The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool
Open accessAbstract
Engineering of the CRISPR/Cas9 system has opened a plethora of new opportunities for site-directed mutagenesis and targeted genome modification. Fundamental to this is a stretch of twenty nucleotides at the 5' end of a guide RNA that provides specificity to the bound Cas9 endonuclease. Since a sequence of twenty nucleotides can occur multiple times in a given genome and some mismatches seem to be accepted by the CRISPR/Cas9 complex, an efficient and reliable in silico selection and evaluation of the targeting site is key prerequisite for the experimental success. Here we present the CRISPR/Cas9 target online predictor (CCTop, http://crispr.cos.uni-heidelberg.de) to overcome limitations of already available tools. CCTop provides an intuitive user interface with reasonable default parameters that can easily be tuned by the user. From a given query sequence, CCTop identifies and ranks all candidate sgRNA target sites according to their off-target quality and displays full documentation. CCTop was experimentally validated for gene inactivation, non-homologous end-joining as well as homology directed repair. Thus, CCTop provides the bench biologist with a tool for the rapid and efficient identification of high quality target sites.
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
Figures



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 cited source says what the claim attributes to it.
In the citing paper
“CCTop and CROP-IT generate scores based on the distances of the mismatches to the PAM (Singh et al., 2015; Stemmer et al., 2015).”
From Off-target effects in CRISPR/Cas9 gene editing (2023) · cites this paper as Stemmer et al., 2015
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
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
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
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400
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
DNA targeting specificity of RNA-guided Cas9 nucleases
Hsu, P. D., Scott, D. A., Weinstein, J. A., Ran, F. A., Konermann, S., Agarwala, V., Li, Y., Fine, E. J., Wu, X., Shalem, O., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2647 · Cited by 4,741
Efficient genome editing in zebrafish using a CRISPR-Cas system
Hwang, W. Y., Fu, Y., Reyon, D., Maeder, M. L., Tsai, S. Q., Sander, J. D., Peterson, R. T., Yeh, J.-R. J., & Joung, J. K. (2013) · Nature Biotechnology · doi:10.1038/nbt.2501 · Cited by 2,937
Genome editing with engineered zinc finger nucleases
Urnov, F. D., Rebar, E. J., Holmes, M. C., Zhang, H. S., & Gregory, P. D. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2842 · Cited by 2,285
A TALE nuclease architecture for efficient genome editing
Miller, J. C., Tan, S., Qiao, G., Barlow, K. A., Wang, J., Xia, D. F., Meng, X., Paschon, D. E., Leung, E., Hinkley, S. J., et al. (2011) · Nature Biotechnology · doi:10.1038/nbt.1755 · Cited by 2,098
Cited by
Sorted by citations
Items 201–300 of 1,000
Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities
Zhang, G., Luo, Y., Dai, X., & Dai, Z. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad333 · Cited by 47
Servetnick, M. D., Steinworth, B. M., Babonis, L. S., Simmons, D., Salinas‐Saavedra, M., & Martindale, M. Q. (2017) · Development · doi:10.1242/dev.145839 · Cited by 45
CRISPR/Cas9‐Mediated Knockout of MicroRNA‐744 Improves Antibody Titer of CHO Production Cell Lines
Raab, N., Mathias, S., Alt, K., Handrick, R., Fischer, S., Schmieder, V., Jadhav, V., Borth, N., & Otte, K. (2019) · Biotechnology Journal · doi:10.1002/biot.201800477 · Cited by 49
Zhu, K., Celwyn, I. J., Guan, D., Yang, X., Wang, X., Hu, W., Jiang, C., Cheng, L., Casellas, R. C., & Lazar, M. A. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.09.010 · Cited by 47
CRISPR/Cas9: the Jedi against the dark empire of diseases
Khan, S., Mahmood, M. S., Rahman, S. U., Zafar, H., Khan, S. H., khan, Z., & Ahmad, A. (2018) · Journal of Biomedical Science · doi:10.1186/s12929-018-0425-5 · Cited by 49
Duy, C., Teater, M. R., Garrett-Bakelman, F. E., Lee, T. C., Meydan, C., Glass, J. L., Li, M., Hellmuth, J. C., Mohammad, H. P., Smitheman, K. N., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0106 · Cited by 45
A benchmark of computational CRISPR-Cas9 guide design methods
Bradford, J., & Perrin, D. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007274 · Cited by 46
Weuring, W. J., Singh, S., Volkers, L., Rook, M. B., Slot, R. V. ‘., Bosma, M., Inserra, M. C., Vetter, I., Verhoeven‐Duif, N. M., Braun, K. P. J., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0219106 · Cited by 46
The Arabidopsis V-ATPase is localized to the TGN/EE via a seed plant-specific motif
Lupanga, U., Rohrich, R. N., Askani, J. C., Hillmer, S., Kiefer, C., Krebs, M., Kanazawa, T., Ueda, T., & Schumacher, K. (2020) · eLife · doi:10.7554/elife.60568 · Cited by 45
Multiplex knockout of trichome-regulating MYB duplicates in hybrid poplar using a single gRNA
Bewg, W. P., Harding, S. A., Engle, N. L., Vaidya, B. N., Zhou, R., Reeves, J., Horn, T. W., Joshee, N., Jenkins, J. W., Shu, S., et al. (2022) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiac128 · Cited by 45
Balke-Want, H., Keerthi, V., Gkitsas, N., Mancini, A., Kurgan, G., Fowler, C., Xu, P., Liu, X.-K., Asano, K., Patel, S., et al. (2023) · Molecular Cancer · doi:10.1186/s12943-023-01799-7 · Cited by 41
Saikia, B., Remya, S., Debbarma, J., Maharana, J., Sastry, G. N., & Chikkaputtaiah, C. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1304381 · Cited by 45
Technologies and Computational Analysis Strategies for CRISPR Applications
Clement, K., Hsu, J. Y., Canver, M. C., Joung, J. K., & Pinello, L. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.06.012 · Cited by 45
Prime Editing Guide RNA Design Automation Using PINE-CONE
Standage-Beier, K., Tekel, S. J., Brafman, D. A., & Wang, X. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.0c00445 · Cited by 47
Correction: CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool
Stemmer, M., Thumberger, T., Keyer, M. D. S., Wittbrodt, J., & Mateo, J. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176619 · Cited by 45
Self-citationEvaluation of Homology-Independent CRISPR-Cas9 Off-Target Assessment Methods
Chaudhari, H. G., Penterman, J. N., Whitton, H. J., Spencer, S. J., Flanagan, N., Zhang, M. C. L., Huang, E., Khedkar, A. S., Toomey, J. M., Shearer, C. A., et al. (2020) · The CRISPR Journal · doi:10.1089/crispr.2020.0053 · Cited by 45
Genetic Engineering in Bacteria, Fungi, and Oomycetes, Taking Advantage of CRISPR
Yang, P., Condrich, A., Lu, L., Scranton, S., Hebner, C., Sheykhhasan, M., & Ali, M. U. (2024) · DNA · doi:10.3390/dna4040030 · Cited by 47
Liu, Z., Hui, Y., Shi, L., Chen, Z., Xu, X., Chi, L., Fan, B., Fang, Y., Liu, Y., Ma, L., et al. (2016) · Stem Cell Reports · doi:10.1016/j.stemcr.2016.07.021 · Cited by 43
Kawabe, Y., Komatsu, S., Komatsu, S., Murakami, M., Itô, A., Sakuma, T., Nakamura, T., Yamamoto, T., & Kamihira, M. (2017) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2017.12.003 · Cited by 45
Krogerus, K., Magalhães, F., Kuivanen, J., & Gibson, B. R. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-10021-y · Cited by 43
Massively targeted evaluation of therapeutic CRISPR off-targets in cells
Pan, X., Qu, K., Yuan, H., Xiang, X., Anthon, C., Pashkova, L., Liang, X., Han, P., Corsi, G. I., Xu, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31543-6 · Cited by 42
Epicardial differentiation drives fibro-fatty remodeling in arrhythmogenic cardiomyopathy
Kohela, A., van Kampen, S. J., Moens, T., Wehrens, M., Molenaar, B., Boogerd, C. J. J., Monshouwer‐Kloots, J., Perini, I., Goumans, M., Smits, A. M., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abf2750 · Cited by 41
CRISPR-Based Genome Editing: Advancements and Opportunities for Rice Improvement
Zegeye, W. A., Tsegaw, M., Zhang, Y., & Cao, L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23084454 · Cited by 49
Cox, M. J., Roman, C. M., Tapper, E. E., Siegler, E. L., Chappell, D. B., Durrant, C., Ahmed, O., Sinha, S., Mwangi, R., Scott, N. E., et al. (2022) · Leukemia · doi:10.1038/s41375-022-01572-7 · Cited by 41
Liu, L., Zou, L., Li, K., Hou, H., Hu, Q., Liu, S., Li, J., Song, C., Chen, J., Wang, S., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111061 · Cited by 38
Rescue of hearing by adenine base editing in a humanized mouse model of Usher syndrome type 1F
Peters, C. W., Hanlon, K. S., Ivanchenko, M. V., Zinn, E. M., Linarte, E., Li, Y., Levy, J. M., Liu, D. R., Kleinstiver, B. P., Indzhykulian, A. A., et al. (2023) · Molecular Therapy · doi:10.1016/j.ymthe.2023.06.007 · Cited by 36
Negative DNA supercoiling induces genome-wide Cas9 off-target activity
Newton, M. D., Losito, M., Smith, Q. M., Parnandi, N., Taylor, B. J., Akçakaya, P., Maresca, M., van Eijk, P., Reed, S. H., Boulton, S. J., et al. (2023) · Molecular Cell · doi:10.1016/j.molcel.2023.09.008 · Cited by 41
CRISPRoffT: comprehensive database of CRISPR/Cas off-targets
Wang, G., Liu, X., Wang, A., Wen, J., Kim, P., Song, Q., Liu, X., & Zhou, X. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1025 · Cited by 42
Eslami-Mossallam, B., Klein, M., van der Smagt, C. P., van der Sanden, K., Jones, S. K., Hawkins, J. A., Finkelstein, I. J., & Depken, M. (2022) · Nature Communications · doi:10.1038/s41467-022-28994-2 · Cited by 40
Flum, M., Dicks, S., Teng, Y.-H., Schrempp, M., Nyström, A., Boerries, M., & Hecht, A. (2022) · Oncogene · doi:10.1038/s41388-022-02190-4 · Cited by 41
Identification of a tomato UDP-arabinosyltransferase for airborne volatile reception
Sugimoto, K., Ono, E., Inaba, T., Tsukahara, T., Matsui, K., Horikawa, M., Toyonaga, H., Fujikawa, K., Osawa, T., Homma, S., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36381-8 · Cited by 40
Niu, R., Peng, J., Zhang, Z., & Shang, X. (2021) · Genes · doi:10.3390/genes12121878 · Cited by 43
CRISPR/Cas12a Mediated Genome Editing Enhances Bombyx mori Resistance to BmNPV
Dong, Z., Qi, Q., Hu, Z., Zhang, X., Miao, J., Huang, L., Chen, P., Lu, C., & Pan, M. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00841 · Cited by 39
multicrispr: gRNA design for prime editing and parallel targeting of thousands of targets
Bhagwat, A. M., Graumann, J., Wiegandt, R., Bentsen, M. S., Welker, J. M., Kuenne, C. T., Preussner, J., Braun, T. W., & Looso, M. (2020) · Life Science Alliance · doi:10.26508/lsa.202000757 · Cited by 39
CD7-deleted hematopoietic stem cells can restore immunity after CAR T cell therapy
Kim, M. Y., Cooper, M. L., Jacobs, M. T., Ritchey, J., Hollaway, J., Fehniger, T. A., & DiPersio, J. F. (2021) · JCI Insight · doi:10.1172/jci.insight.149819 · Cited by 40
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 39
Cromer, M. K., Barsan, V. V., Jaeger, E. B., Wang, M., Hampton, J. P., Chen, F., Kennedy, D., Xiao, J., Khrebtukova, I., Granat, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32233-z · Cited by 39
Rhiel, M., Geiger, K., Andrieux, G., Rositzka, J., Boerries, M., Cathomen, T., & Cornu, T. I. (2023) · Frontiers in Genome Editing · doi:10.3389/fgeed.2023.1130736 · Cited by 35
Unlocking Fungal Potential: The CRISPR-Cas System as a Strategy for Secondary Metabolite Discovery
Leal, K., Rojas, E., Madariaga-Troncoso, D., Contreras, M. J., Núñez-Montero, K., Barrientos, L., Gómez-Espinoza, O., & Iturrieta‐González, I. (2024) · Journal of Fungi · doi:10.3390/jof10110748 · Cited by 41
Schiml, S., Fauser, F., & Puchta, H. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-4931-1_8 · Cited by 37
Targeted in vivo epigenome editing of H3K27me3
Fukushima, H. S., Takeda, H., & Nakamura, R. (2019) · Epigenetics & Chromatin · doi:10.1186/s13072-019-0263-z · Cited by 37
Chang, L.-S., Kim, M., Glinka, A., Reinhard, C., & Niehrs, C. (2020) · eLife · doi:10.7554/elife.51248 · Cited by 39
FCHO controls AP2’s initiating role in endocytosis through a PtdIns(4,5)P 2 -dependent switch
Zaccai, N. R., Kadlecová, Z., Dickson, V. K., Korobchevskaya, K., Kamenický, J., Kovtun, O., Umasankar, P. K., Wrobel, A. G., Kaufman, J. G. G., Gray, S. R., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn2018 · Cited by 36
Debbarma, J., Saikia, B., Singha, D. L., Das, D., Keot, A. K., Maharana, J., Velmurugan, N., Arunkumar, K. P., Reddy, P. S., & Chikkaputtaiah, C. (2023) · Genes · doi:10.3390/genes14020488 · Cited by 36
Synergizing CRISPR/Cas9 off-target predictions for ensemble insights and practical applications
Zhang, S., Li, X., Lin, Q., & Wong, K. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty748 · Cited by 40
The fungal metabolite chaetocin is a sensitizer for pro-apoptotic therapies in glioblastoma
Ozyerli‐Goknar, E., Sur‐Erdem, İ., Seker-Polat, F., Cingöz, A., Kayabölen, A., Kahya, Z., Uyulur, F., Gezen, M., Tolay, N., Erman, B., et al. (2019) · Cell Death and Disease · doi:10.1038/s41419-019-2107-y · Cited by 37
Jhu, M.-Y., Farhi, M., Wang, L., Zumstein, K., & Sinha, N. R. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.764843 · Cited by 37
Nguyen, D. V., Hoang, T. H. T., Le, N. T., Tran, H. T., Nguyen, C. X., Moon, Y., Hà, C. H., & Do, P. T. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2021.770062 · Cited by 37
An Influenza A virus can evolve to use human ANP32E through altering polymerase dimerization
Sheppard, C., Goldhill, D. H., Swann, O. C., Staller, E., Penn, R., Platt, O. K., Sukhova, K., Baillon, L., Frise, R., Peacock, T. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41308-4 · Cited by 36
Creating saponin‐free yellow pea seeds by CRISPR/Cas9‐enabled mutagenesis on β‐amyrin synthase
Hodgins, C. L., Salama, E. M., Kumar, R., Zhao, Y., Roth, S. A., Cheung, I. Z., Chen, J., Arganosa, G. C., Warkentin, T. D., Bhowmik, P., et al. (2024) · Plant Direct · doi:10.1002/pld3.563 · Cited by 36
Review of applications of artificial intelligence (AI) methods in crop research
Bose, S., Banerjee, S., Kumar, S., Saha, A., Nandy, D., & Hazra, S. (2024) · Journal of Applied Genetics · doi:10.1007/s13353-023-00826-z · Cited by 38
CRISPR-M: Predicting sgRNA off-target effect using a multi-view deep learning network
Sun, J., Guo, J. H., & Liu, J. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011972 · Cited by 39
Wang, S., Ren, S., Bai, R., Xiao, P., Qin, Z., Zhou, Y., Zhou, Z., Niu, Y., Ji, W., & Chen, Y. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ac118.004404 · Cited by 36
Phan, Q. T., Tan, W. H., Liu, R., Sundaram, S., Buettner, A., Kneitz, S., Cheong, B., Vyas, H., Mathavan, S., Schartl, M., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2006093117 · Cited by 37
Milner, M. J., Craze, M. S., Hope, M. S., & Wallington, E. J. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.583374 · Cited by 36
Yeap, W., Khan, N. C. M., Jamalludin, N., Muad, M. R., Appleton, D. R., & Kulaveerasingam, H. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.773656 · Cited by 37
Cornean, A., Gierten, J., Welz, B., Mateo, J. L., Thumberger, T., & Wittbrodt, J. (2022) · eLife · doi:10.7554/elife.72124 · Cited by 35
Self-citationLehne, F., Pokrant, T., Parbin, S., Salinas, G., Großhans, J., Rust, K., Faix, J., & Bogdan, S. (2022) · Nature Communications · doi:10.1038/s41467-022-30167-0 · Cited by 35
Olsen, L. A., Levy, M. J., Medley, K., Hassan, H., Miller, B. D., Alexander, R. L., Wilcock, E. B., Yi, K., Florens, L. A., Weaver, K. J., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2204427120 · Cited by 39
Murray, A., Gough, G., Cindrić, A., Vučković, F., Koschut, D., Borelli, V., Petrović, D. J., Bekavac, A., Plećaš, A., Hribljan, V., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104692 · Cited by 38
Bajoghli, B., Kuri, P., Inoue, D., Aghaallaei, N., Hanelt, M., Thumberger, T., Rauzi, M., Wittbrodt, J., & Leptin, M. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1500361 · Cited by 36
Self-citationJacobs, T. B., & Martin, G. B. (2016) · Journal of Visualized Experiments · doi:10.3791/53843 · Cited by 35
Functional interrogation of non-coding DNA through CRISPR genome editing
Canver, M. C., Bauer, D. E., & Orkin, S. H. (2017) · Methods · doi:10.1016/j.ymeth.2017.03.008 · Cited by 36
An efficient system for homology-dependent targeted gene integration in medaka (Oryzias latipes)
Murakami, Y., Ansai, S., Yonemura, A., & Kinoshita, M. (2017) · Zoological Letters · doi:10.1186/s40851-017-0071-x · Cited by 35
Efficient genome editing using CRISPR/Cas9 ribonucleoprotein approach in cultured medaka fish cells
Liu, Q., Yuan, Y., Zhu, F., Hong, Y., & Ge, R. (2018) · Biology Open · doi:10.1242/bio.035170 · Cited by 41
Isken, O., Postel, A., Bruhn, B., Lattwein, E., Becher, P., & Tautz, N. (2018) · Journal of Virology · doi:10.1128/jvi.01714-18 · Cited by 36
Furuhata, Y., Nihongaki, Y., Sato, M., & Yoshimoto, K. (2017) · ACS Synthetic Biology · doi:10.1021/acssynbio.7b00246 · Cited by 35
CRISPR–Cas9/CRISPRi tools for cell factory construction in E. coli
Hashemi, A. (2020) · World Journal of Microbiology and Biotechnology · doi:10.1007/s11274-020-02872-9 · Cited by 34
Harland, L. T. G., Simon, C. S., Senft, A. D., Costello, I., Greder, L. V., Imaz-Rosshandler, I., Göttgens, B., Marioni, J. C., Bikoff, E. K., Porcher, C., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-020-00611-8 · Cited by 30
Comparative Study in Zebrafish and Medaka Unravels the Mechanisms of Tissue Regeneration
Chowdhury, K., Lin, S., & Lai, S.-L. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2022.783818 · Cited by 37
Nieścierowicz, K., Pryszcz, L. P., Navarrete, C., Tralle, E., Sulej, A., Nahia, K. A., Kasprzyk, M. E., Misztal, K., Pateria, A., Pakuła, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33260-6 · Cited by 34
Impairment of Tomato WAT1 Enhances Resistance to Vascular Wilt Fungi Despite Severe Growth Defects
Hanika, K., Schipper, D., Chinnappa, S., Oortwijn, M. E. P., Schouten, H. J., Thomma, B. P. H. J., & Bai, Y. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.721674 · Cited by 34
Perera, L. A., Hattersley, A. T., Harding, H. P., Wakeling, M. N., Flanagan, S. E., Mohsina, I., Raza, J., Gardham, A., Ron, D., & Franco, E. D. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202216491 · Cited by 33
Kwon, M., Hodgins, C. L., Salama, E. M., Dias, K. R., Parikh, A., Mackey, A. V., Catenza, K. F., Vederas, J. C., & Ro, D. (2023) · New Phytologist · doi:10.1111/nph.18994 · Cited by 33
Emerging Gene Therapeutics for Epidermolysis Bullosa under Development
Bischof, J., Hierl, M., & Koller, U. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25042243 · Cited by 36
Targeted genome editing in algae using CRISPR/Cas9
Tanwar, A., Sharma, S., & Kumar, S. B. (2018) · Indian Journal of Plant Physiology · doi:10.1007/s40502-018-0423-3 · Cited by 33
Infrastructures of systems biology that facilitate functional genomic study in rice
Hong, W., Kim, Y., Chandran, A. K. N., & Jung, K. (2019) · Rice · doi:10.1186/s12284-019-0276-z · Cited by 33
Prediction of sgRNA on-target activity in bacteria by deep learning
Wang, L., & Zhang, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3151-4 · Cited by 33
Revisiting CRISPR/Cas-mediated crop improvement: special focus on nutrition
Kaul, T., Sony, S. K., Verma, R., Motelb, K. F. A., Prakash, A. T., Eswaran, M., Bharti, J., Nehra, M., & Kaul, R. (2020) · Journal of Biosciences · doi:10.1007/s12038-020-00094-7 · Cited by 33
Benchmarking and integrating genome-wide CRISPR off-target detection and prediction
Yan, J., Xue, D., Chuai, G., Gao, Y., Zhang, G., & Liu, Q. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa930 · Cited by 35
Žílová, L., Weinhardt, V., Tavhelidse, T., Schlagheck, C., Thumberger, T., & Wittbrodt, J. (2021) · eLife · doi:10.7554/elife.66998 · Cited by 33
Self-citationEndogenous protein tagging in medaka using a simplified CRISPR/Cas9 knock-in approach
Seleit, A., Aulehla, A., & Paix, A. (2021) · eLife · doi:10.7554/elife.75050 · Cited by 33
Chen, M., Wang, M., Shen, X.-G., Liu, H., Zhang, Y., Peng, J., Meng, F., Wang, T.-Y., Bai, Y., Sun, M., et al. (2022) · Journal of Virology · doi:10.1128/jvi.01318-22 · Cited by 35
Cao, L., Wang, Z., Ma, H., Liu, T., Ji, J., & Duan, K. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1048967 · Cited by 33
Interferon regulates neural stem cell function at all ages by orchestrating mTOR and cell cycle
Ibañez, D. C., Skabkin, M. A., Hooli, J., Cerrizuela, S., Göpferich, M., Jolly, A., Volk, K., Zumwinkel, M., Bertolini, M., Figlia, G., et al. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202216434 · Cited by 37
Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities
Zhang, H., Yan, J., Lu, Z., Zhou, Y., Zhang, Q., Cui, T., Li, Y., Chen, H., & Ma, L. (2023) · Cell Discovery · doi:10.1038/s41421-023-00549-9 · Cited by 35
Lee, B. H., Gries, K., Valimukhametova, A. R., McKinney, R. L., González-Rodríguez, R., Topkiran, U. C., Coffer, J. L., Akkaraju, G. R., & Naumov, A. V. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202305506 · Cited by 35
Cerise, M., Falavigna, V. D. S., Rodríguez-Maroto, G., Signol, A., Severing, E. I., Gao, H., van Driel, A. D., Vincent, C. A., Wilkens, S., Iacobini, F. R., et al. (2023) · Development · doi:10.1242/dev.202089 · Cited by 32
van Essen, M. J., Apsley, E. J., Riepsaame, J., Xu, R., Northcott, P. A., Cowley, S. A., Jacob, J., & Becker, E. B. E. (2024) · Disease Models & Mechanisms · doi:10.1242/dmm.050323 · Cited by 32
Data imbalance in CRISPR off-target prediction
Gao, Y., Chuai, G., Yu, W., Qu, S., & Liu, Q. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz069 · Cited by 31
dbGuide: a database of functionally validated guide RNAs for genome editing in human and mouse cells
Gooden, A. A., Evans, C. N., Sheets, T. P., Clapp, M. E., & Chari, R. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa848 · Cited by 35
Hartz, P., Gehl, M., König, L., Bernhardt, R., & Hannemann, F. (2021) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2021.02.006 · Cited by 32
The Off-Targets of Clustered Regularly Interspaced Short Palindromic Repeats Gene Editing
Vicente, M. M., Chaves‐Ferreira, M., Jorge, J. M. P., Proença, J. T., & Barreto, V. M. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.718466 · Cited by 31
Nguyen, N. H. A., Bui, T. P., Le, N. T., Nguyen, C. X., Le, M. T. T., Dao, N. T., Phan, Q., Le, T. V., To, H. T. M., Pham, N. B., et al. (2023) · Planta · doi:10.1007/s00425-023-04089-0 · Cited by 31
rRNA transcription is integral to phase separation and maintenance of nucleolar structure
Dash, S., Lamb, M. C., Lange, J. J., McKinney, M. C., Tsuchiya, D., Guo, F., Zhao, X., Corbin, T. J., Kirkman, M., Delventhal, K. M., et al. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010854 · Cited by 32
Jiang, H., Qu, S., Liu, F., Sun, H., Li, H., Teng, W., Zhan, Y., Li, Y., Han, Y., & Zhao, X. (2025) · Plant Biotechnology Journal · doi:10.1111/pbi.14586 · Cited by 31
Single-cell trajectory analysis of human homogenous neurons carrying a rare RELN variant
Arioka, Y., Shishido, E., Kubo, H., Kushima, I., Yoshimi, A., Kimura, H., Ishizuka, K., Aleksić, B., Maeda, T., Ishikawa, M., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0177-8 · Cited by 31
Hammerschmidt, S. I., Werth, K., Rothe, M., Galla, M., Permanyer, M., Patzer, G. E., Bubke, A., Frenk, D. N., Selich, A., Lange, L., et al. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01949 · Cited by 33
CRISPR/Cas9-Mediated Knock-Out of dUTPase in Mice Leads to Early Embryonic Lethality
Pálinkás, H. L., Rácz, G. A., Gál, Z., Hoffmann, O. I., Tihanyi, G., Róna, G., Gócza, E., Hiripi, L., & Vértessy, B. G. (2019) · Biomolecules · doi:10.3390/biom9040136 · Cited by 31
Dufner‐Almeida, L. G., Nanhoe, S., Zonta, A., Hosseinzadeh, M., Kom‐Gortat, R., Elfferich, P., Schaaf, G., Kenter, A. T., Kümmel, D., Migone, N., et al. (2019) · Human Mutation · doi:10.1002/humu.23963 · Cited by 31
Metadata
- Authors
- Stemmer, M., Thumberger, T., del Sol Keyer, M., Wittbrodt, J., & Mateo, J. L.
- Affiliations
- Heidelberg University (DE)
- Corresponding author
- Thumberger, Thomas
- Wittbrodt, Joachim
- Mateo, Juan L.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-04-24
- Volume
- 10
- Issue
- 4
- Pages
- e0124633
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4409221
- OpenAlex
- W1990149668
- Cited by
- 1,088
- References
- 37
- Citations by year
- 2026: 87 · 2025: 99 · 2024: 106 · 2023: 140 · 2022: 129 · 2021: 114 · 2020: 139 · 2019: 96 · 2018: 72 · 2017: 53 · 2016: 39 · 2015: 13
- FWCI
- 25.61
- Type
- journal article
- Language
- en
- Update notices
- correction (2017-04-20)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (SFB873)
- Universität Heidelberg
- 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/Cas9, sgRNA design, off-target prediction, genome editing, gene inactivation, non-homologous end joining, homology-directed repair, web-based prediction tool
- MeSH terms
- Animals, Binding Sites, Humans, RNA, Messenger, Software, User-Computer Interface, Reproducibility of Results, Gene Targeting, Computational Biology, Internet, Genomics, CRISPR-Cas Systems, Web Browser
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