Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints

Qinchang Chen, Guohui Chuai, Haihang Zhang, Jin Tang, Liwen Duan, Huan Guan, Wenhui Li, Wannian Li, Jiaying Wen, Erwei Zuo (), Qing Zhang () and Qi Liu ()
Additional contact information
Qinchang Chen: Tongji University
Guohui Chuai: Tongji University
Haihang Zhang: Chinese Academy of Agricultural Sciences
Jin Tang: Zhejiang Lab
Liwen Duan: Zhejiang Lab
Huan Guan: Zhejiang Lab
Wenhui Li: Zhejiang Lab
Wannian Li: Tongji University
Jiaying Wen: Tongji University
Erwei Zuo: Chinese Academy of Agricultural Sciences
Qing Zhang: China Innovation Center of Roche
Qi Liu: Tongji University

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract The powerful CRISPR genome editing system is hindered by its off-target effects, and existing computational tools achieved limited performance in genome-wide off-target prediction due to the lack of deep understanding of the CRISPR molecular mechanism. In this study, we propose to incorporate molecular dynamics (MD) simulations in the computational analysis of CRISPR system, and present CRISOT, an integrated tool suite containing four related modules, i.e., CRISOT-FP, CRISOT-Score, CRISOT-Spec, CRISORT-Opti for RNA-DNA molecular interaction fingerprint generation, genome-wide CRISPR off-target prediction, sgRNA specificity evaluation and sgRNA optimization of Cas9 system respectively. Our comprehensive computational and experimental tests reveal that CRISOT outperforms existing tools with extensive in silico validations and proof-of-concept experimental validations. In addition, CRISOT shows potential in accurately predicting off-target effects of the base editors and prime editors, indicating that the derived RNA-DNA molecular interaction fingerprint captures the underlying mechanisms of RNA-DNA interaction among distinct CRISPR systems. Collectively, CRISOT provides an efficient and generalizable framework for genome-wide CRISPR off-target prediction, evaluation and sgRNA optimization for improved targeting specificity in CRISPR genome editing.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42695-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42695-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-42695-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().