Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR
Abstract The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, inclu...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-06013-w |
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| author | Ahtisham Fazeel Abbasi Muhammad Nabeel Asim Andreas Dengel |
| author_facet | Ahtisham Fazeel Abbasi Muhammad Nabeel Asim Andreas Dengel |
| author_sort | Ahtisham Fazeel Abbasi |
| collection | DOAJ |
| description | Abstract The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, including cancers, genetic disorders, and hereditary diseases. CRISPR-Cas9 based genome editing is a multi-step process such as designing a precise gRNA, selecting the appropriate Cas protein, and thoroughly evaluating both on-target and off-target activity of the Cas9-gRNA complex. To ensure the accuracy and effectiveness of CRISPR-Cas9 system, after the targeted DNA cleavage, the process requires careful analysis of the resultant outcomes such as indels and deletions. Following the success of artificial intelligence (AI) in various fields, researchers are now leveraging AI algorithms to catalyze and optimize the multi-step process of CRISPR-Cas9 system. To achieve this goal AI-driven applications are being integrated into each step, but existing AI predictors have limited performance and many steps still rely on expensive and time-consuming wet-lab experiments. The primary reason behind low performance of AI predictors is the gap between CRISPR and AI fields. Effective integration of AI into multi-step CRISPR-Cas9 system demands comprehensive knowledge of both domains. This paper bridges the knowledge gap between AI and CRISPR-Cas9 research. It offers a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process. Furthermore, it provides details of 80 available CRISPR-Cas9 system-related datasets that can be utilized to develop AI-driven applications. Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. In the context of representation learning methods and classifiers/regressors, a thorough analysis of existing predictive pipelines is utilized for recommendations to develop more robust and precise predictive pipelines. |
| format | Article |
| id | doaj-art-7eeaabadcc2743de9998225b72a3dc15 |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-7eeaabadcc2743de9998225b72a3dc152025-02-09T12:52:35ZengBMCJournal of Translational Medicine1479-58762025-02-0123114610.1186/s12967-024-06013-wTransitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPRAhtisham Fazeel Abbasi0Muhammad Nabeel Asim1Andreas Dengel2Smart Data and Knowledge Services, German Research Center for Artificial IntelligenceDepartment of Computer Science, Rhineland-Palatinate Technical University Kaiserslautern-LandauSmart Data and Knowledge Services, German Research Center for Artificial IntelligenceAbstract The revolutionary CRISPR-Cas9 system leverages a programmable guide RNA (gRNA) and Cas9 proteins to precisely cleave problematic regions within DNA sequences. This groundbreaking technology holds immense potential for the development of targeted therapies for a wide range of diseases, including cancers, genetic disorders, and hereditary diseases. CRISPR-Cas9 based genome editing is a multi-step process such as designing a precise gRNA, selecting the appropriate Cas protein, and thoroughly evaluating both on-target and off-target activity of the Cas9-gRNA complex. To ensure the accuracy and effectiveness of CRISPR-Cas9 system, after the targeted DNA cleavage, the process requires careful analysis of the resultant outcomes such as indels and deletions. Following the success of artificial intelligence (AI) in various fields, researchers are now leveraging AI algorithms to catalyze and optimize the multi-step process of CRISPR-Cas9 system. To achieve this goal AI-driven applications are being integrated into each step, but existing AI predictors have limited performance and many steps still rely on expensive and time-consuming wet-lab experiments. The primary reason behind low performance of AI predictors is the gap between CRISPR and AI fields. Effective integration of AI into multi-step CRISPR-Cas9 system demands comprehensive knowledge of both domains. This paper bridges the knowledge gap between AI and CRISPR-Cas9 research. It offers a unique platform for AI researchers to grasp deep understanding of the biological foundations behind each step in the CRISPR-Cas9 multi-step process. Furthermore, it provides details of 80 available CRISPR-Cas9 system-related datasets that can be utilized to develop AI-driven applications. Within the landscape of AI predictors in CRISPR-Cas9 multi-step process, it provides insights of representation learning methods, machine and deep learning methods trends, and performance values of existing 50 predictive pipelines. In the context of representation learning methods and classifiers/regressors, a thorough analysis of existing predictive pipelines is utilized for recommendations to develop more robust and precise predictive pipelines.https://doi.org/10.1186/s12967-024-06013-wAI-driven CRISPR applicationsRepresentation learning in CRISPRML/DL and CRISPRCIRSPR on/off-target activityCRISPR loci and operonsAnti-CRISPR proteins |
| spellingShingle | Ahtisham Fazeel Abbasi Muhammad Nabeel Asim Andreas Dengel Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR Journal of Translational Medicine AI-driven CRISPR applications Representation learning in CRISPR ML/DL and CRISPR CIRSPR on/off-target activity CRISPR loci and operons Anti-CRISPR proteins |
| title | Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR |
| title_full | Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR |
| title_fullStr | Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR |
| title_full_unstemmed | Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR |
| title_short | Transitioning from wet lab to artificial intelligence: a systematic review of AI predictors in CRISPR |
| title_sort | transitioning from wet lab to artificial intelligence a systematic review of ai predictors in crispr |
| topic | AI-driven CRISPR applications Representation learning in CRISPR ML/DL and CRISPR CIRSPR on/off-target activity CRISPR loci and operons Anti-CRISPR proteins |
| url | https://doi.org/10.1186/s12967-024-06013-w |
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