CRISPR-Cas9 is a groundbreaking technology that has revolutionized genetic engineering by allowing precise and efficient genome editing. Despite its potential, the complexity and variability of genomic data present significant challenges. Artificial intelligence (AI) offers powerful tools to enhance CRISPR-Cas9 applications by improving target identification, minimizing off-target effects, optimizing components, and streamlining data analysis. AI tools have significantly enhanced the CRISPR-Cas9 genomic editing technique in precision agriculture, particularly in the area of disease detection and trait improvement. This review explores the various roles that AI plays in advancing CRISPR-Cas9 technology, highlighting current tools and future directions. This review also explores various AI-driven tools and methods that have been developed to optimize CRISPR-Cas9 genomic editing, from target identification to data analysis, off-target prediction, optimizing the efficiency, accuracy, and safety of gene editing, data analysis, and personalized medicine.
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References
1.
TutejaN, TutejaR, PassrichaN., and SaifiS.., eds. 2020. Advancement in Crop Improvement Techniques. Woodhead Publishing, 28–31.
2.
MovahediA, Aghaei-DargiriS, LiH, ZhugeQ, SunW. CRISPR variants for gene editing in plants: Biosafety risks and future directions. Int J Mol Sci, 2023; 24(22):16241.
3.
SherkatghanadZ, AbdarM, CharlierJ, MakarenkovV. Using traditional machine learning and deep learning methods for on- and off-target prediction in CRISPR/Cas9: A Review. Brief Bioinform, 2023; 24(3):bbad131.
4.
ZhangG, LuoY, DaiX, DaiZ. Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA On- and Off-target activities. Brief Bioinform, 2023; 24(6):bbad333.
5.
HasanzadehA, HamblinMR, KianiJ, et al.Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? Nano Today, 2022; 47:101665.
6.
GoshishtMK. Machine learning and deep learning in synthetic biology: Key architectures, applications, and challenges. ACS Omega, 2024; 9(9):9921–9945.
7.
DixitS, KumarA, SrinivasanK, VincentPDR, Ramu KrishnanN. Advancing genome editing with artificial intelligence: Opportunities, challenges, and future directions. Front Bioeng Biotechnol, 2023; 11:1335901.
8.
KumarDN, ChowdharyDL, PathuriT, KattaP, AryaL. 2024. “AI enhanced-smart genome editing: Integration of crispr-cas9 with artificial intelligence for cancer treatment.” In 2024 5th International Conference for Emerging Technology (INCET). IEEE, pp. 1–6.
9.
BaghiniSS, GardanovaZR, ZekiyAO, et al.RETRACTED: Optimizing sgRNA to improve CRISPR/Cas9 knockout efficiency: Special focus on human and animal cell. Front Bioeng Biotechnol, 2021; 9:775309.
10.
ChakrabortySS, Ray DuttaJ, GanesanR, MinaryP. 2024. “The Evolution of nucleic acid–based diagnosis methods from the (Pre-) CRISPR to CRISPR era and the associated machine/deep learning approaches in relevant RNA design.” In: RNA Design: Methods and Protocols: Springer US: New York, NY, pp. 241–300.
11.
RaffaelloT, CasacubertaJ, DalmayT, et al.; European Food Safety Authority (EFSA). Outcome of the public consultation on the draft scientific opinion on the applicability of the efsa opinion on site‐directed nucleases type 3 for the safety assessment of plants developed using site‐directed nucleases type 1 and 2 and oligonucleotide‐directed mutagenesis. EFSA J, 2020; 17(11):1972E.
BarrangouR, DoudnaJA. Applications of CRISPR technologies in research and beyond. Nat Biotechnol, 2016; 34(9):933–941.
14.
DimauroG, BarlettaVS, CatacchioCR, et al.A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage. Comput Struct Biotechnol J, 2022; 20:5813–5823.
15.
LeeCM, CradickTJ, FineEJ, BaoG. Nuclease target site selection for maximizing on-target activity and minimizing off-target effects in genome editing. Mol Ther, 2016; 24(3):475–487.
16.
HsuPD, ScottDA, WeinsteinJA, et al.DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol, 2013; 31(9):827–832.
17.
TsaiSQ, et al.GUIDES: Scalable and efficient predictions of guide RNA efficacy for CRISPR-Cas9. Bioinformatics, 2017.
18.
ZhangF. Development of CRISPR-Cas systems for genome editing and beyond. Quart Rev Biophys, 2019; 52:e6.
19.
MaJ, KösterJ, QinQ, et al.CRISPR-DO for genome-wide CRISPR design and optimization. Bioinformatics, 2016; 32(21):3336–3338.
20.
ListgartenJ, WeinsteinM, ElibolM, et al.Predicting off-target effects for End-to-End CRISPR guide design. bioRxiv, 2016:e078253.
21.
SunH. Navigating the CRISPR-Cas9 frontier: AI-enabled off-target prediction and sgRNA design for unprecedented precision. Trans Mater Biotechnol Life Sci, 2024; 3:522–531.
22.
AliM, ShabbirK, AliS, et al.A new era of discovery: How artificial intelligence has revolutionized biotechnology. Nepal J Biotechnol, 2024; 12(1):1–11.
23.
ListgartenJ, WeinsteinM, KleinstiverBP, et al.Prediction of off-target activities for the End-to-End design of CRISPR guide RNAs. Nat Biomed Eng, 2018; 2(1):38–47.
24.
BlinK, PedersenLE, WeberT, LeeSY. CRISPy-Web: An online resource to design sgRNAs for CRISPR Applications. Synth Syst Biotechnol, 2016; 1(2):118–121.
25.
LeiY, LuL, LiuHY, et al.CRISPR-P: A web tool for synthetic single-guide RNA Design of CRISPR-system in plants. Mol Plant, 2014; 7(9):1494–1496.
26.
FanY, XuH. Prediction of off-target effects in CRISPR/Cas9 system by ensemble learning. Curr Bioinform, 2021; 16(9):1169–1178.
27.
CancellieriS, CanverMC, BombieriN, GiugnoR, PinelloL. CRISPRitz: Rapid, high-throughput, and variant-aware in silico off-target site identification for CRISPR genome editing. Bioinformatics, 2020; 36(7):2001–2008.
28.
AbadiS, YanWX, AmarD, MayroseI. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput Biol, 2017; 13(10):e1005807.
29.
VidanagamachchiSM, WaidyarathnaKMGTR. Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data. Front Digit Health, 2024; 6:1471200.
30.
PinelloL, CanverMC, HobanMD, et al.CRISPResso: A software pipeline for the analysis of CRISPR-Cas9 genome editing outcomes. Nat Biotechnol, 2016; 34(7):695–697.
31.
ShenMW, ArbabM, HsuJY, et al.Predictable and precise template-free CRISPR editing of pathogenic variants. Nature, 2018; 563(7733):646–651.
32.
ChenQ, ChuaiG, ZhangH, et al.Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints. Nat Commun, 2023; 14(1):7521.
33.
HodgkinsA, FarneA, PereraS, et al.WGE: A CRISPR database for genome engineering. Bioinformatics, 2015; 31(18):3078–3080.
34.
ManghwarH, LiB, DingX, et al.CRISPR/Cas systems in genome editing: Methodologies and tools for sgRNA design, off‐target evaluation, and strategies to mitigate off‐target effects. Adv Sci (Weinh), 2020; 7(6):1902312.
35.
KhoshandamM, SoltaninejadH, HosseinkhaniS, et al.CRISPR/Cas and artificial intelligence to improve precision medicine: Future perspectives and potential limitations. Human Gene, 2024; 42:201356.
36.
BorettiA. The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy. Comput Biol Med, 2024; 182:109137.
37.
EslahiA, AlizadehF, AvanA, et al.New advancements in CRISPR based gene therapy of duchenne muscular dystrophy. Gene, 2023; 867:147358.
38.
AnnaA, MonikaG. Splicing mutations in human genetic disorders: Examples, detection, and confirmation. J Appl Genet, 2018; 59(3):253–268.
39.
RahmanMK, RahmanMS. CRISPRpred: A flexible and efficient tool for sgRNAs on-target activity prediction in CRISPR/Cas9 systems. PLoS One, 2017; 12(8):e0181943.
40.
NaeemM, AlkhnbashiOS. Current bioinformatics tools to optimize CRISPR/Cas9 experiments to reduce off-target effects. Int J Mol Sci, 2023; 24(7):6261.
41.
CornetC, Di DonatoV, TerrienteJ. Combining zebrafish and CRISPR/Cas9: Toward a more efficient drug discovery pipeline. Front Pharmacol, 2018; 9:703.
42.
PrykhozhijSV, CaceresL, BermanJN. New developments in CRISPR/Cas-based functional genomics and their implications for research using zebrafish. Curr Gene Ther, 2017; 17(4):286–300.
43.
VarshneyGK, PeiW, LaFaveMC, et al.High-throughput gene targeting and phenotyping in zebrafish using CRISPR/Cas9. Genome Res, 2015; 25(7):1030–1042.
44.
SinghRK, PrasadA, MuthamilarasanM, ParidaSK, PrasadM. Breeding and biotechnological interventions for trait improvement: Status and prospects. Planta, 2020; 252(4):54.
45.
DangY, JiaG, ChoiJ, et al.Optimizing sgRNA structure to improve CRISPR-Cas9 knockout efficiency. Genome Biol, 2015; 16:280.
46.
KonstantakosV, NentidisA, KritharaA, PaliourasG. CRISPR–Cas9 gRNA efficiency prediction: An overview of predictive tools and the role of deep learning. Nucleic Acids Res, 2022; 50(7):3616–3637.
47.
LiuB, JiangH, LiL. The innovative applications and prospects of artificial intelligence in agricultural research: Gene editing, crop improvement, and intelligent data analysis. Adv Resources Res, 2024; 4(4):624–653.
48.
SunL, LaiM, GhouriF, et al.Modern plant breeding techniques in crop improvement and genetic diversity: From molecular markers and gene editing to artificial intelligence—A critical review. Plants (Basel), 2024; 13(19):2676.
49.
LiC, WangM. Pest and disease management in agricultural production with artificial intelligence: Innovative applications and development trends. Adv Resources Res, 2024; 4(3):381–401.
50.
PraveenRVS, HemavathiU, SathyaR, et al.“AI powered plant identification and plant disease classification system.” In 2024 4th International Conference on Sustainable Expert Systems (ICSES), 1610–16. IEEE; 2024.
51.
ChoudharyS, KumarS, GowrojuS, GulhaneM, LakshmiRS., eds. 2024. Genomics at the Nexus of AI, Computer Vision, and Machine Learning. John Wiley & Sons.
52.
WongN, LiuW, WangX. WU-CRISPR: Characteristics of functional guide RNAs for the CRISPR/Cas9 System. Genome Biol, 2015; 16:218–218.
53.
ZhangX, et al.Efficient in silico prediction of CRISPR Off-target sites with CCTop. Nat Biotechnol, 2018; 36(6):542–550.
54.
TeixeiraMA, WeiL, KaloshianI. Root‐knot nematodes induce pattern‐triggered immunity in arabidopsis thaliana roots. New Phytol, 2016; 211(1):276–287.
55.
RanaP, KaurG, WaliaHK, et al.2024. “From data to discoveries: bioinformatics strategies for analyzing metabolomics, proteomics, and gene editing data.” In Metabolomics, Proteomics and Gene Editing Approaches in Biofertilizer Industry: Volume II. Springer Nature: Singapore, pp. 123–140.
56.
KaurB, SandhuKS, KamalR, et al.OMICS for the improvement of abiotic, biotic, and agronomic traits in major cereal crops: Applications, challenges, and prospects. Plants (Basel), 2021; 10(10):1989.
57.
SharmaK, ShivanduSK. Integrating artificial intelligence and internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sensors Int, 2024; 5:100292.
DaraM, DianatpourM, AzarpiraN, OmidifarN. Convergence of CRISPR and artificial intelligence: A paradigm shift in biotechnology. Human Gene, 2024; 41:201297.
60.
NdudzoA, Sibanda MakuviseA, MoyoS, BoboED. CRISPR-Cas9 genome editing in crop breeding for climate change resilience: Implications for smallholder farmers in Africa. J Agric Food Res, 2024; 16:101132.
61.
How AI and data can bring another green revolution in India. Economic Times; 2024: 05: 08.
