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May 15, 2026Cell Discovery0 citationsOpen Access

Decoding the role of chromatin context in the off-target effects of CRISPR gene editing with EGOLD

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HFHu FengJZJitan ZhengNLNana Li

Key Points

  • This research aims to understand how chromatin context influences off-target effects in CRISPR gene editing.
  • Developed the EGOLD method for high-throughput detection of off-target effects.
  • Analyzed 17 base-editing tools over a large-scale endogenous off-target dataset.
  • Utilized machine learning to improve predictions of off-target effects based on chromatin context.
  • Identified 2,145,592 total off-targets, with an event range of 1236–618,774 per tool.
  • Found off-target effects occurred 40% to 80% of the time, highly influenced by chromatin context.
  • Improved prediction accuracy for off-target effects using EGOLD-Seq data to train models.

Abstract

Abstract Despite the power of CRISPR in genome editing, its clinical application is limited by off-target effects; these effects are currently difficult to evaluate at the genome level but are likely to involve chromatin context. Here, we developed the Endogenous Genome-wide Off-target Library Detection (EGOLD) method for high-throughput detection of off-target effects and identification of chromatin context bias in gene editor evaluation. Applying EGOLD to define the off-target characteristics of 17 base-editing tools revealed 2,145,592 total off-targets, with 1236–618,774 events detected per tool. The frequency of off-targets of CRISPR/Cas9 and derivative base editors ranged from 40% to 80% and were strongly influenced by the chromatin context. Using a large-scale endogenous off-target dataset with strict target site conditions to exclude the influence of sequence context, we found that off-target effects occurred in open chromatin genomic regions at a significantly greater frequency than in closed chromatin regions. The incorporation of EGOLD-Seq off-target chromatin context data to train machine learning-based models of gene editor activity substantially improved off-target prediction accuracy. These findings and the accompanying toolkit can guide mechanistic research and the development of safe and precise CRISPR-based tools.

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Cite This Study

Feng et al. (2026) studied this question.

synapsesocial.com/papers/6a06b928e7dec685947abb96https://doi.org/10.1038/s41421-026-00889-2
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comprehensive computational analysis of epigenetic descriptors affecting CRISPR-Cas9 off-target activity2022 · 28 citations
  2. 2RNA-Guided Human Genome Engineering via Cas92013 · 9,508 citations
  3. 3CRISPR/Cas Systems in Genome Editing: Methodologies and Tools for sgRNA Design, Off‐Target Evaluation, and Strategies to Mitigate Off‐Target Effects2020 · 387 citations
  4. 4An APOBEC3A-Cas9 base editor with minimized bystander and off-target activities2018 · 459 citations
  5. 5Off-target effects in CRISPR/Cas9 gene editing2023 · 596 citations