Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions
The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative im...
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MDPI AG
2025-06-01
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| Series: | Current Issues in Molecular Biology |
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| Online Access: | https://www.mdpi.com/1467-3045/47/6/470 |
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| author | Konstantina Athanasopoulou Vasiliki-Ioanna Michalopoulou Andreas Scorilas Panagiotis G. Adamopoulos |
| author_facet | Konstantina Athanasopoulou Vasiliki-Ioanna Michalopoulou Andreas Scorilas Panagiotis G. Adamopoulos |
| author_sort | Konstantina Athanasopoulou |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable. |
| format | Article |
| id | doaj-art-bfcee7272000411ca98aca34ac10de7f |
| institution | Kabale University |
| issn | 1467-3037 1467-3045 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Current Issues in Molecular Biology |
| spelling | doaj-art-bfcee7272000411ca98aca34ac10de7f2025-08-20T03:26:16ZengMDPI AGCurrent Issues in Molecular Biology1467-30371467-30452025-06-0147647010.3390/cimb47060470Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future DirectionsKonstantina Athanasopoulou0Vasiliki-Ioanna Michalopoulou1Andreas Scorilas2Panagiotis G. Adamopoulos3Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, 15701 Athens, GreeceDepartment of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, 15701 Athens, GreeceDepartment of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, 15701 Athens, GreeceDepartment of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, 15701 Athens, GreeceThe integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable.https://www.mdpi.com/1467-3045/47/6/470NGSAImachine learningdeep learninggenomicstranscriptomics |
| spellingShingle | Konstantina Athanasopoulou Vasiliki-Ioanna Michalopoulou Andreas Scorilas Panagiotis G. Adamopoulos Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions Current Issues in Molecular Biology NGS AI machine learning deep learning genomics transcriptomics |
| title | Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions |
| title_full | Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions |
| title_fullStr | Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions |
| title_full_unstemmed | Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions |
| title_short | Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions |
| title_sort | integrating artificial intelligence in next generation sequencing advances challenges and future directions |
| topic | NGS AI machine learning deep learning genomics transcriptomics |
| url | https://www.mdpi.com/1467-3045/47/6/470 |
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