Evidence map›Paper›PMID 42129132›Full record

ArticleCell discovery2026

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

Hu Feng, Jitan Zheng, Nana Li, Long Xie, Erwei Zuo

Abstract read
In one paragraph

Article in Cell discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Hu Feng *State Key Laboratory of Genome and Multi-omics Technologies, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Guangdong, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.ORCID http://orcid.org/0000-0002-0865-0206
Jitan Zheng *State Key Laboratory of Genome and Multi-omics Technologies, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Guangdong, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.
Nana Li *State Key Laboratory of Genome and Multi-omics Technologies, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Guangdong, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.ORCID http://orcid.org/0000-0001-7082-0379
Long Xie *State Key Laboratory of Genome and Multi-omics Technologies, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Guangdong, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.
Erwei ZuoState Key Laboratory of Genome and Multi-omics Technologies, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Guangdong, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China. zuoerwei@caas.cn.ORCID http://orcid.org/0000-0001-8259-0275

Funding

National Natural Science Foundation of China (National Science Foundation of China) 32371549
6 · The paper itself

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.

Identifiers

PMID42129132
PMCPMC13172344

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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.