Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning
Open accessAbstract
Highly specific Cas9 nucleases derived from SpCas9 are valuable tools for genome editing, but their wide applications are hampered by a lack of knowledge governing guide RNA (gRNA) activity. Here, we perform a genome-scale screen to measure gRNA activity for two highly specific SpCas9 variants (eSpCas9(1.1) and SpCas9-HF1) and wild-type SpCas9 (WT-SpCas9) in human cells, and obtain indel rates of over 50,000 gRNAs for each nuclease, covering ~20,000 genes. We evaluate the contribution of 1,031 features to gRNA activity and develope models for activity prediction. Our data reveals that a combination of RNN with important biological features outperforms other models for activity prediction. We further demonstrate that our model outperforms other popular gRNA design tools. Finally, we develop an online design tool DeepHF for the three Cas9 nucleases. The database, as well as the designer tool, is freely accessible via a web server, http://www.DeepHF.com/ .
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
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?
Most gRNA prediction tools rely on either traditional algorithms or deep learning and focus only on SpCas9-WT; few models are specific to variants.
“Although some gRNA prediction tools have been developed, most of them rely on either traditional algorithms (Rule Set 1, CRISPRscan, etc.) or deep learning (DeepCRISPR, DeepCas9, etc.) and focus only on SpCas9-WT; few models are specific to variants (DeepSpCas9variants, DeepSmallCas9, GuideVar, DeepSniper, etc.).”
From Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing (2024) · cites this paper as [37]
The pattern of SpCas9-NG is similar to SpCas9-WT.
“The pattern of SpCas9-NG is similar to SpCas9-WT.”
From Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing (2024) · cites this paper as [37]
The Introduction and Results passages compare WT-SpCas9 with eSpCas9(1.1) and SpCas9-HF1, not SpCas9-NG. Searches for “SpCas9-NG” and its similarity to WT-SpCas9 found no passage addressing that comparison; page numbers were unavailable in our copy. (LLM, conf=0.76)
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.
Cites
1 retracted
Unexpected mutations after CRISPR–Cas9 editing in vivo
Schaefer, K. A., Wu, W.-H., Colgan, D. F., Tsang, S. H., Bassuk, A. G., & Mahajan, V. B. (2017) · Nature Methods · doi:10.1038/nmeth.4293 · Cited by 328 · see citing works
RetractedScikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
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 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
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 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
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 · see citing works
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251 · see citing works
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 · see citing works
Genome-scale CRISPR-Cas9 knockout screening in human cells
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. (2014) · Science · doi:10.1126/science.1247005 · Cited by 5,234 · see citing works
Lorenz, R., Bernhart, S. H., Höner zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011) · Algorithms for Molecular Biology · doi:10.1186/1748-7188-6-26 · Cited by 5,098 · see citing works
Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9
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. (2016) · Nature Biotechnology · doi:10.1038/nbt.3437 · Cited by 4,637 · see citing works
Wang, H., Yang, H., Shivalila, C. S., Dawlaty, M. M., Cheng, A. W., Zhang, F., & Jaenisch, R. (2013) · Cell · doi:10.1016/j.cell.2013.04.025 · Cited by 3,495 · see citing works
Self-citationPredicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
Genetic screens in human cells using the CRISPR-Cas9 system
Wang, T., Wei, J. J., Sabatini, D. M., & Lander, E. S. (2014) · Science · doi:10.1126/science.1246981 · Cited by 2,856 · see citing works
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works
High-fidelity CRISPR-Cas9 nucleases with no detectable genome-wide off-target effects
Kleinstiver, B. P., Pattanayak, V., Prew, M. S., Tsai, S. Q., Nguyen, N. T., Zheng, Z., & Joung, J. K. (2016) · Nature · doi:10.1038/nature16526 · Cited by 2,673 · see citing works
Rationally engineered Cas9 nucleases with improved specificity
Slaymaker, I. M., Gao, L., Zetsche, B., Scott, D. A., Yan, W. X., & Zhang, F. (2016) · Science · doi:10.1126/science.aad5227 · Cited by 2,414 · see citing works
Easy quantitative assessment of genome editing by sequence trace decomposition
Brinkman, E. K., Chen, T., Amendola, M., & van Steensel, B. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku936 · Cited by 2,390 · see citing works
Improving CRISPR-Cas nuclease specificity using truncated guide RNAs
Fu, Y., Sander, J. D., Reyon, D., Cascio, V. M., & Joung, J. K. (2014) · Nature Biotechnology · doi:10.1038/nbt.2808 · Cited by 1,993 · see citing works
HIV-1 Integration in the Human Genome Favors Active Genes and Local Hotspots
Schröder, A. R. W., Shinn, P., Chen, H., Berry, C., Ecker, J. R., & Bushman, F. (2002) · Cell · doi:10.1016/s0092-8674(02)00864-4 · Cited by 1,832 · see citing works
Haeussler, 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 · see citing works
Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation
Doench, J. G., Hartenian, E., Graham, D. B., Tothova, Z., Hegde, M., Smith, I., Sullender, M., Ebert, B. L., Xavier, R. J., & Root, D. E. (2014) · Nature Biotechnology · doi:10.1038/nbt.3026 · Cited by 1,617 · see citing works
Consistent Individualized Feature Attribution for Tree Ensembles
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430 · see citing works
CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo
Moreno-Mateos, M. A., Vejnar, C. E., Beaudoin, J.-D., Fernandez, J. P., Mis, E. K., Khokha, M. K., & Giraldez, A. J. (2015) · Nature Methods · doi:10.1038/nmeth.3543 · Cited by 1,290 · see citing works
Off-target Effects in CRISPR/Cas9-mediated Genome Engineering
Zhang, X.-H., Tee, L. Y., Wang, X.-G., Huang, Q.-S., & Yang, S.-H. (2015) · Molecular Therapy - Nucleic Acids · doi:10.1038/mtna.2015.37 · Cited by 1,273 · see citing works
Enhanced proofreading governs CRISPR-Cas9 targeting accuracy
Chen, J. S., Dagdas, Y. S., Kleinstiver, B. P., Welch, M. M., Sousa, A. A., Harrington, L. B., Sternberg, S. H., Joung, J. K., Yildiz, A., & Doudna, J. A. (2017) · Nature · doi:10.1038/nature24268 · Cited by 1,168 · see citing works
On the Properties of Neural Machine Translation: Encoder-Decoder ApproachesPreprint
Cho, K., van Merriënboer, B., Bahdanau, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.1259 · Cited by 1,112 · see citing works
Retroviral DNA Integration: ASLV, HIV, and MLV Show Distinct Target Site Preferences
Mitchell, R., Beitzel, B., Schroder, A. R. W., Shinn, P., Chen, H., Berry, C., Ecker, J., & Bushman, F. (2004) · PLoS Biology · doi:10.1371/journal.pbio.0020234 · Cited by 1,024 · see citing works
Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells
Wu, X., Scott, D. A., Kriz, A. J., Chiu, A. C., Hsu, P. D., Dadon, D. B., Cheng, A. W., Trevino, A. E., Konermann, S., Chen, S., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2889 · Cited by 944 · see citing works
Quang, D. X., & Xie, X. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw226 · Cited by 840 · see citing works
Vakulskas, C. A., Dever, D. P., Rettig, G. R., Turk, R., Jacobi, A. M., Collingwood, M. A., Bode, N. M., McNeill, M. S., Yan, S., Camarena, J., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0137-0 · Cited by 782 · see citing works
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 · see citing works
Sequence determinants of improved CRISPR sgRNA design
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. (2015) · Genome Research · doi:10.1101/gr.191452.115 · Cited by 625 · see citing works
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields
Wang, S., Peng, J., Ma, J., & Xu, J. (2016) · Scientific Reports · doi:10.1038/srep18962 · Cited by 556 · see citing works
Directed evolution of CRISPR-Cas9 to increase its specificity
Lee, J. K., Jeong, E., Lee, J., Jung, M., Shin, E., Kim, Y.-H., Lee, K., Jung, I., Kim, D., Kim, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05477-x · Cited by 525 · see citing works
Convolutional neural network architectures for predicting DNA–protein binding
Zeng, H., Edwards, M. D., Liu, G., & Gifford, D. K. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw255 · Cited by 513 · see citing works
A highly specific SpCas9 variant is identified by in vivo screening in yeast
Casini, A., Olivieri, M., Petris, G., Montagna, C., Reginato, G., Maule, G., Lorenzin, F., Prandi, D., Romanel, A., Demichelis, F., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4066 · Cited by 493 · see citing works
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 384 · see citing works
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 · see citing works
WU-CRISPR: characteristics of functional guide RNAs for the CRISPR/Cas9 system
Wong, N. W., Liu, W., & Wang, X. (2015) · Genome biology · doi:10.1186/s13059-015-0784-0 · Cited by 374 · see citing works
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
Liu, Z., Chen, C., Li, L., Zhou, J., Li, X., Song, L., & Qi, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014424 · Cited by 350 · see citing works
sgRNA Scorer 2.0: A Species-Independent Model To Predict CRISPR/Cas9 Activity
Chari, R., Yeo, N. C., Chavez, A., & Church, G. M. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.6b00343 · Cited by 260 · see citing works
DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads
Boža, V., Brejová, B., & Vinař, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178751 · Cited by 246 · see citing works
DeepSol: a deep learning framework for sequence-based protein solubility prediction
Khurana, S., Rawi, R., Kunji, K. B., Chuang, G., Bensmail, H., & Mall, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty166 · Cited by 227 · see citing works
In vivo high-throughput profiling of CRISPR–Cpf1 activity
송명재 (2018) · Cited by 201 · see citing works
DNCON2: improved protein contact prediction using two-level deep convolutional neural networks
Adhikari, B., Hou, J., & Cheng, J. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx781 · Cited by 168 · see citing works
Genome-wide identification of CRISPR/Cas9 off-targets in human genome
Duan, J., Lu, G., Xie, Z., Lou, M., Luo, J., Guo, L., & Zhang, Y. (2014) · Cell Research · doi:10.1038/cr.2014.87 · Cited by 137 · see citing works
Zhang, D., Zhang, H., Li, T., Chen, K., Qiu, J., & Gao, C. (2017) · Genome biology · doi:10.1186/s13059-017-1325-9 · Cited by 133 · see citing works
Xu, M., Zeng, W., Chen, N., Chen, T., & Jiang, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx234 · Cited by 125 · see citing works
A unified view of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2017) · arXiv (Cornell University) · doi:10.3929/ethz-b-000237705 · Cited by 120 · see citing works
Pol III Promoters to Express Small RNAs: Delineation of Transcription Initiation
Ma, H., Wu, Y., Dang, Y., Choi, J., Zhang, J., & Wu, H. (2014) · Molecular Therapy — Nucleic Acids · doi:10.1038/mtna.2014.12 · Cited by 117 · see citing works
Xie, Y., Wang, D., Lan, F., Wei, G., Ni, T., Chai, R., Liu, D., Hu, S., Li, M., Li, D., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-02456-y · Cited by 114 · see citing works
Self-citationProtein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural NetworksPreprint
Li, Z., & Yu, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.07176 · Cited by 107 · see citing works
Improved sgRNA design in bacteria via genome-wide activity profiling
Guo, J., Wang, T., Guan, C., Liu, B., Luo, C. Y., Xie, Z., Zhang, C., & Xing, X. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky572 · Cited by 91 · see citing works
Self-citationRahman, M. K., & Rahman, M. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181943 · Cited by 79 · see citing works
Sources of Error in Mammalian Genetic Screens
Sack, L. M., Davoli, T., Xu, Q., Li, M. Z., & Elledge, S. J. (2016) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.116.030973 · Cited by 76 · see citing works
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 54 · see citing works
Gao, Z., Harwig, A., Berkhout, B., & Herrera-Carrillo, E. (2017) · Transcription · doi:10.1080/21541264.2017.1322170 · Cited by 47 · see citing works
GeniePath: Graph Neural Networks with Adaptive Receptive PathsPreprint
Liu, Z., Chen, C., Li, L., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.00910 · Cited by 47 · see citing works
In Silico Predictive Modeling of CRISPR/Cas9 guide efficiencyPreprint
Fusi, N., Smith, I. C. P., Doench, J. G., & Listgarten, J. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/021568 · Cited by 38 · see citing works
Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”
Lareau, C. A., Clement, K., Hsu, J. Y., Pattanayak, V., Joung, J. K., Aryee, M. J., & Pinello, L. (2018) · Nature Methods · doi:10.1038/nmeth.4541 · Cited by 32 · see citing works
Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”
Kim, S., Park, J., Kim, D., Kim, K., Bae, S., Schlesner, M., & Kim, J. (2018) · Nature Methods · doi:10.1038/nmeth.4554 · Cited by 31 · see citing works
Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”
Nutter, L. M. J., Heaney, J. D., Lloyd, K. C. K., et al. (2018) · Nature Methods · doi:10.1038/nmeth.4559 · Cited by 26 · see citing works
Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”
Wilson, C. J., Fennell, T. J., Bothmer, A., Maeder, M. L., Reyon, D., Cotta‐Ramusino, C., Fernández, C. A., Marco, E., Barrera, L. A., Jayaram, H., et al. (2018) · Nature Methods · doi:10.1038/nmeth.4552 · Cited by 26 · see citing works
Towards better understanding of gradient-based attribution methods for Deep Neural NetworksPreprint
Ancona, M., Ceolini, E., Öztireli, C., et al. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.48550/arxiv.1711.06104 · Cited by 26 · see citing works
Xu, M., Zeng, W., Chen, N., Chen, T., & Jiang, R. (2017) · doi:10.7490/f1000research.1114750.1 · Cited by 23 · see citing works
Response to “Unexpected mutations after CRISPR–Cas9 editing in vivo”
Lescarbeau, R. M., Murray, B. A., Barnes, T. M., & Bermingham, N. A. (2018) · Nature Methods · doi:10.1038/nmeth.4553 · Cited by 23 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [55]Krizhevsky, A. et al. in Advances in Neural Information Processing Systems, Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Vol. 25, 1097–1105 (Curran Associates, Inc., 2012).
Cited by
Sorted by citations
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
CRISPR-Cas9 genome editing using targeted lipid nanoparticles for cancer therapy
Rosenblum, D., Gutkin, A., Kedmi, R., Ramishetti, S., Veiga, N., Jacobi, A. M., Schubert, M. S., Friedmann-Morvinski, D., Cohen, Z. R., Behlke, M. A., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc9450 · Cited by 614 · see citing works
Machine learning in healthcare
Habehh, H., & Gohel, S. (2021) · Current Genomics · doi:10.2174/1389202922666210705124359 · Cited by 544 · see citing works
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning
Xiang, X., Corsi, G. I., Anthon, C., Qu, K., Pan, X., Liang, X., Han, P., Dong, Z., Liu, L., Zhong, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23576-0 · Cited by 209 · see citing works
Prediction of the sequence-specific cleavage activity of Cas9 variants
Kim, N., Kim, H. K., Lee, S., Seo, J. H., Choi, J. W., Park, J., Min, S., Yoon, S., Cho, S.-R., & Kim, H. H. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0537-9 · Cited by 198 · see citing works
Engineering a precise adenine base editor with minimal bystander editing
Chen, L., Zhang, S., Xue, N., Hong, M., Zhang, X., Zhang, D., Yang, J., Bai, S., Huang, Y., Meng, H., et al. (2023) · Nature Chemical Biology · doi:10.1038/s41589-022-01163-8 · Cited by 202 · see citing works
EXplainable Artificial Intelligence (XAI)—From Theory to Methods and Applications
Ortigossa, E. S., Gonçalves, T., & Nonato, L. G. (2024) · IEEE Access · doi:10.1109/access.2024.3409843 · Cited by 124 · see citing works
Improved gRNA secondary structures allow editing of target sites resistant to CRISPR-Cas9 cleavage
Riesenberg, S., Helmbrecht, N., Kanis, P., Maricic, T., & Pääbo, S. (2022) · Nature Communications · doi:10.1038/s41467-022-28137-7 · Cited by 109 · see citing works
CRISPR-based bioengineering in microalgae for production of industrially important biomolecules
Dhokane, D., Shaikh, A., Yadav, A., Giri, N., Bandyopadhyay, A., Dasgupta, S., & Bhadra, B. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1267826 · Cited by 106 · see citing works
High-efficiency nonviral CRISPR/Cas9-mediated gene editing of human T cells using plasmid donor DNA
Oh, S. A., Senger, K., Madireddi, S., Akhmetzyanova, I., Ishizuka, I. E., Tarighat, S., Lo, J. H., Shaw, D., Haley, B., & Rutz, S. (2022) · Journal of Experimental Medicine · doi:10.1084/jem.20211530 · Cited by 88 · see citing works
Li, J., Wu, P., Cao, Z., Huang, G., Lu, Z., Yan, J., Zhang, H., Zhou, Y., Liu, R., Chen, H., et al. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113765 · Cited by 26 · see citing works
Self-citationYaish, O., Asif, M., & Orenstein, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac157 · Cited by 19 · see citing works
BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models
Zarate, O. A., Yang, Y., Wang, X., & Wang, J.-P. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04998-z · Cited by 17 · see citing works
Hybrid multitask learning reveals sequence features driving specificity in the CRISPR/Cas9 system
Vora, D. S., Yadav, S., & Sundar, D. (2023) · Biomolecules · doi:10.3390/biom13040641 · Cited by 17 · see citing works
Okada, K., Aoki, K., Tabei, T., Sugio, K., Imai, K., Bonkohara, Y., & Kamachi, Y. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac100 · Cited by 14 · see citing works
- Authors
- Wang, D., Zhang, C., Wang, B., Li, B., Wang, Q., Liu, D., Wang, H., Zhou, Y., Shi, L., Lan, F., & Wang, Y.
- Affiliations
- Fudan University (CN)
- Nantong University (CN)
- Capital Medical University (CN)
- Corresponding author
- Lan, Feng
- Wang, Yongming
- ORCID
- Venue
- Nature Communications
- Published
- 2019-09-19
- Volume
- 10
- Issue
- 1
- Pages
- 4284-4284
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6753114
- OpenAlex
- W2973312556
- Cited by
- 331
- References
- 70
- Citations by year
- 2026: 43 · 2025: 55 · 2024: 47 · 2023: 39 · 2022: 63 · 2021: 45 · 2020: 37 · 2019: 1 · 2018: 1
- FWCI
- 12.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (31720103909, 31471239)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Interference and Gene Delivery
- Keywords
- CRISPR guide RNA design, genome-wide screen, deep learning, recurrent neural networks, genome editing, web-based design tool
- MeSH terms
- Gene Editing, Deep Learning, RNA, Guide, CRISPR-Cas Systems, Animals, Base Sequence, Endonucleases, HeLa Cells, Humans, Promoter Regions, Genetic, RNA, Guide, Kinetoplastida, Internet, Cell Line, Tumor, Mice, INDEL Mutation, HEK293 Cells, Clustered Regularly Interspaced Short Palindromic Repeats, 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
