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.

RNA therapeutics have evolved from passive genetic intermediaries into highly programmable platforms, fundamentally transforming the landscape of precision medicine. This comprehensive review examines the molecular architecture and mechanisms of established platforms in the clinical setting, including mRNA, antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs) and aptamers, alongside next-generation platforms, such as CRISPR-guided systems and circular RNAs (circRNAs). Moreover, we discuss strategies to overcome systemic delivery bottlenecks and evaluate advanced non-viral systems, emphasizing lipid nanoparticles (LNPs), polymers and tissue-specific ligand conjugates that facilitate precise intracellular targeting. Furthermore, we explore the clinical expansion of these platforms across infectious diseases, rare genetic disorders, oncology and cardiovascular conditions. Finally, we highlight how the integration of artificial intelligence (AI) and machine learning (ML) redefines the limits of individualized, programmable RNA therapies by accelerating sequence optimization and nanoparticle formulation.

The human ALKBH gene family comprises nine Fe²⁺/α-ketoglutarate-dependent dioxygenases that catalyze the oxidative demethylation of DNA, RNA, and proteins, thereby influencing key cellular processes. Consequently, dysregulation of these enzymes has been implicated in various human diseases, particularly cancer. Although the transcriptomic profiles of certain members (e.g., ALKBH8, FTO) have been characterized, a comprehensive analysis of the entire ALKBH family remains unclear. In the present study, we investigated the alternative splice variants of the ALKBH genes through direct RNA sequencing across cancerous and non-cancerous cell lines. Novel splicing events were validated by NGS, while RT-qPCR was employed to assess transcript abundance and expression patterns. Additionally, in silico analysis was performed to predict the coding potential of the detected transcripts. Results: Bioinformatics analysis revealed previously uncharacterized alternative transcripts for the human ALKBH gene family members. Expression profiling demonstrated distinct expression patterns between cancerous and non-malignant cells, suggesting a potential role of these demethylases in tumor biology. The investigation of their coding capacity revealed that most of the newly detected transcripts were predicted to encode protein isoforms, highlighting the structural and predicted coding potential of the ALKBH family. Conclusions: Our findings provide the first comprehensive overview of the transcriptional diversity within the human ALKBH gene family. These results enhance our understanding of the demethylation mechanisms and their dysregulation in cancer.