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
CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens
Concordet, J., & Haeussler, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky354 · Cited by 2,392
Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors
Anzalone, A. V., Koblan, L. W., & Liu, D. R. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0561-9 · Cited by 2,237
CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing
Labun, K., Montague, T. G., Krause, M., Cleuren, Y. N. T., Tjeldnes, H., & Valen, E. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz365 · Cited by 2,075
Meyers, R. M., Bryan, J. G., McFarland, J. M., Weir, B. A., Sizemore, A. E., Xu, H., Dharia, N. V., Montgomery, P. G., Cowley, G. S., Pantel, S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3984 · Cited by 1,952
Self-citationHaeussler, M., Schönig, K., Eckert, H., Eschstruth, A., Mianné, J., Renaud, J.-B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1012-2 · Cited by 1,756
Genome-scale CRISPR-Cas9 Knockout and Transcriptional Activation Screening
Joung, J., Konermann, S., Gootenberg, J. S., Abudayyeh, O. O., Platt, R. J., Brigham, M. D., Sanjana, N. E., & Zhang, F. (2017) · Nature Protocols · doi:10.1038/nprot.2017.016 · Cited by 1,243
CRISPR–Cas9 genome editing induces a p53-mediated DNA damage response
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B., & Taipale, J. (2018) · Nature Medicine · doi:10.1038/s41591-018-0049-z · Cited by 1,264
Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117
“We used the STARS algorithm (Doench et al., 2016) to derive 38 hits, 18 of which were shared across at least two cell lines (Figure 7F; STAR Methods).”
MethodOptimized libraries for CRISPR-Cas9 genetic screens with multiple modalities
Sanson, K. R., Hanna, R. E., Hegde, M., Donovan, K. F., Strand, C., Sullender, M. E., Vaimberg, E. W., Goodale, A., Root, D. E., Piccioni, F., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07901-8 · Cited by 1,043
Self-citationHorlbeck, M. A., Gilbert, L. A., Villalta, J. E., Adamson, B., Pak, R. A., Chen, Y., Fields, A. P., Park, C. Y., Corn, J. E., Kampmann, M., et al. (2016) · eLife · doi:10.7554/elife.19760 · Cited by 894
Accurate classification of BRCA1 variants with saturation genome editing
Findlay, G. M., Daza, R. M., Martin, B., Zhang, M. D., Leith, A. P., Gasperini, M., Janizek, J. D., Huang, X., Starita, L. M., & Shendure, J. (2018) · Nature · doi:10.1038/s41586-018-0461-z · Cited by 843
CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering
Labun, K., Montague, T. G., Gagnon, J. A., Thyme, S. B., & Valen, E. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw398 · Cited by 954
Discovering the anticancer potential of non-oncology drugs by systematic viability profiling
Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G., Humeidi, R., Peck, D., Wu, X., Tang, A. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-019-0018-6 · Cited by 878
Self-citationZou, Y., Palte, M. J., Deik, A. A., Li, H., Eaton, J. K., Wang, W., Tseng, Y.-Y., Deasy, R., Kost-Alimova, M., Dančík, V., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09277-9 · Cited by 887
Self-citationTzelepis, K., Koike-Yusa, H., De Braekeleer, E., Li, Y., Metzakopian, E., Dovey, O. M., Mupo, A., Grinkevich, V., Li, M., Mazan, M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.079 · Cited by 761
A major chromatin regulator determines resistance of tumor cells to T cell–mediated killing
Pan, D., Kobayashi, A., Jiang, P., Ferrari de Andrade, L., Tay, R. E., Luoma, A. M., Tsoucas, D., Qiu, X., Lim, K., Rao, P., et al. (2018) · Science · doi:10.1126/science.aao1710 · Cited by 852
Self-citationCRISPR-P 2.0: An Improved CRISPR-Cas9 Tool for Genome Editing in Plants
Liu, H., Ding, Y., Zhou, Y., Jin, W., Xie, K., & Chen, L. (2017) · Molecular Plant · doi:10.1016/j.molp.2017.01.003 · Cited by 843
An atlas of genetic influences on osteoporosis in humans and mice
23andMe Research Team, Morris, J. A., Kemp, J. P., Youlten, S. E., Laurent, L., Logan, J. G., Chai, R. C., Vulpescu, N. A., Forgetta, V., Kleinman, A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0302-x · Cited by 773
TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge
Nugent, A. A., Lin, K., van Lengerich, B., Lianoglou, S., Przybyla, L., Davis, S. S., Llapashtica, C., Wang, J., Kim, D. J., Xia, D., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.007 · Cited by 745
Cytochrome P450 oxidoreductase contributes to phospholipid peroxidation in ferroptosis
Zou, Y., Li, H., Graham, E. T., Deik, A., Eaton, J. K., Wang, W., Sandoval-Gomez, G., Clish, C. B., Doench, J. G., & Schreiber, S. L. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0472-6 · Cited by 753
Self-citationIdentification of CMTM6 and CMTM4 as PD-L1 protein regulators
Mezzadra, R., Sun, C., Jae, L. T., Gomez-Eerland, R., de Vries, E., Wu, W., Logtenberg, M. E. W., Slagter, M., Rozeman, E. A., Hofland, I., et al. (2017) · Nature · doi:10.1038/nature23669 · Cited by 697
DeepCRISPR: Optimized CRISPR guide RNA design by deep learning
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1459-4 · Cited by 641
Pervasive functional translation of noncanonical human open reading frames
Chen, J., Brunner, A., Cogan, J. Z., Nuñez, J. K., Fields, A. P., Adamson, B., Itzhak, D. N., Li, J. Y., Mann, M., Leonetti, M. D., et al. (2020) · Science · doi:10.1126/science.aay0262 · Cited by 654
Gaidt, M. M., Ebert, T. S., Chauhan, D., Ramshorn, K., Pinci, F., Zuber, S., O’Duill, F., Schmid-Burgk, J. L., Hoss, F., Buhmann, R., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.039 · Cited by 638
Off-target effects in CRISPR/Cas9 gene editing
Guo, C., Ma, X., Gao, F., & Guo, Y. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1143157 · Cited by 704
“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).”
BackgroundMethodPyroptosis and Apoptosis Pathways Engage in Bidirectional Crosstalk in Monocytes and Macrophages
Taabazuing, C. Y., Okondo, M. C., & Bachovchin, D. A. (2017) · Cell chemical biology · doi:10.1016/j.chembiol.2017.03.009 · Cited by 630
Past, present, and future of CRISPR genome editing technologies
Pacesa, M., Pelea, O., & Jinek, M. (2024) · Cell · doi:10.1016/j.cell.2024.01.042 · Cited by 609
Evaluation and Design of Genome-Wide CRISPR/SpCas9 Knockout Screens
Hart, T., Tong, A. H. Y., Chan, K., Van Leeuwen, J., Seetharaman, A., Aregger, M., Chandrashekhar, M., Hustedt, N., Seth, S., Noonan, A., et al. (2017) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.117.041277 · Cited by 599
Bock, C., Datlinger, P., Chardon, F. M., Coelho, M. A., Dong, M. B., Lawson, K. A., Lu, T., Maroc, L., Norman, T. M., Song, B., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00093-4 · Cited by 641
Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function
Shifrut, E., Carnevale, J., Tobin, V., Roth, T. L., Woo, J. M., Bui, C. T., Li, P. J., Diolaiti, M. E., Ashworth, A., & Marson, A. (2018) · Cell · doi:10.1016/j.cell.2018.10.024 · Cited by 573
Predictable and precise template-free CRISPR editing of pathogenic variants
Shen, M. W., Arbab, M., Hsu, J. Y., Worstell, D., Culbertson, S. J., Krabbe, O., Cassa, C. A., Liu, D. R., Gifford, D. K., & Sherwood, R. I. (2018) · Nature · doi:10.1038/s41586-018-0686-x · Cited by 569
Multiplexed CRISPR technologies for gene editing and transcriptional regulation
McCarty, N. S., Graham, A. E., Studená, L., & Ledesma‐Amaro, R. (2020) · Nature Communications · doi:10.1038/s41467-020-15053-x · Cited by 567
CRISPR-GE: A Convenient Software Toolkit for CRISPR-Based Genome Editing
Xie, X., Ma, X., Zhu, Q., Zeng, D., Li, G., & Liu, Y. (2017) · Molecular Plant · doi:10.1016/j.molp.2017.06.004 · Cited by 549
Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars
Spanjaard, B., Hu, B., Mitic, N., Olivares-Chauvet, P., Janjuha, S., Ninov, N., & Junker, J. P. (2018) · Nature Biotechnology · doi:10.1038/nbt.4124 · Cited by 531
Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection
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. (2020) · Cell · doi:10.1016/j.cell.2020.10.028 · Cited by 572
Self-citationYoo, S., Mittelstein, D. R., Hurt, R. C., Lacroix, J. J., & Shapiro, M. G. (2022) · Nature Communications · doi:10.1038/s41467-022-28040-1 · Cited by 514
Mumbach, M. R., Satpathy, A. T., Boyle, E. A., Dai, C., Gowen, B. G., Cho, S. W., Nguyen, M. L., Rubin, A. J., Granja, J. M., Kazane, K. R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3963 · Cited by 490
In vivo base editing rescues Hutchinson–Gilford progeria syndrome in mice
Koblan, L. W., Erdos, M. R., Wilson, C., Cabral, W. A., Levy, J. M., Xiong, Z., Tavarez, U. L., Davison, L. M., Gete, Y. G., Mao, X., et al. (2021) · Nature · doi:10.1038/s41586-020-03086-7 · Cited by 489
Zhang, J., Li, X., Li, G., Chen, W., Arakaki, C., Botimer, G. D., Baylink, D. J., Zhang, L., Wen, W., Fu, Y.-W., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1164-8 · Cited by 493
The UCSC Genome Browser database: 2018 update
Casper, J., Zweig, A. S., Villarreal, C., Tyner, C., Speir, M. L., Rosenbloom, K. R., Raney, B. J., Lee, C. M., Lee, B. T., Karolchik, D., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1020 · Cited by 475
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics
Chiou, J., Geusz, R. J., Okino, M.-L., Han, J. Y., Miller, M., Melton, R. L., Beebe, E., Benaglio, P., Huang, S., Korgaonkar, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03552-w · Cited by 447
Massively parallel Cas13 screens reveal principles for guide RNA design
Wessels, H.-H., Méndez-Mancilla, A., Guo, X., Legut, M., Daniloski, Z., & Sanjana, N. E. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0456-9 · Cited by 418
Latest Developed Strategies to Minimize the Off-Target Effects in CRISPR-Cas-Mediated Genome Editing
Naeem, M., Majeed, S., Hoque, M. Z., & Ahmad, I. (2020) · Cells · doi:10.3390/cells9071608 · Cited by 457
Circuit Design Features of a Stable Two-Cell System
Zhou, X., Franklin, R. A., Adler, M., Jacox, J. B., Bailis, W., Shyer, J. A., Flavell, R. A., Mayo, A., Alon, U., & Medzhitov, R. (2018) · Cell · doi:10.1016/j.cell.2018.01.015 · Cited by 430
Am I ready for CRISPR? A user's guide to genetic screens
Doench, J. G. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2017.97 · Cited by 452
Self-citationTargeting REGNASE-1 programs long-lived effector T cells for cancer therapy
Wei, J., Long, L., Zheng, W., Dhungana, Y., Lim, S. A., Guy, C. S., Wang, Y., Wang, Y., Qian, C., Xu, B., et al. (2019) · Nature · doi:10.1038/s41586-019-1821-z · Cited by 447
Self-citationCornerstones of CRISPR–Cas in drug discovery and therapy
Fellmann, C., Gowen, B. G., Lin, P., Doudna, J. A., & Corn, J. E. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.238 · Cited by 452
Metabolic reprogramming of terminally exhausted CD8+ T cells by IL-10 enhances anti-tumor immunity
Guo, Y., Xie, Y., Gao, M., Zhao, Y., Franco, F., Wenes, M., Siddiqui, I., Bevilacqua, A., Wang, H., Yang, H., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-00940-2 · Cited by 455
Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy
Chaffin, M., Papangeli, I., Simonson, B., Akkad, A.-D., Hill, M. C., Arduini, A., Fleming, S. J., Melanson, M., Hayat, S., Kost-Alimova, M., et al. (2022) · Nature · doi:10.1038/s41586-022-04817-8 · Cited by 418
CRISPR in cancer biology and therapy
Katti, A., Diaz, B. J., Caragine, C. M., Sanjana, N. E., & Dow, L. E. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00441-w · Cited by 457
Rees, H. A., Komor, A. C., Yeh, W.-H., Caetano-Lopes, J., Warman, M., Edge, A. S. B., & Liu, D. R. (2017) · Nature Communications · doi:10.1038/ncomms15790 · Cited by 412
The CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K
Słabicki, M., Kozicka, Z., Petzold, G., Li, Y.-D., Manojkumar, M., Bunker, R. D., Donovan, K. A., Sievers, Q. L., Koeppel, J., Suchyta, D., et al. (2020) · Nature · doi:10.1038/s41586-020-2374-x · Cited by 433
Self-citationControl of gasdermin D oligomerization and pyroptosis by the Ragulator-Rag-mTORC1 pathway
Evavold, C. L., Hafner‐Bratkovič, I., Devant, P., D’Andrea, J. M., Ngwa, E. M., Boršić, E., Doench, J. G., LaFleur, M. W., Sharpe, A. H., Thiagarajah, J. R., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.028 · Cited by 426
Self-citationLiu, B., Gao, X., & Zhang, H. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz740 · Cited by 411
Armored CAR T cells enhance antitumor efficacy and overcome the tumor microenvironment
Yeku, O., Purdon, T. J., Koneru, M., Spriggs, D. R., & Brentjens, R. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-10940-8 · Cited by 420
Tumor immune evasion arises through loss of TNF sensitivity
Kearney, C. J., Vervoort, S. J., Hogg, S. J., Ramsbottom, K. M., Freeman, A. J., Lalaoui, N., Pijpers, L., Michie, J., Brown, K. K., Knight, D. A., et al. (2018) · Science Immunology · doi:10.1126/sciimmunol.aar3451 · Cited by 381
A multiprotein supercomplex controlling oncogenic signalling in lymphoma
Phelan, J. D., Young, R. M., Webster, D. E., Roulland, S., Wright, G. W., Kasbekar, M., Shaffer, A. L., Ceribelli, M., Wang, J. Q., Schmitz, R., et al. (2018) · Nature · doi:10.1038/s41586-018-0290-0 · Cited by 416
Brestoff, J. R., Wilen, C. B., Moley, J. R., Li, Y., Zou, W., Malvin, N. P., Rowen, M. N., Saunders, B. T., Ma, H., Mack, M. R., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.11.008 · Cited by 399
Self-citationGenome-Scale Identification of SARS-CoV-2 and Pan-coronavirus Host Factor Networks
Schneider, W. M., Luna, J. M., Hoffmann, H.-H., Rivera, F. J. S., Leal, A. A., Ashbrook, A. W., Pen, J. L., Ricardo-Lax, I., Michailidis, E., Peace, A., et al. (2020) · Cell · doi:10.1016/j.cell.2020.12.006 · Cited by 397
Dempster, J. M., Boyle, I., Vázquez, F., Root, D. E., Boehm, J. S., Hahn, W. C., Tsherniak, A., & McFarland, J. M. (2021) · Genome biology · doi:10.1186/s13059-021-02540-7 · Cited by 433
Self-citationLymph node colonization induces tumor-immune tolerance to promote distant metastasis
Reticker-Flynn, N. E., Zhang, W., Belk, J. A., Basto, P. A., Escalante, N. K., Pilarowski, G. O. W., Bejnood, A., Martins, M. M., Kenkel, J. A., Linde, I. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.019 · Cited by 399
Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions
Shen, J. P., Zhao, D., Sasik, R., Luebeck, J., Birmingham, A., Bojorquez-Gomez, A., Licon, K., Klepper, K., Pekin, D., Beckett, A. N., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4225 · Cited by 359
Deep learning improves prediction of CRISPR–Cpf1 guide RNA activity
Kim, H. K., Min, S., Song, M., Jung, S., Choi, J. W., Kim, Y., Lee, S., Yoon, S., & Kim, S. (2018) · Nature Biotechnology · doi:10.1038/nbt.4061 · Cited by 388
In vivo high-throughput profiling of CRISPR-Cpf1 activity
Kim, H. K., Song, M., Lee, J., Menon, A. V., Jung, S., Kang, Y.-M., Choi, J. W., Woo, E., Koh, H. C., Nam, J.-W., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4104 · Cited by 383
Manghwar, H., Li, B., Ding, X., Hussain, A., Lindsey, K., Zhang, X., & Jin, S. (2020) · Advanced Science · doi:10.1002/advs.201902312 · Cited by 347
The TP53 Apoptotic Network Is a Primary Mediator of Resistance to BCL2 Inhibition in AML Cells
Nechiporuk, T., Kurtz, S. E., Nikolova, O. H., Liu, T., Jones, C. L., D’Alessandro, A., Culp‐Hill, R., d’Almeida, A., Joshi, S. K., Rosenberg, M. W., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0125 · Cited by 392
Hung, V., Lam, S. S., Udeshi, N. D., Svinkina, T., Guzman, G., Mootha, V. K., Carr, S. A., & Ting, A. Y. (2017) · eLife · doi:10.7554/elife.24463 · Cited by 373
Rational discovery of molecular glue degraders via scalable chemical profiling
Mayor-Ruiz, C., Bauer, S., Brand, M., Kozicka, Z., Siklos, M., Imrichova, H., Kaltheuner, I. H., Hahn, E., Seiler, K., Koren, A., et al. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-0594-x · Cited by 369
Mitochondrial Reprogramming Underlies Resistance to BCL-2 Inhibition in Lymphoid Malignancies
Guièze, R., Liu, V. M., Rosebrock, D., Jourdain, A. A., Hernández-Sánchez, M., Martinez Zurita, A., Sun, J., Ten Hacken, E., Baranowski, K., Thompson, P. A., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.08.005 · Cited by 361
Self-citationInflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer
Nicolas, A. M., Pesic, M., Engel, E., Ziegler, P. K., Diefenhardt, M., Kennel, K. B., Buettner, F., Conche, C., Petrocelli, V., Elwakeel, E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.01.004 · Cited by 349
CAR T cell killing requires the IFNγR pathway in solid but not liquid tumours
Larson, R. C., Kann, M. C., Bailey, S. R., Haradhvala, N. J., Llopis, P. D. L. M. M., Bouffard, A. A., Scarfò, I., Leick, M. B., Grauwet, K., Berger, T. R., et al. (2022) · Nature · doi:10.1038/s41586-022-04585-5 · Cited by 365
DPP8/DPP9 inhibitor-induced pyroptosis for treatment of acute myeloid leukemia
Johnson, D. C., Taabazuing, C. Y., Okondo, M. C., Chui, A. J., Rao, S. D., Brown, F. C., Reed, C., Peguero, E., de Stanchina, E., Kentsis, A., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0082-y · Cited by 342
Targeting Mitochondrial Structure Sensitizes Acute Myeloid Leukemia to Venetoclax Treatment
Chen, X., Glytsou, C., Zhou, H., Narang, S., Reyna, D. E., López, A., Sakellaropoulos, T., Gong, Y., Kloetgen, A., Yap, Y. S., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0117 · Cited by 344
Epigenetic Silencing of CDR1as Drives IGF2BP3-Mediated Melanoma Invasion and Metastasis
Hanniford, D., Ulloa-Morales, A. J., Karz, A., Berzoti-Coelho, M. G., Moubarak, R. S., Sánchez‐Sendra, B., Kloetgen, A., Dávalos, V., Imig, J., Wu, P., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2019.12.007 · Cited by 348
Methodologies for Improving HDR Efficiency
Liu, M., Rehman, S., Tang, X., Gu, K., Fan, Q., Chen, D., & Ma, W. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2018.00691 · Cited by 323
A gene-specific T2A-GAL4 library for Drosophila
Lee, P.-T., Zirin, J. D., Kanca, O., Lin, W., Schulze, K. L., Li‐Kroeger, D., Tao, R., Devereaux, C., Hu, Y., Chung, V., et al. (2018) · eLife · doi:10.7554/elife.35574 · Cited by 323
Massively parallel assessment of human variants with base editor screens
Hanna, R. E., Hegde, M., Fagre, C. R., DeWeirdt, P. C., Sangree, A. K., Szegletes, Z., Griffith, A., Feeley, M. N., Sanson, K. R., Baidi, Y., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.012 · Cited by 316
Stem Cell Lineage Infidelity Drives Wound Repair and Cancer
Ge, Y., Gomez, N. C., Adam, R. C., Nikolova, M., Yang, H., Verma, A., Lu, C. P.-J., Polak, L., Yuan, S., Elemento, O., et al. (2017) · Cell · doi:10.1016/j.cell.2017.03.042 · Cited by 342
A Survey of Validation Strategies for CRISPR-Cas9 Editing
Sentmanat, M. F., Peters, S., Florian, C. P., Connelly, J. P., & Pruett‐Miller, S. M. (2018) · Scientific Reports · doi:10.1038/s41598-018-19441-8 · Cited by 351
Enhancing genetic gain in the era of molecular breeding
Xu, Y., Li, P., Zou, C., Lu, Y., Xie, C., Zhang, X., Prasanna, B. M., & Olsen, M. S. (2017) · Journal of Experimental Botany · doi:10.1093/jxb/erx135 · Cited by 351
Editing of the Bacillus subtilis Genome by the CRISPR-Cas9 System
Altenbuchner, J. (2016) · Applied and Environmental Microbiology · doi:10.1128/aem.01453-16 · Cited by 346
Zischewski, J., Fischer, R., & Bortesi, L. (2017) · Biotechnology Advances · doi:10.1016/j.biotechadv.2016.12.003 · Cited by 323
Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs
Listgarten, J., Weinstein, M., Kleinstiver, B. P., Sousa, A. A., Joung, J. K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J. G., et al. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0178-6 · Cited by 336
Self-citationHighly Efficient Mouse Genome Editing by CRISPR Ribonucleoprotein Electroporation of Zygotes
Chen, S., Lee, B. J., Lee, A. Y.-F., Modzelewski, A. J., & He, L. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m116.733154 · Cited by 338
ATP13A2 deficiency disrupts lysosomal polyamine export
van Veen, S., Martin, S., den Haute, C. V., Benoy, V., Lyons, J. A., Vanhoutte, R., Kahler, J. P., Decuypere, J.-P., Gelders, G., Lambie, E. J., et al. (2020) · Nature · doi:10.1038/s41586-020-1968-7 · Cited by 349
Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning
Wang, D., Zhang, C., Wang, B., Li, B., Wang, Q., Liu, D., Wang, H., Zhou, Y., Shi, L., Lan, F., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12281-8 · Cited by 331
CRISPR/Cas9 for cancer research and therapy
Zhan, T., Rindtorff, N., Betge, J., Ebert, M., & Boutros, M. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.04.001 · Cited by 338
Astrocytic interleukin-3 programs microglia and limits Alzheimer’s disease
McAlpine, C. S., Park, J., Griciuc, A., Kim, E., Choi, S. H., Iwamoto, Y., Kiss, M. G., Christie, K. A., Vinegoni, C., Poller, W. C., et al. (2021) · Nature · doi:10.1038/s41586-021-03734-6 · Cited by 322
In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes
Jin, X., Simmons, S. K., Guo, A., Shetty, A. S., Ko, M., Nguyen, L., Jokhi, V., Robinson, E., Oyler, P., Curry, N., et al. (2020) · Science · doi:10.1126/science.aaz6063 · Cited by 317
Takeda, D. Y., Spisák, S., Seo, J.-H., Bell, C., O’Connor, E., Korthauer, K., Ribli, D., Csabai, I., Solymosi, N., Szállási, Z., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.037 · Cited by 317
Tumor innate immunity primed by specific interferon-stimulated endogenous retroviruses
Cañadas, I., Thummalapalli, R., Kim, J. W., Kitajima, S., Jenkins, R. W., Christensen, C. L., Campisi, M., Kuang, Y., Zhang, Y., Gjini, E., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0116-5 · Cited by 324
Machine learning for metabolic engineering: A review
Lawson, C. E., Martí, J. M., Radivojević, T., Jonnalagadda, S. V. R., Gentz, R., Hillson, N. J., Peisert, S., Kim, J., Simmons, B. A., Petzold, C. J., et al. (2020) · Metabolic Engineering · doi:10.1016/j.ymben.2020.10.005 · Cited by 335
Genome editing of model oleaginous microalgae Nannochloropsis spp. by CRISPR /Cas9
Wang, Q., Lu, Y., Xin, Y., Wei, L., Huang, S., & Xu, J. (2016) · The Plant Journal · doi:10.1111/tpj.13307 · Cited by 312
Singh, N., Lee, Y. G., Shestova, O., Ravikumar, P., Hayer, K. E., Hong, S. J., Lu, X. M., Pajarillo, R., Agarwal, S., Kuramitsu, S., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0813 · Cited by 295
Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens
Najm, F. J., Strand, C., Donovan, K. F., Hegde, M., Sanson, K. R., Vaimberg, E. W., Sullender, M. E., Hartenian, E., Kalani, Z., Fusi, N., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4048 · Cited by 279
Self-citationA Genome-wide CRISPR Death Screen Identifies Genes Essential for Oxidative Phosphorylation
Arroyo, J. D., Jourdain, A. A., Calvo, S. E., Ballarano, C. A., Doench, J. G., Root, D. E., & Mootha, V. K. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.017 · Cited by 295
Self-citationA genome-wide CRISPR screen identifies host factors that regulate SARS-CoV-2 entry
Zhu, Y., Feng, F., Hu, G., Wang, Y., Yin, Y., Zhu, Y., Xü, W., Cai, X., Sun, Z., Han, W., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21213-4 · Cited by 307
Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity
Tycko, J., Myer, V. E., & Hsu, P. D. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.07.004 · Cited by 315
AMPK/FIS1-Mediated Mitophagy Is Required for Self-Renewal of Human AML Stem Cells
Pei, S., Minhajuddin, M., Adane, B., Khan, N., Stevens, B. M., Mack, S. C., Lai, S., Rich, J. N., Sheth, A. I., Shannon, K., et al. (2018) · Cell stem cell · doi:10.1016/j.stem.2018.05.021 · Cited by 311
N-terminal degradation activates the NLRP1B inflammasome
Chui, A. J., Okondo, M. C., Rao, S. D., Gai, K., Griswold, A. R., Johnson, D. C., Ball, D. P., Taabazuing, C. Y., Orth-He, E. L., Vittimberga, B. A., et al. (2019) · Science · doi:10.1126/science.aau1208 · Cited by 299
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