CRISPR Screen: Comparison
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Genome-wide CRISPR/Cas9 screen provides a robust and unbiased means for interrogating such genes, and a series of landmark reports since its introduction in 2014 have demonstrated that the technology yields high-quality functional hits. This technology, in combination with other orthogonal methods for studying protein function on a systems scale, can provide valuable functional insights that would take years to establish using conventional methods.

  • CRISPR screen
  • CRISPR/Cas9
  • Methodology

1. Introduction

Prostate cancer (PCa) accounts for 1.6 million cases and 366,000 deaths worldwide each year [1]. Within the USA, PCa is a common cancer and the second leading cause of cancer-related deaths among men [2]. Androgens are critical factors that promote the growth of PCa cells, and androgen-deprivation therapy (ADT) is the mainstay of treatment for men with metastatic PCa [3]. While initially effective, patients with metastatic PCa receiving ADT will eventually develop resistance and progress to inevitably lethal metastatic castration-resistant prostate cancer (mCRPC) [4]. The last decade has seen an expansion of drugs to prolong the life of mCRPC, including second-generation androgen receptor (AR) inhibitors, the chemotherapeutic taxane cabazitaxel and PARP inhibitors for tumors with defects in DNA-damage-repair proteins such as BRCA1/2 and ATM [5,6,7,8,9,10,11][5][6][7][8][9][10][11]. However, identifying new therapeutic targets for mCRPC patients and providing new genetic markers for existing therapies remain a critical challenge.
Recent advances in genome editing technology using the clustered regularly interspaced short palindromic repeats/Cas9 (CRISPR/Cas9) [12] have provided a robust and unbiased tool for conducting genetic screens to study biological systems in a genome-wide manner, which is ideal for the identification of target genes. Prior to the CRISPR/Cas9, the functional genetic screen employed RNA interference (RNAi) oligonucleotides for research on loss-of-function and cDNA overexpression libraries for research on the gain of function [13,14,15,16][13][14][15][16]. However, the construction of cDNA overexpression libraries is challenging. Furthermore, a side-by-side comparison with RNAi knockdown analysis revealed the advantages of functional genomic KO screen using CRISPR/Cas9 [17]. Accordingly, a number of wild-type (WT) Cas9-based CRISPR-KO screens have been performed to date. In more recent years, an increasing number of studies have also utilized mutants of catalytically dead Cas9 (dCas9), in which the nuclease of WT Cas9 is mutated to render it non-functional [18,19][18][19]. dCas9 has been fused to a range of chromatin modifier fusion proteins to convert it into a highly versatile enzyme that can be used to perform CRISPR activation (CRISPRa) or repressive CRISPR interference (CRISPRi) screens [19,20][19][20].

2. Methodology of CRISPR Screen

2.1. Library

Several pooled libraries for KO screen are available from browsing source Addgene, e.g., GeCKO, H1/H2, Brunello and TKO CRISPR-KO libraries [21,22,23,24][21][22][23][24]. These libraries contain over 18,000 genes with 4–10 single guide RNAs (sgRNA) per gene. Similarly, CRISPRa and SAM libraries for activation screen and CRISPRi libraries for repression screen are also shared [25,26,27][25][26][27]. Custom libraries are useful for specific studies of interest [28].

2.2. Viral Packaging of Library and Transduction

The first step in pooled screen using the CRISPR/Cas9 system is to generate a library of perturbed cells with lentiviral infection of an sgRNA library. Viruses are produced by transfecting an sgRNA library into appropriate host cells, e.g., HEK 293FT cells with superior virus production capacity. To avoid confusion in interpretation in case the host cells take up multiple sgRNAs and target multiple genes per cell, low (~0.3) multiplicity of infection (MOI) is ensured by empirically determining the viral titer [27,29,30][27][29][30]. It is important to note the limitation in this step—some models, such as NCI-H660 and VCAP cells of PCa, make it quite challenging to implement tools through lentiviral approaches.

2.3. Viability-Based Screens

One of the most basic experiments to conduct is to identify genes that impact cell fitness. Since perturbations that decrease the cell fitness will be either depleted or completely absent by the end of the screen, this type of screen is termed a negative selection screen. Negative selection screens are most commonly performed in the field of cancer biology to identify dependencies of tumor cells due to specific mutations, copy number alterations, expression patterns and other targets [24,31,32,33,34][24][31][32][33][34]. One of the simplest forms of negative selection screen is to continuously culture cells for extended periods of time in order to identify genes required for cell growth. Such screens have been used to identify both the essential genes required for the cell line tested and a small set of genes that are gene-dependent in a particular cancer cell line [35,36,37][35][36][37]. Another negative selection screen form, performed in cell lines with a given genetic background, is the basis for identifying synthetic lethal interactions, in which simultaneous inhibition of two genes impairs cell viability [38]. The discovery of synthetic interactions will enable targeted therapy of cancer cells, making drugs work only on cells with specific alterations, and can offer a new approach to cancer treatment.
The alternative to the negative selection screen is the positive selection screen, which focuses on cells that have been enriched in the course of time. These screens have been used to identify perturbations which confer resistance to small molecules [14[14][17][39],17,39], conditions [40] and pathogen infections [41,42,43,44,45][41][42][43][44][45]. In a positive selection screen, most of the population is eliminated, and the few surviving perturbations may become over 100-fold enriched. Eventually, a single screen can result in both positively and negatively selected phenotypes. For example, a viability-based KO screen of a cancer cell line can reveal both oncogene depletion and enrichment of tumor suppressors. Typically, an intermediate dose of a small molecule can identify both sensitization genes and resistance genes [46].

2.4. Marker Selection Screen

The marker selection screen aims to identify genetic elements which affect the expression of a particular reporter molecule, and the phenotype is not based on cell viability but on mutations that impact the expression of the marker protein. In this type of screen, the reporter can be genetically engineered by replacing the coding sequence of the gene of interest with the fluorescent marker. Ultimately, fluorescence-activated cell sorting (FACS) allows for the identification of upstream expression regulators by sorting cells with sgRNAs targeting genes which affect the expression of the marker [27,47][27][47].

2.5. Analysis (Algorithms)

Following the selection step, DNA is collected from surviving cells or FACS-selected cells, and PCR is employed to isolate genomic DNA from the cell population and read the genes responsible for the phenotype. Subsequently, large-scale parallel sequencing is performed using next-generation sequencing (NGS) to cover the regions coding for sgRNAs. Several existing algorithms such as Model-based Analysis of Genome-wide CRISPR/Cas9 KO (MAGeCK) [48,49[48][49][50],50], edgeR [51], Bayesian Analysis of Gene EssentiaLity (BAGEL) [52], CRISPR AnalyzeR for Pooled Screens (caRpools) [53], Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py) [54] and DrugZ [55] can then be used to determine the candidate genes responsible for the observed phenotypes through examining the differences in sgRNA abundance between control and phenotypic samples.

2.6. Validation

Analysis of the screen provides a ranked list of candidate genes causing the phenotype. In order to assess which genes contribute to the phenotype and how much, validation is essential. The most critical validation method is to assess whether the selected phenotype is indeed reproducible by introducing sgRNAs targeting the gene of interest. Recent improvements in the specificity of sgRNAs have relieved the need to confirm binding to the target, as long as multiple sgRNAs directed to the same genetic element elicit the phenotype. However, if required, analyses such as genomic PCR, RT-qPCR and Western blotting can be performed to evaluate the functional modification of the targeted gene [25,27][25][27]. It is important to note that the confirmation of on-target activity does not exclude the possibility of phenotyping due to off-target effects. Hence, continuous validation experiments of phenotypes using not only one sgRNA with the highest on-target activity but also multiple sgRNAs for each gene are critical. Rescue experiments are another way to confirm whether the genetic entity confers the phenotype. The goal is to confirm whether restoring candidate expression to physiological levels in CRISPR/Cas9-edited cells returns the cells to their wild-type state [56].

References

  1. Pernar, C.H.; Ebot, E.M.; Wilson, K.M.; Mucci, L.A. The epidemiology of prostate cancer. Cold Spring Harb. Perspect. Med. 2018, 8, a030361.
  2. Siegel, R.L.; Miller, K.D.; Goding Sauer, A.; Fedewa, S.A.; Butterly, L.F.; Anderson, J.C.; Cercek, A.; Smith, R.A.; Jemal, A. Colorectal cancer statistics, 2020. Cancer J. Clin. 2020, 70, 145–164.
  3. Massie, C.; Lynch, A.; Ramos-Montoya, A.; Boren, J.; Stark, R.; Fazli, L.; Warren, A.; Scott, H.; Madhu, B.; Sharma, N.; et al. The androgen receptor fuels prostate cancer by regulating central metabolism and biosynthesis. EMBO J. 2011, 30, 2719–2733.
  4. Attard, G.; Parker, C.; Eeles, R.A.; Schröder, F.; Tomlins, S.A.; Tannock, I.; Drake, C.G.; de Bono, J.S. Prostate cancer. Lancet 2016, 387, 70–82.
  5. Komura, K.; Sweeney, C.J.; Inamoto, T.; Ibuki, N.; Azuma, H.; Kantoff, P.W. Current treatment strategies for advanced prostate cancer. Int. J. Urol. 2018, 25, 220–231.
  6. Nuhn, P.; De Bono, J.S.; Fizazi, K.; Freedland, S.J.; Grilli, M.; Kantoff, P.W.; Sonpavde, G.; Sternberg, C.N.; Yegnasubramanian, S.; Antonarakis, E.S. Update on Systemic Prostate Cancer Therapies: Management of Metastatic Castration-resistant Prostate Cancer in the Era of Precision Oncology. Eur. Urol. 2019, 75, 88–99.
  7. de Wit, R.; de Bono, J.; Sternberg, C.N.; Fizazi, K.; Tombal, B.; Wülfing, C.; Kramer, G.; Eymard, J.-C.; Bamias, A.; Carles, J.; et al. Cabazitaxel versus Abiraterone or Enzalutamide in Metastatic Prostate Cancer. N. Engl. J. Med. 2019, 381, 2506–2518.
  8. Abida, W.; Patnaik, A.; Campbell, D.; Shapiro, J.; Bryce, A.H.; McDermott, R.; Sautois, B.; Vogelzang, N.J.; Bambury, R.M.; Voog, E.; et al. Rucaparib in Men with Metastatic Castration-Resistant Prostate Cancer Harboring a BRCA1 or BRCA2 Gene Alteration. J. Clin. Oncol. 2020, 38, 3763–3772.
  9. de Bono, J.; Mateo, J.; Fizazi, K.; Saad, F.; Shore, N.; Sandhu, S.; Chi, K.N.; Sartor, O.; Agarwal, N.; Olmos, D.; et al. Olaparib for Metastatic Castration-Resistant Prostate Cancer. N. Engl. J. Med. 2020, 382, 2091–2102.
  10. Stopsack, K.H. Efficacy of PARP Inhibition in Metastatic Castration-resistant Prostate Cancer is Very Different with Non-BRCA DNA Repair Alterations: Reconstructing Prespecified Endpoints for Cohort B from the Phase 3 PROfound Trial of Olaparib. Eur. Urol. 2021, 79, 442–445.
  11. de Bono, J.S.; Mehra, N.; Scagliotti, G.V.; Castro, E.; Dorff, T.; Stirling, A.; Stenzl, A.; Fleming, M.T.; Higano, C.S.; Fizazi, K.; et al. Talazoparib monotherapy in metastatic castration-resistant prostate cancer with DNA repair alterations (TALAPRO-1): An open-label, phase 2 trial. Lancet Oncol. 2021, 22, 1250–1264.
  12. Doudna, J.A.; Charpentier, E. Genome editing. The new frontier of genome engineering with CRISPR-Cas9. Science 2014, 346, 1258096.
  13. Berns, K.; Hijmans, M.; Mullenders, J.; Brummelkamp, T.R.; Velds, A.; Heimerikx, M.; Kerkhoven, R.M.; Madiredjo, M.; Nijkamp, W.; Weigelt, B.; et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nat. Cell Biol. 2004, 428, 431–437.
  14. Johannessen, C.M.; Johnson, L.A.; Piccioni, F.; Townes, A.; Frederick, D.T.; Donahue, M.K.; Narayan, R.; Flaherty, K.T.; Wargo, J.A.; Root, D.E.; et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nat. Cell Biol. 2013, 504, 138–142.
  15. Li, W.; Ai, N.; Wang, S.; Bhattacharya, N.; Vrbanac, V.; Collins, M.; Signoretti, S.; Hu, Y.; Boyce, F.M.; Gravdal, K.; et al. GRK3 is essential for metastatic cells and promotes prostate tumor progression. Proc. Natl. Acad. Sci. USA 2014, 111, 1521–1526.
  16. Abdulkadir, S.; Meer, R.V.; Roh, M. Abstract 5185: RNAi screen identifies a synthetic lethal interaction between Pim1 overexpression and Plk1 inhibition. Mol. Cell. Biol. 2013, 20, 3211–3221.
  17. Shalem, O.; Sanjana, N.E.; Hartenian, E.; Shi, X.; Scott, D.A.; Mikkelsen, T.S.; Heckl, D.; Ebert, B.L.; Root, D.E.; Doench, J.G.; et al. Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells. Science 2014, 343, 84–87.
  18. Gilbert, L.; Larson, M.H.; Morsut, L.; Liu, Z.; Brar, G.A.; Torres, S.E.; Stern-Ginossar, N.; Brandman, O.; Whitehead, E.H.; Doudna, J.A.; et al. CRISPR-Mediated Modular RNA-Guided Regulation of Transcription in Eukaryotes. Cell 2013, 154, 442–451.
  19. Mali, P.; Aach, J.; Stranges, P.; Esvelt, K.; Moosburner, M.; Kosuri, S.; Yang, L.; Church, G.M. CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat. Biotechnol. 2013, 31, 833–838.
  20. Larson, M.H.; Gilbert, L.; Wang, X.; Lim, W.; Weissman, J.S.; Qi, L.S. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 2013, 8, 2180–2196.
  21. Sanjana, N.E.; Shalem, O.; Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 2014, 11, 783–784.
  22. Xu, H.; Xiao, T.; Chen, C.-H.; Li, W.; Meyer, C.A.; Wu, Q.; Wu, D.; Cong, L.; Zhang, F.; Liu, J.S.; et al. Sequence determinants of improved CRISPR sgRNA design. Genome Res. 2015, 25, 1147–1157.
  23. Doench, J.G.; Fusi, N.; Sullender, M.; Hegde, M.; Vaimberg, E.W.; Donovan, K.F.; Smith, I.; Tothova, Z.; Wilen, C.; Orchard, R.; et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 2016, 34, 184–191.
  24. Hart, T.; Chandrashekhar, M.; Aregger, M.; Steinhart, Z.; Brown, K.; MacLeod, G.; Mis, M.; Zimmermann, M.; Fradet-Turcotte, A.; Sun, S.; et al. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 2015, 163, 1515–1526.
  25. Gilbert, L.A.; Horlbeck, M.A.; Adamson, B.; Villalta, J.E.; Chen, Y.; Whitehead, E.H.; Guimaraes, C.; Panning, B.; Ploegh, H.L.; Bassik, M.C.; et al. Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation. Cell 2014, 159, 647–661.
  26. Konermann, S.; Brigham, M.; Trevino, A.E.; Joung, J.; Abudayyeh, O.O.; Barcena, C.; Hsu, P.; Habib, N.; Gootenberg, J.; Nishimasu, H.; et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nat. Cell Biol. 2015, 517, 583–588.
  27. Joung, J.; Konermann, S.; Gootenberg, J.S.; Abudayyeh, O.; Platt, R.J.; Brigham, M.D.; Sanjana, N.; Zhang, F. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 2017, 12, 828–863.
  28. Fei, T.; Li, W.; Peng, J.; Xiao, T.; Chen, C.-H.; Wu, A.; Huang, J.; Zang, C.; Liu, X.S.; Brown, M. Deciphering essential cistromes using genome-wide CRISPR screens. Proc. Natl. Acad. Sci. USA 2019, 116, 25186–25195.
  29. Munoz, D.; Stegmeier, F.P.; Schlabach, M. Abstract B21: CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Precis. Med. 2017, 6, 900–913.
  30. Miles, L.A.; Garippa, R.J.; Poirier, J.T. Poirier, Design, execution, and analysis of pooled in vitro CRISPR/Cas9 screens. FEBS J. 2016, 283, 3170–3180.
  31. Luo, B.; Cheung, H.W.; Subramanian, A.; Sharifnia, T.; Okamoto, M.; Yang, X.; Hinkle, G.; Boehm, J.; Beroukhim, R.; Weir, B.A.; et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl. Acad. Sci. USA 2008, 105, 20380–20385.
  32. Wang, T.; Yu, H.; Hughes, N.W.; Liu, B.; Kendirli, A.; Klein, K.; Chen, W.; Lander, E.S.; Sabatini, D.M. Gene Essentiality Profiling Reveals Gene Networks and Synthetic Lethal Interactions with Oncogenic Ras. Cell 2017, 168, 890–903.e15.
  33. Tsherniak, A.; Vazquez, F.; Montgomery, P.G.; Weir, B.A.; Kryukov, G.; Cowley, G.S.; Gill, S.; Harrington, W.F.; Pantel, S.; Krill-Burger, J.; et al. Defining a Cancer Dependency Map. Cell 2017, 170, 564–576.e16.
  34. McDonald, E.R., III; Weck, A.; Schlabach, M.R.; Billy, E.; Mavrakis, K.J.; Hoffman, G.R.; Belur, D.; Castelletti, D.; Frias, E.; Gampa, K.; et al. Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. Cell 2017, 170, 577–592.
  35. Kryukov, G.V.; Wilson, F.H.; Ruth, J.R.; Paulk, J.; Tsherniak, A.; Marlow, S.E.; Vazquez, F.; Weir, B.A.; Fitzgerald, M.E.; Tanaka, M.; et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 2016, 351, 1214–1218.
  36. Tzelepis, K.; Koike-Yusa, H.; De Braekeleer, E.; Li, Y.; Metzakopian, E.; Dovey, O.M.; Grinkevich, V.; Mupo, A.; Li, M.; Mazan, M.; et al. A CRISPR Dropout Screen Identifies Genetic Vulnerabilities and Therapeutic Targets in Acute Myeloid Leukemia. Cell Rep. 2016, 17, 1193–1205.
  37. Chen, L.; Alexe, G.; Dharia, N.; Ross, L.; Iniguez, A.B.; Conway, A.S.; Wang, E.J.; Veschi, V.; Lam, N.; Qi, J.; et al. CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2. J. Clin. Investig. 2017, 128, 446–462.
  38. O’Neil, N.; Bailey, M.L.; Hieter, P. Synthetic lethality and cancer. Nat. Rev. Genet. 2017, 18, 613–623.
  39. Vecchione, L.; Gambino, V.; Raaijmakers, J.; Schlicker, A.; Fumagalli, A.; Russo, M.; Villanueva, A.; Beerling, E.; Bartolini, A.; Mollevi, D.G.; et al. A Vulnerability of a Subset of Colon Cancers with Potential Clinical Utility. Cell 2016, 165, 317–330.
  40. Jain, I.H.; Zazzeron, L.; Goli, R.; Alexa, K.; Schatzman-Bone, S.; Dhillon, H.; Goldberger, O.; Peng, J.; Shalem, O.; Sanjana, N.E.; et al. Hypoxia as a therapy for mitochondrial disease. Science 2016, 352, 54–61.
  41. Orchard, R.C.; Wilen, C.B.; Doench, J.G.; Baldridge, M.T.; McCune, B.T.; Lee, Y.-C.J.; Lee, S.; Pruett-Miller, S.M.; Nelson, C.A.; Fremont, D.H.; et al. Discovery of a proteinaceous cellular receptor for a norovirus. Science 2016, 353, 933–936.
  42. Marceau, C.D.; Puschnik, A.S.; Majzoub, K.; Ooi, Y.S.; Brewer, S.M.; Fuchs, G.; Swaminathan, K.; Mata, M.A.; Elias, J.E.; Sarnow, P.; et al. Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens. Nat. Cell Biol. 2016, 535, 159–163.
  43. Park, R.; Wang, T.; Koundakjian, D.; Hultquist, J.; Lamothe-Molina, P.; Monel, B.; Schumann, K.; Yu, H.; Krupzcak, K.M.; Garcia-Beltran, W.; et al. A genome-wide CRISPR screen identifies a restricted set of HIV host dependency factors. Nat. Genet. 2017, 49, 193–203.
  44. Puschnik, A.S.; Majzoub, K.; Ooi, Y.S.; Carette, J.E. A CRISPR toolbox to study virus–host interactions. Nat. Rev. Genet. 2017, 15, 351–364.
  45. Wei, J.; Alfajaro, M.M.; DeWeirdt, P.C.; Hanna, R.E.; Lu-Culligan, W.J.; Cai, W.L.; Strine, M.S.; Zhang, S.-M.; Graziano, V.R.; Schmitz, C.O.; et al. Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection. Cell 2021, 184, 76–91.
  46. Deans, R.M.; Morgens, D.W.; Ökesli, A.; Pillay, S.; Horlbeck, M.A.; Kampmann, M.; Gilbert, L.A.; Li, A.; Mateo, R.; Smith, M.; et al. Parallel shRNA and CRISPR-Cas9 screens enable antiviral drug target identification. Nat. Chem. Biol. 2016, 12, 361–366.
  47. Parnas, O.; Jovanovic, M.; Eisenhaure, T.M.; Herbst, R.; Dixit, A.; Ye, C.J.; Przybylski, D.; Platt, R.; Tirosh, I.; Sanjana, N.; et al. A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks. Cell 2015, 162, 675–686.
  48. Li, W.; Xu, H.; Xiao, T.; Cong, L.; Love, M.I.; Zhang, F.; Irizarry, R.A.; Liu, J.S.; Brown, M.; Liu, X.S. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 2014, 15, 1–12.
  49. Li, W.; Köster, J.; Xu, H.; Chen, C.H.; Xiao, T.; Liu, J.S.; Brown, M.; Liu, X.S. Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR. Genome Biol. 2015, 16, 1–13.
  50. Wang, B.; Wang, M.; Zhang, W.; Xiao, T.; Chen, C.-H.; Wu, A.; Wu, F.; Traugh, N.; Wang, X.; Li, Z.; et al. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nat. Protoc. 2019, 14, 756–780.
  51. Dai, Z.; Sheridan, J.M.; Gearing, L.J.; Moore, D.L.; Su, S.; Wormald, S.; Wilcox, S.; Dickins, R.A.; Blewitt, M.E.; Ritchie, M.E.; et al. edgeR: A versatile tool for the analysis of shRNA-seq and CRISPR-Cas9 genetic screens. F1000Research 2014, 3, 95.
  52. Hart, T.; Moffat, J. BAGEL: A computational framework for identifying essential genes from pooled library screens. BMC Bioinform. 2016, 17, 1–7.
  53. Winter, J.; Breinig, M.; Heigwer, F.; Brügemann, D.; Leible, S.; Pelz, O.; Zhan, T.; Boutros, M. caRpools: An R package for exploratory data analysis and documentation of pooled CRISPR/Cas9 screens. Bioinformatics 2016, 32, 632–634.
  54. Spahn, P.N.; Bath, T.; Weiss, R.J.; Kim, J.; Esko, J.D.; Lewis, N.E.; Harismendy, O. PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens. Sci. Rep. 2017, 7, 1–8.
  55. Colic, M.; Wang, G.; Zimmermann, M.; Mascall, K.; McLaughlin, M.; Bertolet, L.; Lenoir, W.; Moffat, J.; Angers, S.; Durocher, D.; et al. Identifying chemogenetic interactions from CRISPR screens with drugZ. Genome Med. 2019, 11, 1–12.
  56. Graham, D.B.; Root, D.E. Resources for the design of CRISPR gene editing experiments. Genome Biol. 2015, 16, 1–21.
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