The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
Open accessAbstract
CRISPR-Cas9–based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Additionally, it can identify genes related to drug resistance in cancer cells and facilitate research on overcoming this resistance [8].”
From A Review of Research on CRISPR/Cas9 Gene Editing Technology in Disease Treatment (2024) · cites this paper as [8]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The CFD algorithm is derived from a CRISPR/Cas9 genetic screen experiment that assessed the off-target effects of thousands of sgRNAs (Doench et al., 2016).”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Doench et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“Primarily, the sequence of sgRNAs is a crucial factor affecting on-target and off target efficiency.”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Doench et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“Different sgRNAs targeting the same gene locus can have distinct outcomes”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Doench et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“We used the STARS algorithm (Doench et al., 2016) to derive 38 hits, 18 of which were shared across at least two cell lines”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Doench et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“In addition, although our sgRNA pool was designed in silico to minimize the probability of SpCas9 off-target activity52, the GUIDE-seq assay for sgRNA+119 revealed 11 off-target sites throughout the genome (Supplementary Figs. 11h and 4d).”
From Allele specific repair of splicing mutations in cystic fibrosis through AsCas12a genome editing (2019) · cites this paper as [52]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Although TREM2 has been shown to bind to Aβ and facilitate its uptake80,81, the surface expression changes of other receptors involved in Aβ recognition and uptake might have counteracted the effects of increased TREM2 signaling upon INPP5D haploinsufficiency83,84.”
From INPP5D/SHIP1 regulates endo-lysosomal function and cargo-selective phagocytosis in human microglia (2025) · cites this paper as [83]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, Aβ can bind to lipoproteins such as clusterin (ApoJ) and ApoE in the brain82–84, which facilitates its clearance by microglia via TREM281.”
From INPP5D/SHIP1 regulates endo-lysosomal function and cargo-selective phagocytosis in human microglia (2025) · cites this paper as [83]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
A Programmable Dual-RNA–Guided DNA Endonuclease in Adaptive Bacterial Immunity
Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J. A., & Charpentier, E. (2012) · Science · doi:10.1126/science.1225829 · Cited by 17,116
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
RNA-Guided Human Genome Engineering via Cas9
Mali, P., Yang, L., Esvelt, K. M., Aach, J., Guell, M., DiCarlo, J. E., Norville, J. E., & Church, G. M. (2013) · Science · doi:10.1126/science.1232033 · Cited by 9,210
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
Biopython: freely available python tools for computational molecular biology and bioinformatics
Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., et al. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp163 · Cited by 6,080
Improved vectors and genome-wide libraries for CRISPR screening
Sanjana, N. E., Shalem, O., & Zhang, F. (2014) · Nature Methods · doi:10.1038/nmeth.3047 · Cited by 5,239
Flagged first, then by citations
1 of the 54 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Steiger, J.H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980). · DOI · Paper page
Cited by
Sorted by citations
Items 701–800 of 1,030
cFLIP suppression and DR5 activation sensitize senescent cancer cells to senolysis
Wang, L., Jin, H., Jochems, F., Wang, S., Lieftink, C., Martinez, I. M., Conti, G. D., Edwards, F., de Oliveira, R. L., Schepers, A. G., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00462-2 · Cited by 68
Marchetti, F., Cardoso, R., Chen, C. L., Douglas, G. R., Elloway, J. M., Escobar, P. A., Harper, T. A., Heflich, R. H., Kidd, D., Lynch, A. M., et al. (2023) · Mutation Research/Reviews in Mutation Research · doi:10.1016/j.mrrev.2023.108466 · Cited by 71
Targeting DCAF5 suppresses SMARCB1-mutant cancer by stabilizing SWI/SNF
Radko-Juettner, S., Yue, H., Myers, J., Carter, R. D., Robertson, A. N., Mittal, P., Zhu, Z., Hansen, B. S., Donovan, K. A., Hunkeler, M., et al. (2024) · Nature · doi:10.1038/s41586-024-07250-1 · Cited by 68
Self-citationAnvar, N. E., Lin, C., Ma, X., Wilson, L., Steger, R., Sangree, A. K., Colic, M., Wang, S. H., Doench, J. G., & Hart, T. (2024) · Nature Communications · doi:10.1038/s41467-024-47795-3 · Cited by 62
Self-citationPeng, L., Renauer, P. A., Sferruzza, G., Yang, L., Zou, Y., Fang, Z., Park, J. J.-Y., Chow, R. D.-W., Zhang, Y., Lin, Q., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02282-4 · Cited by 61
In vivo interaction screening reveals liver-derived constraints to metastasis
Borrelli, C., Roberts, M., Eletto, D., Hussherr, M., Fazilaty, H., Valenta, T., Lafzi, A., Kretz, J. A., Vinzoni, E. G., Karakatsani, A., et al. (2024) · Nature · doi:10.1038/s41586-024-07715-3 · Cited by 67
Rastogi, A., Murik, O., Bowler, C., & Tirichine, L. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1143-1 · Cited by 63
Zhao, C., Zheng, X., Qu, W., Li, G., Li, X., Miao, Y., Han, X., Liu, X., Li, Z., Ma, Y., et al. (2017) · International Journal of Biological Sciences · doi:10.7150/ijbs.21312 · Cited by 62
High‐resolution mapping of cancer cell networks using co‐functional interactions
Boyle, E. A., Pritchard, J. K., & Greenleaf, W. J. (2018) · Molecular Systems Biology · doi:10.15252/msb.20188594 · Cited by 63
Murakami, K., Terakado, Y., Saito, K., Jomen, Y., Takeda, H., Oshima, M., & Barker, N. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016806118 · Cited by 62
Cheruiyot, A., Li, S., Srivatsan, S. N., Ahmed, T., Chen, Y., Lemacon, D. S., Li, Y., Yang, Z., Wadugu, B. A., Warner, W. A., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-20-4016 · Cited by 61
ON-Target Adverse Events of CRISPR-Cas9 Nuclease: More Chaotic than Expected
Boutin, J., Cappellen, D., Rosier, J., Amintas, S., Dabernat, S., Bedel, A., & Moreau‐Gaudry, F. (2022) · The CRISPR Journal · doi:10.1089/crispr.2021.0120 · Cited by 59
Smith, T. A., Lopez-Perez, G. S., Herneisen, A. L., Shortt, E., & Lourido, S. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01104-0 · Cited by 52
Coagulation factors directly cleave SARS-CoV-2 spike and enhance viral entry
Kastenhuber, E. R., Mercadante, M., Nilsson-Payant, B. E., Johnson, J. L., Jaimes, J. A., Muecksch, F., Weisblum, Y., Bram, Y., Chandar, V., Whittaker, G. R., et al. (2022) · eLife · doi:10.7554/elife.77444 · Cited by 61
Small-Molecule Inhibition of the Acyl-Lysine Reader ENL as a Strategy against Acute Myeloid Leukemia
Liu, Y., Li, Q., Alikarami, F., Barrett, D. R., Mahdavi, L., Li, H., Tang, S., Khan, T. A., Michino, M., Hill, C., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1307 · Cited by 60
Witkowski, M. T., Lee, S., Wang, E., Lee, A. K., Talbot, A., Ma, C., Tsopoulidis, N., Brumbaugh, J., Zhao, Y., Roberts, K. G., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01314-y · Cited by 62
Exposure of the inner mitochondrial membrane triggers apoptotic mitophagy
Saunders, T. L., Windley, S. P., Gervinskas, G., Balka, K. R., Rowe, C. L., Lane, R. M., Tailler, M., Nguyen, T. N., Ramm, G., Lazarou, M., et al. (2024) · Cell Death and Differentiation · doi:10.1038/s41418-024-01260-2 · Cited by 66
Genome Editing with CRISPR-Cas9: Can It Get Any Better?
Haeussler, M., & Concordet, J. (2016) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2016.04.008 · Cited by 65
Kanjee, U., Grüring, C., Chaand, M., Lin, K., Egan, E. S., Manzo, J., Jones, P. L., Yu, T. M., Barker, R. H., Weekes, M. P., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711310114 · Cited by 63
Bockorny, B., Rusan, M., Chen, W., Liao, R. G., Li, Y. Y., Piccioni, F., Wang, J., Tan, L., Thorner, A. R., Li, T., et al. (2018) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-17-0464 · Cited by 61
Gene disruption through base editing‐induced messenger RNA missplicing in plants
Li, Z., Xiong, X., Wang, F., Liang, J., & Li, J. (2018) · New Phytologist · doi:10.1111/nph.15647 · Cited by 62
Hosmillo, M. D. T., Lu, J. Q., McAllaster, M. R., Eaglesham, J. B., Wang, X., Emmott, E., Domingues, P., Chaudhry, Y., Fitzmaurice, T. J., Tung, M. K. H., et al. (2019) · eLife · doi:10.7554/elife.46681 · Cited by 62
Self-citationEfficient generation of Knock-in/Knock-out marmoset embryo via CRISPR/Cas9 gene editing
Kumita, W., Sato, K., Suzuki, Y., Kurotaki, Y., Harada, T., Zhou, Y., Kishi, N., Sato, K., Aiba, A., Sakakibara, Y., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49110-3 · Cited by 64
Bajaj, J., Hamilton, M., Shima, Y., Chambers, K. R., Spinler, K. R., Nostrand, E. L. V., Yee, B. A., Blue, S. M., Chen, M., Rizzeri, D., et al. (2020) · Nature Cancer · doi:10.1038/s43018-020-0054-2 · Cited by 61
CRISPR/Cas genome editing to optimize pharmacologically active plant natural products
Dey, A. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.105359 · Cited by 62
Dong, C., Fu, S., Karvas, R. M., Chew, B., Fischer, L. A., Xing, X., Harrison, J. K., Popli, P., Kommagani, R., Wang, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30207-9 · Cited by 59
Shen, Y., Cohen, J. L., Nicoloro, S. M. C., Kelly, M., Yenilmez, B., Henriques, F. D. S., Tsagkaraki, E., Edwards, Y. J. K., Hu, X., Friedline, R. H., et al. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.004554 · Cited by 61
Current progress of genome editing in livestock
Lee, K., Uh, K., & Farrell, K. (2020) · Theriogenology · doi:10.1016/j.theriogenology.2020.01.036 · Cited by 60
Kubota, S., Tokunaga, K., Umezu, T., Yokomizo-Nakano, T., Sun, Y., Oshima, M., Tan, K., Yang, H., Kanai, A., Iwanaga, E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09710-z · Cited by 54
Baisya, D. R., Ramesh, A., Schwartz, C., Lonardi, S., & Wheeldon, I. R. (2022) · Nature Communications · doi:10.1038/s41467-022-28540-0 · Cited by 60
A cAMP signalosome in primary cilia drives gene expression and kidney cyst formation
Hansen, J. N., Kaiser, F., Leyendecker, P., Stüven, B., Krause, J., Derakhshandeh, F., Irfan, J., Sroka, T. J., Preval, K. M., Desai, P. B., et al. (2022) · EMBO Reports · doi:10.15252/embr.202154315 · Cited by 66
Microhomology-assisted scarless genome editing in human iPSCs
Kim, S.-I., Matsumoto, T., Kagawa, H., Nakamura, M., Hirohata, R., Ueno, A., Ohishi, M., Sakuma, T., Soga, T., Yamamoto, T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03044-y · Cited by 58
Lloyd, K. A., Wigerblad, G., Sahlström, P., Garimella, M. G., Chemin, K., Stéen, J., Titcombe, P. J., Marklein, B., Zhou, D., Stålesen, R., et al. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2018.03033 · Cited by 60
CRISPR-cas9: a powerful tool towards precision medicine in cancer treatment
Xing, H., & Meng, L. (2019) · Acta Pharmacologica Sinica · doi:10.1038/s41401-019-0322-9 · Cited by 61
Genome-wide detection of CRISPR editing in vivo using GUIDE-tag
Liang, S.-Q., Liu, P., Smith, J. L., Mintzer, E., Maitland, S., Dong, X., Yang, Q., Lee, J., Haynes, C. M., Zhu, L. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28135-9 · Cited by 54
Hennig, S. L., Owen, J. R., Lin, J. C., Young, A. E., Ross, P. J., Eenennaam, A. L. V., & Murray, J. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-78264-8 · Cited by 58
Boyle, E. A., Becker, W. R., Bai, H., Chen, J. S., Doudna, J. A., & Greenleaf, W. J. (2021) · Science Advances · doi:10.1126/sciadv.abe5496 · Cited by 63
Álvarez‐Álvarez, M. M., Biayna, J., & Supek, F. (2022) · Nature Communications · doi:10.1038/s41467-022-32285-1 · Cited by 60
Dewari, P. S., Southgate, B., McCarten, K., Monogarov, G., O’Duibhir, E., Quinn, N., Tyrer, A., Leitner, M.-C., Plumb, C., Kalantzaki, M., et al. (2018) · eLife · doi:10.7554/elife.35069 · Cited by 63
Kujoth, G. C., Sullivan, T. D., Merkhofer, R. M., Lee, T.-J., Wang, H., Brandhorst, T. T., Wüthrich, M., & Klein, B. S. (2018) · mBio · doi:10.1128/mbio.00412-18 · Cited by 58
CRISPR-Local: a local single-guide RNA (sgRNA) design tool for non-reference plant genomes
Sun, J., Liu, H., Liu, J., Cheng, S., Peng, Y., Zhang, Q., Yan, J., Liu, H., & Chen, L. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty970 · Cited by 59
Genome-wide CRISPR screen reveals host genes that regulate SARS-CoV-2 infectionPreprint
Wei, J., Alfajaro, M. M., Hanna, R. E., DeWeirdt, P. C., Strine, M. S., Lu-Culligan, W. J., Zhang, S., Graziano, V. R., Schmitz, C. O., Chen, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.16.155101 · Cited by 59
Self-citationTóth, E., Weinhardt, N., Bencsura, P., Huszár, K., Kulcsár, P. I., Tálas, A., Fodor, E., & Welker, E. (2016) · Biology Direct · doi:10.1186/s13062-016-0147-0 · Cited by 58
Mulligan, M. K., Abreo, T. J., Neuner, S. M., Parks, C., Watkins, C. E., Houseal, M. T., Shapaker, T. M., Hook, M., Tan, H., Wang, X., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00188 · Cited by 56
LXRα activation and Raf inhibition trigger lethal lipotoxicity in liver cancer
Rudalska, R., Harbig, J., Snaebjornsson, M. T., Klotz, S., Zwirner, S., Taranets, L., Heinzmann, F., Kronenberger, T., Förster, M., Cui, W., et al. (2021) · Nature Cancer · doi:10.1038/s43018-020-00168-3 · Cited by 63
Regulation of B cell receptor-dependent NF-κB signaling by the tumor suppressor KLHL14
Choi, J., Phelan, J. D., Wright, G. W., Häupl, B., Huang, D. W., Shaffer, A. L., Young, R. M., Wang, Z., Zhao, H., Yu, X., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921187117 · Cited by 59
Minimized combinatorial CRISPR screens identify genetic interactions in autophagy
Diehl, V., Wegner, M., Grumati, P., Husnjak, K., Schaubeck, S., Gubaš, A., Shah, V. J., Polat, I. H., Langschied, F., Prieto‐Garcia, C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab309 · Cited by 55
Transcriptional landscapes of floral meristems in barley
Thiel, J., Koppolu, R., Trautewig, C., Hertig, C. W., Kale, S. M., Erbe, S., Mascher, M., Himmelbach, A., Rutten, T. L. M., Esteban, E., et al. (2021) · Science Advances · doi:10.1126/sciadv.abf0832 · Cited by 58
Impact of CRISPR-Cas9-Based Genome Engineering in Farm Animals
Singh, P., & Ali, S. A. (2021) · Veterinary Sciences · doi:10.3390/vetsci8070122 · Cited by 64
TRIM28 is a transcriptional activator of the mutant TERT promoter in human bladder cancer
Agarwal, N., Rinaldetti, S. L., Cheikh, B. B., Zhou, Q., Hass, E. P., Jones, R. T., Joshi, M., LaBarbera, D. V., Knott, S., Cech, T. R., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2102423118 · Cited by 58
van der Veer, H. J., van Aalen, E. A., Michielsen, C. M. S., Hanckmann, E. T. L., Deckers, J., van Borren, M. M. G. J., Flipse, J., Loonen, A. J. M., Schoeber, J. P. H., & Merkx, M. (2023) · ACS Central Science · doi:10.1021/acscentsci.2c01467 · Cited by 58
CRISPR-Based Biosensing Strategies: Technical Development and Application Prospects
Tian, T., & Zhou, X. (2023) · Annual Review of Analytical Chemistry · doi:10.1146/annurev-anchem-090822-014725 · Cited by 59
METTL17 is an Fe-S cluster checkpoint for mitochondrial translation
Ast, T., Itoh, Y., Sadre, S., McCoy, J. G., Namkoong, G., Wengrod, J. C., Chicherin, I., Joshi, P. R., Kamenski, P., Suess, D. L. M., et al. (2024) · Molecular Cell · doi:10.1016/j.molcel.2023.12.016 · Cited by 58
Huang, B., Zeng, Z., Kim, S., Fausto, C. C., Koppitch, K. A., Li, H., Li, Z., Chen, X. X., Guo, J., Zhang, C. C., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.04.002 · Cited by 65
Saurat, N. G., Minotti, A. P., Rahman, M. T., Sikder, T., Zhang, C., Cornacchia, D., Jungverdorben, J., Ciceri, G., Betel, D., & Studer, L. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.06.001 · Cited by 60
Recent advances in therapeutic gene-editing technologies
Liu, D., Cao, D., & Han, R. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.03.026 · Cited by 70
Barrow, J. J., Balsa, E., Verdeguer, F., Tavares, C. D. J., Soustek, M. S., Hollingsworth, L. R., Jedrychowski, M. P., Vogel, R. O., Paulo, J. A., Smeitink, J. A. M., et al. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.08.023 · Cited by 57
Self-citationCRISPR Screen Reveals that EHEC’s T3SS and Shiga Toxin Rely on Shared Host Factors for Infection
Pacheco, A., Lazarus, J. E., Sit, B., Schmieder, S. S., Lencer, W., Blondel, C., Doench, J. G., Davis, B. M., & Waldor, M. (2018) · mBio · doi:10.1128/mbio.01003-18 · Cited by 51
Self-citationIdentification of Antinorovirus Genes in Human Cells Using Genome-Wide CRISPR Activation Screening
Orchard, R. C., Sullender, M. E., Dunlap, B. F., Balce, D. R., Doench, J. G., & Virgin, H. W. (2018) · Journal of Virology · doi:10.1128/jvi.01324-18 · Cited by 56
Self-citationStefely, J. A., Zhang, Y., Freiberger, E. C., Kwiecien, N. W., Thomas, H. E., Davis, A. M., Lowry, N. D., Vincent, C. E., Shishkova, E., Clark, N. A., et al. (2020) · Autophagy · doi:10.1080/15548627.2020.1719746 · Cited by 58
Correction of the Exon 2 Duplication in DMD Myoblasts by a Single CRISPR/Cas9 System
Lattanzi, A., Duguez, S., Moiani, A., Izmiryan, A., Barbon, E., Martin, S., Mamchaoui, K., Mouly, V., Bernardi, F., Mavilio, F., et al. (2017) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2017.02.004 · Cited by 65
CRISPR/Cas systems: opportunities and challenges for crop breeding
Biswas, S., Zhang, D., & Shi, J. (2021) · Plant Cell Reports · doi:10.1007/s00299-021-02708-2 · Cited by 58
DNA methylation enzymes and PRC1 restrict B-cell Epstein–Barr virus oncoprotein expression
Guo, R., Zhang, Y., Teng, M., Jiang, C., Schineller, M., Zhao, B., Doench, J. G., O’Reilly, R. J., Cesarman, E., Giulino-Roth, L. G., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0724-y · Cited by 58
Self-citationPlant drought tolerance provided through genome editing of the trehalase gene
Núñez-Muñoz, L. A., Vargas-Hernández, B. Y., Hinojosa-Moya, J., Ruíz‐Medrano, R., & Xoconostle‐Cázares, B. (2021) · Plant Signaling & Behavior · doi:10.1080/15592324.2021.1877005 · Cited by 57
G3BP1 binds to guanine quadruplexes in mRNAs to modulate their stabilities
He, X., Yuan, J. J., & Wang, Y. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab873 · Cited by 56
Zhao, H., Liu, Y., Wang, L., Jin, G., Zhao, X., Xu, J., Zhang, G., Ma, Y., Yin, N., & Peng, M. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110083 · Cited by 57
PPM1D mutations are oncogenic drivers of de novo diffuse midline glioma formation
Khadka, P., Reitman, Z. J., Lu, S. D., Buchan, G. B. J., Gionet, G. L., Dubois, F. P. B., Carvalho, D. M., Shih, J., Zhang, S., Greenwald, N. F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28198-8 · Cited by 58
Self-citationPredicting base editing outcomes using position-specific sequence determinants
Pallaseni, A., Peets, E. M., Koeppel, J., Weller, J., Vanderstichele, T., Ho, U. L., Crepaldi, L., van Leeuwen, J. S., Allen, F., & Parts, L. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac161 · Cited by 59
Development and Applications of CRISPR/Cas9-Based Genome Editing in Lactobacillus
Mu, Y., Zhang, C., Li, T., Jin, F., Sung, Y.-J., Oh, H., Lee, H., & Jin, L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232112852 · Cited by 62
Identification of purine biosynthesis as an NADH-sensing pathway to mediate energy stress
Yang, R., Yang, C., Ma, L., Zhao, Y., Guo, Z., Niu, J., Chu, Q., Ma, Y., & Li, B. (2022) · Nature Communications · doi:10.1038/s41467-022-34850-0 · Cited by 60
Adaptive introgression of a visual preference gene
Rossi, M., Hausmann, A. E., Alcamí, P., Möst, M., Roussou, R., Belleghem, S. M. V., Wright, D. S., Kuo, C., Lozano-Urrego, D., Maulana, A., et al. (2024) · Science · doi:10.1126/science.adj9201 · Cited by 57
Mapping multimodal phenotypes to perturbations in cells and tissue with CRISPRmap
Gu, J., Iyer, A., Wesley, B., Taglialatela, A., Leuzzi, G., Hangai, S., Decker, A., Gu, R., Klickstein, N., Shuai, Y., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02386-x · Cited by 69
Gene editing in mouse zygotes using the CRISPR/Cas9 system
Wefers, B., Bashir, S., Rossius, J., Wurst, W., & Kühn, R. (2017) · Methods · doi:10.1016/j.ymeth.2017.02.008 · Cited by 60
Next-Generation Sequencing of Genome-Wide CRISPR Screens
Yau, E. H., & Rana, T. M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7514-3_13 · Cited by 57
CRISPR/Cas9 cleavage efficiency regression through boosting algorithms and Markov sequence profiling
Peng, H., Zheng, Y., Blumenstein, M., Tao, D., & Li, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty298 · Cited by 57
CRISPR-Based Tools in Immunity
Simeonov, D. R., & Marson, A. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041522 · Cited by 57
Restoring the p53 ‘Guardian’ Phenotype in p53-Deficient Tumor Cells with CRISPR/Cas9
Chira, S., Gulei, D., Hajitou, A., & Berindan-Neagoe, I. (2018) · Trends in Biotechnology · doi:10.1016/j.tibtech.2018.01.014 · Cited by 56
Reversal of mcr-1-Mediated Colistin Resistance in Escherichia coli by CRISPR-Cas9 System
Wan, P., Cui, S., Ma, Z.-B., Chen, L., Li, X., Zhao, R., Xiong, W., & Zeng, Z. (2020) · Infection and Drug Resistance · doi:10.2147/idr.s244885 · Cited by 57
Dabelsteen, S., Pallesen, E. M. H., Marinova, I. N., Nielsen, M. I., Adamopoulou, M., Rømer, T. B., Rudjord-Levann, A. M., Aasted, M. K. M., Ye, Z., Thein, D., et al. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.06.039 · Cited by 55
Lucas, E. D., Schafer, J. B., Matsuda, J. L., Kraus, M., Burchill, M. A., & Tamburini, B. A. J. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108258 · Cited by 54
Non-canonical Targets of HIF1a Impair Oligodendrocyte Progenitor Cell Function
Allan, K. C., Hu, L. R., Scavuzzo, M. A., Morton, A. R., Gevorgyan, A. S., Cohn, E. F., Clayton, B. L. L., Bederman, I. R., Hung, S., Bartels, C. F., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.09.019 · Cited by 59
Activation of Clustered IFNγ Target Genes Drives Cohesin-Controlled Transcriptional Memory
Siwek, W., Tehrani, S., Mata, J. F., & Jansen, L. E. T. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.10.005 · Cited by 60
UFMylation inhibits the proinflammatory capacity of interferon-γ–activated macrophages
Balce, D. R., Wang, Y., McAllaster, M. R., Dunlap, B. F., Orvedahl, A., Hykes, B. L., Droit, L., Handley, S. A., Wilen, C. B., Doench, J. G., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011763118 · Cited by 58
Self-citationA proteome-wide map of 20(S)-hydroxycholesterol interactors in cell membranes
Cheng, Y.-S., Zhang, T., Ma, X., Pratuangtham, S., Zhang, G. C., Ondrus, A. A., Mafi, A., Lomenick, B., Jones, J. J., & Ondrus, A. E. (2021) · Nature Chemical Biology · doi:10.1038/s41589-021-00907-2 · Cited by 56
Zhang, G., Zeng, T., Dai, Z., & Dai, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.001 · Cited by 64
NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences
Ao, C., Zou, Q., & Yu, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab480 · Cited by 56
Loss of Rnf31 and Vps4b sensitizes pancreatic cancer to T cell-mediated killing
Frey, N., Tortola, L., Egli, D., Janjuha, S., Rothgangl, T., Marquart, K. F., Ampenberger, F., Köpf, M., & Schwank, G. (2022) · Nature Communications · doi:10.1038/s41467-022-29412-3 · Cited by 57
Huang, Y., Chen, C.-W. D., Sundaramurthy, V., Slabicki, M. M., Hao, D., Watson, C. J., Tovy, A., Reyes, J. M., Dakhova, O., Crovetti, B. R., et al. (2021) · Apollo (University of Cambridge) · doi:10.17863/cam.74923 · Cited by 56
Zhou, W., Gao, F., Romero-Wolf, M., Jo, S., & Rothenberg, E. V. (2022) · Science Immunology · doi:10.1126/sciimmunol.abm1920 · Cited by 52
Ren, X., Peng, M., Peng, X., Yao, W., Galbo, P. M., Corrigan, D. T., Wang, H., Su, Y., Dong, X., Sun, Q., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci163620 · Cited by 59
Jin, S., Lin, Q., Gao, Q., & Gao, C. (2022) · Nature Protocols · doi:10.1038/s41596-022-00773-9 · Cited by 56
Chung, K., Cheng, C., Chen, Y., Hsu, C., Huang, Y.-L., Hsieh, M., Kuo, H., Lin, Y., Juan, Y., Nakahira, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51683-1 · Cited by 61
Systematic discovery of CRISPR-boosted CAR T cell immunotherapies
Datlinger, P., Pankevich, E. V., Arnold, C. D., Pranckevicius, N., Lin, J., Romanovskaia, D., Schaefer, M., Piras, F., Orts, A.-C., Nemc, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09507-9 · Cited by 63
CRISPR/Cas9: From Genome Engineering to Cancer Drug Discovery
Luo, J. (2016) · Trends in cancer · doi:10.1016/j.trecan.2016.05.001 · Cited by 57
The Conspicuity of CRISPR-Cpf1 System as a Significant Breakthrough in Genome Editing
Bayat, H., Modarressi, M. H., & Rahimpour, A. (2017) · Current Microbiology · doi:10.1007/s00284-017-1406-8 · Cited by 56
Protein Inhibitors of CRISPR-Cas9
Bondy‐Denomy, J. (2017) · ACS Chemical Biology · doi:10.1021/acschembio.7b00831 · Cited by 56
Resistance to nonribosomal peptide antibiotics mediated by d-stereospecific peptidases
Li, Y., Zhong, Z., Hou, P., Zhang, W., & Qian, P. (2018) · Nature Chemical Biology · doi:10.1038/s41589-018-0009-4 · Cited by 54
Bandopadhayay, P., Piccioni, F., O’Rourke, R., Ho, P., Gonzalez, E. M., Buchan, G. B. J., Qian, K., Gionet, G. L., Girard, E. J., Coxon, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10307-9 · Cited by 54
Self-citationOberhofer, G., Ivy, T., & Hay, B. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921698117 · Cited by 56
CHK1 Inhibitor Blocks Phosphorylation of FAM122A and Promotes Replication Stress
Li, F., Kozono, D., Deraska, P. V., Branigan, T. B., Dunn, C. E., Zheng, X.-F., Parmar, K., Nguyen, H. Q., DeCaprio, J. A., Shapiro, G. I., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.10.008 · Cited by 56
Metadata
- Authors
- Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H. W., Listgarten, J., & Root, D. E.
- Affiliations
- Broad Institute (US)
- Washington University in St. Louis (US)
- Microsoft Research New England (United States) (US)
- Corresponding author
- Doench, John G
- Listgarten, Jennifer
- Root, David E
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2016-01-18
- Volume
- 34
- Issue
- 2
- Pages
- 184-191
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4744125
- OpenAlex
- W2252568502
- Semantic Scholar
- b9e5fa707e804d6008e5011b058244437c656a93
- Cited by
- 4,637
- References
- 54
- Citations by year
- 2026: 494 · 2025: 658 · 2024: 569 · 2023: 572 · 2022: 538 · 2021: 499 · 2020: 463 · 2019: 337 · 2018: 258 · 2017: 170 · 2016: 75
- FWCI
- 144.68
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (K12CA087723)
- National Institute of Allergy and Infectious Diseases (U19AI109725, T32AI007163)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Keywords
- sgRNA design, CRISPR/Cas9, off-target effects, on-target activity, genetic screen, off-target prediction, genome editing, human genome, mouse genome
- MeSH terms
- RNA, Guide, CRISPR-Cas Systems, Animals, Drug Resistance, Genetic Engineering, Humans, Gene Library, Genome, Genomics, Cell Line, Tumor, Mice, CRISPR-Cas Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review