Niraj Rayamajhi, Tianyang Xu, Tianci Liu, Jing Gao, Kranthi Varala, Ying Li
Synthesized recent advances in AI and ML for plant genomics. Summarized AI and LLM applications in predicting epigenomic features, regulatory DNA elements, gene expressions, gene regulatory networks, and post-transcriptional regulation in plants. AI and LLM tools are transforming plant functional genomics to inform crop improvement.
One of the central goals of plant functional genomics is to uncover regulatory mechanisms that shape agriculturally important traits to inform crop improvement. Recent advances in machine learning (ML) and artificial intelligence (AI), especially Large Language Models (LLMs), have greatly transformed our ability to derive regulatory information from complex genomics data. This review starts with a brief introduction of recent advances in AI and ML. We then present a plant-focused synthesis of emerging applications of AI- and LLM tools to: (i) predict epigenomic features, regulatory DNA elements, and gene expressions; (ii) infer gene regulatory network; and (iii) estimate post-transcriptional regulation.