A Foundational Large Language Model for Edible Plant Genomes

Abstract

AbstractIn recent years, significant progress has been made in the field of plant genomics, demonstrated by the increased use of high-throughput methodologies that allow for the characterization of multiple genome-wide molecular phenotypes. These results have provided valuable insights into plant traits and their underlying genetic mechanisms, especially in well-researched model plant species. Nonetheless, although acquiring and characterizing these molecular phenotypes can offer valuable insights into plant traits, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a novel foundational large language model trained on reference genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for many genomic elements, including polyadenylation sites, splice sites, open chromatin and enhancer regions. Furthermore, AgroNT can be used to predict the strength of promoter sequences and tissue-specific gene expression levels or prioritize functional variants. Using the cassava genome as an example of an understudied species, we perform a large-scalein silicosaturation mutagenesis analysis to assess the impact of>10 million mutations on gene expression levels and enhancer elements in the cassava genome, and provide the results as a valuable resource for regulatory causal variant characterization. Furthermore, owing to the lack of comprehensive benchmarks in the context of deep learning-based methods in plant genomic research, we propose the use of the multiple datasets encompassing seven distinct genomic prediction tasks, which have been compiled here, as the Plants Genomic Benchmark (PGB). The pre-trained AgroNT model is now publicly available on Hugging Face athttps://huggingface.co/InstaDeepAI/agro-ntfor future research purposes.