Evidence map›Paper›PMID 36646933›Full record

ArticleNature biotechnology2023

Predicting prime editing efficiency and product purity by deep learning.

Nicolas Mathis, Ahmed Allam, Lucas Kissling, Kim Fabiano Marquart, Lukas Schmidheini, Cristina Solari, Zsolt Balázs, Michael Krauthammer, Gerald Schwank

Abstract read
In one paragraph

Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

109 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Prime Editing: An Overview.Methods in molecular biology (Clifton, N.J.) · 2027
    Review
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Genomic repeats for single-cell molecular recording.bioRxiv : the preprint server for biology · 2026
    Article
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Prime editing of a pathogenicScience translational medicine · 2026
    Article

49 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Nicolas Mathis *Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-4051-8344
Ahmed Allam *Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-0871-1977
Lucas KisslingInstitute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-4989-0695
Kim Fabiano MarquartInstitute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-7393-4398
Lukas SchmidheiniInstitute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0001-5993-9985
Cristina SolariInstitute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Zsolt BalázsDepartment of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
Michael KrauthammerDepartment of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland. michael.krauthammer@uzh.ch.ORCID http://orcid.org/0000-0002-4808-1845
Gerald SchwankInstitute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland. schwank@pharma.uzh.ch.ORCID http://orcid.org/0000-0003-0767-2953

Funding

Swiss National Science Foundation 185293
6 · The paper itself

Abstract

Prime editing is a versatile genome editing tool but requires experimental optimization of the prime editing guide RNA (pegRNA) to achieve high editing efficiency. Here we conducted a high-throughput screen to analyze prime editing outcomes of 92,423 pegRNAs on a highly diverse set of 13,349 human pathogenic mutations that include base substitutions, insertions and deletions. Based on this dataset, we identified sequence context features that influence prime editing and trained PRIDICT (prime editing guide prediction), an attention-based bidirectional recurrent neural network. PRIDICT reliably predicts editing rates for all small-sized genetic changes with a Spearman's R of 0.85 and 0.78 for intended and unintended edits, respectively. We validated PRIDICT on endogenous editing sites as well as an external dataset and showed that pegRNAs with high (>70) versus low (<70) PRIDICT scores showed substantially increased prime editing efficiencies in different cell types in vitro (12-fold) and in hepatocytes in vivo (tenfold), highlighting the value of PRIDICT for basic and for translational research applications.

Indexed as

Deep LearningCRISPR-Cas SystemsGene EditingHepatocytesHumansMutationNeural Networks, Computer

Identifiers

PMID36646933
PMCPMC7614945

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.