Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

被引：27

作者：

Dixit, Shriniket ^{[1
]}

Kumar, Anant ^{[2
]}

Srinivasan, Kathiravan ^{[1
]}

Vincent, P. M. Durai Raj ^{[3
]}

Krishnan, Nadesh Ramu ^{[3
]}

机构：

[1] Vellore Inst Technol, Sch Comp Sci & Engn, Vellore, India

[2] Vellore Inst Technol, Sch Biosci & Technol, Vellore, India

[3] Vellore Inst Technol, Sch Comp Sci Engn & Informat Syst, Vellore, India

来源：

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | 2024年 / 11卷

关键词：

genome editing and CRISPR/Cas9; base editing and AI; AI models for gRNA design; prime editing and AI; genome editing outcome prediction using AI; genomics and AI; off-target prediction; precision medicine and AI; NEURAL-NETWORK; CRISPR/CAS9; DESIGN; DNA; DETERMINANTS; EFFICIENCY; CLEAVAGE; OUTCOMES; SGRNAS; MODEL;

D O I：

10.3389/fbioe.2023.1335901

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for understanding genes and improving medical treatments. On the other hand, Artificial intelligence (AI) helps genome editing achieve more precision, efficiency, and affordability in tackling various diseases, like Sickle cell anemia or Thalassemia. AI models have been in use for designing guide RNAs (gRNAs) for CRISPR-Cas systems. Tools like DeepCRISPR, CRISTA, and DeepHF have the capability to predict optimal guide RNAs (gRNAs) for a specified target sequence. These predictions take into account multiple factors, including genomic context, Cas protein type, desired mutation type, on-target/off-target scores, potential off-target sites, and the potential impacts of genome editing on gene function and cell phenotype. These models aid in optimizing different genome editing technologies, such as base, prime, and epigenome editing, which are advanced techniques to introduce precise and programmable changes to DNA sequences without relying on the homology-directed repair pathway or donor DNA templates. Furthermore, AI, in collaboration with genome editing and precision medicine, enables personalized treatments based on genetic profiles. AI analyzes patients' genomic data to identify mutations, variations, and biomarkers associated with different diseases like Cancer, Diabetes, Alzheimer's, etc. However, several challenges persist, including high costs, off-target editing, suitable delivery methods for CRISPR cargoes, improving editing efficiency, and ensuring safety in clinical applications. This review explores AI's contribution to improving CRISPR-based genome editing technologies and addresses existing challenges. It also discusses potential areas for future research in AI-driven CRISPR-based genome editing technologies. The integration of AI and genome editing opens up new possibilities for genetics, biomedicine, and healthcare, with significant implications for human health.

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