Deep learning applications advance plant genomics research

Abstract

With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data, deep learning (DL) has emerged as a powerful tool to solve complex biological problems, with particular promise in plant genomics. This review systematically examines the progress of DL applications in DNA, RNA, and protein sequence analysis, covering key tasks such as gene regulatory element identification, gene function annotation, and protein structure prediction, and highlighting how these DL applications illuminate research of plants, including horticultural plants. We evaluate the advantages of different neural network architectures and their applications in different biology studies, as well as the development of large language models (LLMs) in genomic modelling, such as the plant-specific models PDLLMs and AgroNT. We also briefly introduce the general workflow of the basic DL model for plant genomics study. While DL has significantly improved prediction accuracy in plant genomics, its broader application remains constrained by several challenges, including the limited availability of well-annotated data, computational capacity, innovative model architectures adapted to plant genomes, and model interpretability. Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.