Generative AI and Large-Language Models in Genomics: From Sequence to Function, Prediction and Interpretation

The explosion of genomic data has created a need for powerful computational tools. Generative AI and large-language-model (LLM) approaches are now entering genomics: treating DNA/RNA/protein sequences as “language”, generating hypotheses, predicting variant effects, automating annotation, and integrating multimodal data. For example, LLMs are being applied to genomic sequence modelling, single-cell analysis, and structure/function prediction.To gather further insights in generative AI and language modeling in genomics, we welcome articles addressing, but not limited to, the following themes:• Foundations of sequence modeling for DNA, RNA, and protein using generative and language models• In-silico generation and design of variants, regulatory elements, and synthetic genes• AI-driven prediction of variant effects, functional annotation, and disease risk assessment• Translational and clinical applications, including diagnostics, biomarker discovery, and genomic pipelines• Fusion and integration of multimodal data sources (genomics, imaging, spatial omics, clinical data)• Interpretability, reproducibility, and robustness in AI models• Human–AI collaboration and the future of generative design in synthetic biology Topic editors: Rosalba Giugno, Zhirong Yang Source: https://www.frontiersin.org/research-topics/77552/generative-ai-and-large-language-models-in-genomics-from-sequence-to-function-prediction-and-interpretation