MultiCRISPR-EGA: Optimizing Guide RNA Array Design for Multiplexed CRISPR Using the Elitist Genetic Algorithm.

IF 3.7 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

ACS Synthetic Biology Pub Date : 2025-02-20 DOI:10.1021/acssynbio.4c00860

Yangyu Zhang, Guanlin Chen, Ce Liang, Bin Yang, Xin Lei, Tao Chen, Huaiguang Jiang, Wei Xiong

{"title":"MultiCRISPR-EGA: Optimizing Guide RNA Array Design for Multiplexed CRISPR Using the Elitist Genetic Algorithm.","authors":"Yangyu Zhang, Guanlin Chen, Ce Liang, Bin Yang, Xin Lei, Tao Chen, Huaiguang Jiang, Wei Xiong","doi":"10.1021/acssynbio.4c00860","DOIUrl":null,"url":null,"abstract":"Multiplexed CRISPR design, which allows for the concurrent and efficient editing of multiple genomic sites, is a powerful tool for complex genetic modifications. However, designing effective multiplexed guide RNA (gRNA) arrays remains challenging due to the exponential increase in potential gRNA array candidates and the significant impact of different target site selections on efficiency and specificity. Recognizing that more stable gRNAs, characterized by lower minimum free energy (MFE), have prolonged activity and thus higher efficacy, we developed MultiCRISPR-EGA, a graphical user interface (GUI)-based tool that employs the Elitist Genetic Algorithm (EGA) to design optimized single-promoter-driven multiplexed gRNA arrays. Computational experiments on Escherichia coli gene targets demonstrate that the EGA can rapidly optimize multiplexed gRNA arrays, outperforming other intelligent optimization algorithms in CRISPR interference (CRISPRi) applications, while the GUI provides real-time design progress control and compatibility with various CRISPR-Cas systems. This tool aims to advance the multiplexed gRNA array design process, enabling more efficient and cost-effective genome editing for synthetic biology.","PeriodicalId":26,"journal":{"name":"ACS Synthetic Biology","volume":" ","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Synthetic Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1021/acssynbio.4c00860","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Multiplexed CRISPR design, which allows for the concurrent and efficient editing of multiple genomic sites, is a powerful tool for complex genetic modifications. However, designing effective multiplexed guide RNA (gRNA) arrays remains challenging due to the exponential increase in potential gRNA array candidates and the significant impact of different target site selections on efficiency and specificity. Recognizing that more stable gRNAs, characterized by lower minimum free energy (MFE), have prolonged activity and thus higher efficacy, we developed MultiCRISPR-EGA, a graphical user interface (GUI)-based tool that employs the Elitist Genetic Algorithm (EGA) to design optimized single-promoter-driven multiplexed gRNA arrays. Computational experiments on Escherichia coli gene targets demonstrate that the EGA can rapidly optimize multiplexed gRNA arrays, outperforming other intelligent optimization algorithms in CRISPR interference (CRISPRi) applications, while the GUI provides real-time design progress control and compatibility with various CRISPR-Cas systems. This tool aims to advance the multiplexed gRNA array design process, enabling more efficient and cost-effective genome editing for synthetic biology.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

ACS Synthetic Biology 生物-

CiteScore

8.00

自引率

10.60%

发文量

380

审稿时长

6-12 weeks

期刊介绍： The journal is particularly interested in studies on the design and synthesis of new genetic circuits and gene products; computational methods in the design of systems; and integrative applied approaches to understanding disease and metabolism. Topics may include, but are not limited to: Design and optimization of genetic systems Genetic circuit design and their principles for their organization into programs Computational methods to aid the design of genetic systems Experimental methods to quantify genetic parts, circuits, and metabolic fluxes Genetic parts libraries: their creation, analysis, and ontological representation Protein engineering including computational design Metabolic engineering and cellular manufacturing, including biomass conversion Natural product access, engineering, and production Creative and innovative applications of cellular programming Medical applications, tissue engineering, and the programming of therapeutic cells Minimal cell design and construction Genomics and genome replacement strategies Viral engineering Automated and robotic assembly platforms for synthetic biology DNA synthesis methodologies Metagenomics and synthetic metagenomic analysis Bioinformatics applied to gene discovery, chemoinformatics, and pathway construction Gene optimization Methods for genome-scale measurements of transcription and metabolomics Systems biology and methods to integrate multiple data sources in vitro and cell-free synthetic biology and molecular programming Nucleic acid engineering.