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REVIEW 4 major objections 5 minor 42 references

Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A review argues that explainable AI can open the black box of CRISPR guide design and sharpen off-target safety.

desk verdict Unfinished review with a load-bearing unsupported claim linking explainable AI to clinical CRISPR successes; not publishable in current form. read the letter →

arxiv 2508.20130 v1 pith:C3GRSJY6 submitted 2025-08-26 q-bio.QM cs.AIcs.LG

Alireza Abbaszadeh , Armita Shahlai This is my paper · ORCID
classification q-bio.QMcs.AIcs.LG
keywords CRISPRguideRNAdesigndeeplearningexplainableAIoff-targetpredictiongenomeeditingsafetybaseclinicaltranslation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review assembles evidence from 2020–2025 that modern machine learning, especially deep learning, now outperforms older rule-based methods at predicting how efficiently a guide RNA cuts its intended site and how likely it is to cut elsewhere. The authors' central thesis is that explainable AI techniques—attention weights, SHAP values, and interpretable linear models—are what make these powerful black boxes usable in high-stakes genome editing, because they turn a single prediction score into a sequence-position map that matches known biology and can be audited. If true, the practical consequence is that guide selection can move from expensive trial-and-error to in silico screening followed by targeted validation, with a more defensible off-target safety case for regulators.

What carries the argument

The central object is the guide RNA's 20-nucleotide targeting sequence treated as a sequence-prediction input. The machinery that carries the argument is a pipeline of deep learning architectures—CNNs for motif detection, recurrent and attention layers for positional dependencies, and BERT-style pre-training for guide-target pairs—each equipped with an attribution method (attention weights, SHAP, integrated gradients) that projects the model's decision back onto individual nucleotide positions. Genome-wide off-target assays such as CHANGE-seq and DISCOVER-Seq+ supply the empirical ground truth on which both the predictive models and the explainability claims rest.

What would settle it

Inspect the methods or supplementary files of the exa-cel trials [24], the NTLA-2001 trial [27], and the EDIT-101 trial [31]: if the guide sequences are justified by standard rule-based or expert selection without any machine-learning or explainable-AI step, the paper's assertion that modern AI tools aided sgRNA selection is contradicted.

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Extended reading notes

Core claim

The paper's discovery, in its own terms, is a convergence: deep learning models like CRISPRon and CRISPR-Net show that sequence context plus epigenetic data materially improve gRNA efficiency forecasts, while interpretability studies such as AttCRISPR and a BERT-based off-target model show that attention and SHAP analyses can reveal biologically meaningful position sensitivities—for instance, PAM-proximal seed-region positions driving cleavage. On the safety side, the review contends that off-target prediction has shifted from mismatch-counting rules to learned models trained on genome-wide empirical maps, and that these predictions are credible enough to shape clinical guide selection, espe

Load-bearing premise

The paper's strongest claim depends on believing that the approved clinical CRISPR therapies it cites, especially exa-cel, actually relied on AI-based guide selection when their guides were chosen; the cited trial reports do not describe such AI, so if that premise fails the review loses much of its 'explainable AI underpins clinical success' narrative.

Editorial extensions

If this is right

  • Guide design shifts from wet-lab screening of many candidates to in silico pre-screening, with explainability telling experimentalists which positions to verify first.
  • Off-target safety becomes a per-patient question: variant-aware models can flag guides whose risk profile changes with a person's SNPs, making population-aware guide panels feasible.
  • Newer editors—base editors, prime editors, and integrase-based PASTE—need the same explanatory machinery for outcomes that no longer follow the classic double-strand-break off-target pattern.
  • Jointly modeling on-target potency and off-target risk lets designers optimize the trade-off explicitly rather than applying a fixed mismatch threshold.
  • Explainable reasons, such as a mismatch at a position learned to be critical for Cas9, can be packaged into regulatory and clinical risk documentation for approved therapies.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper treats explainability as a trust-building feature, but it implies a stronger hypothesis: that interpretable models will discover genuinely new sequence determinants of Cas activity, not merely rediscover known seed-region and PAM effects. That hypothesis is testable by prospectively validating a novel motif flagged by attribution analysis.
  • The clinical-translation narrative would be directly testable by auditing how the guides for exa-cel, NTLA-2001, and EDIT-101 were actually chosen; the cited clinical reports are not explicit about AI involvement, so the review's claim that AI underpins those successes should be read as an aspiration until such an audit is performed.
  • The same attention/SHAP machinery can be extended to base editors and prime editors, attributing their distinct byproducts—off-target base conversions, RNA edits, pegRNA-related indels—to sequence context, a measurable extension the paper does not carry out.
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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This manuscript is a narrative review of AI and explainable AI (XAI) for CRISPR guide RNA design and off-target safety. It surveys recent machine-learning models for on-target activity prediction (CRISPRon, Kim et al., AttCRISPR, CRISPRedict, etc.), interpretability methods (attention, SHAP, integrated gradients), off-target prediction and experimental detection (CHANGE-seq/DISCOVER-Seq, etc.), and clinical applications of CRISPR (exa-cel, NTLA-2001, EDIT-101, base-edited CAR7 T cells). The authors argue that XAI is becoming a key enabler of efficient, safe, and clinically viable CRISPR therapies, and they conclude with a normative statement that XAI is an 'empowering enabler' of genome engineering. The review contains a large reference list (2020--2024) and two summary tables, but also includes several placeholder citations '(Ref.docx)', truncated sentences, and at least one unsupported clinical attribution.

Significance. The review addresses an important and timely topic: the role of explainable AI in CRISPR guide design and safety. If properly revised, it could serve as a useful interdisciplinary introduction, especially for experimental biologists seeking orientation in the fast-growing AI/CRISPR literature. The paper usefully compiles recent models, tools, and clinical studies, and it explicitly acknowledges the need for experimental validation and for ethical/regulatory reflection. Its main limitation is that it is a broad narrative review with no systematic methodology, no critical benchmarking, and no quantitative comparison of the tools it discusses. The central thesis that XAI is a key clinical enabler rests partly on an unsupported historical claim about exa-cel's guide selection, and the current draft contains visible incompleteness artifacts. Therefore, the manuscript is not yet in publishable form, but the flaws are reparable within the scope of a review.

major comments (4)
  1. [Section 1.1 (Ex Vivo Hematopoietic Stem Cell Therapies)] The sentence 'Modern AI tools further aided in selecting an optimal sgRNA with high on-target activity and low off-target propensity' and the later claim that 'explainable AI-driven design has underpinned' clinical development are load-bearing for the review's thesis. The cited references [24]–[26] (Frangoul et al. and Locatelli et al.) report clinical outcomes for exa-cel but do not describe AI-driven or explainable-AI-driven guide selection; no other primary source is given. This is an unsupported factual attribution, not a matter of interpretation. The authors should either provide a documented primary source showing that AI/XAI was used in guide selection for exa-cel, or explicitly reframe these statements as recommendations or as plausible future uses of XAI rather than as historical facts.
  2. [Conclusion (Section 4)] The concluding assertion—'The role of explainable AI in enhancing CRISPR precision and safety is that of an empowering enabler'—is presented as if it were a finding of the reviewed literature. The manuscript surveys examples of AI and XAI in gRNA design, but it does not provide comparative or causal evidence that XAI specifically improved clinical outcomes, safety, or regulatory decisions. Since this normative claim is the capstone of the review, it should be explicitly framed as the authors' perspective or as a research agenda, not as a conclusion derived from the cited studies.
  3. [Throughout: Introduction, AI Models, Off-target Prediction] The manuscript contains multiple placeholder citations '(Ref.docx)' and truncated sentences that indicate an incomplete draft. Examples include: '(Ref.docx) (Ref.docx)' after 'cleavage efficiency' in the Introduction; '(Ref.docx)' after 'with unprecedented sensitivity' in the Off-target Prediction section; the broken sentence 'a model might predict 90Explainable AI techniques thus enrich...' in the XAI section; and 'they found that around 6Another layer of complexity...' in the Off-target section. These artifacts prevent a reader from evaluating the completeness and accuracy of the review. They must be fixed before the manuscript can be considered further.
  4. [Section 3 (Off-target Prediction) and Reference [7]] The text repeatedly refers to the experimental method as 'CHANGE-seq', but reference [7] is titled 'COCHANGE-seq'. This is a factual inconsistency in a central methodological term. (The corresponding Nature Biotechnology article is normally cited as 'CHANGE-seq'; the 'CO' prefix may be an error in the reference list.) The authors should verify the correct assay name and ensure that text, citations, Table/Figure captions, and the bibliography are consistent.
minor comments (5)
  1. [Abstract] The parenthetical '(2020–2025, updated to reflect current year if needed)' is a template artifact and should be removed or replaced with the actual coverage dates.
  2. [Keywords] The keyword list contains 'Nature Biology', which appears to be a stray phrase rather than a keyword. It should be deleted.
  3. [Figures] All four figures are labeled '(Conceptual figure placeholder)'. If these are not final figures, they should be either implemented as proper, informative schematic figures or removed; placeholders are not appropriate for a journal submission.
  4. [Section 1.1] There are typographical errors in the text: 'nowexagamglogene' should be 'now exagamglogene'; 'editit' appears in the AI Models section; 'AA V' is written with a space in several places; 'H”oijer' has a mangled quote character in the Off-target section; 'P ASTEsystem' should be 'PASTE system'. A careful proofreading pass is needed.
  5. [Section numbering] Earlier sections (AI Models for gRNA Design, Explainable AI Techniques, Off-target Prediction and Safety) are unnumbered, while '1 Clinical Applications' begins numbered sections. The numbering scheme should be made consistent throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is a narrative review that summarizes external results; the only concerns are unsupported attributions, not circular reasoning.

full rationale

This manuscript is a review paper with no equations, no fitted parameters, no original model, and no derivation chain. Its claims about AI-based gRNA design tools are summaries of independently published, externally cited works (e.g., CRISPRon, AttCRISPR, SHAP-based analyses), and the review does not use its own conclusions as evidence for those summaries. The passages asserting that 'Modern AI tools further aided in selecting an optimal sgRNA' for exa-cel and that 'explainable AI-driven design has underpinned' clinical development are not backed by the cited clinical papers, but this is an evidentiary/support problem, not circularity: the review's conclusion about XAI as an 'empowering enabler' is an opinion, not a result derived from those claims, and the assertion is not definitionally tied to the conclusion. There are no self-citations by the present authors, no uniqueness theorem invoked from prior work, no ansatz smuggled in via citation, and no renaming of a known result. Per the hard rules, a paper that is self-contained against external sources and whose central content is a survey of other work should receive a low circularity score. The identified weakness belongs to correctness/verifiability, not circularity.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This is a review with no new derivation, fitted parameters, or postulated entities. The ledger reflects only the domain assumptions needed to accept the review's narrative.

assumptions (2)
  • domain assumption The cited primary studies (e.g., [1], [4], [16]) are accurately summarized, including their performance claims and clinical outcomes.
    The review relies on the correctness of the papers it cites for all of its substantive content, and it adds no independent verification.
  • domain assumption The claim that explainable AI was used in the design of clinically approved therapies such as exa-cel is true.
    Section 1.1 asserts that AI tools aided sgRNA selection for exa-cel without citing any primary source; this is a load-bearing attribution for the review's thesis.

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Cite this review

Pith. "Pith review of Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety." pith.science (2026). https://pith.science/paper/C3GRSJY6

@misc{pith2026250820130,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence for CRISPR Guide RNA Design: Explainable Models and Off-Target Safety},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C3GRSJY6}},
  note         = {Machine review of arXiv:2508.20130}
}
read the original abstract

CRISPR-based genome editing has revolutionized biotechnology, yet optimizing guide RNA (gRNA) design for efficiency and safety remains a critical challenge. Recent advances (2020--2025, updated to reflect current year if needed) demonstrate that artificial intelligence (AI), especially deep learning, can markedly improve the prediction of gRNA on-target activity and identify off-target risks. In parallel, emerging explainable AI (XAI) techniques are beginning to illuminate the black-box nature of these models, offering insights into sequence features and genomic contexts that drive Cas enzyme performance. Here we review how state-of-the-art machine learning models are enhancing gRNA design for CRISPR systems, highlight strategies for interpreting model predictions, and discuss new developments in off-target prediction and safety assessment. We emphasize breakthroughs from top-tier journals that underscore an interdisciplinary convergence of AI and genome editing to enable more efficient, specific, and clinically viable CRISPR applications.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 40 canonical work pages

  1. [7]

    Cameron, P., Fuller, C.K., Donohoue, P.D., Jones, B.N., Thompson, M.S., Carter, M.M., Gradia, S., Vidal, B., Garner, E., Slorach, E.M., Laufer, M., Strouse, B., Brugmann, S.A., Beisel, C.L., Stadtmauer, E.A., Weissman, I., Chew, A., Listgarten, J., Bejerano, G.: Mapping the genomic landscape of CRISPR-Cas9 off-target activity with COCHANGE-seq. Nat. Biote...

    work page doi:10.1038/s41587-020-0536-x 2020

  2. [24]

    Frangoul, H., Altshuler, D., Cappellini, M.D., Chen, Y.S., Domm, J., Eustace, B.K., Foell, J., de la Fuente, J., Grupp, S., Handgretinger, R., Ho, T.M., Imren, S., Jani, V.V., Maestro, S., Mapara, M., de Montalembert, M., Rondelli, D., Sharma, A., Steinberg, M.H., Wall, D., Yen, A., Corbacioglu, S.: CRISPR-Cas9 Gene Editing for Sickle Cell Disease and β-T...

    work page doi:10.1056/nejmoa2031054 2021

  3. [26]

    Frangoul, H., Locatelli, F., Sharma, A., Walters, M.C., Wembere, J., Wigg, A., Adewoye, A.H., Al-Samkari, H., Asnani, M., August, K., Bobruff, Y., Boruchov, A., Bourget, P., Thompson, A.A., Tisdale, J.F., Traina, F., Vichinsky, E., Walters, M.C., Yan, J., Yawn, B.P., Zacharoulis, S., Zeigler, C., (CLIMB SCD-121 Study Group): Exagamglogene Autotemcel for S...

    work page doi:10.1056/nejmoa2309676 2024

  4. [1]

    Xiang, X., Corsi, G., Anthon, C., Qu, K., Pan, X., Liang, X., Paszkowski-Rogacz, M., Son, W.I., Tesfu, A., Myklebust, L., Bøhn, E., Hovig, E., Hildebrand, F., ¨Ozbek, U., Korneliussen, T.S., Han, Y., Li, W., Jauch, R., Li, J., Ang, Y.S.: Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning. Nat. Commun. 12(1), 3238 (2021)...

    doi:10.1038/s41467-021- 2021

  5. [2]

    Kim, N., Kim, H., Lee, S., Seo, J.H., Choi, J.W., Park, J.S., Kim, J.S.: Predic- tion of the sequence-specific cleavage activity of Cas9 variants. Nat. Biotechnol. 38(11), 1328–1336 (2020). DOI 10.1038/s41587-020-0537-9

    work page doi:10.1038/s41587-020-0537-9 2020

  6. [3]

    BMC Bioinform

    Xiao, L.M., Wan, Y.Q., Jiang, Z.R.: AttCRISPR: a spatiotemporal interpretable model for prediction of sgRNA on-target activity. BMC Bioinform. 22(1), 589 (2021). DOI 10.1186/s12859-021-04509-6

    work page doi:10.1186/s12859-021-04509-6 2021

  7. [4]

    Huang, X., Li, Y., Yang, J., Wang, J., Yin, H.: Interpretable CRISPR/Cas9 off-target activities with mismatches and indels using a BERT-based deep learning model. Comput. Biol. Med. 146, 105569 (2022). DOI 10.1016/j.compbiomed.2022.105569

    work page arXiv 2022

  8. [5]

    Bioinformatics 37(15), 2299–2307 (2021)

    Charlier, J., Nadon, R., Makarenkov, V.: Accurate deep learning off-target predic- tion with novel sgRNA–DNA sequence encoding in CRISPR–Cas9 gene editing. Bioinformatics 37(15), 2299–2307 (2021). DOI 10.1093/bioinformatics/btab112

    work page doi:10.1093/bioinformatics/btab112 2021

Show all 42 references
  1. [6]

    Entropy 23(5), 608 (2021)

    Vinodkumar, P.K., Ozcinar, C., Anbarjafari, G.: Prediction of sgRNA off-target activity in CRISPR/Cas9 gene editing using graph convolution network. Entropy 23(5), 608 (2021). DOI 10.3390/e23050608

    2021 doi

  2. [8]

    H¨ oijer, I., Emmanouilidou, A., ¨Ostlund, R., van Schendel, R., Bozorgpana, S., Tijsterman, M., Feuk, L., Gyllensten, U., den Hoed, M., Ameur, A.: CRISPR- Cas9 induces large structural variants at on-target and off-target sites in vivo that segregate across generations. Nat. ...

    2022 doi

  3. [9]

    Cancellieri, S., Zeng, J., Lin, L.Y., Tognon, M., Nguyen, M.A., Lin, J., Bombieri, N., Maitland, S.A., Ciuculescu, M.F., Katta, V., Tsai, S.Q., Armant, M., Wolfe, S.A., Giugno, R., Bauer, D.E., Pinello, L.: Human genetic diversity alters off- target outcomes of therapeutic gen...

    2023 doi

  4. [10]

    Nucleic Acids Res

    Zhang, W., Yin, J., Ding, Z., Xin, C., Liu, M., Wang, Y., Yang, H., Zhang, F.: In- depth assessment of the PAM compatibility and editing activities of Cas9 variants. Nucleic Acids Res. 49(15), 8785–8795 (2021). DOI 10.1093/nar/gkab507

    2021 doi

  5. [11]

    Lin, J., Zhang, Z., Zhang, S., Chen, J., Wong, K.C.: CRISPR-Net: A recurrent convolutional network quantifies CRISPR off-target activities with mismatches and indels. Adv. Sci. 7(18), 1903562 (2020). DOI 10.1002/advs.201903562 25

    2020 doi

  6. [12]

    Baisya, D., Ramesh, A., Schwartz, C., Lonardi, S., Wheeldon, I.: Genome- wide functional screens enable the prediction of high-activity CRISPR-Cas9 and Cas12a guides in Yarrowia lipolytica. Nat. Commun. 13(1), 922 (2022). DOI 10.1038/s41467-022-28540-0

    2022 doi

  7. [13]

    Marquart, K.F., Allam, A., Janjuha, S., Sintsova, A., Villiger, L., Frey, N., Jeker, L.T., Paganetti, P., Thony, B., Schwank, G.: Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens. Nat. Commun. 12...

    2021 doi

  8. [14]

    Bioin- formatics 37(Suppl 1), i342–i348 (2021)

    Li, V.R., Zhang, Z., Troyanskaya, O.G.: Croton: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes. Bioin- formatics 37(Suppl 1), i342–i348 (2021). DOI 10.1093/bioinformatics/btab268

    2021 doi

  9. [15]

    Biomolecules 13(4), 641 (2023)

    Vora, D.S., Yadav, S., Sundar, D.: Hybrid multitask learning reveals sequence features driving specificity in the CRISPR/Cas9 system. Biomolecules 13(4), 641 (2023). DOI 10.3390/biom13040641

    2023 doi

  10. [16]

    Nucleic Acids Res

    Konstantakos, V., Nentidis, A., Krithara, A., Paliouras, G.: CRISPRedict: a CRISPR-Cas9 web tool for interpretable efficiency predictions. Nucleic Acids Res. 50(W1), W191–W198 (2022). DOI 10.1093/nar/gkac466

    2022 doi

  11. [17]

    Nucleic Acids Res

    Kirillov, B., Savitskaya, E., Panov, M., Ogurtsov, A.Y., Shabalina, S.A., Koonin, E.V., Severinov, K.: Uncertainty-aware and interpretable evaluation of Cas9– gRNA and Cas12a–gRNA specificity for fully matched and partially mismatched targets with deep kernel learning. Nucleic...

    2022 doi

  12. [18]

    Cells 11(11), 1847 (2022)

    Liu, X., Wang, S., Ai, D.: Predicting CRISPR/Cas9 repair outcomes by an attention-based deep learning framework. Cells 11(11), 1847 (2022). DOI 10.3390/cells11111847

    2022 doi

  13. [19]

    Genes 12(12), 1878 (2021)

    Niu, R., Peng, J., Zhang, Z., Shang, X.: R-CRISPR: a deep learning network to predict off-target activities with mismatch, insertion and deletion in CRISPR- Cas9 system. Genes 12(12), 1878 (2021). DOI 10.3390/genes12121878

    2021 doi

  14. [20]

    Wiener, D., Gill, S., Ferrante, F., Bohlooly, Y., Hsu, P.D.: Improving the sensitiv- ity of in vivo CRISPR off-target detection with DISCOVER-Seq+. Nat. Methods 20(5), 800–809 (2023). DOI 10.1038/s41592-023-01811-7

    2023 doi

  15. [21]

    Kim, H.Y., Lee, S.H., Kim, J.S.: High-throughput analysis of the activities of xCas9, SpCas9-NG and SpCas9 at matched and mismatched target sequences in human cells. Nat. Biomed. Eng. 4(2), 111–124 (2020). DOI 10.1038/s41551-019- 0505-1 26

    2020 doi

  16. [22]

    Wandera, K.G., Alkhnbashi, O.S., Bassett, H.V., Mitrofanov, A., Hauns, S., Migur, A., Tiksnadi, S., Robertson, D.L., Nagel, M.A., Jost, D., Backofen, R., K¨ unne, T., Brouns, S.J.J.: Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases. Mol. Cell...

    2022 doi

  17. [23]

    Yarnall, M.T.N., Ioannidi, E.I., Schmitt-Ulms, C., Krajeski, R.N., Lim, J., Vil- liger, L., Zhou, W., Jiang, K., Garushyants, S.K., Roberts, N., Zhang, L., Vakulskas, C.A., Walker, J.A., Kadina, A.P., Zepeda, A.E., Holden, K., Ma, H., Xie, J., Gao, G., Foquet, L., Bial, G., Do...

    2023

  18. [25]

    Locatelli, F., Rangarajan, H., Walters, M.C., Wembere, J., Wigg, A., Adewoye, A.H., Al-Samkari, H., Asnani, M., August, K., Bobruff, Y., Boruchov, A., Bour- get, P., Thompson, A.A., Tisdale, J.F., Traina, F., Vichinsky, E., Walters, M.C., Yan, J., Yawn, B.P., Zacharoulis, S., ...

    2024 doi

  19. [27]

    Gillmore, J.D., Gane, E., Taubel, J., Kao, J., Fontana, M., Maitland, M.L., Seitzer, J., O’Connell, D., Walsh, K.R., Wood, K., Phillips, J., Xu, Y., Ama- ral, A., Boyd, A.P., Cehelsky, J.E., McKee, M., Schiermeier, A., Harari, O., Murphy, A., Kyratsous, C.A., Zambrowicz, B., S...

    2021 doi

  20. [28]

    Science367(6481), eaba7365 (2020)

    Stadtmauer, E.A., Fraietta, J.A., Davis, M.M., Cohen, A.D., Weber, K.L., Lancaster, E., Mangan, P.A., Kulikovskaya, I., Gupta, M., Chen, F., Tian, 27 L., Gonzalez, V.E., Xu, J., Jung, I.Y., Melenhorst, J.J., Plesa, G., Shea, J., Matlawski, T., Cervini, A., Gaymon, A.L., Desjar...

    2020

  21. [29]

    Lu, Y., Xue, J., Deng, T., Zhou, X., Yu, K., Deng, L., Huang, M., Yi, X., Liang, M., Wang, Y., Shen, H., Tong, R., Wang, W., Li, L., Song, J., Li, J., Su, X., Ding, Z., Gong, Y., Zhu, J., Wang, Y., Zou, B., Zhang, Y., Li, Y., Zhou, L., Liu, Y., Yu, M., Wang, Y., Zhang, X., Yin...

    2020

  22. [30]

    Chiesa, R., Georgiadis, C., Syed, F., Zhan, H., Gkazi, S.A., Preece, R., Ottaviano, G., Braybrook, T., Chu, J., Kubat, A., Uwanogho, D., Djelou, N., Kouremenos, N., Harris, J., Farzaneh, F., Nathwani, A.C., Menger, L., Pule, M., Qasim, W.: Base-Edited CAR7 T Cells for Relapsed...

    2023 doi

  23. [31]

    Pierce, E.A., Aleman, T.S., Jayasundera, K.T., Ashimatey, B.S., Kim, K., Rashid, A., Jaskolka, M.C., Myers, R.L., Lam, B.L., Bailey, S.T., Comander, J.I., Lauer, A.K., Maguire, A.M., Pennesi, M.E.: Gene Editing for CEP290- Associated Retinal Degeneration. N. Engl. J. Med. 390(...

    1972 doi

  24. [32]

    World Health Organization News Release (2021)

    World Health Organization: WHO issues new recommendations on human genome editing for the advancement of public health. World Health Organization News Release (2021). https://www.who.int/news/item/12-07-2021-who-issues-n ew-recommendations-on-human-genome-editing-for-the-advan...

    2021

  25. [33]

    The CRISPR Journal 3(5), 365–377 (2020)

    Baylis, F., Darnovsky, M., Hasson, K., Krahn, T.M.: Human germline and heri- table genome editing: The global policy landscape. The CRISPR Journal 3(5), 365–377 (2020). DOI 10.1089/crispr.2020.0082

    2020

  26. [34]

    Marx, V.: The CRISPR children. Nat. Biotechnol. 39(11), 1302–1305 (2021). DOI 10.1038/s41587-021-01094-0

    2021 doi

  27. [35]

    Muigai, A.W.T.: Expanding global access to genetic therapies. Nat. Biotechnol. 40(1), 20–21 (2022). DOI 10.1038/s41587-021-01191-0

    2022 doi

  28. [36]

    Zhang, D.: The morality and ethics governing CRISPR–Cas9 patents in China. Nat. Biotechnol. 39(4), 393–395 (2021). DOI 10.1038/s41587-021-00872-0 28

    2021 doi

  29. [37]

    Replogle, J.M., Norman, T.M., Xu, A., Hussmann, J.A., Chen, J., Cogan, J.Z., Meer, E.J., Terry, M., Riordan, D.P., Srinivas, N., Leonard, J.A., Gilbert, L.A., Horlbeck, M.A., Lim, D.A., Weissman, J.S., Basu, S.: Combinatorial single-cell CRISPR screens by direct guide RNA capt...

    2020 doi

  30. [38]

    Jost, M., Santos, D.A., Saunders, R.A., Horlbeck, M.A., Hawkins, J.S., Scaria, S.M., Norman, T.M., Hussmann, J.A., Liem, D.A., Gross, C.A., Weissman, J.S.: Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs. Nat. Biotechnol. 38(3), 355–364...

    2020 doi

  31. [39]

    Kanfer, G., Sarraf, S., Maman, Y., Baldwin, H., Dominguez-Martin, E., John- son, K.R., Cho, D., Gubser Keller, C., Venken, K.J.T., M¨ uller, U., Perrimon, N., Shilo, B.Z., Schuldiner, M.: Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes. J. Cell Bio...

    2021 doi

  32. [40]

    An, M., Raguram, A., Du, S.W., Banskota, S., Davis, J.R., Newby, G.A., Chen, P.Z., Palczewski, K., Liu, D.R.: Engineered virus-like particles for transient deliv- ery of prime editor ribonucleoprotein complexes in vivo. Nat. Biotechnol. 42(10), 1526–1537 (2024). DOI 10.1038/s4...

    2024 doi

  33. [41]

    Davis, J.R., Wang, X., Witte, I.P., Huang, T.P., Levy, J.M., Raguram, A., Banskota, S., Seidah, N.G., Musunuru, K., Liu, D.R.: Efficient in vivo base editing via single adeno-associated viruses with size-optimized genomes encod- ing compact adenine base editors. Nat. Biomed. E...

    2022 doi

  34. [42]

    Davis, J.R., Banskota, S., Levy, J.M., Wang, P., Newby, G.A., Ord´ o˜ nez, M., Raguram, A., Carter, L., Born, D.A., Kornfeld, J.E., Jayaram, H., Bao, S., Liu, D.R.: Efficient prime editing in mouse brain, liver and heart with dual AA Vs. Nat. Biotechnol. 42(2), 253–264 (2024)....

    2024 doi

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Reviewed August 5, 2026 · model on record in the stance chip above.