RNA, an essential molecule in all organisms, carries genetic instructions and regulates critical biological functions. It is composed of nucleotide sequences and structures, forming patterns comparable to the syntax of human language. Understanding these intricate patterns has been a significant challenge in plant biology.
Professor Yiliang Ding's team at the John Innes Centre, which specializes in RNA structures, collaborated with Dr. Ke Li's group at the University of Exeter to develop PlantRNA-FM. This AI model, trained on 54 billion pieces of RNA data from 1,124 plant species, utilizes methodologies similar to AI tools like ChatGPT, which analyze human language.
By studying this extensive dataset, PlantRNA-FM has learned to interpret the "grammar" of RNA sequences and structures. The model has already demonstrated its capability to predict RNA functions and identify functional RNA structural patterns, with experimental validation confirming its findings. These identified structures influence how genetic information is translated into proteins.
Dr. Haopeng Yu, a postdoctoral researcher in Professor Ding's team, explained: "While RNA sequences may appear random to the human eye, our AI model has learned to decode the hidden patterns within them."
The collaboration also involved contributions from Northeast Normal University and the Chinese Academy of Sciences. Professor Ding highlighted the significance of the innovation: "PlantRNA-FM is just the beginning. We are working closely with Dr. Li's group to develop more advanced AI approaches to uncover the hidden DNA and RNA languages in nature. This breakthrough opens new possibilities for understanding and programming plants, potentially transforming crop improvement and AI-driven gene design. AI is playing a critical role in addressing global challenges such as food security and climate-resilient agriculture."
Research Report:An Interpretable RNA Foundation Model for Exploration Functional RNA Motifs in Plants
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