深度学习在植物基因组学与作物育种中的应用现状与展望

Purpose/Significance] Advances in single-cell sequencing and high-throughput technology have made it possible for plantgenomics to accumulate large quantities of data describing multidimensional genomic-wide molecular phenotypes at low cost. Aspowerful data mining tools, deep learning techniques can be utilized to further predict and interpret the acquired molecular phenotypes.In recent studies, deep learning has been shown to yield significant results in plant genomics and crop breeding research. However, acomplete review of deep learning applications in plant genomics is lacking. [Method/Process] The input to deep learning applied togenomics is usually biological sequences and molecular phenotypes as predictor and target variables, respectively. We introduced theworkflow from four views: input data pre-processing includes retrieval, coding, and splitting; model construction and training includesthe selection of model architecture and hyperparameters; model evaluation and interpretability. Specifically, this paper introduces thebackground of deep learning approaches, including the latest graph neural networks; then it discusses two prominent issues in theintersection of genomics and deep learning with respect to gene characterization and protein characterization: 1) how to model the flowof information from plant genomic DNA sequences to molecular phenotypes; and 2) how deep learning models can be utilized toidentify functional variation in natural populations? Specifically, the paper summarizes the current status of deep learning applications inrelated fields, which include deep learning and DNA and gene characterization research, interpretability of deep learning in genomicsapplications, graph neural networks in genomics, deep learning and genomic variation research, deep learning in protein prediction,ALPHAFOLD in protein prediction, deep learning and crop breeding research, and unsupervised learning in genomics and proteincharacterization. [Results/Conclusions] This article summarizes how traditional deep-learning algorithms, graph deep-learning,generative adversarial networks and interpretable AI are applied in current research in order to address these two problems. Finally, theprospects for deep learning in future plant genomics research and crop improvement are discussed. Overall, deep learning has providedbetter results than conventional methods in many genomics research directions, and the application of deep learning in genomics hasyielded early applications of scientific and economic significance. Deep learning offers two distinct advantages: 1) end-to-end learning,with the ability to integrate multiple pre-processing steps into a single model; and 2) multimodal data processing capabilities that canhandle extremely heterogeneous data in genomics. The advancement of deep learning has the potential to expand new researchperspectives in genomics and crop breeding, and to facilitate larger-scale association studies in both phenotypic and genotypic genomicsas algorithms become more accurate.