RNA's diverse biological functions, including gene regulation, enzymatic catalysis, signal transduction, and even therapeutic activity, ultimately depend on its three-dimensional (3D) structure. Departing from the classical task of inferring structure directly from sequence, this study investigates how to efficiently encode and generate RNA 3D structures, thereby enabling systematic exploration of the molecules' vast folding landscape. We propose a generative framework, RNA-FrameEncoder (RNA-FE). First, a Vector-Quantised Variational AutoEncoder compresses continuous RNA backbones into sequences of discrete structural tokens, “verbalizing” the geometry while suppressing high-frequency noise. Next, a Transformer language model is trained on these token sequences to capture their joint distribution, and a decoder subsequently projects the tokens back into Cartesian coordinates. This design sidesteps the complexity of explicit equivariance modelling, lowers computational cost, and markedly enhances the representational capacity. Comprehensive benchmarks show that RNA-FE can unconditionally generate realistic, novel, and designable RNA structures without any sequence or structural priors. Overall, RNA-FE provides a scalable approach for efficiently charting RNA 3D structural space and opens a data-efficient avenue for a range of downstream applications. The code and pretrained models are publicly available at: https://github.com/users/Hanbo24-bit/RNA-FE.

Rice blast, caused by the fungal pathogen Magnaporthe oryzae (M. oryzae), remains one of the most devastating threats to global rice production, capable of reducing yields by up to 30%. Current integration methods for single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) treat tissues as static snapshots, overlooking the temporal dynamics crucial for host-pathogen analysis. Here, we introduce Rice Blast Temporal Graph Neural Network (RBTN), the first computational framework specifically designed to integrate scRNA-seq and ST data across the three-dimensional spot-cell-time manifold during rice blast infection. RBTN constructs a unified graph whose nodes represent either individual cell transcriptomes or spatial spots sampled at three infection time points (0 h,$\text{1 2 h}, \text{2 4 h}$). It learns stage-specific mappings via bidirectional cosine loss and enforces temporal smoothness through a cosinebased consistency term. Alternating self-attention and temporal-attention layers capture local spatial organization and cross-time transitions. When applied to a public rice blast dataset, RBTN consistently delivers high-fidelity spatial reconstructions across different sets of highly variable marker genes (HVGs) and produces interpretable embeddings that jointly encode spatial, cellular, and temporal relationships. RBTN therefore establishes a robust, temporally-aware computational platform for elucidating the spatiotemporal dynamics of host-pathogen interactions during rice blast infection. All code in this paper is available at https://github.com/zhuyuheng111/RBTN.

MOTIVATION: Accurate prediction of RNA secondary structure remains challenging due to the presence of pseudoknots, long-range dependencies, and limited labeled data. RESULTS: We propose TVAE, a novel framework that integrates a Transformer encoder with a Variational Autoencoder (VAE). The Transformer captures global dependencies in the sequence, while the VAE models structural variability by learning a probabilistic latent space. Unlike deterministic models, TVAE generates diverse and biologically plausible secondary structures, enabling more comprehensive structure discovery. To obtain discrete predictions, we introduce GHA-Pairing, a fast and biologically constrained base-pairing algorithm. TVAE demonstrates strong generalization across different RNA families and achieves state-of-the-art performance on benchmark datasets, reaching an F1 score of 0.89 and 83% accuracy, surpassing existing methods by 10%. These results highlight the advantage of probabilistic modeling for RNA structure prediction and its potential to enhance biological insights. AVAILABILITY AND IMPLEMENTATION: Code and pretrained models are available at https://github.com/mei-rna/TVAE-RNA. The released version of the dataset and models can also be accessed via DOI: 10.5281/zenodo.16946114.

Rice blast, caused by Magnaporthe oryzae(M.oryzae), is a destructive rice disease that reduces rice yield by 10% to 30% annually. It also affects other cereal crops such as barley, wheat, rye, millet, sorghum, and maize. Small RNAs (sRNAs) play an essential regulatory role in fungus-plant interaction during the fungal invasion, but studies on pathogenic sRNAs during the fungal invasion of plants based on multi-omics data integration are rare. This paper proposes a novel approach called Graph Embedding combined with Random Walk with Restart (GERWR) to identify pathogenic sRNAs based on multi-omics data integration during M.oryzae invasion. By constructing a multi-omics network (MRMO), we identified 29 pathogenic sRNAs of rice blast fungus. Further analysis revealed that these sRNAs regulate rice genes in a many-to-many relationship, playing a significant regulatory role in the pathogenesis of rice blast disease. This paper explores the pathogenic factors of rice blast disease from the perspective of multi-omics data analysis, revealing the inherent connection between pathogenic factors of different omics. It has essential scientific significance for studying the pathogenic mechanism of rice blast fungus, the rice blast fungus-rice model system, and the pathogen-host interaction in related fields.