 5, e12677 (2016).

  87. 87

    Horlbeck, M. A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5, e19760 (2016).

  88. 88

    Read, A., Gao, S., Batchelor, E. & Luo, J. Flexible CRISPR library construction using parallel oligonucleotide retrieval. Nucleic Acids Res. 45, e101 (2017).

  89. 89

    Hough, S. H. et al. Guide Picker is a comprehensive design tool for visualizing and selecting guides for CRISPR experiments. BMC Bioinformatics 18, 167 (2017).

  90. 90

    Shi, J. et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015).

  91. 91

    Munoz, D. M. et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov. 6, 900–913 (2016).

  92. 92

    Rodriguez, J. M. et al. APPRIS: annotation of principal and alternative splice isoforms. Nucleic Acids Res. 41, D110–D117 (2013).

  93. 93

    Xu, H. et al. Sequence determinants of improved CRISPR sgRNA design. Genome Res. 25, 1147–1157 (2015).

  94. 94

    Haeussler, M. et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17, 148 (2016). This study presents a systematic comparison of on-target algorithms and off-target algorithms for designing sgRNAs.

  95. 95

    van Overbeek, M. et al. DNA repair profiling reveals nonrandom outcomes at Cas9-mediated breaks. Mol. Cell 63, 633–646 (2016).

  96. 96

    Bae, S., Kweon, J., Kim, H. S. & Kim, J.-S. Microhomology-based choice of Cas9 nuclease target sites. Nat. Methods 11, 705–706 (2014).

  97. 97

    Tsai, S. Q. & Joung, J. K. Defining and improving the genome-wide specificities of CRISPR-Cas9 nucleases. Nat. Rev. Genet. 17, 300–312 (2016).

  98. 98

    Listgarten, J. et al. Predicting off-target effects for end-to-end CRISPR guide design. Cold Spring Harb. Lab. (2017).

  99. 99

    Radzisheuskaya, A., Shlyueva, D., Müller, I. & Helin, K. Optimizing sgRNA position markedly improves the efficiency of CRISPR/dCas9-mediated transcriptional repression. Nucleic Acids Res. 44, e141 (2016).

  100. 100

    Lizio, M. et al. Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals. Nucleic Acids Res. 45, D737–D743 (2017).

  101. 101

    Graham, D. B. & Root, D. E. Resources for the design of CRISPR gene editing experiments. Genome Biol. 16, 260 (2015).

  102. 102

    Heigwer, F., Kerr, G. & Boutros, M. E-CRISP: fast CRISPR target site identification. Nat. Methods 11, 122–123 (2014).

  103. 103

    Meier, J. A., Zhang, F. & Sanjana, N. E. GUIDES: sgRNA design for loss-of-function screens. Nat. Methods 14, 831–832 (2017).

  104. 104

    Bae, S., Park, J. & Kim, J.-S. Cas-OFFinder: a fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 1473–1475 (2014).

  105. 105

    Fulco, C. P. et al. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science 354, 769–773 (2016).

  106. 106

    Canver, M. C. et al. Variant-aware saturating mutagenesis using multiple Cas9 nucleases identifies regulatory elements at trait-associated loci. Nat. Genet. 49, 625–634 (2017).

  107. 107

    Canver, M. C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).

  108. 108

    Liu, S. J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355, eaah7111 (2017).

  109. 109

    Winter, J. et al. caRpools: an R package for exploratory data analysis and documentation of pooled CRISPR/Cas9 screens. Bioinformatics 32, 632–634 (2016).

  110. 110

    Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

  111. 111

    Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 7, 2719–2727 (2017).

  112. 112

    Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733 (2014). This study presents a systematic catalogue of essential and nonessential genes for benchmarking screen performance.

  113. 113

    Donovan, K. F. et al. Creation of novel protein variants with CRISPR/Cas9-mediated mutagenesis: turning a screening by-product into a discovery tool. PLoS ONE 12, e0170445 (2017).

  114. 114

    Ipsaro, J. J. et al. Rapid generation of drug-resistance alleles at endogenous loci using CRISPR-Cas9 indel mutagenesis. PLoS ONE 12, e0172177 (2017).

  115. 115

    Aguirre, A. J. et al. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov. 6, 914–929 (2016).

  116. 116

    Rosenbluh, J. et al. Complementary information derived from CRISPR Cas9 mediated gene deletion and suppression. Nat. Commun. 8, 15403 (2017).

  117. 117

    Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

  118. 118

    Caskey, C. T. & Kruh, G. D. The HPRT locus. Cell 16, 1–9 (1979).

  119. 119

    Sagi, I. et al. Derivation and differentiation of haploid human embryonic stem cells. Nature 532, 107–111 (2016).

  120. 120

    Rauscher, B., Heigwer, F., Breinig, M., Winter, J. & Boutros, M. GenomeCRISPR — a database for high-throughput CRISPR/Cas9 screens. Nucleic Acids Res. 45, D679–D686 (2017).

  121. 121

    Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).

  122. 122

    Wong, A. S. L. et al. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc. Natl Acad. Sci. USA 113, 2544–2549 (2016).

  123. 123

    Shen, J. P. et al. Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 17, 10–19 (2017).

  124. 124

    Bassik, M. C. et al. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152, 909–922 (2013).

  125. 125

    Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

  126. 126

    van Leeuwen, J. et al. Exploring genetic suppression interactions on a global scale. Science 354, aag0839 (2016).

  127. 127

    Lu, Q. et al. Applications of CRISPR genome editing technology in drug target identification and validation. Expert Opin. Drug Discov. 12, 541–552 (2017).

  128. 128

    Schenone, M., Dancík, V., Wagner, B. K. & Clemons, P. A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).

  129. 129

    Chavez, A. et al. Comparison of Cas9 activators in multiple species. Nat. Methods 13, 563–567 (2016). This study systematically compares numerous CRISPRa approaches.

  130. 130

    Liu, X. S. et al. Editing DNA methylation in the mammalian genome. Cell 167, 233–247.e17 (2016).

  131. 131

    Laufer, B. I. & Singh, S. M. Strategies for precision modulation of gene expression by epigenome editing: an overview. Epigenet. Chromatin 8, 34 (2015).

  132. 132

    Tsai, S. Q. et al. GUIDE-seq enables genome-wide profiling of off-target cleavage by CRISPR-Cas nucleases. Nat. Biotechnol. 33, 187–197 (2015).

Download references


The author thanks K. Donovan for assistance in manuscript preparation, M. Hegde for assistance with analysis and the entire Genetic Perturbation Platform (GPP) at the Broad Institute. For insightful discussions, the author thanks J. Arroyo, O. Parnas and Z. Tothova (Broad Institute); J. Listgarten and N. Fusi (Microsoft Research); C.Wilen and R. Orchard (Washington University); J. Klappenbach (Merck); L. Brody (Desktop Genetics); and M. Fan (Addgene). This work is dedicated to Francis Edward Sheehan.

Author information

Correspondence to John G. Doench.

Ethics declarations

Competing interests

J.G.D. is a consultant for Tango Therapeutics.

Related links

PowerPoint slides

Supplementary information

Supplementary information S1 (box)

Description of Simulations from Figure 1 (DOC 29 kb)


Confirmation bias

The tendency to focus on information that confirms a pre-existing belief to the exclusion of contradictory information. In genetic screens, this can manifest in choosing to follow up a gene that scores with marginal statistical significance in the primary screen, rather than focusing on the experimentally identified top hits.

Single-guide RNAs

(sgRNAs). The first CRISPR systems characterized in prokaryotes required two RNAs to program the Cas9 protein: a CRISPR RNA (crRNA) and a transactivating crRNA (tracrRNA). To simplify the system, these two independent RNAs can instead be merged into a single transcript, the sgRNA, which has practical benefits especially for ease of expression in mammalian cells.


The titre of a lentivirus is the number of infectious particles per unit of volume, and the ratio of lentiviral integrants to cells is the multiplicity of infection (MOI). Importantly, cells differ in their inherent infectivity, and thus the volume of virus that is sufficient to achieve a given infection efficiency in cell type A is not necessarily the same in cell type B.


The transplantation of cells from one species to another. Often, it involves introducing human cancer cells into a mouse model to study their behaviour in complex microenvironments that are difficult to model in cell culture. Mice with an active immune system will recognize foreign cells and clear them out; thus, such experiments must be performed in immunodeficient mice.


Two genes that are produced by a gene duplication event and that, owing to their shared sequence, may have the same or similar functions. Thus, loss of one of them is often insufficient to manifest a phenotype, as the other paralogue can compensate.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Doench, J. Am I ready for CRISPR? A user's guide to genetic screens. Nat Rev Genet 19, 67–80 (2018).

Download citation

Further reading